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Evie Stergiakoulis (lead), Dr Sarah Lewis, Prof Michael Owen Prof Marianne van den Bree
Cleft of the lip and/or palate is a common birth defect worldwide and occurs at a rate of one in 650 live births in the UK. Being born with cleft places a significant burden on children, their families and the health system as they require surgery (multiple times depending on cleft type), and other interventions to improve appearance, speech, hearing, dentition and other adverse outcomes. They are also at increased risk of psychological, psychiatric and cognitive problems [1]. The UK-based Cleft Collective is a unique resource comprising the world’s largest cohort study of individuals affected by cleft and their families [2]. Rich longitudinal information is collected on the children’s mental health, parental, prenatal and early life factors as well as genetic data, providing unique opportunities to study genetic as well as environmental influences on risk of development of mental health conditions over time.
The PhD project will provide the first detailed description of neurodevelopmental and mental health outcomes in children with cleft and examine the contributions of genetic and environmental factors. We will use two unique genetically informative clinical cohorts of children; the University of Bristol Cleft Collective and the Cardiff University longitudinal ExperiencCes of people witH cOpy number variants (ECHO) study. Control samples will consist of the Avon Longitudinal Study of Parents and Children (ALPSAC) and the Millennium cohort which are deeply-phenotyped cohorts of typically developing children.
The aims of the study are: 1) To improve understanding of risk of neurodevelopmental and mental health outcomes in children born with cleft. This will be achieved by comparing children born with cleft to those at high genetic risk of neurodevelopmental and mental health outcomes but without cleft (children from the ECHO study) and typically developing children.
2) To improve understanding of the causes of neurodevelopmental and mental health outcomes in children born with cleft. This will be achieved by determining in children born with cleft the contribution of: a) composite genetic (polygenic) risk scores for neurodevelopmental and psychiatric disorders and b) rare genetic mutations.
3) To improve understanding of non-genetic factors, the project will also examine contributions of early developmental problems, family socio-economic status, family relationship quality, and traumatic experiences to risk of childhood psychiatric disorders in children born with cleft.
This multidisciplinary project will integrate expertise across cleft, genetic epidemiology, psychiatry, and epidemiology. Advanced epidemiological methods will be applied to study repeated measures and compare outcomes across cohorts. Copy Number Variants (CNVs) and other rare variants will be identified in children from the Cleft Collective and compared with mutations identified in children with neurodevelopmental disorders and children from the general population. Genetic epidemiological methods, such as polygenic risk scores, will be used to study the contribution of common variants and causally informative designs will be employed to test the causal link between cleft and mental health problems. The candidate will have the opportunity to develop into one of few experts globally with in depth understanding across these fields. The project will include training in advanced epidemiology, genetic epidemiology, bioinformatics and a mini-MD in the MRC Centre for Neuropsychiatric Genetics and Genomics at Cardiff.
[1] doi: 10.1016/j.jaac.2018.06.024
[2] http://www.bristol.ac.uk/dental/cleft-collective/
[3] doi: 10.1038/s41380-020-0654-3
[4] doi: 10.1371/journal.pgen.1007501
[5] doi: 10.1016/S2215-0366(19)30123-3
Dr Evie Stergiakouli (lead), Prof Claire Haworth, Dr Oliver Davis, Dr Alexandra Havdahl
Children are now exposed to and use digital media from a very young age both for entertainment and educational purposes. Parents and clinicians are concerned about the potential harmful effects that digital media use may have on children’s mental and physical health [1,2,3]. However, most studies have been cross-sectional and have shown positive as well as negative effects (with the exception of excessive use and extreme online harmful behaviour) [4] and they have not considered the effect of digital media use on children from the general population. COVID-19 and lockdown increased digital media use for the majority of children and this has been linked to positive effects, such as increased connectedness, at the time that it was most needed [5].
The aim of this project is to examine if digital media use is having an impact (positive or negative) on children’s mental health and explore the mechanisms behind any associations. We also aim to determine if there are sensitive windows in development or situations (such as lockdown) when digital media are most/least harmful and if there are groups of children (e.g. with pre-existing neurodevelopmental problems) that are particularly vulnerable.
The PhD student will use genetic epidemiological methods to test if digital media use is causally associated with mental health in children. Methods will include performing GWAS for digital media use, polygenic risk score analysis [5] and Multivariable Mendelian randomization [6]. These methods will be applied across large cohorts of children from the general population with mental health data across time, such as the Avon Longitudinal Study of Parents and Children (ALSPAC), the Millennium Cohort and the Norwegian Mother, Father and Child Cohort Study (MoBa).
1. Canadian Paediatric Society, Paediatr Child Health. 2019; 24(6): 402–408.
2. Ra CK, et al. JAMA. 2018;320(3):255–263. doi:10.1001/jama.2018.8931
3. Hoge E et al. Pediatrics. 2017;140(Suppl 2):S76-S80. doi: 10.1542/peds.2016-1758G.
4. Orben, A. Soc Psychiatry Psychiatr Epidemiol 2020; 55, 407–414.
5. Widnall E et al. 24th August 2020, NIHR School for Public Health Research
6. Davey Smith G, Hemani G, Human Molecular Genetics 2014;23(R1):R89–R98, https://doi.org/10.1093/hmg/ddu328
7. Leppert B et al. JAMA Psychiatry. 2019;76(8):834–842. doi:10.1001/jamapsychiatry.2019.0774
Dr. Siddhartha Kar (lead), Prof. Paul Brennan (IARC), , Prof. Tom Gaunt
Cancer is a disease of the genome. Certain changes that are acquired over the course of life in the genomes of healthy cells in the human body (somatic genomic changes) dysregulate the fine balance between cell death and proliferation. These somatic genomic aberrations are the cornerstone of malignant cellular transformation. Targeting somatic genomic changes is fundamental to the practice of precision cancer medicine. We understand that common exposures and cancer risk factors such as ultraviolet light and smoking accelerate the acquisition of these changes. However, little is actually known about how everyday exogeneous and endogenous factors such as diet, obesity, and insulin resistance relate to, and likely drive, carcinogenic changes in the somatic genome. This is because it is difficult to measure lifelong trajectories of the factors retrospectively at cancer diagnosis and expensive to measure them prospectively in large numbers of individuals until some of them develop cancer. Such one-time "snapshot" measures, even where feasible, are prone to bias and confounding. Specific inherited or germline genetic variants have been found to be robustly associated with these exposures or factors. Since genetic variants are allocated at random at conception and fixed thereafter, they are less affected by bias and confounding. The factor-associated variants provide remarkable proxies for the lifetime levels of these factors even in patients in whom the factor itself has not been measured. These variants collected into polygenic scores can serve as instruments to evaluate association between the germline genetically inferred levels of the factor and somatic/tumour molecular features and mechanisms that operate within the cancer.
1. To identify tumour molecular features associated with common exposures or putative cancer risk factors
Genome-wide association studies involving hundreds of thousands of individuals have identified germline variants that are robustly associated with different factors, ranging from body mass index to blood-levels of protein markers. This variation will be leveraged to generate personalised life-course profiles of these factors in cancer patients using germline genotype data. The association of these profiles with tumour gene expression, methylation, copy number, and mutations will then be evaluated at the level of single genes and multi-gene biological pathways in >11,000 tumours that have been subjected to deep germline-somatic molecular and clinical phenotyping in The Cancer Genome Atlas (TCGA) project.
2. To investigate the association between common exposures or putative cancer risk factors and cancer drug sensitivity
Over 1,000 cancer cell lines from the Genomics of Drug Sensitivity in Cancer project have been screened for their response to >450 cancer drugs either approved for use in patients or in development. Germline genotypes from the cell lines will be used to index the factors and the association of each index with therapeutic response assessed.
A key aspect of this project is flexibility in terms of the scientific direction taken with these rich data sets that will be provided to the student. It is envisioned that such flexibility may manifest in various ways such as (but certainly not limited to) encouraging an investigation of cancer risk factors in the context of genetic ancestry and sex (for example, the student may wish to study whether the impact of body mass index on the tumour genome differs by sex or ancestry).
The student will receive exceptional training in the handling and statistical analysis of large-scale, high-dimensional cancer genetic, genomic, transcriptomic, and epigenomic data sets and in the interpretation of findings based on these data sets. The student will apply a range of computational techniques including state-of-the-art Mendelian randomisation methods implemented in MR-Base, polygenic scoring approaches such as LD-Pred, and expression quantitative trait locus analysis using the R package Matrix eQTL. The project seeks to encourage a high-degree of flexibility both in terms of the scientific questions being asked and in terms of the methods being applied and should the student choose to do so, there is ample scope for implementation of artificial intelligence/machine learning-based methods in these data sets, etc. Relevant training in these methods will be provided. It is envisioned that the work will lead to multiple high-profile and highly interdisciplinary publications that will be led by the student, providing an excellent foundation for future scientific leadership.
The Cancer Genome Atlas project: Ding, L. et al. Cell 173, 305-320.e10 (2018).
The Genomics of Drug Sensitivity in Cancer project: Iorio, F. et al. Cell 166, 740–754 (2016).
Dr Anya Skatova (lead), Prof Deborah Lawlor ,
Shopping history records collected by supermarkets contain population level health information which could be missing from traditional health research data such as medical records. For example, shopping transactions can provide granular and objective data on under/unreported behaviours and outcomes in reproductive health domain – related to pain and weight management, vitamins consumption, infant feeding, etc - that can be tracked longitudinally. Combining shopping history datasets with epidemiological methods has potential for health research and might improve diagnosis, disease prevention and planning of interventions.
The aim of the PhD is to explore the potential of shopping history data to identify key reproductive events and lifestyle choices around these in real time. The specific focus of the PhD will be developed by the student, with potential objectives including: (1) determining the accuracy of shopping history to determine one or more reproductive events, such as conception, pregnancy, breastfeeding or parenthood; (2) whether shopping histories can identify lifestyle changes around these events, such as pre-conception, pregnancy and breastfeeding related changes in diet; (3) the extent to which shopping histories enhance repeat data collected in cohort studies, for example, shopping histories with data in real time might be able to pinpoint the timing of events such as planning a pregnancy and conception, whereas cohort data collected from movement sensors over periods that coincide with the timing of these events might better identify changes in physical activity and sleep patterns. The PhD will work with standalone population level supermarket shopping histories data, as well as a subset of shopping histories data linked into Avon Longitudinal Study of Parents and Children (ALSPAC).
The student will mainly work with shopping histories data of a large UK health and beauty retailer, both standalone (>12.5m customers, >1.5 billion transactions) and linked into ALSPAC (for ~1,500 index ALPSAC participants). There is a scope for additional new quantitative data collection with ALSPAC participants where it is needed to meet research aims of the PhD project.
Shopping histories data will be used first to identify a reproductive life event of interest (e.g., pregnancy) and a time window associated with it. Products that are bought during this time window will be then explored. This will allow to identify other behaviours (e.g., pain management, fertility issues) and health outcomes (e.g., miscarriages) associated with this life event. Those behaviours and outcomes will be then validated through the contextual variables using the data available in ALSPAC (and new data collected through surveys) related to causes and consequences of the life event. The student will be expected to explore the structure of the repeat shopping data and identify appropriate methods for analysing those data. For example, repeat purchasing of sanitary products (indicative of menstruation) which change over time might be analysed by multilevel models or structural equations depending on the structure of the data and the specific research question.
This is a data intensive quantitative PhD. The successful candidate would be expected to have had experience of statistical analysis in their first degree, be competent in handling complex large-scale data and eager to learn new quantitative methods and/or about new topic areas in a multidisciplinary team. Depending on their previous experience the successful candidate will obtain training in epidemiology, survey design and data collection, advanced statistical methods, and data science, including the ethics and governance and management and use of data, through the completion of the research project and through postgraduate short courses.
Dr Philip Haycock (lead), Dr Gibran Hemani , Prof Tom Gaunt
Summary data from genome-wide association studies (GWAS) are a valuable resource for many post-GWAS analytical tools, including Mendelian randomisation (MR), fine-mapping and linkage disequilibrium score regression. Access to GWAS summary data is increasingly supported by a number of online repositories, such as Open GWAS (https://gwas.mrcieu.ac.uk/), PhenoScanner (http://www.phenoscanner.medschl.cam.ac.uk/), the GWAS catalog https://www.ebi.ac.uk/gwas/ and GWAS central (https://www.gwascentral.org/). Online repositories may, however, be susceptible to meta-data errors, such as mis-specification of the effect allele or allele frequency columns, which has the potential to introduce substantial bias into downstream analyses. These errors occur because conventions for the inclusion or naming of data fields that avoid ambiguity have not been widely adopted by the GWAS community. Anecdotal evidence suggests that around 2% of datasets in Open GWAS may be affected by such errors. In this project, the student will conduct a systematic quantification of meta-data errors in online repositories of GWAS summary data. The work will be supported by the CheckSumStats R package (https://github.com/MRCIEU/CheckSumStats).
To quantify the extent of meta-data errors in online post-GWAS summary data platforms
Comparison of meta-data fields between GWAS studies and reference studies using the CheckSumStats R package. https://github.com/MRCIEU/CheckSumStats
Dr Amanda Hughes (lead), Professor Laura Howe, Dr Helen Bould
In high-income countries, tackling obesity is seen as a key priority by policymakers and health inequality researchers. As a result, underweight is largely ignored, despite clear links between low body weight and worse health and higher mortality. Underweight has diverse causes: it can result from mental illnesses including eating disorders, but it may also reflect food insecurity and socioeconomic deprivation. In the UK, changes to welfare policy during the 2010s were accompanied by increased foodbank use(1) and a starkly raised risk of underweight was in this period reported among British adult jobseekers(2). More recently, the COVID-19 pandemic brought profound economic and lifestyle changes. Alongside increased food insecurity in low-income families(3) and a record rise in foodbank use(4), there was a stark rise in referrals and hospitalizations for eating disorders(5). These changes mean that the prevalence, risk factors, and social patterning of underweight must now be reassessed.
This project aims to comprehensively investigate the prevalence, social distribution, and risk factors of low body weight in the contemporary UK. To shed light on potential causes, it will explore changes between the early 2000s and the early 2020s. It will consider several stages of the life-course, including adolescence, young adulthood, and middle-age. In each historical period, it will explore the contribution of sociodemographic risk factors, physical and mental health, and genetics.
Data will come from birth cohort studies: the 1946, 1958 and 1970 Birth Cohort Studies, Avon Longitudinal Study of Parents and Children (ALSPAC), and Millennium Cohort Study (MCS). All studies contain rich information including sociodemographic factors, objective height and weight measurements, self-perception of body weight, mental health, and physical health. Repeat measurements allow investigation of change within cohorts. Between-cohort change can be examined by comparing aged-matched participants across surveys. The contribution of mental illness will be examined using measures of common mental disorder (depression/anxiety symptoms) and, where available, of eating disorders. Where genetic information is available, genetic causal inference methods will be used to further unpick the contribution of health and health-related behaviours (e.g. smoking). To capture the effects of the COVID-19 pandemic, the project will use post-pandemic information from ALSPAC, MCS, and the 1958 cohort.
1. Reeves A, Loopstra R. J Soc Policy. 2020.
2. Hughes A, Kumari M. Prev Med. 2017.
3. Parnham JC et al. Public Health. 2020.
4. Baraniuk C. BMJ. 2020.
5. Solmi F et al. Lancet Child Adolesc Heal. 2021.
Dr Denize Atan (lead), Dr Theresa Redaniel, Dr Tim Jones, Senior Research Associate & former cancer analyst at Public Health England, Applied Research Collaboration-West Dr Beth Stuart, Medical Statistician, University of Southampton Dr Samiel Merriel, GP with expertise in early cancer diagnosis and prevention, University of Exeter
Brain tumours affect 8 per 100,000 people in the UK each year. Brain tumours often affect vision before causing any other symptoms. Unless diagnosed early, many people with brain tumours will die or suffer long-term disabilities, like permanent sight loss.
In 2016, an 8-year-old boy called Vincent Barker was in the news. During a routine sight test, his optometrist, Honey Rose, failed to detect optic nerve swelling at the back of his eyes - a sign indicating raised intracranial pressure. He died soon afterwards. As a result, Honey Rose was convicted for gross negligence manslaughter.
Since the widespread media coverage of the Rose/Barker case, optometrists have been referring more people to hospital over concerns they might have optic nerve swelling. Because of this, we think more patients with brain tumours are diagnosed earlier and more frequently by eye specialists than 5 years ago.
Our primary aims are to find out the number of people diagnosed with brain tumours every year between 2013 to 2018 in England and the proportion who were diagnosed by eye specialists before and after the Rose/Barker case in 2016.
Our secondary aims are to determine whether patients with brain tumours were diagnosed earlier by hospital eye specialists compared with other routes-to-diagnosis and whether they lived longer and had better treatment outcomes as a result.
Public Health England and the National Cancer Registry and Analysis Service (NCRAS) routinely collect data on everyone diagnosed with benign and malignant brain tumours in England. We have National Research Ethics Committee approval to access NCRAS data linked to Hospital Episode Statistics; and we have obtained the data on all new cases of benign and malignant brain tumours diagnosed between 2013 and 2018.
Trends in the data will be investigated in the 3 years before and after exposure to the widespread media coverage Rose/Barker case in 2016 by generalised linear regression techniques. We will determine the change in:
(i) Adjusted odds ratios for the number of brain tumours diagnosed via hospital eye services
(i) Time to diagnosis
(ii) WHO tumour grade at diagnosis
(iii) Cancer stage at diagnosis
(iv) Time between diagnosis and treatment
(v) Mortality
Age, sex, ethnicity, geographical location, deprivation index, and smoking history will be used as covariates in these analyses.
1. Poostchi A, et al. Spike in neuroimaging requests following the conviction of the optometrist Honey Rose. Eye 2018.
2. Elliss-Brookes L, et al. Routes to diagnosis for cancer. B J Cancer 2012.
3. Koo MM, et al. Presenting symptoms of cancer and stage at diagnosis. Lancet Oncol 2020.
Dr Denize Atan (lead), Dr Theresa Redaniel, Dr Tim Jones, Senior Research Associate & former cancer analyst at Public Health England, Applied Research Collaboration-West Dr Beth Stuart, Medical Statistician, University of Southampton Dr Samiel Merriel, GP with expertise in early cancer diagnosis and prevention, University of Exeter
Brain tumours affect 8 per 100,000 people in the UK each year. Brain tumours often affect vision before causing any other symptoms. Unless diagnosed early, many people with brain tumours will die or suffer long-term disabilities, like permanent sight loss.
In 2016, an 8-year-old boy called Vincent Barker was in the news. During a routine sight test, his optometrist, Honey Rose, failed to detect optic nerve swelling at the back of his eyes - a sign indicating raised intracranial pressure. He died soon afterwards. As a result, Honey Rose was convicted for gross negligence manslaughter.
Since the widespread media coverage of the Rose/Barker case, optometrists have been referring more people to hospital over concerns they might have optic nerve swelling. Because of this, we think more patients with brain tumours are diagnosed earlier and more frequently by eye specialists than 5 years ago.
Our primary aim is to find out the number of people diagnosed with brain tumours every year between 2013 to 2018 in England and the proportion who were diagnosed by eye specialists before and after the Rose/Barker case in 2016.
Public Health England and the National Cancer Registry and Analysis Service (NCRAS) routinely collect data on everyone diagnosed with benign and malignant brain tumours in England. We have National Research Ethics Committee approval to access NCRAS data linked to Hospital Episode Statistics; and we have obtained the data on all new cases of benign and malignant brain tumours diagnosed between 2013 and 2018.
Trends in the data will be investigated in the 3 years before and after exposure to the widespread media coverage Rose/Barker case in 2016 by generalised linear regression techniques. We will then determine the change in adjusted odds ratios for the number of brain tumours diagnosed via hospital eye services before and after the Rose/Barker case.
1. Poostchi A, et al. Spike in neuroimaging requests following the conviction of the optometrist Honey Rose. Eye 2018.
2. Elliss-Brookes L, et al. Routes to diagnosis for cancer. B J Cancer 2012.
3. Koo MM, et al. Presenting symptoms of cancer and stage at diagnosis. Lancet Oncol 2020.
Dr Philip Haycock (lead), Prof Richard Martin,
Telomeres are DNA-protein structures at the end of chromosomes that protect the genome from damage, shorten progressively over time in most somatic tissues, and are proposed physiological markers of aging. In 2017 the Telomeres Mendelian Randomization Collaboration (TMRC) established that telomere length increases risk of several site specific cancers but reduces risk for some non-neoplastic diseases, including cardiovascular diseases. The analysis was based on a genetic instrument with 12 single nucleotide polymorphisms that together explained 2% to 3% of the variance in circulating telomere length. A recently published genome-wide association study in 472,174 participants in the UK Biobank has increased the number of independent GWAS hits for telomere length to 197 explaining 4.54% of the variance. The primary aims of this project are to: 1) update the genetic instrument for telomere length to include the newly discovered hits; 2) to conduct MR analyses of the association of telomere length with risk of cancer and non-neoplastic diseases; 3) conduct sensitivity analyses for violations of instrumental variable assumptions. Time permitting, the student might additionally: 4) assess the shape of the association between genetically instrumented telomere length and risk of neoplastic and non-neoplastic diseases; and 5) compare MR findings to observational analyses of directly measured telomere length and disease risk in UK Biobank.
The primary aims are: 1) update a genetic instrument for telomere length; 2) conduct MR analyses of the association of telomere length with risk of cancer and non-neoplastic diseases; 3) conduct sensitivity analyses for violations of instrumental variable assumptions; 4) compare MR findings to observational analyses of directly measured telomere length and disease risk in UK Biobank; 5) assess the shape of the association between genetically instrumented telomere length and risk of neoplastic and non-neoplastic diseases
Two-sample Mendelian randomization, using 197 genetic polymorphisms to instrument telomere length and summary data from genome-wide association studies of cancer and non-neoplastic diseases. Primary analyses will be based on random effects inverse variance weighted linear regression. Sensitivity analyses will include MR-Egger regression, the weighted mode estimators and weighted median estimator.
https://www.medrxiv.org/content/10.1101/2021.03.23.21253516v1
https://jamanetwork.com/journals/jamaoncology/fullarticle/2604820
Professor Laura Howe (lead), Dr Amanda Hughes, Dr Matt Dickson, University of Bath, Institute for Policy Research Professor Frances Rice, Cardiff University, Division of Psychological Medicine and Clinical Neurosciences
The transition to higher education (HE) is a key point in a person’s life course; decisions at this time can have lifelong influences. The experience of a mental health or neurodevelopmental condition could lead some people to make decisions that undermine their academic potential, e.g. choose not to participate in HE despite receiving sufficient qualifications, choose a less prestigious university than their grades would make them eligible for, or choose to attend a local university and live at home. These differences may be exacerbated by parental mental health or by genetic factors. As such, own and parental experiences of mental health and neurodevelopmental conditions may entrench intergenerational patterns of socioeconomic (dis)advantage.
1. Assess the influence of life course trajectories of depressive symptoms and neurodevelopmental conditions with decisions about higher education.
2. Assess the influence of parental mental health with decisions about higher education, and the degree to which these are mediated by own mental health.
3. Quantify the role of mental health and neurodevelopmental conditions in the intergenerational transmission of socioeconomic (dis)advantage.
4. Evaluate the influence of genetic risk scores for educational attainment and ADHD on decisions about higher education.
The project involves statistical analysis of data from the Avon Longitudinal Study of Parents and Children (ALSPAC), including:
- Measures of depression, anxiety, ADHD and ASD, repeatedly across childhood through to adulthood
- Measures of parental depression and anxiety
- A detailed questionnaire on reasons for (not) participating in HE, reasons for choices of university and course, and details of university attended
- Linked data on educational test performance across childhood and adolescence
- Genetics
Methods will include regression modelling, techniques for modelling longitudinal data, mediation analysis, and analysis of genetic data. Where possible, we will draw on additional studies for genetic analyses to boost statistical power.
Reflecting the interdisciplinary nature of the project, this PhD involves collaborations with the Institute for Policy Research at the University of Bath and the Division of Psychological Medicine and Clinical Neurosciences at Cardiff University. The student will be based in the MRC Integrative Epidemiology Unit and the Department of Population Health Sciences at the University of Bristol, but will have the opportunity to spend time in all centres.
López-López JA et al. BJPsych Open. 2019:6(1):e6
https://www.medrxiv.org/content/10.1101/2020.05.21.20108928v2
Dardani et al. Int J Epidemiol. 2021 dyab107
Hughes A et al. NPJ Sci Learn. 2021;6(1):1.
Belfields C, et al. The impact of undergraduate degrees on early-career earnings. IFS 2020
Sullivan A, et al. Br J Sociol. 2018 Sep 1;69(3):776–98.
Dr Gemma Sharp (lead), Dr Dan Bernie, Dr Chin Yang Shapland Prof Kate Tilling
Climate change will affect human health through changes to heat stress, sanitation, access to sufficient food and safe drinking water, disease patterns, migration, and frequency of extreme weather events.
Pregnant women and the developing fetus are considered amongst the most vulnerable and marginalised members of society, and could therefore be uniquely sensitive to the effects of climate change. Previous research has shown that babies of mothers exposed to higher ambient temperatures during pregnancy are at higher risk of preterm birth and low birthweight. These perinatal outcomes are associated with greater risk of neonatal ICU admission and infant mortality, as well as long term outcomes such as neurodevelopmental delays and cardiovascular disease.
Research is required to characterise the potential effects of climate change on these outcomes, both now and in the future, and to inform strategies to reduce or manage an increase in their prevalence in line with climate change.
This exciting project aims to explore how epidemiological data and approaches can be best applied to study the effects of climate change. The project focuses on how exposure to specific aspects of climate during pregnancy relate to perinatal health outcomes: gestational age at birth and birth weight.
We will link health and demographic data from sources including UK Biobank, ALSPAC and Born in Bradford to detailed historical climate data from the UK Met Office including 1km resolution data from HadUK-Grid data, supplemented with complimentary observational station data and climate reanalysis products as necessary. We will then explore associations between climate exposures (as measured by ambient temperature, precipitation, barometric pressure, hours of sunlight, or compound measure of environmental stress) during pregnancy in relation to birth weight and gestational age at delivery. Severity and duration of exposure will be sampled on daily to monthly time scales. Results will be then used to define parameters in projection models of the impact of climate change on these outcomes going forward under different scenarios. The application to future climate projections will link to the UK Climate Projections (UKCP18) which for the first time provide national scale climate projections at a resolution comparable to weather forecasts.
In addition to working with a team of highly experienced experts in epidemiology and population health, the student will work with climate experts, including Dr Dan Bernie, who has a joint appointment with the Met office. There will also be opportunities for training and gaining experience in public and policy engagement to help translate findings and create impact.
Climate change and the potential effects on maternal and pregnancy outcomes: an assessment of the most vulnerable – the mother, fetus, and newborn child. Rylander et al. 2013 Glob Health Action https://www.ncbi.nlm.nih.gov/pubmed/23481091
Exploring associations of maternal exposure to ambient temperature with duration of gestation and birth weight: a prospective study. Li et al 2018 BMC Pregnancy Childbirth https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6311008/
Systematic review on adverse birth outcomes of climate change. Poursafa et al 2015 J Res Med Sci https://www.ncbi.nlm.nih.gov/pubmed/26109998
Dr Gemma Sharp (lead), Prof Sarah Lewis, Dr Evie Stergiakouli
Worldwide, roughly one in every 750 babies is born with a cleft of the lip and/or palate. These children face multiple issues with feeding, teeth, speech and hearing, which can last throughout their lives, even after surgery to repair the cleft(s).
There is a high degree of heterogeneity in terms of severity of outcomes. Some individuals have very few issues and require minimal clinical care or adjustment, whereas for others the impact is huge and necessitates lifelong care from multiple disciplines. This presents a challenge to health services looking to allocate finite resources, as a one-size-fits-all approach is unlikely to be economically efficient and could take resources away from those most in need. A better ability to predict which children born with a cleft are at risk of poor outcomes would help clinicians tailor care. This could ultimately result in better cleft care outcomes.
Outcomes like speech, neurodevelopment, mental wellbeing and educational attainment are likely to be influenced by a combination genetics and environment. Epigenetics, which will be influenced by genetics and the environment, could also play a role. Therefore, integrating genetic and epigenetic data with non-molecular data about a child’s cleft phenotype, family history and home environment could help predict risk of outcomes.
This project will use genetic, epigenetic, questionnaire and clinical data to develop and test new risk scores to predict health and social outcomes in children born with a cleft lip/palate. Working closely with the cleft patient and clinical community, the student’s findings will help inform development of clinically useful risk factor profiles to help tailor cleft care and ultimately improve outcomes.
In this project, the student will use information from questionnaires and medical records to identify which clinical (e.g. cleft type, feeding and speech problems) and social (e.g parent’s educational level, parent’s mental health) factors predict worse cleft-related outcomes.
They will use published genetic and epigenetic studies to identify SNPs and CpGs associated with various child health and social outcomes (e.g. obesity, ADHD, social communication, speech traits, depression, etc). They will then generate several polygenic risk scores (PRS) and DNA methylation-based risk scores (MRS) for these outcomes in data from the Cleft Collective cohort studies, which is a detailed longitudinal study of ~3000 children born with a cleft in the UK.
The student will use regression analyses and ROC-curves to assess the ability of these PRSs and MRSs to predict relevant outcomes in children born with a cleft. The student will also examine the predictive ability of non-molecular factors, such as cleft type, socioeconomic position and infant feeding, and compare predictive performance of various combinations of environmental factors, PRS and MRS in predicting different outcomes.
Finally, the student will select the highest performing combination of predictive factors and develop risk scores for each outcome. They will then test the ability of these risk scores to predict outcomes in (an) independent dataset(s) of children born with a cleft. Such datasets could include a subset of the Cleft Collective retained for testing purposes (I.e. not included in the development of the risk scores), or other cohorts of children born with a cleft with information on outcomes and genetics and/or epigenetics from our collaborators in Norway, Germany and/or the USA.
Dr Robert Thibault (lead), Prof Marcus Munafò,
BACKGROUND: Statistics training in many undergraduate courses, particularly in basic science disciplines, rarely extends beyond t-tests, ANOVAs, and basic linear regression. Without an introduction to core statistical concepts (e.g., central limit theorem, randomness) and additional, broader concepts (e.g., confidence intervals, coding), students may begin to perform ‘mindless statistics’ (Gigerenzer 2004), conducting statistical tests without understanding why they are doing so, or what the tests can (and cannot) tell us.
PROBLEM: This poor foundational training may have broader consequences—statistical misconceptions appear to extend to postgraduate researchers and even faculty (Gigerenzer, 2004; Hoekstra, Morey, Rouder, & Wagenmakers, 2014; Tversky & Kahneman, 1971). Better designed undergraduate statistics training may improve statistical knowledge and understanding, and in turn improve the quality of research outputs.
THIS PROJECT: This mini-project is one part of a larger research programme that aims to improve statistical training in the basic sciences. A previous mini-project documented and synthesised the current state of statistical training in psychology undergraduate programmes in the UK (TARG Meta-Research Group, 2020). For this project, you will develop statistical training standards in psychology by coordinating a Delphi process with various stakeholders.
The aim of this mini-project is to develop statistical training standards. The specific objectives include:
1. Assemble a panel of relevant stakeholders (e.g., instructors, researchers, learned societies, statisticians, students).
2. Conduct a Delphi process with these stakeholders.
3. Write a report that outlines the conclusions of the Delphi process.
You will lead a modified Delphi process, which is a structured method to elicit the opinions of various stakeholders and synthesize them into a meaningful conclusion.
BENEFITS FOR YOU: This project will help you gain an appreciation for the importance of good statistical practice in scientific research, including epidemiology. You will connect with researchers, instructors, and statisticians at various universities. You will learn about the field of meta-research and help prepare an article for publication which you will be an author on.
WHO TO CONTACT: If you are interested, please get in touch with me at robert.thibault@bristol.ac.uk – I can provide more details and perhaps tailor the project to your interests.
Gigerenzer, G. (2004). Mindless statistics. Journal of Socio-Economics. https://doi.org/10.1016/j.socec.2004.09.033
Hoekstra, R., Morey, R. D., Rouder, J. N., & Wagenmakers, E. J. (2014). Robust misinterpretation of confidence intervals. Psychonomic Bulletin and Review, 21(5), 1157–1164. https://doi.org/10.3758/s13423-013-0572-3
Tversky, A., & Kahneman, D. (1971). Belief in the law of small numbers. Psychological Bulletin, 76(2), 105–110. https://doi.org/10.1037/h0031322
TARG Meta-Research Group. (2020). Statistics education in undergraduate psychology: A survey of UK course content.
Dr Evie Stergiakouli (lead), Prof Laura Howe, Christina Dardani Dr Rachel Blakey
Attention Deficit Hyperactivity Disorder (ADHD) is a chronic neurodevelopmental condition, characterised by persistent difficulties in the areas of attention span/impulse control. It typically first manifests early in childhood and often persists into adulthood and has been associated with impaired education attainment and social disadvantages.
We have previously shown that ADHD genetic risks are associated with younger maternal age at birth, lower educational attainment and other indicators of social disadvantage in mothers from the general population (1). Using Mendelian randomization (MR) we have also found evidence of causal effects of genetic liability to ADHD on educational attainment, and evidence of effects of genetic liability to higher educational attainment on risk of ADHD which was independent of cognitive ability (2). Since ADHD manifests at a very young age, the causal effects of genetic liability to education on ADHD are likely to indicate parental effects. However, disentangling the individual effects of each factor as well as assessing for genetic confounding is required.
In this project, we will explore the links between educational attainment, reproductive outcomes (age at first birth, age at first sexual intercourse, number of live births), socioeconomic status and other indicators of social disadvantage on ADHD.
A. We will use two-sample Mendelian randomization (3) to investigate any causal links between genetic liability to educational attainment, reproductive outcomes (age at first birth, age at first sexual intercourse, number of live births), socioeconomic status and ADHD bidirectionally
B. We will perform Multivariable Mendelian randomization (4) to account for the exposures simultaneously
C. Apply sensitivity analyses including weighted median, weighted mode, MR-Egger regression, MR-PRESSO and colocalization analyses to assess and adjust for pleiotropy
1. Leppert et al. Association of Maternal Neurodevelopmental Risk Alleles With Early-Life Exposures. JAMA Psychiatry. 2019;76(8):834–842. doi:10.1001/jamapsychiatry.2019.0774
2. Dardani et al. Is genetic liability to ADHD and ASD causally linked to educational attainment?, International Journal of Epidemiology, 2021;, dyab107, https://doi.org/10.1093/ije/dyab107
3. Davey Smith, G. & Hemani, G. Mendelian randomization: genetic anchors for causal inference in epidemiological studies. Hum. Mol. Genet. 23, R89–R98 (2014)
4. Sanderson, E., Davey, G. S., Windmeijer, F. & Bowden, J. An examination of multivariable Mendelian randomization in the single-sample and two-sample summary data settings. Int. J. Epidemiol. (2018)
Dr Tim Morris (lead), Prof Kate Tilling, Prof George Davey Smith
Participants of research studies are often not fully representative of the general population at baseline. Furthermore, attrition from research studies is often patterned by a range of demographic, health and socioeconomic factors, making these studies less representative with greater elapsed time. This non-random participation has implications for the validity of results obtained from datasets [1,2], particularly where there is interaction in the selection mechanisms [3]. Non-random participation and attrition can be seen in the genomes of study participants; participation has been shown to be related to genetic risk for traits including higher BMI, neuroticism, schizophrenia, attention-deficit hyperactivity disorder (ADHD) and depression [4]. A clearer understanding of the genotypic and phenotypic patterns of study participation and attrition could help to improve interpretation of future study findings.
1. To investigate genetic signal in non-response in cohort studies such as the Avon Longitudinal Study of Parents and Children (ALSPAC), the Millennium Cohort Study (MCS), and Understanding Society (USoc).
2. To investigate the heterogeneity of genetic signal for participation across cohorts.
3. To investigate whether genetic signal for participation varies over time.
This project will provide experience in using ALSPAC data, handling genetic data, and statistical methods for analysing genetic data. We will support you to write the work up for publication if you wish to do so.
This mini project may make use of several methods outlined below; the student can initially choose which of these areas they would like to focus on. In each case, the student will be required to create indicators of non-response across all questionnaire and direct assessment timepoints for study participants. 1) Applying Genome-wide Complex Trait Analysis (GCTA-GREML) to measures of participation in cohorts to estimate the SNP heritability of participation. These results may then be meta-analysed across cohorts. 2) Conducting a GWAS of age and sex in cohorts as a negative control outcome analysis to identify selection bias. This may be supplemented by investigating associations between polygenic scores for traits with age and sex. 3) Investigating how polygenic scores for a range of traits (e.g. BMI, schizophrenia, educational attainment) associate with participation and attrition in cohorts. The results of these analyses may be meta-analysed with the above cohorts and others that the IEU has access to.
1. Griffith, G.J., Morris, T.T., Tudball, M.J. et al. Collider bias undermines our understanding of COVID-19 disease risk and severity. Nat Commun 11, 5749 (2020). https://doi.org/10.1038/s41467-020-19478-2.
2. Marcus R Munafò, Kate Tilling, Amy E Taylor, David M Evans, George Davey Smith, Collider scope: when selection bias can substantially influence observed associations, International Journal of Epidemiology, Volume 47, Issue 1, February 2018, Pages 226–235, https://doi.org/10.1093/ije/dyx206.
3. North TL, Davies NM, Harrison S, Carter AR, Hemani G, Sanderson E, Tilling K, Howe LD. Using Genetic Instruments to Estimate Interactions in Mendelian Randomization Studies. Epidemiology. 2019 Nov;30(6):e33-e35. doi: 10.1097/EDE.0000000000001096. PMID: 31469698.
4. Amy E Taylor, Hannah J Jones, Hannah Sallis, Jack Euesden, Evie Stergiakouli, Neil M Davies, Stanley Zammit, Debbie A Lawlor, Marcus R Munafò, George Davey Smith, Kate Tilling, Exploring the association of genetic factors with participation in the Avon Longitudinal Study of Parents and Children, International Journal of Epidemiology, Volume 47, Issue 4, August 2018, Pages 1207–1216, https://doi.org/10.1093/ije/dyy060.
Dr Liam Mahedy (lead), Prof. Marcus Munafò,
Growing evidence suggests that levels and changes in hand grip strength may be sensitive to subtle changes in brain health (Carson et al., 2018). Evidence from longitudinal studies have revealed mixed findings in terms of the direction of this relationship. For example, studies indicated that low hand grip strength was associated with cognitive impairment in ageing populations (Cooper et al., 2013), even when measured decades earlier in midlife (Dercon et al., 2021). Other studies found that poorer cognitive functioning was associated with low hand grip strength (van Dam et al., 2018). Findings examining the bidirectional association have also been unclear (McGrath et al., 2019, Ritchie et al., 2016). One recent genetic study that examined this association reported a positive association between genetic risk score for hand grip strength and cognitive functioning but did not examine the potential for a bidirectional relationship (Tikkanen et al., 2018).
Using genetic variants, which are fixed at conception, this project will help to determine if there is a potential causal association between hand grip strength and cognitive functioning and examine the potential for reverse causation between cognitive functioning and hand grip strength.
Summary level two-level bi-directional Mendelian randomisation.
Apply several sensitivity analyses including weighted median, weighted mode, MR-Egger regression, and MR-PRESSO to rule out pleiotropy.
Carson R.G. (2018). Get a grip: individual variations in grip strength are a marker of brain health. Neurobiol Aging. 71:189–222.
Cooper R., et al. (2014). Physical capability in mid-life and survival over 13 years of follow-up: British birth cohort study. BMJ. 348.
Dercon, Q., et al. (2021). Grip strength from midlife as an indicator of later-life brain health and cognition: evidence from a British birth cohort. BMC Geriatrics, 21, 475.
McGrath R., et al. (2019). Handgrip Strength Is Associated with Poorer Cognitive Functioning in Aging Americans. J Alzheimer’s Dis. 70:1187–96.
Ritchie S.J., et al. (2016). Do Cognitive and Physical Functions Age in Concert from Age 70 to 76? Evidence from the Lothian Birth Cohort 1936.Span J Psychol. 5; 19:E90.
Tikkanen, E., et al. (2018). Biological Insights into Muscular Strength: Genetic Findings in the UK Biobank. 8, 6451.
van Dam R., (2018). Cognitive Function in Older Patients with Lower Muscle Strength and Muscle Mass. Dement Geriatr Cogn Disord. 45,243-250.
Professor Richard Martin (lead), Dr James Yarmolinsky, Dr Philip Haycock
Circulating proteins play fundamental roles in biology and disease aetiology, including development of cancer, and are the direct targets of many drugs (1). Recent large-scale genome-wide association studies (GWAS) of circulating protein have identified thousands of protein quantitative trait loci (pQTLs) that influence protein levels (2-5). These variants can be used in a Mendelian randomization (MR) framework to estimate the causal effects on circulating proteins on cancer risk or progression (6). Robust identification of circulating proteins that causally influence risk or subsequent progression of cancer can then be used inform development of pharmacological interventions for cancer prevention and treatment.
The overall aim of this project is to identify circulating proteins that influence cancer risk and/or progression (i.e. spread or death). The specific objectives of this project are as follows:
i) Develop genetic instruments for circulating protein concentrations using recent GWAS to estimate the effect of these proteins on risk of overall and subtype-specific breast, colorectal, lung, ovarian, and prostate cancer.
ii) Examine the effect of circulating proteins on progression of breast, colorectal, lung, ovarian and prostate cancer.
iii) Examine agreement between the effect of circulating proteins on cancer risk compared with their effect on progression.
iv) Validate top findings from objectives i and ii using observational epidemiological analysis in UK Biobank or the UK wide Clinical Practice Research Datalink (where a protein discovered is also the drug target for an approved medication).
Genome-wide significant (P < 5 x 10-8) single-nucleotide polymorphisms (SNPs) associated with circulating protein concentrations will be used to develop genetic instruments for these proteins to estimate their causal effect on risk and progression of overall and subtype-specific breast, colorectal, lung, and prostate cancer using two-sample Mendelian randomization (1-5). Various sensitivity analyses will be performed to examine robustness of findings to violations of Mendelian randomization assumptions (e.g., colocalization analysis, heterogeneity testing, Steiger filtering, SloperHunter for cancer progression analyses). Concordance of effect estimates across cancer risk and progression analyses (corrected for potential index event bias) will be assessed. Top findings (i.e. those that are robust to multiple testing correction) will then be validated using observational epidemiological analysis within the UK Biobank cohort study or the Clinical Practice Research Datalink (CPRD).
This project will provide comprehensive and advanced training in genetic epidemiology and cancer, specifically Mendelian randomization applied to molecular traits and cancer biology, and experience writing up findings for publication and presentation. You will join a large cohort of fellow doctoral students and be part of a vibrant, intellectually generous and supportive Department.
1. Schmidt et al. Nature Communications, 2020. doi: 10.1038/s41467-020-16969-0
2. Folkersen et al. Nature Metabolism, 2020. doi: 10.1038/s42255-020-00287-2
3. Pietzner et al. Nature Communications, 2020. doi: 10.1038/s41467-020-19996-z
4. Gilly et al. Nature Communications, 2020. doi: 10.1038/s41467-020-20079-2
5. Sun et al. Nature, 2018. doi: 10.1038/s41586-018-0175-2
6. Zheng et al. Nature Genetics 2020. doi: 10.1038/s41588-020-0682-6
Luisa Zuccolo (lead), Carolina Borges, Nancy McBride
Breastfeeding is sustainable, the biological norm, and potentially life-saving, particularly for premature babies. Evidence-based strategies to support breastfeeding have been successful, but inequalities in breastfeeding rates are proving difficult to reduce, affecting the most vulnerable of mothers and babies. Successfully establishing and sustaining breastfeeding can be facilitated by both removing structural and cultural barriers, and overcoming individual challenges. Common factors such as obesity and depression/anxiety could play an important part in explaining some of the variability (and inequality) in breastfeeding duration. Conversely, maternal factors reflecting good mental and physical health could increase resilience to contexts with low systemic and cultural support for breastfeeding, such as the UK. However, the evidence on the individual determinants causally influencing successful and sustained breastfeeding is of poor quality. The identification of causal determinants of early cessation will improve breastfeeding support activities.
This project aims to establish the causal role of individual and environmental factors on successfully establishing and sustaining breastfeeding.
In particular, it will investigate:
1. Individual maternal factors as causal determinants of breastfeeding outcomes.
2. Joint and conditional effects of determinants of breastfeeding outcomes.
3. Contextual effects of determinants of breastfeeding outcomes, depending on high/low support.
Data:
Cohort studies participating in the MR-PREG consortium (MoBa, HUNT, ALSPAC, BiB...).
Maternal Exposures:
obesity, diabetes, internalising (depression/anxiety) and externalising (ADHD) problems, smoking/alcohol/caffeine use, sleep traits, eczema and skin conditions, education.
Outcomes:
successful establishment of breastfeeding (i.e. breastfeeding for 6+ weeks Vs <6 weeks); sustained breastfeeding (i.e. breastfeeding for 6+ months Vs <6 months).
Analysis:
1. 2-Samples Mendelian Randomization for each factor (exposure) in each cohort, then meta-analysis.
2. Mediation analysis to investigate to what extent the various individual factors explain education effects, thus mediating health inequalities - MultiVariable MR.
3. We will evaluate whether systemic factors underpinning high and low breastfeeding rates (level of support for breastfeeding) modify the effects attributed to the individual factors - meta-regression and cross-cohort analyses based on the summary data.
Victora CG et al. Lancet 2016; 387(10033): 474-90
Rollins NC et al. The Lancet 2016; 387(10017): 491-504
Dr Luisa Zuccolo (lead), Dr Carolina Borges, Dr Rachel Freathy
Breastfeeding is the biological norm and sustainable. It is also potentially life-saving, particularly for premature babies and those without access to clean water. Strategies to support breastfeeding have been successful, but inequalities persist and rates remain low in high-income countries such as the UK.
Although the short-term effects of breastfeeding are well documented, several questions about the epidemiology of breastfeeding remain unresolved, including which maternal and infant long-term outcomes are affected by different breastfeeding practices, and what the mechanisms behind these are.
This project will benefit mothers and babies by improving our understanding of practices and behaviours for optimal child development and long-term maternal health.
This project will improve our understanding of the effects (to the mother and the infant) of successfully establishing and sustaining breastfeeding.
Specific aims:
1. To identify genetic predictors of breastfeeding traits (to be used in genetic analyses to inform Aims 2. and 3.)
2. To estimate causal effects of breastfeeding on maternal and offspring health
3. To explore mechanisms for the long-term effects of breastfeeding
Breastfeeding traits include initiation, successful establishment, duration, exclusivity, breastfeeding problems.
This project aims to answer the above questions by combining cutting-edge methods that improve causal inference in observational studies, e.g. Mendelian Randomization and causal mediation, with classic epidemiological designs, e.g. cross-context comparisons. Results from each of these methods are likely to suffer from different biases, sometimes in opposite directions. We will exploit this using a triangulation approach, consistent results will provide stronger evidence for causality.
A key and novel component of the project will also be the identification of genetic predictors of breastfeeding traits through well-powered genome-wide association studies, to inform the Mendelian randomization analyses.
In order to fulfill the study’s objectives, an international network of collaborating cohorts has been established (N>120,000) to analyse existing data on breastfeeding traits, and putative determinants and consequences.
Victora CG et al. Lancet 2016; 387(10033): 474-90.
Dr Evie Stergiakouli (lead), Dr Gemma Sharp, Prof Sarah Lewis Christina Dardani
Cleft of the lip and/or palate (CL/P) is one of the most common congenital anomalies requiring corrective surgery within the first year of life. Children born with CL/P have 3.2 admissions and spend 13.2 days in hospital in the first two years of life. Despite treatment to repair the cleft in infancy, being born with a cleft frequently results in multiple adverse outcomes across the lifespan, including facial disfigurement, impaired speech and low intelligibility, with potentially poor educational, vocational, social, mental and physical health outcomes. We have previously shown, using data from a large cohort of children born with cleft, the Cleft Collective, that children born with CL/P have higher levels of behavioural problems than children in the general population at 5 and 10 years (1). However, we do not know whether there is genetic correlation between cleft and psychiatric disorders or whether factors associated to the cleft phenotype cause psychiatric disorders in children born with cleft. We have previously used Mendelian randomization to show that being born with cleft does not cause children to underperform at school (2).
In this project, we will explore the links between cleft and psychiatric disorders (ADHD, autism spectrum disorder (ASD), anxiety, depression, schizophrenia).
A. We will use two-sample Mendelian randomization (3) to investigate any causal links between genetic liability to cleft of the lip and/or palate, and ADHD, ASD, anxiety, depression, schizophrenia bidirectionally
B. Apply sensitivity analyses including weighted median, weighted mode, MR-Egger regression, MR-PRESSO and colocalization analyses to assess and adjust for pleiotropy
C. We will perform Linkage Disequilibrium (LD)-score regression (4) to estimate any genetic correlation between cleft of the lip and/or palate, and ADHD, ASD, anxiety, depression, schizophrenia
1. Berman et al. Prevalence and Factors Associated with Behavioural Problems in 5-year-old Children Born with Cleft Lip and/or Palate from the Cleft Collective. medRxiv 2021.08.04.21261594; https://doi.org/10.1101/2021.08.04.21261594
2. Dardani et al. Is genetic liability to ADHD and ASD causally linked to educational attainment?, International Journal of Epidemiology, 2021;, dyab107, https://doi.org/10.1093/ije/dyab107
3. Davey Smith, G. & Hemani, G. Mendelian randomization: genetic anchors for causal inference in epidemiological studies. Hum. Mol. Genet. 23, R89–R98 (2014)
4. Bulik-Sullivan et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat Genet 2015;47:291–95.
Dr Luisa Zuccolo (lead), Dr Gemma Sharp, Dr Doretta Caramaschi (Exeter) Dr Anita Thapar (Cardiff)
There is growing concern that paternal exposures before conception have been greatly neglected. Studying how these impact on future generations’ health could open new avenues for prevention-prospective fathers are not generally advised to change their behaviour. In animal studies, one of the paternal exposures showing the largest and most consistently reported effects relative to the prenatal period is alcohol, however convincing human evidence is lacking to date. In this project, we propose to investigate the relationship between pre-conception paternal alcohol use and offspring mental health, and in particular attention deficit hyperactivity disorder (ADHD).
1 Is paternal alcohol use before conception associated with offspring ADHD, and/or its brain morphology correlates?
2 Is this due to shared genetic influences?
3 Is this association robust to different causal inference methods of analyses such as Mendelian randomization, and the use of negative controls (eg non-biological fathers)?
4 Are offspring cord blood DNA methylation and ADHD structural brain correlates on the causal pathway from paternal alcohol exposure childhood ADHD?
To improve the chance of disentangling correlation from causation, we propose to use a paradigm similar to that of (lab studies of) rodent models of paternal effects: a pseudo-experimental study design (employing Mendelian randomization and other analytical approaches to improve causal inference), restricting to families with no intrauterine exposure to alcohol (like in the animal studies), focusing on early manifestation of the outcome occurring before the onset of own drinking to remove confounding by own drinking, and even earlier potential biological mediators of the effects (eg cord-blood DNA methylation, eliminating confounding by paternal drinking in the postnatal period, both set to ‘zero’ in animal studies).
Finegersh A, Alcohol 2015 49:461-70
Rogers JC, JAMA Psychiatry 2016 73:64-72
Chen YC, Mol Psychiatry 2017 Mar 21
Dr Eleanor Sanderson (lead), Dr Rhian Daniel, Professor Laura Howe
Many of an individual’s traits are observationally associated with their health outcomes. Understanding the relationships between these factors is critical to effective public health intervention. When multiple traits are potentially associated with a disease or health outcome, it is often not clear how much of the observed effect of each single trait is due to the effect of that trait or behaviour on other traits and behaviours, which then affect the outcome, and how much is “direct” in the sense that it is not mediated by the other traits being considered.
Causal mediation analysis is one approach that can be used to determine the proportion of the effect of a trait on an outcome that is via a mediating variable. However, this method relies on many strong assumptions. An alternative approach, relying on different assumptions, is Mendelian Randomisation (MR). MR is a method of instrumental variable analysis which utilises genetic variation between individuals to help understand causal effects.
The aim of this project is to conduct research on the strengths and limitations of MR when trying to understand the causal effects of multiple exposures on a health outcome. This will include investigation of how novel methods of MR analysis such as Multivariable MR relate to mediation analysis and developing and extend existing MR methods to deal with multiple mediators. This project will also explore possibilities of combining the two approaches.
In this PhD you will have the opportunity to work with leading researchers in the fields of population health, statistics, and Mendelian randomisation to further develop statistical methods for causal analysis based around MR mediation analysis.
This project will involve mathematical derivation of the properties of the extended methods and verification using simulation studies. This project will also involve analysis of both individual-level and summary-level data to apply the methods developed.
Although this project will be methodological in focus, the student will have the opportunity to develop a relevant application of these methods based on their personal research interests. Prospective applicants should have a strong quantitative background and an interest in developing methods for causal analysis within a population health setting; however, no particular background knowledge is required.
Dr Gibran Hemani (lead), Mr Matthew Lyon, Prof Tom Gaunt
Programming is an increasingly important skill to have in epidemiological research and this project will
The OpenGWAS project hosts about 200 billion genetic association records in an online database, using a technology known as ElasticSearch. The way it is currently implemented is extremely fast for users but it is not particularly efficient in terms of how much disk space it uses in the cloud, and as a consequence it is very expensive.
An alternative way to host the data would be in GWAS VCF files that are more disk space efficient. The problem here is that we need to re-write the software used to query the database to lookup data in these GWAS VCF files rather than the ElasticSearch database.
This is not the most traditional of mini projects but we think it would be a really valuable one for both the student and the community. The student will gain:
a) An opportunity to learn python programming
b) A chance to contribute to a large open source software project
c) Contribution to the next OpenGWAS paper
1. A python package already exists that can query GWAS VCF files (https://github.com/MRCIEU/pygwasvcf). Update the pyGWASVCF package to use a faster VCF parsing library
2. The OpenGWAS database is queried via an API. In order for the API to query GWAS VCF files directly we will update the API to use pyGWASVCF
3. Performance comparisons of ElasticSearch vs pyGWASVCF
The project will entail creating a fork of the OpenGWAS API (https://github.com/MRCIEU/opengwas-api/) and updating it to perform queries against a directory of GWAS VCF files. We will then compare its performance against the original API that queries the ElasticSearch database.
At the onset of the project we will consider whether we should adopt the GenomicsDB approach for hosting the GWAS VCF files, depending on its developmental maturity.
1. Elsworth et al. The MRC IEU OpenGWAS data infrastructure. Biorxiv 2020 https://www.biorxiv.org/content/10.1101/2020.08.10.244293v1
2. Hemani et al. The MR-Base platform supports systematic causal inference across the human phenome. eLife 2018. https://elifesciences.org/articles/34408
3. Lyon et al. The variant call format provides efficient and robust storage of GWAS summary statistics. Genome Biology 2021. https://genomebiology.biomedcentral.com/articles/10.1186/s13059-020-02248-0
Dr Gibran Hemani (lead), Dr Josephine Walker, Dr Neil Davies
The gender pay gap grows substantially after women become parents (Kleven et al 2019). Encouraging more equitable sharing of parental leave amongst mothers and fathers may be a critical part of reducing the gender pay gap (Johansson 2010). Though a shared parental leave scheme was introduced in the UK in 2015, only 2-3% of fathers have enrolled in the scheme. Amongst the reasons for this is that it is governed by a set of complex, inaccessible rules that vary across work places (Maternity Action, 2021). For example, the University of Bristol offers more pay to fathers than the baseline shared parental leave scheme (http://www.bristol.ac.uk/hr/policies/shared-parental-leave.html).
The Bristol branch of the Universities and Colleges Union (UCU) along with the University of Bristol HR department would like to help parents-to-be plan and understand their leave and pay options through a user-friendly calculator.
The aim of this project is to develop user friendly software to plan shared parental leave and calculate the pay and other aspects of parental leave. It is a great opportunity to learn web application development skills, whilst providing a valuable resource to the community.
An R package already exists that calculates various aspects of shared parental leave (https://github.com/explodecomputer/shared-parental-leave/). The primary task of this project will be to develop a web app that makes this R package more user friendly.
We will approach this project in a similar manner to how the USS pension modeller was developed (http://www.uss-pension-model.com/). Here, an R package was developed to perform the analysis and calculations (https://github.com/explodecomputer/USSpensions), and an R shiny app was developed as a wrapper for the calculations in the R package (https://github.com/explodecomputer/USSpensions-shiny/).
1. Kleven et al. Children and Gender Inequality: Evidence from Denmark. American Economic Journal: Applied Economics. 2019.
2. Johansson. The effect of own and spousal parental leave on earnings. Working Papers, IFAU - Institute for Evaluation of Labour Market and Education Policy. 2010. https://www.econstor.eu/handle/10419/45782
3. Maternity Action, 2021. https://maternityaction.org.uk/wp-content/uploads/Shared-Parental-Leave-briefing-May-2021.pdf
Dr Neil Ryan (lead), Prof Sir John Burn ,
The aim of this proof-of-concept study is to analyse urine samples, collected with Colli-Pee, in women with microsatellite instability high (MSI-H) endometrial cancers as to determine if MSI-H can be detected in their urine. This could potentially provide an alternative means of endometrial cancer surveillance in women with Lynch syndrome (LS) that would enable painless self-sampling and the triage of those who need invasive investigations.
LS is the most common inherited cancer predisposition[1]; as many as many as one in 278 individuals have LS [2]. For a woman with LS, the lifetime risk of endometrial cancer is up to 60%[3]. However, these women have limited options as to how to mitigate their risk. Risk reducing surgery has been shown to be effective[4], yet it is not suitable for all women; such surgery leads to infertility and the surgical risk may be too high for some individuals. Endometrial cancer surveillance is often offered in leu of risk reducing surgery in the form of annual endometrial biopsy[5]. Endometrial sampling can be painful, and the fear of pain can prompt some women to leave surveillance programs or opt for risk reducing surgery sooner then they may have otherwise[6]. Due to the costly and labour-intensive nature of such screening programs many centres fail to offer a comprehensive service[7]. Therefore, there is a need for alternative methods by which to detect early endometrial neoplasms in women with LS.
Endometrial cancers that arise in women with LS often display MSI-H [8]. This is an early event in the natural history of the cancer[9]. What is more, MSI-H can be detected in the urine of those with cancer [10,11]. It has also been detected in the hysteroscopic wash of patients with endometrial cancer[12,13]. It is known that those with endometrial cancer shed malignant cells that can be detected in their urine[14]. Therefore, it is theoretically possible that MSI-H could be detectable in the urine of those with MSI-H in their endometrial cancer; this has yet to be explored.
Around 20% of endometrial have MSI because of somatic events or LS[15]. As all endometrial cancers are screened for MSI in the UK, it is possible to identify women early in their cancer journey. Women with MSI-H endometrial cancer would be recruited into this study and asked to use the Colli-Pee to collect up to three morning urine samples which would be analysed for MSI. The technology to explore MSI in urine samples already exists [16–20]. These data will inform a potential future study in which those with LS could trial the use of the Colli-Pee based urine MSI analysis as to provide an alternative means of endometrial cancer surveillance in women with LS.
Aim: The overarching aim of this study is to explore the promise of a urine based endometrial cancer detection tool that could be used to provide endometrial surveillance in women with Lynch syndrome (LS). If successful, this would avoid the current need for invasive, repetitive, and costly diagnostic tests and allow women with LS to self-test.
Objectives:
Main:
To explore if microsatellite instability (MSI) can be detected in the urine of those with microsatellite high (MSI-H) endometrial cancers using the Colli-Pee device.
Specific:
• Seek and acquire ethical approval for multi-cite recruitment upon confirmation of a successful application.
• Recruit between 30-40 women with proven microsatellite instability high endometrial cancer, vaginal bleeding and before their hysterectomy from centres in the Southwest of England (Bristol, Bath and Taunton) over the course of a year.
• Recruit 5 controls without endometrial cancer but with vaginal bleeding.
• Collect up to three morning urine samples, to increase cellular yield, from each study participant.
• Within the first two months of recruitment, optimise sample preservation from collection to analysis; namely trial the use of Novosanis Urine Conservation Medium.
• Within in the first two months of recruitment, optimise material extraction for the use of a MSI Analysis System.
• Explore the use of an extended MSI panel to see if this can improve diagnostic accuracy.
• Published a peer reviewed manuscript detailing the outcomes of this study within a year of the study’s closure.
Subject identification:
Women will with microsatellite instability high (study arm) will be identified by members of the research team during the weekly multidisciplinary team meeting for their respective cancer centre. It is routine clinical practice in the Southwest of England for microsatellite analysis to be done on the diagnostic sample. This will allow for the identification of suitable study subjects before they hysterectomy. In total these centres treat around 350 endometrial cancers a year; assuming 20% [1] have microsatellite instability, there would be 70 potential women to recruit each year. Written and informed consent will be required before entry into the study. In addition, five women without cancer but vaginal bleeding will be recruited from the benign gynaecology workload as a control arm. All women will be approached during their routine clinical care.
Inclusion criteria:
• Ability to given informed and written consent
• Greater than or equal to 18 years old
• Uterus still in situ
• Histological confirmed endometrial cancer with microsatellite instability (study arm) or no clinically confirmed cancer (control arm)
• Current vaginal bleeding
• Ability to self-collect a urine sample
Exclusion Criteria
• < 18 years old
• Unable to given informed and written consent
• Concurrent urogenital or colorectal cancer
• No active vaginal bleed
• No residual cancer at hysterectomy (study arm)
• No uterus
• Significant urinary incontinence (defined as continuous urinary leak)
• Vagino-visceral fistula
• Previous pelvic radiotherapy
• Congenital urogenital abnormality/Procidentia
• Inability to self-collect urine sample.
Sample Size:
With a prevalence of disease (MSI-H endometrial cancer) of 100%, and the values of both sensitivity or specificity of the screening or diagnostic test {for both null (Ho) and alternative (Ha) hypotheses} being set at 0.6 and 0.9 respectively, a sample size of 35 is required[2].
In addition, 5 controls will be recruited in which one urine sample will be analysed. Finally, we assume some women will have no residual disease on hysterectomy and therefore 5 additional women may need to be recruited to the study arm[3].
Sample collection:
Women will be asked to produce between one to three morning urine samples; morning collection should increase the cellular yield as endometrial shed pools in the vagina overnight. These will be collected in the Colli-Pee device in combination with the Novosanis Urine Conservation Medium to maximise DNA preservation. If this leads to suboptimal DNA quality for microsatellite instability analysis other mediums will be trailed, for example the Novosanis Urine Analyte Stabiliser. For samples collected by patients at home, the Colli-Pee postal kit could be utilised for direct transportation to the genomics laboratory.
In addition, a heperinized venous blood sample will be taken from the patient as a source of reference DNA. This will be stored at below 4 degrees Celsius in registered clinical laboratory. We will aim to time this blood draw to coincide with routine clinical bloods as to minimise discomfort for the patient.
Sample analysis:
Protocols for microsatellite instability analysis in urine have been described in the literature[4–8]. These will be optimised for the detection of microsatellite instability in endometrial cancer cells found within the urine. In the first instance, the urine samples were spun at 3000 rpm for 6 minutes within 5 hours after voiding. The pellet was rinsed with 10 mL phosphate-buffered saline (PBS) and spun again at 3000 rpm for 6 minutes. The pellet then was resuspended in 800 μL PBS, brought to a 1.5-mL Eppendorf tube, and spun down at 10,000 rpm for 2 minutes. The cells were stored at −80 °C until DNA isolation. DNA extraction will follow the clinical laboratory’s current standard operating procedures. If these methods prove suboptimal, the literature will be searched for alternative protocols.
References
1 Ryan NAJ, Glaire MA, Blake D, et al. The proportion of endometrial cancers associated with Lynch syndrome: a systematic review of the literature and meta-analysis. Genet Med 2019;21:2167–80. doi:10.1038/s41436-019-0536-8
2 Bujang MA. Requirements for Minimum Sample Size for Sensitivity and Specificity Analysis. J Clin Diagnostic Res Published Online First: 2016. doi:10.7860/jcdr/2016/18129.8744
3 O’Flynn H, Ryan NAJ, Narine N, et al. Diagnostic accuracy of cytology for the detection of endometrial cancer in urine and vaginal samples. Nat Commun 2021;12:952. doi:10.1038/s41467-021-21257-6
4 Rhijn BWG van, Lurkin I, Kirkels WJ, et al. Microsatellite analysis—DNA test in urine competes with cystoscopy in follow‐up of superficial bladder carcinoma. Cancer 2001;92:768–75. doi:10.1002/1097-0142(20010815)92:4<768::aid-cncr1381>3.0.co;2-c
5 Szarvas T, Kovalszky I, Bedi K, et al. Deletion analysis of tumor and urinary DNA to detect bladder cancer: urine supernatant versus urine sediment. Oncol Rep 2007;18:405–9.
6 Utting M, Werner W, Dahse R, et al. Microsatellite analysis of free tumor DNA in urine, serum, and plasma of patients: a minimally invasive method for the detection of bladder cancer. Clin Cancer Res Official J Am Assoc Cancer Res 2002;8:35–40.
7 Utting M, Werner W, Muller G, et al. A Possible Noninvasive Method for the Detection of Bladder Cancer in Patients. Ann Ny Acad Sci 2001;945:31–5. doi:10.1111/j.1749-6632.2001.tb03861.x
8 Goessl C, Müller M, Straub B, et al. DNA Alterations in Body Fluids as Molecular Tumor Markers for Urological Malignancies. Eur Urol 2002;41:668–76. doi:10.1016/s0302-2838(02)00126-4
9 Mead LJ, Jenkins MA, Young J, et al. Microsatellite Instability Markers for Identifying Early-Onset Colorectal Cancers Caused by Germ-Line Mutations in DNA Mismatch Repair Genes. Clin Cancer Res 2007;13:2865–9. doi:10.1158/1078-0432.ccr-06-2174
10 Ryan NA, McMahon RF, Ramchander NC, et al. Lynch syndrome for the gynaecologist. Obstetrician Gynaecol 2021;23:9–20. doi:10.1111/tog.12706
11 Crosbie EJ, Ryan NAJ, Bosse T, et al. The Manchester International Consensus Group recommendations for the management of gynecological cancers in Lynch syndrome. Genetics in Medicine 2019.
12 Ryan N, Nobes M, Sedgewick D, et al. A mismatch in care: results of a United Kingdom‐wide patient and clinician survey of gynaecological services for women with Lynch syndrome. Bjog Int J Obstetrics Gynaecol 2021;128:728–36. doi:10.1111/1471-0528.16432
13 Auranen A, Joutsiniemi T. A systematic review of gynecological cancer surveillance in women belonging to hereditary nonpolyposis colorectal cancer (Lynch syndrome) families. Acta Obstet Gyn Scan 2011;90:437–44. doi:10.1111/j.1600-0412.2011.01091.x
14 Helder-Woolderink J, Bock G de, Hollema H, et al. Pain evaluation during gynaecological surveillance in women with Lynch syndrome. Fam Cancer 2017;16:205–10. doi:10.1007/s10689-016-9937-x
15 Yang KY, Caughey AB, Little SE, et al. A cost-effectiveness analysis of prophylactic surgery versus gynecologic surveillance for women from hereditary non-polyposis colorectal cancer (HNPCC) Families. Fam Cancer 2011;10:535–43. doi:10.1007/s10689-011-9444-z
16 Ryan NAJ, Davison NJ, Payne K, et al. A Micro-Costing Study of Screening for Lynch Syndrome-Associated Pathogenic Variants in an Unselected Endometrial Cancer Population: Cheap as NGS Chips? Frontiers Oncol 2019;9:61. doi:10.3389/fonc.2019.00061
Dr Siang Boon Koh (lead), Dr Timothy Robinson, Dr Siddhartha Kar
RAS is a family of three small GTPases deregulated in at least 30% of human cancers. Classically, RAS deregulation is associated with genetic mutations of the RAS genes. Recently, RAS regulators (namely RAS-GEFs and RAS-GAPs) have been recognised as critical players through which non-genetic RAS deregulation arises. RAS-GEFs promote RAS activity, while RAS-GAPs inhibit RAS activity. Some RAS-GEFs and -GAPs also have other oncogenic roles that remain obscure (1).
Less than 5% of breast cancer harbours RAS genetic mutations, thus RAS deregulation is not widely suspected as an oncogenic event in breast cancer. Surprisingly, our recent work identified a non-genetic RAS deregulation in triple-negative breast cancer (TNBC) (2). TNBC is an aggressive breast cancer subtype, where the only systemic treatment for most patients is chemotherapy. We discovered that a RAS-GAP called RASAL2 promotes chemoresistance (2). Remarkably, RASAL2 also drives sensitivity to RAS pathway inhibitors (2). This exciting discovery suggests that RAS regulators may represent a new class of cancer biomarkers and drug targets.
There are at least 20 RAS-GEFs and -GAPs, but little is known about their roles in tumour progression and treatment response. Studying RAS regulators that can promote cancer progression (prognostic) or predict treatment response (predictive) may lead to effective therapeutic interventions. The aims of this project are to:
1) Assess the prognostic value of RAS regulators in breast cancer
2) Assess the predictive value of RAS regulators in breast cancer
3) Assess the potential causal role of RAS regulators in breast cancer risk
We have identified at least two RAS regulators to be prognostic/predictive in breast cancer (2). Here, we will use Mendelian randomisation (MR) to validate these candidates, and evaluate the relevance of other RAS regulators in breast cancer. We have successfully applied this method in similar studies (3), and anticipate this project to support our ongoing effort in defining the emerging role of RAS regulators.
The MR approach will be applied to cancer and control cases in datasets such as TCGA and the Breast Cancer Association Consortium. Based on prior experience, single nucleotide polymorphisms (SNPs) marking expression quantitative trait loci (eQTLs) that are associated with gene expression level at genome-wide significance (P<5x10-8) can be selected as genetic instruments. To retain independent SNPs, linkage disequilibrium clumping with a threshold of r2≤0.01 based on the 1000 Genomes European ancestry reference panel data will be used. R2 and F-statistics will be calculated to assess the strength of the genetic instruments. To account for multiple testing, Bonferroni corrections will be used to establish P thresholds for evidence of a causal effect.
1. O Maertens, et al. Adv Biol Regul 55, 1–14 (2014).
2. SB Koh, et al. Clin Cancer Res 27, 4883–4897 (2021).
3. T Robinson, et al. Int J Cancer 147, 1597–1603 (2020).
Dr Gemma Sharp (lead), Dr Hannah Jones, Dr Katherine Ruth, Genetics of Complex Traits, University of Exeter Medical School Dr Anna Murray, Genetics of Complex Traits, University of Exeter Medical School Dr Arianna Di Florio, Cardiff University
Problematic menstrual symptoms such as heavy menstrual bleeding (HMB) are common and distressing, impacting substantially on quality of life and mental wellbeing of affected women. As well as this likely effect of HMB on poor mental health (MH), there could also be an effect in the reverse direction: psychological stress is also a known disruptor of menstrual cycles and can be associated with heavier bleeding. Additionally, HMB is almost always self-reported and measured subjectively (with reduced quality of life now being part of the official diagnosis), so an association between HMB and MH might be explained by women with mood disorders being more likely to assess their level of bleeding as abnormal and substantially affecting their quality of life.
Both HMB and MH are influenced by inflammation. Increasing evidence suggests that inflammation plays a causal role in the pathogenesis of psychiatric disorders, including depression, and inflammation is also known to show cyclical variation, partly influenced by fluctuations in reproductive hormones throughout the menstrual cycle. There is also some evidence that systemic and local endometrial inflammation is associated with severity of menstrual symptoms like HMB.
This project aims to explore direct effects of HMB on MH, and of MH on HMB, as well as the potential confounding or mediating role of inflammation. A better understanding of these relationships would help inform ways to predict whether women with HMB are at greater risk of MH conditions, and whether women with MH conditions are at greater risk of menstrual issues like HMB. This would help clinicians tailor care to improve outcomes for groups of women. Understanding whether these associations are likely to be causal, and whether inflammation plays a role in the manifestation of both HMB and MH conditions will help shed light on the pathogenesis of HMB (and underlying pathologies) and MH and suggest whether both could be effectively treated using anti-inflammatory medications.
This project will use a genetic epidemiology approach to examine bi-directional associations between HMB and MH, as well as the role of inflammation in explaining any relationship. The student will first examine the association between HMB and MH phenotypes in UK Biobank and the Avon Longitudinal Study of Parents and Children (ALSPAC). Then, using results from our previous genomewide association study (GWAS) of HMB in UK Biobank, the student will generate polygenic risk scores (PRS) for HMB UKB and ALSPAC and conduct reduced-phenomewide association studies (PheWAS) to identify MH variables and inflammatory biomarkers associated with genetic liability to HMB. The student will also use LD score regression (LDSR) to explore the genetic correlation between all traits of interest using our HMB GWAS results and results from previously published GWAS of MH conditions and inflammatory biomarkers. Any associations from the pheWAS and LDSR will be followed up using Mendelian Randomization (MR) to explore if relationships are likely to be causal. The student will learn to apply 2 MR approaches: 1) 1-sample MR (this will allow us to test MR assumptions using individual-level data), 2) 2-sample MR using GWAS summary statistics (this will improve power above the one sample MR). Sensitivity analyses will allow the student to explore assumptions of both methods.
The student will also explore effects in the opposite direction by using results of previous GWAS of MH conditions and inflammatory biomarkers to generate PRS in ALSPAC and UK Biobank and test for associations with HMB, as well as in 1 and 2 sample MR analyses to explore causality.
The student will triangulate findings from across the PRS and MR analyses to inform network MR analysis including HMB, MH and inflammation to explore the strength and direction of direct and indirect and combined and independent causal effects.
Dr Evie Stergiakouli (lead), Prof Laura Howe, Dr Rachel Blakey, Christina Dardani
Neurodevelopmental and mental health problems in childhood have been linked to adverse physical health outcomes in both childhood and adult life. For example, genetic risk scores for ADHD have been associated with adverse lifestyle and physical health outcomes even in individuals from the general population who do not necessarily exhibit the disorder (1). Causal analyses have linked ADHD to childhood obesity and coronary artery disease (2). However, neurodevelopmental disorders and more specifically ADHD are also associated with adverse socioeconomic factors and impaired educational attainment (3).
In this project, we aim to investigate the effect of neurodevelopmental and mental health problems in childhood on child and adult physical health while taking into account the strong associations of neurodevelopmental problems and mental health with socioeconomic factors, education and parental psychopathology.
We will construct polygenic risk scores for childhood neurodevelopmental and mental health conditions in parents and children from the general population to investigate their associations with adult physical health and perform Polygenic Transmission Disequilibrium test (4) to asses genetic risk transmitted from parents to children. We will perform Multivariable Mendelian randomization (5) to disentangle the effects of genetic liability to neurodevelopmental problems on physical health accounting for socioeconomic factors. We will apply sensitivity analyses including weighted median, weighted mode, MR-Egger regression, MR-PRESSO and colocalization analyses as well as other causally informative designs to assess and adjust for pleiotropy. There is also the opportunity to contribute to large-scale meta-analyses including the Norwegian Mother, Father and Child Cohort Study (MoBa) and initiate collaborations with external researchers.
1) Leppert et al. Association of Maternal Neurodevelopmental Risk Alleles With Early-Life Exposures. JAMA Psychiatry 2019;76(8):834–842. doi:10.1001/jamapsychiatry.2019.0774
2) Leppert et al. The Effect of Attention Deficit/Hyperactivity Disorder on Physical Health Outcomes: A 2-Sample Mendelian Randomization Study, AJE 2021; 190 (6), 1047–1055, https://doi.org/10.1093/aje/kwaa273
3) Dardani et al. Is genetic liability to ADHD and ASD causally linked to educational attainment?, IJE 2021;, dyab107, https://doi.org/10.1093/ije/dyab107
4) Weiner et al. Polygenic transmission disequilibrium confirms that common and rare variation act additively to create risk for autism spectrum disorders. Nat. Genet. 2017 49, 978
5) Sanderson et al. An examination of multivariable Mendelian randomization in the single-sample and two-sample summary data settings. IJE 2018; 1;48(3):713-727. doi: 10.1093/ije/dyy262.
Dr Eleanor Sanderson (lead), Professor Kate Tilling, Professor George Davey Smith
Mendelian randomization (MR) uses genetic variants as instrumental variables to estimate the causal effect of an exposure on an outcome, free from bias due to unobserved confounding. E.g. the effect of BMI on cancer incidence.
Multivariable MR (MVMR) is an extension of MR that jointly estimates the causal effect of multiple exposures on an outcome. MVMR can be used to adjust for pleiotropic effects, where genetic variants are associated with multiple traits biasing MR estimates.
Many exposures, such as BMI, vary across an individual’s lifetime, however genetic variants are fixed. Recent research has focused on (1) how MR estimates should be interpreted when the exposure varies across the lifecourse (2) the degree to which it is possible to separate the causal effect of the same exposure at different stages of the lifecourse. Understanding the effect of an exposure across the lifecourse would identify windows for prevention or treatment, and shed light on the aetiology of disease.
The aim of this project is to conduct research on the estimation and interpretation of different MR methods with time varying exposures. There will be a particular focus on the estimation of the causal effects of multiple exposures that vary over time through MVMR. MVMR can be used to estimate the proportion of an effect that is mediated through other exposures and this project will also consider how MVMR for mediation can be interpreted when both the exposure and mediators vary over time.
The focus of this project is on developing and applying methods for MVMR. The project will use simulation analysis to understand the methods and interpretation considered. This project will also involve the analysis of individual and summary level data for MR and MVMR estimation to illustrate the results obtained.
The project is methodological in focus, and the student will have the opportunity to develop an application of the methods considered. This could be on a topic that is relevant to their own applied research interests, or can be developed in consultation with the supervisors.
Professor Andrew Dowsey (lead), Dr Brian Sullivan, Professor Alastair Hay, alastair.hay@bristol.ac.uk
Urinary catheters are a widely used and sometimes may lead to urinary tract infections (UTIs) and progression to urosepsis, a serious bacterial infection of the blood stream. Our goal is to understand what modifiable community factors are associated with increased risk of a UTI and urosepsis in individuals using catheters, with a focus on reducing antimicrobial resistance (AMR).
Catheters are commonly used across age groups, for many reasons including neurological problems, congenital conditions, or cancer, and may increase the risk of UTIs/Urosepsis. Using the Bristol region NHS systemwide dataset, we want to identify modifiable risk factors to help patient outcomes and improve clinical decision making.
Our dataset is novel containing anonymised patient level data on UTI history, comorbidities and other factors. The data will require curation and management. Once prepared, the dataset will be visualised (population plots and individual timelines) and statistically modelled using Bayesian logistic regression to generate odds ratios for each risk factor.
Professor Andrew Dowsey (lead), Dr Brian Sullivan, Elizabeth Beech
The Bristol region has experienced a recent increase in cases of Clostridioides difficile infection (CDI). CDI can cause diarrhoea, abdominal pain, and fever, with severe disease occasionally requiring surgery or causing death. We seek to identify risk factors for this increase as well as model historical and fluctuations in CDI.
While there a several known risk factors for CDI, it not clear what underlies the increase in the Bristol region. Using the Bristol region NHS systemwide dataset we have the ability to statistically model many potentially influential variables (e.g. history of bacterial infections, gastrointestinal comorbidities) over time.
Our dataset is novel containing anonymised patient level data on CDI history, comorbidities and other factors. The data will require curation and management. The dataset will be visualised (population plots and individual timelines) and statistically modelled using Bayesian logistic regression and Bayesian timeseries modelling.
Dr Duleeka Knipe (lead), TBC,
Suicide bereavement is a strong predictor of suicidal behaviour, with evidence that paternal suicide (especially maternal) increases risk of suicide and self-harm behaviour in offspring. There is evidence that suggests that the time since suicide bereavement also impacts on suicide and self-harming behaviour in the individual bereaved.
This evidence primarily originates from high income countries. Roughly 80% of all suicide and self-harm occurs in low- and middle-income countries (LMICs), but only 15% of research evidence originates from these settings. Given the difference in suicide and self-harm rates between countries, and the important contextual and cultural differences (including responses to suicidal behaviour), research evidence generated from high income countries may not be applicable in LMICs . Additionally in contexts, like Sri Lanka, where extended family networks are stronger, the impact of suicidal behaviour in other members of family (and community) may increase suicide or self-harm risk in the individual exposed to that behaviour.
This project will investigate the influence of exposure to self-harm behaviour and suicide in others (family and community) on subsequent suicide and self-harm risk using an established cohort dataset in Sri Lanka.
It will answer the following questions:
1) What is the risk of suicide and self-harm in individuals who are exposed to these behaviours in family and community members?
2) Does the risk vary by the sex of the individual, the kinship relationship, and the timing of the exposure (especially in young people)?
The project involves statistical analysis of data from the locked boxed trial in Sri Lanka. This will involve developing skills in survival and spatial analysis, as well as logistic regression techniques.
Pearson M, Metcalfe C, Jayamanne S, et al. Effectiveness of household lockable pesticide storage to reduce pesticide self-poisoning in rural Asia: a community-based, cluster-randomised controlled trial. Lancet 2017
Pitman A, Osborn D, King M, Erlangsen A. Effects of suicide bereavement on mental health and suicide risk. Lancet Psychiatry 2014; 1(1): 86-94.
Professor Matthew Ridd (lead), Dr Sarah Sullivan, Dr Ketaki Bhate
Acne is an inflammatory skin disorder comprising papules/pustules, comedones, hyper-pigmentation and scarring. Almost all teenagers are affected to some degree, with 20% being moderately-to-severely affected. There is accompanying psychosocial morbidity and the physical impairment/disfigurement caused by hyper-pigmentation or scarring can be permanent. Attendance in both primary and secondary care consume considerable NHS resources. However, there little is published on natural history and conflicting evidence surrounding the relationships between acne and diet, psychological-stress and obesity.
Further research is needed to better understand both risk factors for the development and persistence of acne; and the psychological consequences of having acne. This work could provide evidence-leading to healthcare improvements and better understanding of the link between acne and mental health in adolescence, which is a vulnerable period for mental health disorders.
This study has three aims:
1. To investigate how common acne is
2. To investigate risk factors for acne onset and persistence
3. To study the psychosocial consequences of having acne
Using data from the Avon Longitudinal Study of Parents and Children (ALSPAC) birth cohort, first the prevalence, incidence-rate and cumulative-incidence of acne between will be estimated using data from study clinics where acne was examined in detail by trained healthcare professionals. The sex, age, ethnic-group and socioeconomic distributions of young people with acne according to disease severity and comparing with those who do not have acne will be described. Persistence of acne across examinations at ages 9-13 will be described.
Data will be used to test hypotheses that: dietary factors such as dairy-rich or high glycaemic-index diets, psychological- stress and obesity early in childhood are positively associated with early onset of acne, and the progression and severity of acne; and the risk of depression, low self-esteem and time-off-school are increased in those patients who have had acne.
Bhate K, Williams H C. Epidemiology of acne vulgaris. BJD 2013; 168: 474-485.
Layton A, Eady EA, Peat M, et al. Identifying acne treatment uncertainties via a James Lind Alliance Priority Setting Partnership. BMJ Open 2015; 5: e008085 DOI: 10.1136/bmjopen-2015-008085
Prof Laura Howe (lead), Prof Abigail Fraser, Prof Alun Hughes, UCL
The response to exercise is an integrative measure of circulatory function and predicts future cardiovascular disease. Lifetime maximum aerobic capacity (VO2peak) is established by age ~30 and subsequently declines. The determinants of peak circulatory capacity are not well understood. This PhD will investigate key likely influences on peak adult circulatory capacity: adiposity/physical inactivity, and adversity/disadvantage. Measurements of Avon Longitudinal Study of Parents and Children (ALSPAC) participants at age 30 are currently underway, including assessing VO2peak using cycle-ergometry/gas- exchange, concurrent cardiac output by inert gas-rebreathing, and skeletal muscle microvascular function using near-infrared spectroscopy. The availability of multiple repeated measurements means that outcomes can be related to key antecedent exposures over the whole of fetal, child and early adult life. This will provide important insights into improvement of future cardiovascular health.
The aims of this PhD are:
1. To assess the association of life course trajectories of adiposity and physical (in)activity with VO2peak.
2. To assess socioeconomic inequalities in VO2peak, and the degree to which these are explained by adiposity and physical (in)activity.
3. To assess the association between adverse childhood experiences and VO2peak, and the degree to which these are explained by adiposity and physical (in)activity.
In this PhD, you will draw on the detailed longitudinal data available in ALSPAC, to look at the life course determinants of VO2peak. In particular, you will draw on repeated measures of adiposity (including BMI and DXA-determined fat mass) and physical (in)activity (including accelerometry-based measured), and detailed data from two generations on socioeconomic position (occupation, education, financial difficulties) and adversity (child maltreatment and measures of family dysfunction).
You will have the opportunity to develop skills in advanced statistical methodology, including multilevel models and structural equation models to estimate trajectories, techniques for mediation analysis, and methods for dealing with missing data. You will join a supportive and collaborative research team, in the vibrant environment of a leading centre for epidemiology, and will have the opportunity to lead on publishing the results of your research.
Professor Stafford Lightman (lead), Dr Thomas Upton, Prof Wuge Briscoe (Department of Chemistry, University of Bristol) Prof Krasi Tsaneva-Atanasova (Mathematics, University of Exeter) Collaborators: Mr Robin Crossley (DesignWorks Windsor), Prof Colin Dayan (Cardiff University), Prof Ido Kema(University Medical Centre Groningen), Dr Suvi Ruuskanen (University of Turku, Finland)
Rhythms characterise all living things, and our physiology can be a considered as a state of continuous dynamic
equilibrium. Despite this, almost all clinical tests of human health consist of single time point measurements, which inevitably do not reflect normal and inherent daily or even hourly variation. To overcome this, we have developed a novel microdialysis-based ambulatory technology which allows 24-hour ambulatory, minimally invasive, blood free sampling (URHYTHM, www.designworks.studio/ultradian-u-rhythm, www.u-rhythm.co.uk/).
Using the technique we have successfully demonstrated the dynamics of adrenal hormones including the stress hormone cortisol in hundreds of human participants (www.ultradian.eu).
To broaden the use and impact of the technique we now wish to investigate the use of U-RHYTHM to understand dynamics of other hormones crucial to normal growth and development, in particular sex and thyroid hormones that exhibit differential effects across tissues and the lifespan.
The student will undertake a multidisciplinary programme of work to test the hypothesis that sex and thyroid hormone dynamics can be measured in subcutaneous tissue. This will involve learning and applying the technique of U-RHYTHM microdialysis, using state of the art physical chemistry methods to describe the interaction of hormones with the U-RHYTHM microdialysis system and conducting a proof-of-principle clinical trial in human participants.
The project will be based at the University of Bristol within the Labs for Integrative Neuroscience and Endocrinology and the Department of Chemistry. The student will learn techniques for the analysis and interpretation of dynamic data under supervision of the Department of Mathematics for Healthcare at the University of Exeter. The project will be supported by clinical experts at the University of Cardiff and Bristol.
Stafford Lightman, Professor of Medicine. (lead), Dr Thomas Upton, Clinical Research Fellow., Wuge Briscoe, Professor of Chemistry
Rhythms characterise all living things, and our physiology can be a considered as a state of continuous dynamic
equilibrium. Despite this, almost all clinical tests of human health consist of single time point measurements, which inevitably do not reflect normal and inherent daily or even hourly variation. To overcome this, we have developed a novel microdialysis-based ambulatory technology which allows 24-hour ambulatory, minimally invasive, blood free sampling (URHYTHM, www.designworks.studio/ultradian-u-rhythm, www.u-rhythm.co.uk/).
Using the technique we have successfully demonstrated the dynamics of adrenal hormones including the stress hormone cortisol in hundreds of human participants (www.ultradian.eu).
The project will be based at the University of Bristol within the Labs for Integrative Neuroscience and Endocrinology and the Department of Chemistry. The student will also learn techniques for the analysis and interpretation of dynamic data
This project will develop the use and impact of our novel technology to understand the importance of dynamic changes in hormone levels over the day on human health. In particular the stuent will investigate the use of U-RHYTHM to understand dynamics of the hormones crucial to normal growth and development, in particular sex and thyroid hormones that exhibit differential effects across tissues and the lifespan. The student will undertake a multidisciplinary programme of work to test the hypothesis that sex and thyroid hormone dynamics can be measured in subcutaneous tissue, and that abnormalities in these rhythms is a cause of ill health.
This will involve learning and applying:
1) The technique of U-RHYTHM microdialysis in human subjects.
2) The use of state of the art physical chemistry methods to describe the interaction of hormones with the URHYTHM microdialysis system.
3) Conducting a proof-of-principle clinical trial in human participants.
4) the use of mathematical techniques to evaluate time series measurements of dynamic changes of hormone levels over time, together with the use of artificial intelligence and machine learning
www.u-rhythm.co.uk
www.ultradian.eu
Bhake R, et al J Clin Endocrinol Metab. 2020 Apr 1;105(4):dgz002. doi: 10.1210
Bhake et al J Clin Endocrinol Metab. 2019 Dec 1;104(12):5935-5947. doi: 10.1210
Russell GM, et al Clin Endocrinol (Oxf). 2014 Aug;81(2):289-93. doi: 10.1111
Bhake RC, et al J Med Eng Technol. 2013 Apr;37(3):180-4. doi: 10.3109
Kalafatakis et al Proc Natl Acad Sci U S A. 2018 Apr 24;115(17):E4091-E4100.
Dr Lavinia Paternoster (lead), Prof George Davey Smith, Dr April Hartley
Typical epidemiological studies aim to identify causal risk factors for onset of disease by comparing diseased cases with disease free controls. However, if the aim is to identify effective treatments for use after the onset of disease, then factors that explain why some patients progress through disease stages quickly whilst others recover or progress more slowly may be the more pertinent comparison.
To identify genetic and non-genetic causal risk factors for disease progression that may be useful for identification of new treatments
You will use existing in-house datasets for a disease of your interest.
You will use state-of-the-art methods developed in the Integrative Epidemiology Unit to identify factors associated with progression of disease, taking account of collider/selection bias that can be introduced in such analyses.
You will use Mendelian Randomization to determine if relationships observed are causal.
doi.org/10.1371/journal.pgen.1006944
Prof Nicky Welton (lead), Dr. David Phillippo,
In the UK, decisions as to which medical treatments and interventions to make available on the NHS take into consideration both the costs and benefits of the treatment over a patients lifetime. This is usually based on a cost-utility analysis which identifies the treatment with the highest expected net benefit, based upon a cost-effectiveness model. Cost-effectiveness models are typically non-linear functions of the model input parameters, such as a Markov models which track patient movements between health states over time. Key inputs to cost-effectiveness models are estimates of the relative impact of different treatments on transitions between health-states. It’s common for there to be multiple treatment options that policy makers need to compare (the decision set). Randomised controlled trials (RCTs) provide the most robust evidence of the relative efficacy of different treatment options. However, when there are multiple treatment options it is unlikely that a single RCT has compared all treatment options, and instead there are multiple RCTs that have each compared different sets of treatments within our decision set (and can even include additional treatments outside of the decision set). When the RCT evidence forms a network of treatment comparisons, then the evidence can be pooled in a network meta-analysis (NMA) (Dias et al. 2018), to obtain pooled relative treatment effect estimates that can be used as inputs to a cost-effectiveness model. The validity of NMA relies on the consistency assumption, where direct estimates from RCTs that have compared two treatments (eg AvB) head-to-head are not in conflict with indirect estimates obtained from the rest of the network (eg AvC and BvC). One reason why there may be inconsistency is due to a lack of methodological rigour in some of the included RCTs, leading to biased estimates. Studies included in an NMA are assessed for risk of bias, however this provides no indication of the impact of potential bias on a decision based on the NMA. For example, the AvB study may overestimate the effect of B against A, but if C clearly has the highest expected net benefit, then adjusting for the bias in the AvB study is unlikely to have any impact on the treatment recommendation. A threshold analysis method has previously been developed to address this question when the net benefit function is a simple linear function of a single measure of treatment efficacy (Phillippo et al 2018).
The aim of this project is to develop algorithms to assess the impact of bias in relative treatment effect inputs to cost-effectiveness models, where the relative estimates have come from a network meta-analysis (NMA), accounting for (i) non linear relationships between the NMA estimates and the net benefit function and (ii) the complex relationship between RCT evidence and resulting NMA estimates via the hierarchical NMA model. The methods will answer the question: “how biased would the RCT evidence have to be before a rational risk neutral decision maker changed its recommendations?”
This project will extend the work of Phillippo et al 2018 to decisions based on non linear relationships between the NMA estimates and the net benefit function. The project will also consider alternative decision rules (such as recommend all treatments within a minimally clinically significant margin), and decisions that are based on multiple outcomes via a multi-criteria decision analysis (MCDA). The project will draw on recent methodological developments for value of information analysis (Heath et al. 2018), such as generalised additive models, integrated nested Laplace approximations, and multi-level Monte-Carlo methods. These meta-modelling techniques have the potential to obtain fast and accurate computational tools that can evaluate the thresholds of input parameters for which decisions change, based on maximising any kind of net benefit function.
An important output of the project will be the creation of software tools (using R and R Shiny) to allow easy application for health economists without advanced statistical training. The methods will be applied to examples from the National Institute of Health and Care Excellence (NICE) guidelines. Case studies may include social anxiety, non-small cell lung-cancer, headaches, and atrial fibrillation.
Phillippo DM, Dias S, Ades AE, Didelez V, Welton NJ. Sensitivity of treatment recommendations to bias in network meta-analysis. JRSSA. 2018. 181:843-867. https://doi.org/10.1111/rssa.12341
Heath A, Manolopoulou I, Baio G. A Review of Methods for Analysis of the Expected Value of Information. Medical Decision Making. 2017. 37: 747-758 https://doi.org/10.1177/0272989X17697692
Dias S, Ades AE, Welton NJ, Jansen JP, Sutton AJ. Network Meta-analysis for Comparative Effectiveness Research. Wiley. Hoboken NJ. 2018.
Dr Sarah Watkins (lead), Dr Matthew Suderman,
For a number of years governments around Europe have discussed using molecular biomarkers as a potential tool to confirm the age of some individuals seeking asylum. This is now a measure being actively considered by the UK government. Although there have been a number of commentaries published addressing issues with the accuracy of such biomarkers, including DNA methylation age, little attention has been paid to how social, psychological, and other external factors associated with individuals needing to seek asylum might impact these biomarkers and therefore bias age estimates.
This project will review the literature to determine how external factors might impact age estimates of individuals seeking asylum obtained from molecular biomarkers. It could involve a simulation to illustrate how good estimates would need to be before they could be reliably used to estimate the age of individuals.
The first step will be to identify from the literature the most important exposures and experiences that impact individuals seeking asylum, particularly children and young people. This includes exposures such as trauma, poor access to health services, and extreme poverty. Then the molecular biomarker literature will be reviewed to assess the current evidence (and the extent to which it exists) on the impact of the identified exposures and experiences on epigenetic age. There will be a possible meta-analysis of results, and it may be possible to conduct a simulation.
https://post.parliament.uk/research-briefings/post-pn-0666/
Sauer PJ, Nicholson A, Neubauer D; Advocacy and Ethics Group of the European Academy of Paediatrics. Age determination in asylum seekers: physicians should not be implicated. Eur J Pediatr. 2016 Mar;175(3):299-303. doi: 10.1007/s00431-015-2628-z. Epub 2015 Sep 18. PMID: 26385241.
Dupras, C., Beck, S., Rothstein, M. A., Berner, A., Saulnier, K. M., Pinkesz, M., ... & Joly, Y. (2019). Potential (mis) use of epigenetic age estimators by private companies and public agencies: human rights law should provide ethical guidance. Environmental Epigenetics, 5(3), dvz018.
Dr Sarah Watkins (lead), Dr Matthew Suderman,
Epigenetic clocks are algorithms that utilise DNA methylation (DNAm) data to predict a person’s age. Over 10 epigenetic clocks are frequently used in the literature, containing between 10 and 514 DNAm sites. As technology to measure DNAm changes, so too do the sites that are measured; this means that sites used as part of clock algorithms may be missing in some datasets. Because of this, the age predictors may be biased, but this has been inadequately assessed in previous literature, and so the robustness of the predictors to losing sites is unclear.
The aim of this project is to calculate epigenetic age with ten commonly used epigenetic clocks, in multiple publicly available datasets that have measured chronological age. We will utilise datasets measured on the 450k array (as this is typically the set of probes used to derive the predictors) and test the robustness of the age predictors by removing sites. This will include a demonstration of how clock estimates change depending on the technology used to measure DNA methylation.
Using R you will calculate the ten epigenetic clocks, and then will re-calculate them with iterations of missing sites. This will be done across multiple datasets. You will then use those iterations to build a picture of how robust the clock estimates are to missing features of the predictor. This project will introduce you to using R (if you are not already familiar with it), analysis of DNA methylation data, calculating epigenetic age, and writing up and presenting findings.
BERGSMA, T. & ROGAEVA, E. 2020. DNA Methylation Clocks and Their Predictive Capacity for Aging Phenotypes and Healthspan. Neurosci Insights, 15, 2633105520942221.
HORVATH, S. 2013. DNA methylation age of human tissues and cell types. Genome Biol, 14, R115.
Dr Anya Skatova (lead), Dr Philip Newall,
Gambling is now seen as a public health issue in the UK, with recent government estimates suggesting a societal cost of £1.2 billion a year (Public Health England, 2021). It is generally considered that disadvantaged groups bear a disproportionate share of this burden, as supported by the observation for example that bookmakers tend to cluster in areas of relative socioeconomic deprivation (Newall, 2015; Wardle et al., 2017). However, as with much gambling research, these observations are limited by methodological factors, for example via the selection of nonrepresentative retrospective samples (Muggleton et al., 2021).
These previous observations would be backed-up by confirmation in prospective representative samples, such as via Avon Longitudinal Study of Parents And Children (ALSPAC), a dataset which has already been used in several pieces of gambling research (Emond et al., 2022; Hollén et al., 2020).
Since many gambling measures are available in the ALSPAC dataset, and has already been used in previous gambling research (Emond et al., 2022; Hollén et al., 2020), we expect that a significant amount of work can be conducted during this mini project. Specifically, we expect that a descriptive piece of work on the distribution of gambling-related harm across distinct socioeconomic groups can be conducted and written-up, and submitted to a field journal such as Journal of Gambling Studies.
Descriptive statistics, correlation and regression analysis using previously collected data within ALSPAC.
Emond, A., Nairn, A., Collard, S., & Hollén, L. (2022). Gambling by young adults in the UK during COVID-19 lockdown. Journal of Gambling Studies, 38(1), 1–13.
Hollén, L., Dörner, R., Griffiths, M. D., & Emond, A. (2020). Gambling in young adults aged 17–24 years: A population-based study. Journal of Gambling Studies, 36(3), 747–766. https://doi.org/10.1007/s10899-020-09948-z
Muggleton, N., Parpart, P., Newall, P., Leake, D., Gathergood, J., & Stewart, N. (2021). The association between gambling and financial, social, and health outcomes in big financial data. Nature Human Behaviour, 5, 319–326. https://doi.org/10.1038/s41562-020-01045-w
Newall, P. W. S. (2015). How bookies make your money. Judgment and Decision Making, 10(3), 225–231.
Public Health England. (2021). Landmark report reveals harms associated with gambling estimated to cost society at least £1.27 billion a year. GOV.UK. https://www.gov.uk/government/news/landmark-report-reveals-harms-associated-with-gambling-estimated-to-cost-society-at-least-1-27-billion-a-year
Wardle, H., Asbury, G., & Thurstain-Goodwin, M. (2017). Mapping risk to gambling problems: A spatial analysis of two regions in England. Addiction Research & Theory, Journal Article.
Dr. Paul Yousefi (lead), Professor. Richard Martin, Dr. Matthew Suderman Dr. Athene Lane Dr. Sam Merriel
Prostate Cancer (PCa) is a leading cause of male mortality, with 336,000 deaths worldwide each year (1). Although most PCa cases are indolent, slow-growing, and tend not to progress, a subset of PCa cases are more aggressive and will progress to metastases, treatment resistance and death. Aggressive cases are a major driver of PCa mortality, which is the second most frequent cause of UK male cancer deaths (2). Currently, performance of even NICE recommended post-diagnostic clinical prediction models of PCa progression (based on clinical variables and prostate biopsy) is limited, with C-index at 10 years ranging from 0.73 to 0.81. Tools that improve discrimination of PCa progression and reduce harm due to both under- and over-treatment are required to guide treatment clinically.
DNA methylation (DNAm) is an epigenetic modification that regulates tissue-specific gene expression and is disrupted in cancer development where it’s a hallmark of oncogenesis and progression pathophysiology (3). Attempts to identify DNAm differences in PCa have been underpowered and featured substantial design flaws (e.g. insufficient treatment adjustment, inappropriate follow-up window, etc.). However, blood DNAm is increasingly being appreciated as valuable for predicting cancer progression and prognosis (4), which merits further exploration with adequate sample size and sufficient methodology.
Given the role of DNAm in tumorigenesis, the potential for DNAm to capture signal of early disease progression, and the poor performance of existing biomarkers, this project aims to improve discrimination of the rate and severity of PCa progression by:
(1) Identifying genome-wide blood DNAm patterns that differ prospectively between aggressive and indolent forms of PCa
(2) Developing a DNAm signature using machine learning techniques for predicting aggressive PCa among patients with confirmed localised disease that could inform the need for radical therapeutic intervention
(3) Evaluating whether such a DNAm signature can improve upon existing NICE-recommended clinical progression methods
This project will use participants from the Prostate Testing for Cancer and Treatment (ProtecT) trial which included 1,600 UK men with confirmed, localised PCa and prospectively evaluated two forms of radical PCa treatment (prostatectomy and radiotherapy) to active monitoring. Median follow-up was 10-years and PCa mortality was the primary outcome. Using DNA isolated from whole blood samples collected at baseline, genome-wide DNAm levels will be quantified by Illumina HumanMethylationEPIC BeadChip for N = 850 (125 aggressive cases, 725 indolent controls) age and treatment matched ProtecT participants.
An Epigenome Wide Association Study (EWAS) will be performed to identify where DNAm is differentially methylated between aggressive and indolent PCa cases at over >850k CpG sites measured on the Illumina HumanMethylationEPIC BeadChip. Analysis for identification of differentially methylated regions (DMRs) will also be performed. To determine the CpGs and CpG combinations most predictive of PCa progression, we will apply feature selection and engineering approaches, and a library of pre-specified supervised machine learning methods (e.g. elastic-net regression, tree-ensembles, etc.). All models will be evaluated by outcome-stratified repeated k-fold cross-validation to robustly assess out-of-sample predictive performance and tune relevant hyperparameters. C-statistic/AUC will be the primary performance metric, but several alternatives will be considered.
1. Pernar CH, Ebot EM, Wilson KM, Mucci LA. The Epidemiology of Prostate Cancer. Cold Spring Harb Perspect Med TA - TT -. 2018;8(12):a030361.
2. Prostate Cancer incidence statistics: Cancer Research UK (CRUK) [Internet]. 2017. Available from: https://www.cancerresearchuk.org/health-professional/cancer-statistics/statistics-by-cancer-type/prostate-cancer/incidence
3. Zhang J, Huang K. Pan-cancer analysis of frequent DNA co-methylation patterns reveals consistent epigenetic landscape changes in multiple cancers. BMC Genomics. 2017. Available from: https://pubmed.ncbi.nlm.nih.gov/28198667/
4. Yousefi PD, Suderman M, Langdon R, Whitehurst O, Davey Smith G, Relton CL. DNA methylation-based predictors of health: applications and statistical considerations. Nat Rev Genet. 2022 Mar 18;1–15.
Dr Ashley Budu-Aggrey (lead), Dr Lavinia Paternoster,
Several traits such as cardiovascular disease[1] and mental health disorders[2] have been reported to be observationally associated with atopic dermatitis (AD). The most recent genome-wide association study for AD has also uncovered genetic correlations[3]. However, the causal relationships and direction of effect is yet to be determined. Establishing causality will aid the early detection of AD or later health outcomes and determine whether intervention on one condition will affect the other.
To investigate causal risk factors and outcomes of AD using Mendelian Randomization (MR)
1. The literature will be screened to identify hypotheses for examination with MR.
2. Observational analysis will be performed using datasets available in-house such as the UK Biobank
3. Suitable datasets will be identified, and genetic instruments for AD and other traits will be refined
4. 2 sample MR analyses will be performed to investigate causal relationships
5. Sensitivity analyses will be performed to ensure the assumptions of the MR analyses have not been violated
1. Standl, M. et al. Association of Atopic Dermatitis with Cardiovascular Risk Factors and Diseases. Journal of Investigative Dermatology 137, 1074–1081 (2017).
2. Budu-Aggrey, A. et al. Investigating the causal relationship between allergic disease and mental health. Clin Exp Allergy 51, 1449–1458 (2021).
3. Budu-Aggrey, A. et al. European and multi-ancestry genome-wide association meta-analysis of atopic dermatitis highlights importance of systemic immune regulation. Submitted to Nature Communications (2022).
Dr Ashley Budu-Aggrey (lead), Dr Lavinia Paternoster,
An observational relationship has been reported between atopic dermatitis (AD) and BMI, where evidence of a causal relationship has also been found for higher BMI increasing the risk of AD. Up until now, this causal relationship has not been investigated with respect to childhood and adulthood BMI separately especially given the AD mostly presents in early childhood.
To investigate the causal relationship with AD and childhood and adulthood BMI separately using Mendelian Randomization (MR)
Two-sample MR will be used to investigate causality between AD and childhood and adulthood BMI separately, and also determine the direction of effect (AD -> childhood/adulthood BMI or childhood/adulthood BMI -> AD). Genetic instruments for the MR analyses will be derived from the most recent genome-wide association studies (GWAS) for AD and childhood/adulthood BMI. Sensitivity analyses will also be applied to ensure the assumptions of the MR analyses have been met.
- Budu-Aggrey, A. et al. European and multi-ancestry genome-wide association meta-analysis of atopic dermatitis highlights importance of systemic immune regulation. Submitted to Nature Communications (2022).
- Budu-Aggrey, A. et al. Assessment of a causal relationship between body mass index and atopic dermatitis. Journal of Allergy and Clinical Immunology 147, 400–403 (2021).
- Richardson, T. G., Sanderson, E., Elsworth, B., Tilling, K., & Smith, G. D. (n.d.). Use of genetic variation to separate the effects of early and later life adiposity on disease risk: mendelian randomisation study. https://doi.org/10.1136/bmj.m1203
Prof Julian Higgins (lead), Prof Kate Tilling, Prof Marcus Munafò
Triangulation, in which multiple methods are strategically used to answer a single question, is a currently developing area. Lawlor, Tilling and Davey Smith (2016) explained how causal inferences can be strengthened by integrating results from several approaches with different key sources of potential bias. The statistical methods for combining the results from multiple sources of evidence within a triangulation framework are, however, underdeveloped. This PhD seeks to develop, illustrate and evaluate such methods.
The project seeks to develop and implement quantitative methods for triangulation of multiple lines of evidence addressing the same underlying epidemiological question
Work is expected to focus on three key areas as follows.
1) At its simplest, triangulation involves comparison and combination of studies of the same exposure-outcome effect that use different designs or analytic methods. For example, randomized trials, Mendelian randomization studies and traditional multivariable regression analyses of observational evidence might all tackle a question relating to the same exposure-outcome effect. The studies may produce different effect estimates because they are (i) asking subtly different questions (e.g. in relation to the period or patterns of exposure), (ii) compromised by different biases and/or (iii) subject to chance. Triangulation combines these issues in a statistical model and assesses the extent to which the observed data fit together – an approach known as multiparameter evidence synthesis. Methods for producing these models, assessing coherence and drawing conclusions about causal effects of the exposure on the outcome will be developed. The project will primarily explore Bayesian methods, because they are flexible and allow incorporation of external information through prior distributions.
2) Another form of triangulation arises when some (or all) studies address only a component of the underlying question. For example, if the exposure-outcome effect occurs through an intermediate, then studies of the exposure-outcome effect might be triangulated with a combination of studies (i) of the effect of exposure on the intermediate and (ii) of the effect of the intermediate on the outcome. Methods will be developed to synthesise these three sets of studies, and account for true differences, biases and chance.
3) In addition to working on novel statistical methods, the student may explore other methodological questions. First, how should we define and identify studies suitable for a triangulation exercise? Automation tools may help here, such as MELODI (http://melodi.biocompute.org.uk), which we have developed to identify studies examining intermediates between exposure and outcome. Second, how should we evaluate the risk of bias in studies for which formal frameworks (such as RoB 2 and ROBINS-I; http://riskofbias.info) have not been developed? Third, what sources of information are available about biases, to inform prior distributions, and how can more information be generated?
Methods developed in these three areas will be illustrated through application to important causal questions in epidemiology.
Lawlor DA, Tilling K, Davey Smith G. Triangulation in aetiological epidemiology. Int J Epidemiol. 2016 Dec 1;45(6):1866-1886. doi: 10.1093/ije/dyw314.
Munafò MR, Davey Smith G. Robust research needs many lines of evidence. Nature. 2018 Jan;553(7689):399-401. doi: 10.1038/d41586-018-01023-3.
Munafò MR, Higgins JPT, Davey Smith G. Triangulating Evidence through the Inclusion of Genetically Informed Designs. Cold Spring Harb Perspect Med. 2021 Aug 2;11(8):a040659. doi: 10.1101/cshperspect.a040659.
Turner RM, Spiegelhalter DJ, Smith GC, Thompson SG. Bias modelling in evidence synthesis. J R Stat Soc Ser A Stat Soc. 2009 Jan;172(1):21-47. doi: 10.1111/j.1467-985X.2008.00547.x.
Ades AE, Welton NJ, Caldwell D, Price M, Goubar A, Lu G. Multiparameter evidence synthesis in epidemiology and medical decision-making. J Health Serv Res Policy. 2008 Oct;13 Suppl 3:12-22. doi: 10.1258/jhsrp.2008.008020.
Dr Evie Stergiakouli (lead), Prof Laura Howe, Alexandra Havdhal, University of Oslo Department of Psychology
Attention Deficit Hyperactivity Disorder (ADHD) is a chronic neurodevelopmental condition, characterised by persistent difficulties in the areas of attention span/impulse control. Approximately 65% of children diagnosed with ADHD have symptoms and impairment that persist into adulthood and ADHD can lead to educational, social, and occupational difficulties (1).
We have previously shown that higher genetic risk for ADHD is associated with younger maternal age at birth, lower educational attainment and other indicators of social disadvantage in mothers from the general population (2). Using Mendelian randomization (MR) we have also found evidence of higher genetic liability to ADHD causing lower educational attainment, and evidence of genetic liability to lower educational attainment increasing risk to ADHD independent of cognitive ability (3). Since ADHD manifests at a very young age, the causal effects of genetic liability to education on ADHD are likely to indicate parental effects. The causal link between parental education and ADHD could be mediated by optimal lifestyle and general health factors during pregnancy and/or socioeconomic factors linked to better access to educational resources. Disentangling the individual effects of each factor as well as assessing for genetic confounding is required.
In this project, we will explore the links between educational attainment, reproductive outcomes (age at first birth, number of live births), prenatal factors, socioeconomic status and other indicators of social disadvantage on ADHD.
Our aims are: 1. To assess the contribution of parental educational attainment on trajectories of ADHD traits in offspring and 2. To disentangle it from the offspring own genetic background and investigate the causal pathways linking parental educational attainment and offspring ADHD.
This is an exciting opportunity for a student to perform advanced genetic epidemiological analyses on large multigenerational longitudinal cohorts from two countries: the Avon Longitudinal Study of Parents and Children in the UK and the Norwegian Mother and Child Cohort Study (MoBa) in Norway.
For aim 1, we will compare the associations of maternal and paternal genetic liability of educational attainment on ADHD trajectories from two general population samples with very different educational systems and social structures. Polygenic Transmission Disequilibrium tests will be used to assess if genetic liability to lower educational attainment is overtransmitted to offspring with ADHD (4).
For aim 2, we will use within-families MR (5) in MoBa to investigate causal effects of the offspring’s own genetic liability to education attainment while adjusting for parental genetic liability. We will also perform Multivariable Mendelian randomization (6) to account for multiple exposures (educational attainment, reproductive outcomes, prenatal factors, socioeconomic status) simultaneously. Finally, we will apply sensitivity analyses including weighted median, weighted mode, MR-Egger regression, MR-PRESSO and colocalization analyses to assess and adjust for pleiotropy.
Thapar A, Cooper M. Attention deficit hyperactivity disorder. Lancet. 2016 Mar 19;387(10024):1240-50. doi: 10.1016/S0140-6736(15)00238-X.
Leppert B et al. Association of Maternal Neurodevelopmental Risk Alleles With Early-Life Exposures. JAMA Psychiatry. 2019;76(8):834–842. doi:10.1001/jamapsychiatry.2019.0774
Dardani et al. Is genetic liability to ADHD and ASD causally linked to educational attainment?, Int. J. Epidemiol. 2021;, dyab107, https://doi.org/10.1093/ije/dyab107
Weiner D et al. Polygenic transmission disequilibrium confirms that common and rare variation act additively to create risk for autism spectrum disorders. Nat Genet 49, 978–985 (2017). https://doi.org/10.1038/ng.3863
Brumpton, B et al. Avoiding dynastic, assortative mating, and population stratification biases in Mendelian randomization through within-family analyses. Nat Commun 11, 3519 (2020). https://doi.org/10.1038/s41467-020-17117-4
Sanderson E et al. An examination of multivariable Mendelian randomization in the single-sample and two-sample summary data settings. Int J Epidemiol. 2019 Jun 1;48(3):713-727. doi: 10.1093/ije/dyy262.
Prof Tom Gaunt (lead), Dr Maria Sobczyk,
Mendelian Randomization (MR) is a genetic epidemiology method which utilises variants sourced from genome-wide association studies (GWAS) to assess causality between risk/protective factors and disease outcomes in a manner less biased to observational studies(1).
Recently, applications of MR to drug target prioritization gained a lot of interest. One approach is to use expression or protein quantitative trait loci (QTL) for a drug target gene/protein as exposures with the aim of establishing the effects of perturbing the intended target directly (“on-target”)(2). However, most drugs will have broader molecular consequences, either in parallel (“off-target”) or downstream of the intended target which can be exploited to repurpose drugs for novel indications. One way to identify such off-targets is to mine high-throughput expression datasets of various cell lines exposed to small molecule drugs and genetic perturbations(3).
Using a variety of high-throughput genomics/other -omics resources, the project will aim to discover and triangulate with Mendelian Randomization:
1) Drug off-target side effects
2) Drug repurposing opportunities
You will use data on transcriptional responses to drugs from expression perturbation databases and other resources to identify additional genes and proteins for off-target side effect prediction, using multivariable MR to identify direct effects. In addition, you will use protein-protein interaction data collated in our locally developed resource: EpiGraphDB(4) to further refine gene sets to instrument.
You will also use drug perturbation data sources to evaluate the potential to repurpose drugs with a previously-published approach(5) that compares disease-associated transcriptomic profiles to in vitro drug transcriptomic profiles to identify profiles that may reverse the effect of disease on gene expression. You will consider tissue-specific and pathway-specific transcriptomic profiles for a variety of diseases and explore whether this identifies additional repurposing opportunities. You will validate drug repurposing and off-target side effect predictions using observational data from UK Biobank and electronic health records, in addition to MR.
1. Sanderson, E. et al. Mendelian randomization. Nat. Rev. Methods Prim. 2, 6 (2022).
2. Gill, D. et al. Mendelian randomization for studying the effects of perturbing drug targets. Wellcome open Res. 6, 16 (2021).
3. Keenan, A. B. et al. The Library of Integrated Network-Based Cellular Signatures NIH Program: System-Level Cataloging of Human Cells Response to Perturbations. Cell Syst. 6, 13–24 (2018).
4. Liu, Y. et al. EpiGraphDB: a database and data mining platform for health data science. Bioinformatics 0–0 (2020).
5. Wu, P. et al. Integrating gene expression and clinical data to identify drug repurposing candidates for hyperlipidemia and hypertension. Nat. Commun. 13, 46 (2022).
Dr Eleanor Sanderson (lead), Dr Kaitlin Wade,
Mendelian randomization (MR) uses genetic variants to estimate the causal effect of an exposure on an outcome of interest in a way that aims to overcome from bias from unobserved confounding. Under a set of standard assumptions, the estimates obtained from MR can be interpreted as ‘population average causal effects’ (i.e., the average causal effect for the population studied). However, for many exposure-outcome relationships of interest, the causal effect of the exposure on the outcome may differ for different groups of individuals. For example, the causal effect of body mass index (BMI) on blood pressure (BP) may differ by whether an individual is male or female. There are different approaches that could be used to estimate whether there is a difference in the causal effect across different groups, but which approach is the most appropriate is unknown.
• To estimate whether the causal effect of BMI on BP by differs by sex.
• To determine which method is the most appropriate to test for differences in the causal effect of BMI on BP by sex.
This project will use individual-level data from UK Biobank to apply different approaches to estimating whether there is a difference in the causal effect of BMI on BP by sex. These approaches will include
1. Conducting sex-specific MR analyses and testing for a difference in the causal effect between these groups
2. Including a sex interaction term in a single MR estimate using data from the whole population
3. Any other approaches identified that can be applied in individual-level MR estimation.
The student may also have the opportunity to conduct a small simulation study to identify any bias in the approaches used; however, this part of the project is optional and will depend on available time.
1. Davies, Neil M., Michael V. Holmes, and George Davey Smith. "Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians." bmj 362 (2018).
2. Sanderson, Eleanor, et al. "Mendelian randomization." Nature Reviews Methods Primers 2.1 (2022): 1-21.
Gibran Hemani (lead), George Davey Smith,
Estimating the influence of genotypes and traits on fitness is a fundamental question in biology. While the question requires causal inference, many analytical frameworks (e.g. Price equation) are embedded in a statistical framework that are unable to separate cause from correlation. Mendelian randomisation is an analytical framework that is widely used in epidemiological studies, and we have shown it can be used to make causal inference of traits on fitness. In this project we will investigate the theoretical properties of this approach and apply it to a vast array of genetic data to understand the landscape of the genotype-phenotype map on fitness. This mapping will allow us to make inference about critically important questions, such as the degree to which pleiotropy modulates directional selection, and the degree to which stabilising selection maintains natural genetic variation.
1. One approach to estimate the influence of traits on fitness is to estimate the causal influences on reproductive traits such as fecundity, age of first menarche, age of first birth and age of menopause. We will investigate the way in which these traits approximate fitness, using multi-generation samples and latent modelling through genomic structural equation modelling
2. We will perform an exhaustive scan of traits on measures of fitness determined from (1), to build a profile of the genotypes-trait-fitness landscape
3. We will use (2) to model and infer the degree to which important mechanisms such as pleiotropy and trait network effects either accelerate or constrain directional selection, and to what degree they maintain natural genetic variation.
Throughout the project we will use a mixture of genome-wide association studies, a suite of methods developed for Mendelian randomisation, as well as theoretical and simulation analyses of selection processes.
Evershed RP, Davey Smith G et al. Dairying, diseases and the evolution of lactase persistence in Europe. Nature. 2022 Aug;608(7922):336-345. doi: 10.1038/s41586-022-05010-7 – Example of how, in principle, Mendelian randomization could be used in future to infer fitness relationships
Hemani G, Knott S, Haley C. An evolutionary perspective on epistasis and the missing heritability. PLoS Genet. 2013 Feb;9(2):e1003295. doi: 10.1371/journal.pgen.1003295 – Exploration of the mechanisms maintaining genetic variation in the context of widespread additive genetic variation
Mills, M.C., Tropf, F.C., Brazel, D.M. et al. Identification of 371 genetic variants for age at first sex and birth linked to externalising behaviour. Nat Hum Behav 5, 1717–1730 (2021). https://doi.org/10.1038/s41562-021-01135-3 - A GWAS on reproductive traits
Okasha, S. & Otsuka, J. The Price equation and the causal analysis of evolutionary change. Philos. Trans. R. Soc. B Biol. Sci. 375, 20190365 (2020) – The relationship between causal inference and evolutionary modelling
Dr Nabila Kazmi (lead), Prof Sarah Lewis,
Prostate cancer is the second most common male cancer worldwide, but there is substantial geographical variation, suggesting a potential role for modifiable risk factors in prostate carcinogenesis.
Mendelian randomization (MR) analysis is a method which uses genetic variation as instrumental variables to investigate the casual relationship between exposure and outcome. In our previous MR analysis, we found that serum iron was protective against overall prostate cancer, where, OR = 0.92 (95% CI =0.86–0.98 and p-value = 0.007).
Now, it is known that blood cell characteristics are associated with prostate cancer risk. We know that iron is essential for haemoglobin production and has an impact on other blood cell characteristics. The purpose of this study is to determine whether effects of iron on blood cell characteristics could be responsible for the protective effect of iron on prostate cancer risk. To investigate this hypothesis, student will perform a Two-step MR project, with iron as the exposure, blood cell counts are the intermediate and prostate cancer as the outcome.
Students choosing this project must have taken the Molecular Epidemiology unit.
The aim of this study is to apply two- sample Mendelian randomization analyses to evaluate the evidence of a causal link between serum iron and red blood cell count and red cell distribution width (RDW).
Objective：
1. To find genetic variants that satisfy the instrumental variable assumptions and to test their associations with the outcome in the largest available dataset that is relevant to the causal question.
2. Using Inverse variance weighted (IVW) method to evaluate the primary causal effect estimate.
3. Using MR-Egger regression, weighted median method (WME), mode-based simple estimation, Mode-based weighted estimation to test the reliability and stability of the results.
Using data from large-scale genome-wide association study (GWAS) of exposure and intermediates and a GWAS of prostate cancer, we will investigate the causality between iron and intermediate biomarkers including red blood cell count and RDW using IVW method. Sensitivity tests will be performed to evaluate the robustness of the estimated results. IVW will be used as the primary causal effect estimate. We will adopt another four methods to test for pleiotropy, these are MR-Egger regression, weighted median method (WME), mode-based simple estimation, Mode-based weighted estimation. If the above five different MR models produce similar estimates of causal effects, we consider that serum iron level has a causal effect on the pathways investigated.
Bell S et al. (2021) A genome-wide meta-analysis yields 46 new loci associating with biomarkers of iron homeostasis. Communications Biology. 4(1):156.
Watts E et al. (2020) Hematologic Markers and Prostate Cancer Risk: A Prospective Analysis in UK Biobank. Cancer Epidemiology, Biomarkers and Prevention. 29(8):1615-1626.
Kazmi N et al. (2020) Appraising causal relationships of dietary, nutritional and physical-activity exposures with overall and aggressive prostate cancer: two-sample Mendelian randomization study based on 79,148 prostate cancer cases and 61,106 controls. International Journal of Epidemiology. 49(2), 587-596
Davey Smith G ES. (2003) “Mendelian randomisation”: can genetic epidemiology contribute to understanding environmental determinants of disease? International Journal of Epidemiology. 32, 1-22
Natalia Lewis (lead), Dr Elizabeth Cook, Dr Estela Capelas Barbosa Dr Sally McManus
Intimate partner violence has a negative impact on social and economic outcomes. However, it has not been established how long those social and economic consequences last and how the duration varies with the type of violence. Duration of such effects is needed to cost the social and economic harms from violence.
To quantify the average length of time for which social and economic outcomes are affected by intimate partner violence, by type of violence (sexual, physical, emotional, financial).
A focused systematic review of studies reported in peer reviewed literature that recruited adults, had multiple time points, a social and/or economic outcome(s) and where intimate partner violence was a predictor, independent variable, or inclusion criterion.
Support will be provided by supervisors and subject librarian.
Training on quantitative evidence synthesis is available via short course and guided self-learning.
Patton SC, Szabo YZ, Newton TL. Mental and Physical Health Changes Following an Abusive Intimate Relationship: A Systematic Review of Longitudinal Studies. Trauma Violence Abuse. 2022 Oct;23(4):1079-1092. doi: 10.1177/1524838020985554. Epub 2021 Jan 20. PMID: 33468040.
Page, M.J., McKenzie, J.E., Bossuyt, P.M. et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. Syst Rev 10, 89 (2021). https://doi.org/10.1186/s13643-021-01626-4
Dr Qian Yang (lead), Dr Carolina Borges,
Previous observational studies suggested (1) maternal antenatal depression was associated with higher risks of preterm birth, low birthweight and intrauterine growth restriction; (2) antidepressants use during pregnancy were associated with higher risks of miscarriage, stillbirth and preeclampsia, and low Apgar scores. However, these findings may be vulnerable to residual confounding.
(1) To explore effects of maternal genetic liability to depression on pregnancy and perinatal outcomes using two-sample MR;
(2) To write-up methods and findings and co-author a paper of this study.
Genetic instruments and their associations with depression will be obtained from Psychiatric Genomics Consortium GWAS. Instrument-outcome associations will be extracted from MR-PREG GWAS, combining UKB, ALSPAC, BiB, MoBa and FinnGen. In ‘TwoSampleMR’ R package, inverse variance weighted will be used for main analyses with sensitivity analyses to assess MR assumptions.
Dr Qian Yang (lead), Prof Deborah Lawlor, Dr Charlie Hatcher
Hypertensive disorders of pregnancy (HDP) influences ~10% of pregnancies. MR found Bifidobacterium protected pre-eclampsia using FinnGen. However, it is important to replicate this independently. It might be useful to explore whether there is evidence for causal effects on gestational hypertension, and small-for-gestational age which is increased in women with HDP.
(1) To explore effects of gut microbiome reported in previous MR (PMID: 36380372) on pre-eclampsia (for replication), gestational diabetes, and small-for-gestational age using two-sample MR in an independent (to FinnGen) sample.
(2) To learn about additional methods for testing results validity.
We will follow the previous MR to extract genetic instruments and their associations with gut microbiome from MiBioGen consortium GWAS. Instrument-outcome associations will be extracted from MR-PREG GWAS, combining UKB, ALSPAC, BiB and MoBa, or InterPregGen GWAS. Two-sample MR will be conducted using ‘TwoSampleMR’ R package.
Josine Min (lead), Gibran Hemani, Johann Hawe (Illumina)
Genome wide associations studies (GWASs) have discovered many genetic associations with a large range of human traits, but the functional consequences of GWAS signals often remain elusive, as most GWAS signals reside in non-coding genomic regions. However, GWAS signals are enriched in DNA regulatory elements and cell type specific annotations, and thus it is likely that GWAS signals confer their effects through modulating gene regulatory mechanisms.
Genetic factors for molecular traits (DNA methylation, gene expression, protein levels) are being discovered at an astonishing rate. A major hope for these genetic factors is that they can be used to identify causal mechanism of complex traits.[1] Fascinatingly, the dimensionality of molecular phenotyping is bound to surpass the density of human genetic variation, meaning that genetic pleiotropy (where one variant influences multiple phenotypes) is a necessary feature amongst molecular phenotypes. This has critical downstream implications for being able to use genetics to make valid causal inference of putative molecular targets on disease incidence and progression.
This project will build a resource for storing and querying harmonized molecular QTL data in a computational efficient manner, and then use that resource to build pleiotropy maps of human molecular phenotypes. These maps will subsequently be used in evolutionary modelling and in collaboration with Illumina using machine learning and artificial intelligence approaches to understand the basis of molecular pleiotropy. This will include a research visit to Illumina AI lab in Germany.
1. Develop a computational framework for storing and querying molecular QTLs that will integrate with the OpenGWAS project
2. Generate pleiotropy maps using fine mapping and colocalization
3. Use evolutionary models to understand the impact of pleiotropy on natural selection processes
4. Use deep learning to predict disease mechanisms and disease progression from molecular pleiotropy maps
Currently summary statistics are stored for each GWA dataset separately. However this is not sustainable for QTL summary statistics with millions of molecular features. Therefore a new framework will be developed to store complete molecular QTL statistics for each dataset. Fine mapping and colocalization analysis will be used to integrate methylation QTL statistics from the Genetics of DNA Methylation Consortium, expression QTL statistics from eQTLGen and protein QTL statistics from SCALLOP and ALSPAC. This will result in maps of colocalized molecular traits. We will investigate biological models of pleiotropy, for example by using evolutionary models and gene-environmental interactions. We will use deep learning to identify molecular pleiotropy maps that correspond to distinct phenotypic patient subgroups.
1. Neumeyer S, Hemani G, Zeggini E. Strengthening Causal Inference for Complex Disease Using Molecular Quantitative Trait Loci. Trends Mol Med. 2020;26(2):232-41.
Josine Min (lead), Gibran Hemani, Johann Hawe (Illumina)
DNA methylation (DNAm) plays a central role in gene regulation. However, it is unknown how DNAm patterns change. For example, through genetic factors or physiological states.
Longitudinal birth cohort studies such as ALSPAC provide an unique opportunity to study DNAm patterns over time and to link it to varying physiological states. Quantitative traits comprising your physiological state (such as BMI, glucose and inflammation levels) have varying patterns over time. Identifying changes in DNAm preceding a change in physiological state may lead to identification of a marker that predicts disease outcome.(1)
Multiple studies have identified genetic variants associated with DNAm (mQTL: methylation quantitative trait locus) by combining genome wide genotype information with DNAm levels.(2) The Genetics of DNA methylation Consortium brought together a large number of cohorts to identify mQTLs in blood and investigated whether the mQTLs play a role in disease etiology.(3) Modelling DNAm trajectories with genetic variation could improve our understanding of biological mechanisms.
Aims: In this project we will identify longitudinal mQTL associations and examine whether these associations are involved in change of physiological state.
Objectives:
1) To identify longitudinal mQTLs across 4 timepoints in children and 2 timepoints in mums
2) To examine whether varying DNAm patterns predict varying disease/trait patterns
3) To examine whether longitudinal mQTLs are involved in disease progression
A longitudinal mQTL resource will be developed through genome-wide association analysis on DNAm levels in ALSPAC. Longitudinal trajectories of inflammation proteins and physiological traits (glucose, BMI) will be examined. To examine the relationship between longitudinal mQTL trajectories and these age-dependent health outcomes, we will use machine learning methods and mathematical modelling. Colocalization methods will be used to examine whether GWA loci for disease progression are shared with longitudinal mQTLs.
1. Chen R, Xia L, Tu K, Duan M, Kukurba K, Li-Pook-Than J, et al. Longitudinal personal DNA methylome dynamics in a human with a chronic condition. Nat Med. 2018;24(12):1930-9.
2. Gaunt TR, Shihab HA, Hemani G, Min JL, Woodward G, Lyttleton O, et al. Systematic identification of genetic influences on methylation across the human life course. Genome Biol. 2016;17:61.
3. Min JL, Hemani G, Hannon E, Dekkers KF, Castillo-Fernandez J, Luijk R, et al. Genomic and phenotypic insights from an atlas of genetic effects on DNA methylation. Nat Genet. 2021;53(9):1311-21.
Dr Eleanor Sanderson (lead), Dr Rebecca Richmond,
Multivariable MR (MVMR) is an extension of Mendelian randomization that allows for multiple exposures to be included in a single estimation. MVMR can be implemented with Genome-wide association study summary statistics. This enables the causal effect of multiple correlated traits on a single outcome, such as the causal effect of multiple lipids traits on coronary heart disease, to be estimated with existing publicly available data. However with many traits, such as lipids, it is not possible to include all potential exposures in a single model. This occurs because high correlation between the traits leads to low power and weak instruments. One potential approach to deciding how to include different traits is ‘stepwise’ MVMR where exposures are selected sequentially to include as many as possible while avoiding weak instrument bias.
The aim of this project is to explore different approaches to stepwise MVMR to establish the strengths and weaknesses of each approach.
This project will use an exemplar application of the effect of lipid traits on coronary heart disease to explore the application of different approaches to stepwise MVMR. We will use summary-data MR and MVMR methods applied through the ‘TwoSampleMR’ and ‘MVMR’ packages in R.
1. Sanderson, E., et al., An examination of multivariable Mendelian randomization in the single-sample and two-sample summary data settings. International journal of epidemiology, 2019. 48(3): p. 713-727.
2. Sanderson, E., et al., Mendelian randomization. Nature Reviews Methods Primers, 2022. 2(1): p. 6.
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Dr Charlotte Archer (lead), Professor Nicola Wiles, Professor Debbi Caldwell
EMDR has been established as an effective treatment for post-traumatic stress disorder (PTSD). However, PTSD has been re-categorised as a trauma/stressor-related disorder instead of anxiety. A meta-analysis to evaluate the effectiveness of EMDR on reducing symptoms of anxiety reports RCTs published up to 2018 (Faretta & Del Farra, 2019; Yunitri et al, 2020). However, further studies have been published (Inci Izmir et al, 2023; Azimisefat et al, 2022).
To assess the effectiveness eye movement desensitisation and reprocessing for anxiety disorders.
Systematic review, meta-analysis if applicable.
• Azimisefat et al, 2022. (2022). Efficacy of virtual reality exposure therapy and eye movement desensitization and reprocessing therapy on symptoms of acrophobia and anxiety sensitivity in adolescent girls: A randomized controlled trial. Frontiers in Psychology. 13, doi: 10.3389/fpsyg.2022.919148.
• Inci Izmir, SB., Korkmazlar, Ü., Ercan, ES. (2023) Eye Movement Desensitization and Reprocessing Therapy in Adolescents With Panic Disorder: A Twelve-Week Follow-Up Study. Clinical Child Psychology and Psychiatry. 2023;0(0). doi:10.1177/13591045231184757
• Faretta, E., & Dal Farra, M. (2019) Efficacy of EMDR Therapy for Anxiety Disorders. Journal of EMDR Practice and Research, 13(4), DOI:
• 10.1891/1933-3196.13.4.325.
• Yunitri et al, 2020. The effectiveness of eye movement desensitization and reprocessing toward anxiety disorder: A meta-analysis of randomized controlled trials. Journal of Psychiatric Research, 123, p102-113.
Dr Neil Goulding (lead), Prof Abigail Fraser, Matthew Suderman
The aetiology of the hypertensive disorders of pregnancy (HDP; preeclampsia and gestational hypertension) is likely complex, involving both maternal vascular and inflammatory components. We hypothesize that women who experience a HDP will have a more adverse inflammatory profile later in life.
To investigate the associations of HDP with 92 individual inflammatory proteins measured in women at mean (standard deviation; SD) age 47.5 (4.4) in a population based prospective pregnancy cohort study (ALSPAC).
1. Conduct a review of the literature to gain insight into the pathogenesis of HDP.
2. Identify potential confounders of the relationship of interest.
3. Conduct multivariable regression analyses to estimate the relationships between HDP and the 92 proteins measured using the Olink Target 96 Inflammation Panel (Olink, Uppsala, Sweden).
4. Write up a report and if the student wishes to, prepare a manuscript for publication.
LA Magee et al. N Engl J Med 2022;386:1817-1832.
Fraser et. al. Int J Epidemiol 2013;97-110.
Dr Laura Corbin (lead), Prof Nic Timpson, Dr Lucy Goudswaard
Obesity is a risk factor for disease including cardiovascular disease and types of cancer. The circulating proteome includes thousands of proteins naturally secreted from cells or present because of cell damage or cell death, including cytokines, growth factors and hormones. Exploring protein changes associated with excess adiposity by using causal analyses could help with identifying targets to prevent or reduce disease. Integrating evidence from independent sources (triangulation) is useful in overcoming specific limitations to any one study design. In this context, clinical trials of bariatric surgery present an opportunity for the examination of the circulating proteome in the context of variation in body mass index (BMI).
The aim of this project is to use proteomic data from a bariatric surgery randomized controlled trial (RCT) to identify the effect of surgical weight loss on circulating proteins. Results from this analysis will be compared to the proteomic signature of BMI derived from a range of complementary study designs including observational and Mendelian randomization analyses.
Proteomics data will be available for a subset of 125 patients from the By-Band-Sleeve trial of bariatric surgery. Samples collected before and after bariatric surgery have been analysed using the Olink Explore 3072 platform which provides relative quantification of approx. 3000 circulating proteins. These data will be available to analyse alongside a selection of clinical variables collected during the trial including BMI. Statistical methods will include linear (mixed) models but could also include multivariate modelling techniques (implemented in R).
The Olink platform is widely used in epidemiology, and comparative datasets exist both within our team (for example, from a clinical trial of a dietary intervention) and in publicly available resources such as UK Biobank and ALSPAC. Genome-wide association studies of Olink protein data have also been performed providing an opportunity for Mendelian randomization analyses. This provides a useful opportunity to compare results across study designs.
This project represents a continuation of work carried out by Dr Lucy Goudswaard on an earlier release of these data.
Skills developed in this project:
• Working with omic data;
• Working with clinical trial data;
• Coding in R;
• Using Git to develop code;
By-Band-Sleeve Collaborative Group. Roux-en-Y gastric bypass, gastric banding, or sleeve gastrectomy for severe obesity: Baseline data from the By-Band-Sleeve randomized controlled trial. Obesity (Silver Spring). 2023; 31( 5): 1290- 1299. doi:10.1002/oby.23746
Corbin, L. J., Hughes, D. A., Bull, C. J., et al. The metabolomic signature of weight loss in the Diabetes Remission Clinical Trial (DiRECT). medRxiv 2022.07.15.22277671; doi: https://doi.org/10.1101/2022.07.15.22277671
Fang, S., Wade, K.H., Hughes, D.A., et al. A multivariant recall-by-genotype study of the metabolomic signature of BMI. Obesity (Silver Spring). 2022; 30: 1298– 1310. doi:10.1002/oby.23441
Lawlor, D. A., Tilling, K. & Davey Smith, G. Triangulation in aetiological epidemiology. Int J Epidemiol 45, 1866-1886 (2016). https://doi.org:10.1093/ije/dyw314
Rogers, C. A. et al. The By-Band study: gastric bypass or adjustable gastric band surgery to treat morbid obesity: study protocol for a multi-centre randomised controlled trial with an internal pilot phase. Trials 15, 53 (2014). https://doi.org:10.1186/1745-6215-15-53
Olink Proteomics. PEA – a high-multiplex immunoassay technology with qPCR or NGS readout [White paper],
Dr Qian Yang (lead), Dr Carolina Borges, Prof Deborah Lawlor
Hypertensive disorders of pregnancy (HDP) affect approximately 10% of all pregnancies, contribute to maternal and fetal mortality, and are associated with the development of chronic hypertension. HDP encompasses a spectrum of conditions, incl. gestational hypertension, preeclampsia and eclampsia, and identifying their causal metabolites could help understand their pathologies and pave the way for targeted interventions. Nuclear magnetic resonance (NMR) metabolomics enables profiling of the lipidome, some amino acids and other small-molecule metabolites. Circulating NMR metabolites have been associated with complex diseases, incl. hypertension and HDP. We have observed associations of maternal pregnancy NMR metabolites (fatty acids, amino acids) with a higher risk of preeclampsia in Born in Bradford. These associations may be explained by residual confounding and reverse causality. Mendelian randomization (MR) provides an alternative approach to probe causal associations of NMR metabolites with HDP. Previous MR studies have identified some NMR metabolites affect blood pressure, outside of pregnancy and offspring birthweight.
The aim of this study is to explore the causal effect of maternal metabolites on the risk of HDP and its subtypes using MR.
This study will be undertaken within the MR-PREG collaboration, which aims to explore causes and consequences of different pregnancy and perinatal outcomes. The PhD candidate will have access to the largest genome-wide association studies (GWAS) currently available for metabolites and HDP. The student will select genetic variants strongly associated with NMR metabolites using data from ~300,000 UK Biobank individuals. For the selected genetic variants, the student will extract female-specific GWAS data for NMR metabolites (N ~150,000), as well as for GWAS meta-analyses of HDPs (N cases: ~26,000, N controls: ~416,000), gestational hypertension (N cases: ~17,000, N controls: ~405,000), preeclampsia (N cases: ~14,000, N controls: ~474,000).
The student will have the opportunity to use, and further develop, methods that account for high levels of biological and environmental correlation between metabolites. For example, we have shown how univariable and multi-variable MR can be used to account for correlation between metabolites, and the student would likely use this method for some of the work. This was used only after prior selection based on biology and strong correlations. The student will also be encouraged to explore the possibility of instrumenting more metabolites with higher correlations using newly developed MR methods.
1. Taylor K, Ferreira DLS, West J, Yang T, Caputo M, Lawlor DA. Differences in Pregnancy Metabolic Profiles and Their Determinants between White European and South Asian Women: Findings from the Born in Bradford Cohort. Metabolites. 2019;9.
2. Zhao J, Stewart ID, Baird D, Mason D, Wright J, Zheng J, et al. Causal effects of maternal circulating amino acids on offspring birthweight: a Mendelian randomisation study. EBioMedicine. 2023;88:104441.
3. Barry CS, Lawlor DA, Shapland CY, Sanderson E, Borges MC. Using Mendelian Randomisation to Prioritise Candidate Maternal Metabolic Traits Influencing Offspring Birthweight. Metabolites. 2022;12.
4. Yang Q, Borges MC, Sanderson E, Magnus MC, Kilpi F, Collings PJ, et al. Associations between insomnia and pregnancy and perinatal outcomes: Evidence from mendelian randomization and multivariable regression analyses. PLoS Med. 2022;19:e1004090.
Professor Sarah Lewis (lead), Dr Jelena Savovic, Professor Roger Bayston, University of Nottingham Professor Ian Kimber, University of Manchester
Background - Breast Implant Illness is a term recently used to describe a range of symptoms including; fatigue, chest pain, hair loss, headaches, chills, photosensitivity, rash, and chronic pain, which individuals have attributed to breast implants. However, a causal association has not yet been established for this. The Medicines and Healthcare Regulatory Authority (MHRA) would like to know what evidence exists linking health outcomes to implants (https://www.gov.uk/guidance/symptoms-sometimes-referred-to-as-breast-implant-illness).
This project will systematically search the literature to identify studies which assess health outcomes in relation to breast implants; it will also document information on the types of study identified and the methods used in these studies.
The student will write a search strategy, inclusion and exclusion criteria and methods for screening for relevant studies. The student will conduct a search of the medical literature for studies relating symptoms of breast implant illness to silicone implants. The student will screen abstracts and full text of articles to determine whether studies meet the inclusion criteria. They will then extract the pertinent information from included studies into a preprepared table. The student will review the study types, the methods used in the studies identified and the evidence presented in those studies. The findings of this study will be written up as a report which includes recommendations for future research and provided to the MHRA.
Glasberg SB. More Research Is What We Need Now for Breast Implant Illness. Aesthet Surg J. 2022 Oct 13;42(11):NP704-NP705. doi: 10.1093/asj/sjac187. PMID: 3578039
Dr Natalia Lewis (lead), Dr Estela Capelas Barbosa, e.capelasbarbosa@bristol.ac.uk, Sally McManus, sally.mcmanus@city.ac.uk, Senior Lecturer, Violence and Society Centre, City, University of London
Research on the cost of sexual violence to the economy and society is underdeveloped. Costing can draw on different types of inputs (e.g., quality of life lost, service costs, lost earning potential) and can process the data collected in different ways (annual/lifetime, per event or per person, discounting or not).
This project aims to: map what cost-estimate studies have been done on sexual violence and what methods they have used; assess if the costing of sexual violence is gender disaggregated.
A scoping review of studies that estimated the costs of sexual violence and were published since the landmark 2014 review by Walby and Olive.
Walby S, Olive P. (2014). Estimating the costs of gender-based violence in the European Union.
Peters MD et al. (2015) Guidance for conducting systematic scoping reviews. Int J Evid Based Healthc. Sep;13(3):141-6. doi: 10.1097/XEB.0000000000000050
Dr Emma Vincent (lead), Dr Jie Zheng, Emma Hazelwood
We recently found evidence for a protective effect of lymphocyte count on colorectal cancer (1). However, the term ‘lymphocyte’ encompasses several different immune cell types and for this information to be useful therapeutically, we need to investigate the cell types and pathways involved in more detail. CD4+ T cells are a type of lymphocyte which have a known role in priming and directing anti-tumour immune responses. While the role of CD4+ T cells in the tumour microenvironment has been investigated, their role in cancer risk is less well studied.
The aim is to investigate the role of CD4+ T cells in colorectal cancer development.
The student will perform a transcriptome-wide Mendelian randomization (MR) study to estimate the causal effect of CD4+ T cell gene expression profiles on colorectal cancer.
During activation, CD4+ T cells undergo extensive gene expression changes that shape their effector function. However, until recently it has only been possible to assess static, non-dynamic gene expression profiles in resting cells. This was extremely limiting given that T cells completely remodel their transcriptional profile upon activation. A recent publication by Soskic et al (2) mapped genetic effects on gene expression using single-cell transcriptomics, capturing the transcriptional states of CD4+ T cells at rest and following activation at three time points in healthy individuals. Data from this publication allows us to investigate the role of both resting and activated CD4+ T cells in colorectal cancer development.
(1) Constantinescu et al. International Journal of Cancer. 2023. https://doi.org/10.1002/ijc.34691
(2) Soskic et al. Nature Genetics. 2022. https://doi.org/10.1038/s41588-022-01066-3
Dr Eleanor Sanderson (lead), Dr Gareth Griffith, Professor Kate Tilling
Mendelian randomization (MR) uses genetic variants to estimate the causal effect of an exposure on an outcome of interest in a way that aims to overcome bias from unobserved confounding. The genetic variants used in MR are often selected as those most strongly associated with the exposure from a genome-wide association study (GWAS). These GWAS are conducted on datasets such as UK Biobank where many of the individuals are on medication such as blood pressure (BP) lowering medication. This medication use may bias effect estimates from GWAS studies or induce associations between SNPs for other characteristics and blood pressure, in turn biasing any MR studies which include BP as an exposure or an outcome.
It is not clear how to adjust for such bias in MR studies; excluding all individuals who report taking BP medication could induce selection bias as individuals taking medication will have had higher BP prior to medication use than those who do not. However, ignoring the potential issue is also likely to bias MR effect estimates. Alternative strategies include applying a BP adjustment to those reporting medication use to account for the effect of medication or restricting the sample to only younger individuals who are less likely to be taking medication.
The aim of this project is to explore how the different potential adjustments for medication use that could be applied to GWAS studies affect the results obtained from summary data MR analyses.
In this project the student will run GWAS on UK Biobank using the following adjustments to account for reported blood pressure medication
o Increasing the measured blood pressure by a set amount for those reporting medication use.
o Excluding those reporting medication use
o Restricting the sample to those under 50 where medication use is lower.
Each of these set of GWAS results will then be used in the same summary data MR analyses to compare the results obtained from the different adjustments. Separate MR analyses will be conducted with blood pressure as the exposure and the outcome. The results for each analysis will be compared to each other and to results with no adjustment for medication use to understand how these different adjustments bias the results from MR studies.
Dr Mark Gormley (lead), Dr Rebecca Richmond,
Ankyloglossia (‘tongue-tie’) is a common condition characterised by a short lingual frenulum. It has been associated with feeding difficulties, predominantly among breastfed infants, which can lead to inadequate nutrition and failure to thrive.
Tongue-tie is being more frequently diagnosed, particularly in high income countries. This may be due to increased breastfeeding or awareness of the condition. Another hypothesis is that rates are increasing due to folic acid (FA) supplementation during pregnancy, but the evidence remains insufficient.1
FA is protective against the development of neural tube defects and is recommended during the periconceptional period. It would not be advisable for expectant mothers to stop taking FA since the benefits outweigh the potential harms. Nonetheless, if tongue-tie was found to be influenced by maternal FA intake, this would raise further awareness of the condition and potential for early treatment through frenectomy, a simple and low risk procedure.
This project will investigate the relationship between maternal folate status and offspring tongue tie using data from mothers and children in the Avon Longitudinal Study of Parents and Children (ALSPAC).
Exposures will be maternal FA supplementation at 17 and 32 weeks of pregnancy, folate intake in the diet at 32 weeks of pregnancy and the methyltetrahydrofolate reductase (MTHFR) genotype.2
Outcomes will include reported tongue tie, infant feeding difficulties, breastfeeding cessation, and weight gain in infancy.
The analysis will involve multivariable linear and logistic regression analysis with adjustment for potential confounding factors (including maternal smoking, alcohol, body mass index, social class, education, dietary intake).
MTHFR genotype will be used to assess the causal role of maternal folate status in relation to tongue tie and related outcomes within a Mendelian randomization framework.2
Analyses will be further stratified by mode of infant feeding (breastfeeding exclusivity and duration).
1. Rubin, G., Stewart, C., McGowan, L., Woodside, J., Barrett, G., Godfrey, K., & Hall, J. (2023). Maternal folic acid supplementation and the risk of ankyloglossia (tongue-tie) in infants; a systematic review. Authorea, doi. 10.22541/au.168007870.09307215/v1.
2. Lewis, S. J., Leary, S., Smith, G. D., & Ness, A. (2009). Body composition at age 9 years, maternal folate intake during pregnancy and methyltetrahydrofolate reductase (MTHFR) C677T genotype. British Journal of Nutrition, 102(4), 493-496.
Dr Rebecca Richmond (lead), Mr Benji Woolf,
In previous work, we found that a large proportion of the Mendelian randomization literature to be poorly reported (1). The STROBE-MR checklist was subsequently introduced as a guideline to improve reporting of studies using Mendelian randomization (2). The effect that issuing the STROBE-MR guidelines has had on reporting quality in MR studies requires evaluation.
To compare reporting quality of Mendelian randomization studies published before and after issuing of the STROBE-MR guidelines to see whether this has improved reporting of MR studies.
There may also be scope for considering if there are useful changes which should be made to the STROBE-MR checklist – e.g. simplifications or additional items.
Study selection and evaluation will use similar systematic review methods as those in the previous study (1). Rather than evaluating all published studies, we expect that this project will use random sampling to select a more manageable number of studies. The actual evaluation will use a difference-in-difference approach, which is an econometric method for evaluating the effect of an intervention (here issuing of the STROBE-MR guidelines) after selecting suitable control group (e.g. published randomised controlled trails).
transparency of reporting in two-sample summary data Mendelian randomization studies using the MR-Base platform. Int J Epidemiol. 2022 Apr 6;dyac074.
2. Skrivankova VW, Richmond RC, Woolf BAR, Davies NM, Swanson SA, VanderWeele TJ, et al. Strengthening the reporting of observational studies in epidemiology using mendelian randomisation (STROBE-MR): explanation and elaboration. BMJ. 2021 Oct 26;375:n2233.
Dr Natalia Lewis (lead), Professor Gene Feder,
Theory of change (programme theory) describes how an intervention can produce long-term outcomes through a logical sequence of intermediate outcomes. Theories of change have been widely used to design and evaluate health interventions. The development of a theory of change typically occurs through a co-production process, requiring stakeholders to reflect on how their programmes can bring change. There is comparatively little literature on applying a theory of change approach to multidisciplinary programmes of research addressing complex public health and clinical problems.
The Violence, Health, and Society (VISION) consortium is a 5-year UKPRP-funded multi-disciplinary collaboration which aims to improve the measurement of data on violence to influence policy and practice and reduce violence and the health inequalities that result. VISION worked with partner organisations and other professional stakeholders to co-produce theories of change for research strands within the consortium.
This study aims to evaluate the process of developing theories of change for research strands within the VISION consortium. The research questions are: 1) How were the theories of change co-produced? 2) What factors were important in facilitating or hindering the co-production?
This is a qualitative process evaluation with a case study comprising of participant observations of stakeholders’ workshops, analysis of physical artefacts and documents, semi-structured interviews with workshops attendees. Ethics approval for the study has been obtained. We will provide observation fieldnotes, artefacts, and documents. The student will conduct up to 20 semi-structured qualitative interviews with VISION researchers and stakeholders who took part in the development of theories of change. The interview topic guide will explore participants’ perspective on the stakeholders’ workshops on co-producing theories of change including any things which drew them to it initially, how they experienced it, what factors facilitated and hindered the process of theories development, and whether there are things that could have been done differently. Interviews will be audio recorded and professionally transcribed. The student will use the principles of co-production and framework approach for organising primary data into codes, candidate themes, and analytical themes.
1. Breuer et al 2016.Using theory of change to design and evaluate public health interventions: a systematic review. doi: 10.1186/s13012-016-0422-6.
2. Boblin et al 2013. Using Stake’s Qualitative Case Study Approach to Explore Implementation of Evidence-Based Practice. doi:10.1177/1049732313502128
Dr Natalia Lewis (lead), Professor Gene Feder,
Theory of change (programme theory) describes how an intervention can produce long-term outcomes through a logical sequence of intermediate outcomes. Theories of change have been widely used to design and evaluate health interventions. The development of a theory of change typically occurs through a co-production process, requiring stakeholders to reflect on how their programmes can bring change. There is comparatively little literature on applying a theory of change approach to multidisciplinary programmes of research addressing complex public health and clinical problems.
The Violence, Health, and Society (VISION) consortium is a 5-year UKPRP-funded multi-disciplinary collaboration which aims to improve the measurement of data on violence to influence policy and practice and reduce violence and the health inequalities that result. VISION worked with partner organisations and other professional stakeholders to co-produce theories of change for research strands within the consortium.
This study aims to evaluate the process of developing theories of change for research strands within the VISION consortium. The research questions are: 1) How were the theories of change co-produced? 2) What factors were important in facilitating or hindering the co-production?
This is a qualitative process evaluation with a case study comprising of participant observations of stakeholders’ workshops, analysis of physical artefacts and documents, semi-structured interviews with workshops attendees. Ethics approval for the study has been obtained. We will provide observation fieldnotes, artefacts, and documents. The student will conduct up to 20 semi-structured qualitative interviews with VISION researchers and stakeholders who took part in the development of theories of change. The interview topic guide will explore participants’ perspective on the stakeholders’ workshops on co-producing theories of change including any things which drew them to it initially, how they experienced it, what factors facilitated and hindered the process of theories development, and whether there are things that could have been done differently. Interviews will be audio recorded and professionally transcribed. The student will use the principles of co-production and framework approach for organising primary data into codes, candidate themes, and analytical themes.
1. Breuer et al 2016.Using theory of change to design and evaluate public health interventions: a systematic review. doi: 10.1186/s13012-016-0422-6.
2. Boblin et al 2013. Using Stake’s Qualitative Case Study Approach to Explore Implementation of Evidence-Based Practice. doi:10.1177/1049732313502128
Dr Rachael Hughes (lead), Professor Kate Tilling, Dr Emily Kawabata Dr Audinga-Dea Hazewinkel [LSHTM]
Participants in epidemiologic and genetic studies are rarely true random samples of the populations they are intended to represent, and both known and unknown factors can influence participation in a study (known as selection into a study). Selection bias is attributable to conditioning on common effects (e.g., of the outcome and exposure) and is a type of collider-stratification bias. An estimate of the exposure effect in the study sample is biased by selection when it systematically differs to the value of the exposure effect in the target population. Selection bias arising from a non-probability sampling design or non-participation can be especially challenging as the factors influencing selection into the study are rarely observed on the unselected individuals. Consequently, an analyst cannot directly adjust for the selection bias in their analysis. Alternatives include evaluating using a risk of bias tool to assess if a study could be affected by selection bias or to conduct a quantitative bias analysis (QBA; also known as a sensitivity analysis) to assess sensitivity of a study’s conclusions to plausible assumptions about the selection bias. Currently, QBA methods and risk of bias tools are not routinely implemented, partly due to a lack of knowledge about accessible software.
The main aim of the scoping review is to identify the latest state-of-the art, publicly available software implementations of QBAs and risk to bias tools to selection bias and to describe the key properties of each software program.
The project objectives are: (i) conduct a systematic search of the Comprehensive R Archive Network (CRAN) and Stata’s Statistical Software Components (SSC) archive to identify R packages and Stata commands, respectively, that implement a QBA or risk of bias tool, (ii) extract information on the key properties of each relevant program, and (iii) illustrate the application of a subset of these software programs to real data from the UK Biobank study.
Note that, using Web of Science, Drs Hazewinkel and Hughes have recently conducted a scoping review of the published literature for software implementations of QBAs and risk of bias tools to selection bias. The proposed mini project complements this previous review of the published literature, and the aim is to publish a manuscript on the combined output from this project and the review of the published literature.
To avoid overlap with another software review underway on QBAs to data missing not at random, we shall restrict the review to selection bias where there is no information available on the unselected individuals (e.g., arising from a non-probability sampling design or non-participation).
The search terms for the CRAN and SSC archive searches can be derived from those used for the Web of Science search of the published literature. As the tools for searching CRAN and the SSC archive are less sophisticated than that of Web of Science, the first step will be to simplify the Web of Science search terms used during the review of the published literature.
Software submitted to CRAN, and the SSC archive are accompanied by a brief description of the program. Users can search for software based on words or phrases mentioned in the title or the brief description. R packages available.packages() and pkgsearch() can be used to search all packages currently available on CRAN (archived packages are excluded) and the IDEAS/RePEC website (https://ideas.repec.org/) can be used to search the SSC archive.
After potential R packages and Stata commands have been identified, the student will decide, based on the software’s title and description, if the software implements either a QBA or risk of bias tool to selection bias. Dr Hughes will act as a second reviewer.
Among the relevant software programs, the student will extract information about the software features such as:
• QBA or risk of bias tool
• applicable to which type of target analysis:
o e.g., standard regression, multilevel analysis, mediation analysis, meta-analysis
o e.g., type of outcome variable, type of exposure variable
• graphical or tabular outputs
For a subset of software programs (e.g., those applicable when the analysis of interest is a linear regression) the student will describe, illustrate, and compare these programs when applied to a real data example. The example will depend on the features of the software programs under comparison and so will be decided at a later stage. Dr Hughes has a UK Biobank project applicable for analyses of QBAs.
Fox MP, MacLehose RF, Lash TL. Applying quantitative bias analysis to epidemiologic data. Second Edition. Springer Nature Switzerland 2021.
Hernan MA, Harnandez-Diaz S, Robins JM. A structural approach to selection bias. Epidemiology 2004; 15: 615-625.
Lu H, Cole SR, Howe C, Westreich D. Toward a clearer definition of selection bias when estimating causal effects. Epidemiology 2022; 33: 699-706.
An example of a scoping review of software implementations of QBAs:
Kawabata K, Tilling K, Groenwold RHH, Hughes RA. Quantitative bias analysis in practice: review of software for regression with unmeasured confounding. BMC Medical Research Methodology 2023; 23: 111.
Dr Rachael Hughes (lead), Professor Kate Tilling, Dr Emily Kawabata Dr Audinga-Dea Hazewinkel [LSHTM]
Participants in epidemiologic and genetic studies are rarely true random samples of the populations they are intended to represent, and both known and unknown factors can influence participation in a study (known as selection into a study). Selection bias is attributable to conditioning on common effects (e.g., of the outcome and exposure) and is a type of collider-stratification bias. An estimate of the exposure effect in the study sample is biased by selection when it systematically differs to the value of the exposure effect in the target population. Selection bias arising from a non-probability sampling design or non-participation can be especially challenging as the factors influencing selection into the study are rarely observed on the unselected individuals. Consequently, an analyst cannot directly adjust for the selection bias in their analysis. Alternatives include evaluating using a risk of bias tool to assess if a study could be affected by selection bias or to conduct a quantitative bias analysis (QBA; also known as a sensitivity analysis) to assess sensitivity of a study’s conclusions to plausible assumptions about the selection bias. Currently, QBA methods and risk of bias tools are not routinely implemented, partly due to a lack of knowledge about accessible software.
The main aim of the scoping review is to identify the latest state-of-the art, publicly available software implementations of QBAs and risk to bias tools to selection bias and to describe the key properties of each software program.
The project objectives are: (i) conduct a systematic search of the Comprehensive R Archive Network (CRAN) and Stata’s Statistical Software Components (SSC) archive to identify R packages and Stata commands, respectively, that implement a QBA or risk of bias tool, (ii) extract information on the key properties of each relevant program, and (iii) illustrate the application of a subset of these software programs to real data from the UK Biobank study.
Note that, using Web of Science, Drs Hazewinkel and Hughes have recently conducted a scoping review of the published literature for software implementations of QBAs and risk of bias tools to selection bias. The proposed mini project complements this previous review of the published literature, and the aim is to publish a manuscript on the combined output from this project and the review of the published literature.
To avoid overlap with another software review underway on QBAs to data missing not at random, we shall restrict the review to selection bias where there is no information available on the unselected individuals (e.g., arising from a non-probability sampling design or non-participation).
The search terms for the CRAN and SSC archive searches can be derived from those used for the Web of Science search of the published literature. As the tools for searching CRAN and the SSC archive are less sophisticated than that of Web of Science, the first step will be to simplify the Web of Science search terms used during the review of the published literature.
Software submitted to CRAN, and the SSC archive are accompanied by a brief description of the program. Users can search for software based on words or phrases mentioned in the title or the brief description. R packages available.packages() and pkgsearch() can be used to search all packages currently available on CRAN (archived packages are excluded) and the IDEAS/RePEC website (https://ideas.repec.org/) can be used to search the SSC archive.
After potential R packages and Stata commands have been identified, the student will decide, based on the software’s title and description, if the software implements either a QBA or risk of bias tool to selection bias. Dr Hughes will act as a second reviewer.
Among the relevant software programs, the student will extract information about the software features such as:
• QBA or risk of bias tool
• applicable to which type of target analysis:
o e.g., standard regression, multilevel analysis, mediation analysis, meta-analysis
o e.g., type of outcome variable, type of exposure variable
• graphical or tabular outputs
For a subset of software programs (e.g., those applicable when the analysis of interest is a linear regression) the student will describe, illustrate, and compare these programs when applied to a real data example. The example will depend on the features of the software programs under comparison and so will be decided at a later stage. Dr Hughes has a UK Biobank project applicable for analyses of QBAs.
Fox MP, MacLehose RF, Lash TL. Applying quantitative bias analysis to epidemiologic data. Second Edition. Springer Nature Switzerland 2021.
Hernan MA, Harnandez-Diaz S, Robins JM. A structural approach to selection bias. Epidemiology 2004; 15: 615-625.
Lu H, Cole SR, Howe C, Westreich D. Toward a clearer definition of selection bias when estimating causal effects. Epidemiology 2022; 33: 699-706.
An example of a scoping review of software implementations of QBAs:
Kawabata K, Tilling K, Groenwold RHH, Hughes RA. Quantitative bias analysis in practice: review of software for regression with unmeasured confounding. BMC Medical Research Methodology 2023; 23: 111.
Dr Daniel Major-Smith (lead), Dr Isaac Halstead,
The sharing of data and analysis code supporting scientific publications is essential for open-science, improving research reproducibility, and disseminating knowledge on statistical methods (Munafó et al., 2017). However, these practices are relatively rare in the medical sciences, with only an estimated 2% of studies between 2016 and 2021 making data publicly available, with code sharing rates at <0.5% (Hamilton et al., 2023). To the best of our knowledge, recent comparable figures specifically for epidemiology are unknown, although based on a small sample of 90 studies from 2005, none fully-shared either data or code (Peng et al., 2006). While there may be valid reasons for not sharing epidemiological data (e.g., confidentiality concerns), methods such as synthetic data generation are a viable alternative to allow ‘quasi-reproducible’ research (Shepherd et al., 2017).
Given the importance of sharing research data and analysis code for reproducible research – particularly in epidemiology which is heavily-reliant on expensive/difficult-to-conduct data and advanced statistical methods – here we aim to assess:
• The prevalence of data and code sharing in leading epidemiology journals
• Whether this varies by the type of data (e.g., observational vs trials vs simulations)
• Reasons for not sharing data (if provided)
• Whether these trends have changed over time
We will review all research articles in leading general epidemiology journals (International Journal of Epidemiology, American Journal of Epidemiology, European Journal of Epidemiology and Epidemiology) from 2010 to present. For all original research articles, we will code:
• Journal
• Study year
• Study type (e.g., trial, cohort, simulation study, etc.)
• Data availability (including synthetic data)
• Reasons for data not being available (if applicable and provided)
• Analysis code availability
In addition to assessing overall prevalence of data and code sharing, analyses will also explore whether these trends have changed over time, whether they differ by journal and whether they differ by study type.
Hamilton et al. (2023) https://doi.org/10.1136/bmj-2023-075767
Munafò et al. (2017) https://doi.org/10.1038/s41562-016-0021
Peng et al. (2006) https://doi.org/10.1093/aje/kwj093
Shepherd et al. (2017) https://doi.org/10.1093/aje/kwx066
Dr Daniel Major-Smith (lead), Dr Isaac Halstead,
The sharing of data and analysis code supporting scientific publications is essential for open-science, improving research reproducibility, and disseminating knowledge on statistical methods (Munafó et al., 2017). However, these practices are relatively rare in the medical sciences, with only an estimated 2% of studies between 2016 and 2021 making data publicly available, with code sharing rates at <0.5% (Hamilton et al., 2023). To the best of our knowledge, recent comparable figures specifically for epidemiology are unknown, although based on a small sample of 90 studies from 2005, none fully-shared either data or code (Peng et al., 2006). While there may be valid reasons for not sharing epidemiological data (e.g., confidentiality concerns), methods such as synthetic data generation are a viable alternative to allow ‘quasi-reproducible’ research (Shepherd et al., 2017).
Given the importance of sharing research data and analysis code for reproducible research – particularly in epidemiology which is heavily-reliant on expensive/difficult-to-conduct data and advanced statistical methods – here we aim to assess:
• The prevalence of data and code sharing in leading epidemiology journals
• Whether this varies by the type of data (e.g., observational vs trials vs simulations)
• Reasons for not sharing data (if provided)
• Whether these trends have changed over time
We will review all research articles in leading general epidemiology journals (International Journal of Epidemiology, American Journal of Epidemiology, European Journal of Epidemiology and Epidemiology) from 2010 to present. For all original research articles, we will code:
• Journal
• Study year
• Study type (e.g., trial, cohort, simulation study, etc.)
• Data availability (including synthetic data)
• Reasons for data not being available (if applicable and provided)
• Analysis code availability
In addition to assessing overall prevalence of data and code sharing, analyses will also explore whether these trends have changed over time, whether they differ by journal and whether they differ by study type.
Hamilton et al. (2023) https://doi.org/10.1136/bmj-2023-075767
Munafò et al. (2017) https://doi.org/10.1038/s41562-016-0021
Peng et al. (2006) https://doi.org/10.1093/aje/kwj093
Shepherd et al. (2017) https://doi.org/10.1093/aje/kwx066
Dr Isaac Halstead (lead), Dr Daniel Major-Smith ,
Personality traits have a large impact on health and health-related behaviours (e.g., smoking, disease monitoring, etc.). Understanding the stability of personality traits, as well as the factors which may cause changes in personality, may therefore be important for improving public health. Previous research has suggested that personality traits are largely stable over adolescence and into adulthood (Atherton et al., 2020), although life events – such as going to university, long-term relationships, becoming a parent, etc. – may also shape subsequent personality (Bühler et al., 2023). While previous longitudinal work has been conducted, often sample sizes are small, and most studies focus on a small number of life events (Bühler et al., 2023).
In this project, we aim to use longitudinal data from ALSPAC (the Avon Longitudinal Study of Parents and Children), with detailed confounder and repeated exposure and outcome data, to try and answer the following research questions:
1: How stable are personality traits from adolescence to adulthood (from age 13 to age 29 years)
2: Do life events cause a change in personality over this time period?
Exposures: A range of life events occurring in adulthood (prior to age 29), including: attending/graduating university, marriage/long-term relationship, divorce/separation, becoming a parent, (un)employment, death of a parent/loved one, long-term illness (e.g., diabetes, depression, anxiety).
Outcome: Big-5 personality traits measured at age 29 (also measured at age 13)
Confounders: socioeconomic position (via multiple proxies), parental mental health, parental exposure behaviours, offspring life events occurring prior to age 13 (e.g., mental and physical health, educational attainment).
Correlations will be used to assess the stability of personality over time. Using this detailed longitudinal data – hopefully removing many sources of bias, such as reverse causality and unmeasured confounding (VanderWeele et al., 2016) – we also plan to conduct linear regression to estimate the causal relationship between life events and subsequent changes in personality.
Atherton, et al., 2021 https://doi.org/10.1177/0146167220949362
Bühler et al., 2023 https://doi.org/10.1177/08902070231190219
VanderWeele et al., 2016 https://doi.org/10.1007/s00127-016-1281-9
Dr Isaac Halstead (lead), Dr Daniel Major-Smith ,
Personality traits have a large impact on health and health-related behaviours (e.g., smoking, disease monitoring, etc.). Understanding the stability of personality traits, as well as the factors which may cause changes in personality, may therefore be important for improving public health. Previous research has suggested that personality traits are largely stable over adolescence and into adulthood (Atherton et al., 2020), although life events – such as going to university, long-term relationships, becoming a parent, etc. – may also shape subsequent personality (Bühler et al., 2023). While previous longitudinal work has been conducted, often sample sizes are small, and most studies focus on a small number of life events (Bühler et al., 2023).
In this project, we aim to use longitudinal data from ALSPAC (the Avon Longitudinal Study of Parents and Children), with detailed confounder and repeated exposure and outcome data, to try and answer the following research questions:
1: How stable are personality traits from adolescence to adulthood (from age 13 to age 29 years)
2: Do life events cause a change in personality over this time period?
Exposures: A range of life events occurring in adulthood (prior to age 29), including: attending/graduating university, marriage/long-term relationship, divorce/separation, becoming a parent, (un)employment, death of a parent/loved one, long-term illness (e.g., diabetes, depression, anxiety).
Outcome: Big-5 personality traits measured at age 29 (also measured at age 13)
Confounders: socioeconomic position (via multiple proxies), parental mental health, parental exposure behaviours, offspring life events occurring prior to age 13 (e.g., mental and physical health, educational attainment).
Correlations will be used to assess the stability of personality over time. Using this detailed longitudinal data – hopefully removing many sources of bias, such as reverse causality and unmeasured confounding (VanderWeele et al., 2016) – we also plan to conduct linear regression to estimate the causal relationship between life events and subsequent changes in personality.
Atherton, et al., 2021 https://doi.org/10.1177/0146167220949362
Bühler et al., 2023 https://doi.org/10.1177/08902070231190219
VanderWeele et al., 2016 https://doi.org/10.1007/s00127-016-1281-9
Dr Isaac Halstead (lead), Dr Daniel Major-Smith ,
A large amount of research has investigated whether personality traits are associated with health-promoting behaviours, such as smoking, alcohol consumption, etc (Kotov et al., 2010; Zvolensky et al., 2015). Previous research indicates that neuroticism as a risk factor for substance use (Kotov et al., 2010; Zvolensky et al., 2015), and conscientiousness as a protective factor (Kotov et al., 2010), with inconsistent patterns of association in agreeableness, openness and extraversion. However, much of this previous work is cross-sectional, meaning it is difficult to rule out reverse causality, or other sources of confounding. Although some work has used longitudinal data to try and overcome these biases, or using other methods (e.g., Sallis et al., 2019), these are rare and studies often small. The extent to which personality may potentially cause such behaviours is therefore still an open question.
In this project, we aim to use longitudinal data from ALSPAC (the Avon Longitudinal Study of Parents and Children), with detailed confounder and repeated exposure and outcome data, to try and answer the causal question: “Do aspects of personality cause health-promoting behaviours?”
Exposures: Big-5 personality traits measured at age 29 (also measured at age 13)
Outcomes: Health-promoting behaviour outcomes measured at age 30 (also assessed during adolescence and early adulthood). This includes measures of smoking, alcohol consumption, cannabis use and other illicit drug use
Confounders, including: socioeconomic position (via multiple proxies), parental mental health, parental health-promoting behaviours, offspring life events (e.g., being a parent).
Using this detailed longitudinal data, we plan to conduct regression analyses (depending on the outcome; e.g., logistic regression for binary outcomes) adjusting for this wide range of confounders, in addition to baseline exposures and outcomes. By utilising this longitudinal repeated data, such an approach will hopefully provide a less-biased estimate of the causal relationship between personality and health-promoting behaviours (VanderWeele et al., 2016).
Sallis et al., 2019 https://doi.org/10.1017/S0033291718003069
Zvolensky et al., 2015 https://doi.org/10.1016/j.jpsychires.2015.02.008
Kotov et al., 2010 https://doi.org/10.1037/a0020327
VanderWeele et al., 2016 https://doi.org/10.1007/s00127-016-1281-9
Dr Isaac Halstead (lead), Dr Daniel Major-Smith ,
A large amount of research has investigated whether personality traits are associated with health-promoting behaviours, such as smoking, alcohol consumption, etc (Kotov et al., 2010; Zvolensky et al., 2015). Previous research indicates that neuroticism as a risk factor for substance use (Kotov et al., 2010; Zvolensky et al., 2015), and conscientiousness as a protective factor (Kotov et al., 2010), with inconsistent patterns of association in agreeableness, openness and extraversion. However, much of this previous work is cross-sectional, meaning it is difficult to rule out reverse causality, or other sources of confounding. Although some work has used longitudinal data to try and overcome these biases, or using other methods (e.g., Sallis et al., 2019), these are rare and studies often small. The extent to which personality may potentially cause such behaviours is therefore still an open question.
In this project, we aim to use longitudinal data from ALSPAC (the Avon Longitudinal Study of Parents and Children), with detailed confounder and repeated exposure and outcome data, to try and answer the causal question: “Do aspects of personality cause health-promoting behaviours?”
Exposures: Big-5 personality traits measured at age 29 (also measured at age 13)
Outcomes: Health-promoting behaviour outcomes measured at age 30 (also assessed during adolescence and early adulthood). This includes measures of smoking, alcohol consumption, cannabis use and other illicit drug use
Confounders, including: socioeconomic position (via multiple proxies), parental mental health, parental health-promoting behaviours, offspring life events (e.g., being a parent).
Using this detailed longitudinal data, we plan to conduct regression analyses (depending on the outcome; e.g., logistic regression for binary outcomes) adjusting for this wide range of confounders, in addition to baseline exposures and outcomes. By utilising this longitudinal repeated data, such an approach will hopefully provide a less-biased estimate of the causal relationship between personality and health-promoting behaviours (VanderWeele et al., 2016).
Sallis et al., 2019 https://doi.org/10.1017/S0033291718003069
Zvolensky et al., 2015 https://doi.org/10.1016/j.jpsychires.2015.02.008
Kotov et al., 2010 https://doi.org/10.1037/a0020327
VanderWeele et al., 2016 https://doi.org/10.1007/s00127-016-1281-9
Dr Tom Bond (lead), Dr Apostolos Gkatzionis, Carolina Borges Paul Madley-Dowd Kate Tiling Deborah Lawlor
Studies of pregnancy outcomes are often conducted in samples that are restricted to women who are currently pregnant. This involves conditioning on pregnancy “incidence”, analogous to conditioning on disease incidence in studies of disease progression. In this situation pregnancy becomes a collider variable, which could induce spurious associations between causes of pregnancy that are truly independent in the source population. Furthermore, if any of the causes of pregnancy also cause the pregnancy outcome of interest then this could result in biased estimates of the true causal associations between risk factors and the pregnancy outcome. This is an example of index event bias (1), and the extent to which this could affect Mendelian randomization (MR) studies of pregnancy outcomes is currently unknown. This mini project will inform and influence a large number of MR investigations that we are currently conducting within the MR-PREG consortium.
1. Explore, via simulations and empirical analyses, the extent to which index event bias may affect MR studies of pregnancy outcomes.
2. Establish, via simulations and empirical analyses, whether existing methods can mitigate index event bias in MR studies of pregnancy outcomes.
We will conduct simulation studies and (where possible) real data analyses, to explore the extent to which two sample MR estimates of causal effects on pregnancy outcomes may be affected by index event bias under realistic assumptions. There will also be scope to conduct further simulation studies, along with applied example analyses, to test whether existing methods can mitigate index event bias in a two sample MR study of pregnancy outcomes. Relevant methods will include Slope-Hunter (2) and the method of Dudbridge et al. (3), which requires a GWAS of pregnancy incidence.
1. Mitchell RE, et al. PLOS Genetics. 2023;19(2):e1010596.
2. Mahmoud O, et al. Nature Communications. 2022;13(1):619.
3. Dudbridge F, et al. Nature Communications. 2019;10(1):1561.
Dr Tom Bond (lead), Dr Apostolos Gkatzionis, Carolina Borges Paul Madley-Dowd Kate Tiling Deborah Lawlor
Studies of pregnancy outcomes are often conducted in samples that are restricted to women who are currently pregnant. This involves conditioning on pregnancy “incidence”, analogous to conditioning on disease incidence in studies of disease progression. In this situation pregnancy becomes a collider variable, which could induce spurious associations between causes of pregnancy that are truly independent in the source population. Furthermore, if any of the causes of pregnancy also cause the pregnancy outcome of interest then this could result in biased estimates of the true causal associations between risk factors and the pregnancy outcome. This is an example of index event bias (1), and the extent to which this could affect Mendelian randomization (MR) studies of pregnancy outcomes is currently unknown. This mini project will inform and influence a large number of MR investigations that we are currently conducting within the MR-PREG consortium.
1. Explore, via simulations and empirical analyses, the extent to which index event bias may affect MR studies of pregnancy outcomes.
2. Establish, via simulations and empirical analyses, whether existing methods can mitigate index event bias in MR studies of pregnancy outcomes.
We will conduct simulation studies and (where possible) real data analyses, to explore the extent to which two sample MR estimates of causal effects on pregnancy outcomes may be affected by index event bias under realistic assumptions. There will also be scope to conduct further simulation studies, along with applied example analyses, to test whether existing methods can mitigate index event bias in a two sample MR study of pregnancy outcomes. Relevant methods will include Slope-Hunter (2) and the method of Dudbridge et al. (3), which requires a GWAS of pregnancy incidence.
1. Mitchell RE, et al. PLOS Genetics. 2023;19(2):e1010596.
2. Mahmoud O, et al. Nature Communications. 2022;13(1):619.
3. Dudbridge F, et al. Nature Communications. 2019;10(1):1561.
Dr Bruna Rubbo (lead), Dr Duleeka Knipe, Additional collaborators include researchers from the following institutions: World Health Organisation Food and Agriculture Organisation of the United Nations University of Edinburgh’s Centre for Pesticide Suicide Prevention
Suicide is the one of the leading causes of mortality worldwide, with more deaths than due to HIV/AIDS, breast cancer, or war and homicide. Approximately 80% of global suicides occurs in low- and middle-income countries (LMIC). The most common methods of suicide globally are ingestion of pesticides, hanging, and firearms, of which pesticide self-poisoning accounts for up to 20% of cases.
Suicide by pesticide poisoning is one of the most common methods of suicide in LMICs due to easy access to highly hazardous pesticides (HHP), particularly in rural settings (1). Brazil’s economy relies heavily on the agricultural industry, and the country is among the world’s largest consumer of pesticides (2, 3). HHP are highly lethal and restricting access to them is one of the few proven approaches to reduce overall suicide rates (4, 5). Based on work involving researchers at the University of Bristol, the World Health Organisation (WHO) are promoting the restriction of HHPs as part of their ‘LIVE LIFE’ programme, which has limiting access to the means of suicide, especially pesticides, as one of their four strategies to reduce suicide deaths.
This mini-project is an important component of a larger international project examining pesticide suicides and the impact of changes in regulation restricting access to HHP in LMICs. You will have the opportunity to significantly expand your network by working with researchers from the WHO, the University of Edinburgh’s Centre for Pesticide Suicide Prevention (CPSP), the Food and Agriculture Organisation (FAO) of the United Nations (UN), and collaborators in Brazil. Importantly, the findings from your analyses will have real-world implications, with potential to influence policy at national and local levels.
The aim of the mini-project is to describe trends of overall and method-specific suicide rates in Brazil, stratified by age group, sex, and method of suicide. Methods include suicide by pesticide ingestion, use of firearms, hanging, and poisoning, among others.
This project will provide the opportunity to build data linkage and analysis skills in R (option to use Stata, if preferred) using open data, a skill that will be valuable for future projects across multiple fields of expertise.
You will use longitudinal population-level suicide data (ICD-10 codes X60-X84 for intentional self-harm, ICD-10 X68 for pesticide suicides) derived from the Brazilian national SIM database (mortality information system). Monthly data on suicides are available from 1996 to 2021, stratified by region, sex, age group, ethnicity, education level, and marital status. Additional data on type of substance used for self-harm and suicides are available from the SINAN database (notifiable disease information system), also stratified by region. Population data will be derived from IBGE database (Brazilian Institute of Geography and Statistics). All these datasets will be formatted in machine-readable files and available for use; we will provide close assistance in the data linkage process.
In this project, you will:
1) link the mortality and population datasets in order to calculate overall and method-specific age-standardised mortality rates and mortality rates by region;
2) provide descriptive statistics and produce figures to visually inspect the data;
3) calculate suicide mortality rates by region and plot these onto maps to visualise regional trends; and
4) write up results for publication.
1. Gunnell and Eddleston. Suicide by intentional ingestion of pesticides: a continuing tragedy in developing countries. Int J Epidemiol. 2003; 32(6): 902–909.
2. McDonald, et al. Trends in method-specific suicide in Brazil from 2000 to 2017. Soc Psychiatry Psychiatr Epidemiol. 2021 Oct;56(10):1779-1790. doi: 10.1007/s00127-021-02060-6. Epub 2021 Mar 29.
3. Neves, et al. Poisoning by agricultural pesticides in the state of Goias, Brazil, 2005-2015: analysis of records in official information systems. Ciencias & Saude Coletiva, 2020; 25(7):2743-2754.
4. Knipe, et al. Preventing deaths from pesticide self-poisoning – learning from Sri Lanka’s success. Lancet Glob Health. 2017 Jul;5(7):e651-e652. doi: 10.1016/S2214-109X(17)30208-5.
5. Gunnell, Knipe, et al. Prevention of suicide with regulations aimed at restricting access to highly hazardous pesticides: a systematic review of the international evidence. Lancet Glob Health 2017; 5:e1026-37
Dr Bruna Rubbo (lead), Dr Duleeka Knipe, Additional collaborators include researchers from the following institutions: World Health Organisation Food and Agriculture Organisation of the United Nations University of Edinburgh’s Centre for Pesticide Suicide Prevention
Suicide is the one of the leading causes of mortality worldwide, with more deaths than due to HIV/AIDS, breast cancer, or war and homicide. Approximately 80% of global suicides occurs in low- and middle-income countries (LMIC). The most common methods of suicide globally are ingestion of pesticides, hanging, and firearms, of which pesticide self-poisoning accounts for up to 20% of cases.
Suicide by pesticide poisoning is one of the most common methods of suicide in LMICs due to easy access to highly hazardous pesticides (HHP), particularly in rural settings (1). Brazil’s economy relies heavily on the agricultural industry, and the country is among the world’s largest consumer of pesticides (2, 3). HHP are highly lethal and restricting access to them is one of the few proven approaches to reduce overall suicide rates (4, 5). Based on work involving researchers at the University of Bristol, the World Health Organisation (WHO) are promoting the restriction of HHPs as part of their ‘LIVE LIFE’ programme, which has limiting access to the means of suicide, especially pesticides, as one of their four strategies to reduce suicide deaths.
This mini-project is an important component of a larger international project examining pesticide suicides and the impact of changes in regulation restricting access to HHP in LMICs. You will have the opportunity to significantly expand your network by working with researchers from the WHO, the University of Edinburgh’s Centre for Pesticide Suicide Prevention (CPSP), the Food and Agriculture Organisation (FAO) of the United Nations (UN), and collaborators in Brazil. Importantly, the findings from your analyses will have real-world implications, with potential to influence policy at national and local levels.
The aim of the mini-project is to describe trends of overall and method-specific suicide rates in Brazil, stratified by age group, sex, and method of suicide. Methods include suicide by pesticide ingestion, use of firearms, hanging, and poisoning, among others.
This project will provide the opportunity to build data linkage and analysis skills in R (option to use Stata, if preferred) using open data, a skill that will be valuable for future projects across multiple fields of expertise.
You will use longitudinal population-level suicide data (ICD-10 codes X60-X84 for intentional self-harm, ICD-10 X68 for pesticide suicides) derived from the Brazilian national SIM database (mortality information system). Monthly data on suicides are available from 1996 to 2021, stratified by region, sex, age group, ethnicity, education level, and marital status. Additional data on type of substance used for self-harm and suicides are available from the SINAN database (notifiable disease information system), also stratified by region. Population data will be derived from IBGE database (Brazilian Institute of Geography and Statistics). All these datasets will be formatted in machine-readable files and available for use; we will provide close assistance in the data linkage process.
In this project, you will:
1) link the mortality and population datasets in order to calculate overall and method-specific age-standardised mortality rates and mortality rates by region;
2) provide descriptive statistics and produce figures to visually inspect the data;
3) calculate suicide mortality rates by region and plot these onto maps to visualise regional trends; and
4) write up results for publication.
1. Gunnell and Eddleston. Suicide by intentional ingestion of pesticides: a continuing tragedy in developing countries. Int J Epidemiol. 2003; 32(6): 902–909.
2. McDonald, et al. Trends in method-specific suicide in Brazil from 2000 to 2017. Soc Psychiatry Psychiatr Epidemiol. 2021 Oct;56(10):1779-1790. doi: 10.1007/s00127-021-02060-6. Epub 2021 Mar 29.
3. Neves, et al. Poisoning by agricultural pesticides in the state of Goias, Brazil, 2005-2015: analysis of records in official information systems. Ciencias & Saude Coletiva, 2020; 25(7):2743-2754.
4. Knipe, et al. Preventing deaths from pesticide self-poisoning – learning from Sri Lanka’s success. Lancet Glob Health. 2017 Jul;5(7):e651-e652. doi: 10.1016/S2214-109X(17)30208-5.
5. Gunnell, Knipe, et al. Prevention of suicide with regulations aimed at restricting access to highly hazardous pesticides: a systematic review of the international evidence. Lancet Glob Health 2017; 5:e1026-37
Dr Ben Faber (lead), Dr Rhona Beynon (rhona.beynon@bristol.ac.uk),
Osteoarthritis (OA) is a leading cause of disability, particularly among older people (1). It can affect any joint in the body, but two of the most commonly afflicted sites are the knees and hip (2). There is currently no cure for the disease, leaving patients to trial a combination of supportive therapies to attempt to alleviate symptoms. If unsuccessful, joint replacement is the last resort.
Previous studies indicate that hip OA affects men and women at similar rates, while knee OA is more prevalent among women (3). Alongside age and gender, obesity has been found to significantly contribute to the onset and progression of OA at the knee, and to a lesser extent, at the hip (4-6).
Genetic studies have suggested knee and hip OA have shared and independent genetic causes (7). However, the interrelation between OA in these joints is unclear, raising the question of whether OA in one joint increases susceptibility in the other or if they exist as distinct and separate conditions.
This project uses data already derived from the left knee and hip of approximately 40,000 individuals in UK Biobank. It aims to:
(i) Assess the prevalence of radiographic OA (rOA) at the hip and knee joint, unilaterally and bilaterally.
(ii) Compare associations of known OA risk factors (e.g., age, sex, weight) with the risk of rOA at the hip and knee.
(iii) Explore potential associations between rOA at the knee and hip.
This epidemiological study involves cross-sectional analyses. The student will gain skills in using descriptive statistics (means, frequencies, standard deviations) to explore the dataset, create plots for visualising the relationships between rOA and established risk factors, and employ contingency tables and chi-squared tests to assess the relationship between rOA at the hip and knee.
Subsequently, students will receive guidance on conducting regression analyses, including logistic regression models, to quantify associations between established OA risk factors and rOA. This analysis will assess whether these associations differ between joints. Furthermore, regression techniques will be used to explore whether the presence of rOA in one joint increases the likelihood of having rOA in the other joint. All analyses will be executed using the STATA software package.
The student will have the opportunity to draft an abstract for submission to both national and international conferences and potentially expand their dissertation into a research paper suitable for journal submission.
1. Cross M, Smith E, Hoy D, Nolte S, Ackerman I, Fransen M, et al. The global burden of hip and knee osteoarthritis: estimates from the global burden of disease 2010 study. Ann Rheum Dis. 2014;73(7):1323-30.
2. NHS. Osteoarthritis 2023 [17.08.23]. Available from: https://www.nhs.uk/conditions/osteoarthritis/.
3. Srikanth VK, Fryer JL, Zhai G, Winzenberg TM, Hosmer D, Jones G. A meta-analysis of sex differences prevalence, incidence and severity of osteoarthritis. Osteoarthritis Cartilage. 2005;13(9):769-81.
4. King LK, March L, Anandacoomarasamy A. Obesity & osteoarthritis. Indian J Med Res. 2013;138(2):185-93.
5. Lohmander LS, Gerhardsson de Verdier M, Rollof J, Nilsson PM, Engstrom G. Incidence of severe knee and hip osteoarthritis in relation to different measures of body mass: a population-based prospective cohort study. Ann Rheum Dis. 2009;68(4):490-6.
6. Funck-Brentano T, Nethander M, Moverare-Skrtic S, Richette P, Ohlsson C. Causal Factors for Knee, Hip, and Hand Osteoarthritis: A Mendelian Randomization Study in the UK Biobank. Arthritis Rheumatol. 2019;71(10):1634-41.
7. Boer CG, Hatzikotoulas K, Southam L, Stefansdottir L, Zhang Y, Coutinho de Almeida R, et al. Deciphering osteoarthritis genetics across 826,690 individuals from 9 populations. Cell. 2021;184(18):4784-818 e17.
Dr Ben Faber (lead), Dr Rhona Beynon (rhona.beynon@bristol.ac.uk),
Osteoarthritis (OA) is a leading cause of disability, particularly among older people (1). It can affect any joint in the body, but two of the most commonly afflicted sites are the knees and hip (2). There is currently no cure for the disease, leaving patients to trial a combination of supportive therapies to attempt to alleviate symptoms. If unsuccessful, joint replacement is the last resort.
Previous studies indicate that hip OA affects men and women at similar rates, while knee OA is more prevalent among women (3). Alongside age and gender, obesity has been found to significantly contribute to the onset and progression of OA at the knee, and to a lesser extent, at the hip (4-6).
Genetic studies have suggested knee and hip OA have shared and independent genetic causes (7). However, the interrelation between OA in these joints is unclear, raising the question of whether OA in one joint increases susceptibility in the other or if they exist as distinct and separate conditions.
This project uses data already derived from the left knee and hip of approximately 40,000 individuals in UK Biobank. It aims to:
(i) Assess the prevalence of radiographic OA (rOA) at the hip and knee joint, unilaterally and bilaterally.
(ii) Compare associations of known OA risk factors (e.g., age, sex, weight) with the risk of rOA at the hip and knee.
(iii) Explore potential associations between rOA at the knee and hip.
This epidemiological study involves cross-sectional analyses. The student will gain skills in using descriptive statistics (means, frequencies, standard deviations) to explore the dataset, create plots for visualising the relationships between rOA and established risk factors, and employ contingency tables and chi-squared tests to assess the relationship between rOA at the hip and knee.
Subsequently, students will receive guidance on conducting regression analyses, including logistic regression models, to quantify associations between established OA risk factors and rOA. This analysis will assess whether these associations differ between joints. Furthermore, regression techniques will be used to explore whether the presence of rOA in one joint increases the likelihood of having rOA in the other joint. All analyses will be executed using the STATA software package.
The student will have the opportunity to draft an abstract for submission to both national and international conferences and potentially expand their dissertation into a research paper suitable for journal submission.
1. Cross M, Smith E, Hoy D, Nolte S, Ackerman I, Fransen M, et al. The global burden of hip and knee osteoarthritis: estimates from the global burden of disease 2010 study. Ann Rheum Dis. 2014;73(7):1323-30.
2. NHS. Osteoarthritis 2023 [17.08.23]. Available from: https://www.nhs.uk/conditions/osteoarthritis/.
3. Srikanth VK, Fryer JL, Zhai G, Winzenberg TM, Hosmer D, Jones G. A meta-analysis of sex differences prevalence, incidence and severity of osteoarthritis. Osteoarthritis Cartilage. 2005;13(9):769-81.
4. King LK, March L, Anandacoomarasamy A. Obesity & osteoarthritis. Indian J Med Res. 2013;138(2):185-93.
5. Lohmander LS, Gerhardsson de Verdier M, Rollof J, Nilsson PM, Engstrom G. Incidence of severe knee and hip osteoarthritis in relation to different measures of body mass: a population-based prospective cohort study. Ann Rheum Dis. 2009;68(4):490-6.
6. Funck-Brentano T, Nethander M, Moverare-Skrtic S, Richette P, Ohlsson C. Causal Factors for Knee, Hip, and Hand Osteoarthritis: A Mendelian Randomization Study in the UK Biobank. Arthritis Rheumatol. 2019;71(10):1634-41.
7. Boer CG, Hatzikotoulas K, Southam L, Stefansdottir L, Zhang Y, Coutinho de Almeida R, et al. Deciphering osteoarthritis genetics across 826,690 individuals from 9 populations. Cell. 2021;184(18):4784-818 e17.
Dr Eleanor Sanderson (lead), Dr Rebecca Richmond, Dan Rosoff
Chronotype (evening preference) has previously been shown to have a causal effect on breast cancer through univariable Mendelian randomization (MR), although the mechanisms underlying this effect are unclear. Chronotype is correlated with alcohol consumption and may have a shared genetic aetiology or there may be a causal effect between these traits. MR uses genetic variants to estimate the causal effect of an exposure on an outcome of interest in a way that aims to overcome bias from unobserved confounding. However, MR is biased if the genetic variants are associated with the outcome through a pathway that does not include the exposure, such as if genetic variants associated with chronotype are also associated with alcohol consumption. Multivariable MR is an extension of MR that can include multiple exposures and so can account for such pathways. Multivariable MR can also be used to establish the extent to which alcohol consumption mediates or confounders the effect of chronotype on breast cancer.
The aim of this project is to estimate to what extent the effect of chronotype on breast cancer can be explained by alcohol consumption.
This project will use individual level data from UK Biobank and apply multivariable MR method to estimate the extent to which the effect of chronotype on breast cancer is explained by alcohol consumption. Multivariable MR can also be biased by pathways from the genetic instruments to the outcome that do not act through the exposure and therefore this project will apply a novel multivariable MR that estimates which genetic variants are likely to have pleiotropic effects and obtains effect estimates that are robust to that pleiotropy.
Richmond, Rebecca C., et al. "Investigating causal relations between sleep traits and risk of breast cancer in women: Mendelian randomisation study." bmj 365 (2019).
Sanderson, Eleanor, et al. "An examination of multivariable Mendelian randomization in the single-sample and two-sample summary data settings." International journal of epidemiology 48.3 (2019): 713-727.
Dr Eleanor Sanderson (lead), Dr Rebecca Richmond, Dan Rosoff
Chronotype (evening preference) has previously been shown to have a causal effect on breast cancer through univariable Mendelian randomization (MR), although the mechanisms underlying this effect are unclear. Chronotype is correlated with alcohol consumption and may have a shared genetic aetiology or there may be a causal effect between these traits. MR uses genetic variants to estimate the causal effect of an exposure on an outcome of interest in a way that aims to overcome bias from unobserved confounding. However, MR is biased if the genetic variants are associated with the outcome through a pathway that does not include the exposure, such as if genetic variants associated with chronotype are also associated with alcohol consumption. Multivariable MR is an extension of MR that can include multiple exposures and so can account for such pathways. Multivariable MR can also be used to establish the extent to which alcohol consumption mediates or confounders the effect of chronotype on breast cancer.
The aim of this project is to estimate to what extent the effect of chronotype on breast cancer can be explained by alcohol consumption.
This project will use individual level data from UK Biobank and apply multivariable MR method to estimate the extent to which the effect of chronotype on breast cancer is explained by alcohol consumption. Multivariable MR can also be biased by pathways from the genetic instruments to the outcome that do not act through the exposure and therefore this project will apply a novel multivariable MR that estimates which genetic variants are likely to have pleiotropic effects and obtains effect estimates that are robust to that pleiotropy.
Richmond, Rebecca C., et al. "Investigating causal relations between sleep traits and risk of breast cancer in women: Mendelian randomisation study." bmj 365 (2019).
Sanderson, Eleanor, et al. "An examination of multivariable Mendelian randomization in the single-sample and two-sample summary data settings." International journal of epidemiology 48.3 (2019): 713-727.
Dr Naomi Warne (lead), Dr Helen Bould,
Mental health conditions are common in young people and are associated with significant functional impairment and mortality. Visual art-based interventions, including art therapy and non-therapeutic engagement with the visual arts, can help young people express their feelings without the need for verbal communication. Importantly, visual art interventions can also reduce mental health symptoms in young people (Easwaran et al., 2021).
Systematic reviews have found preliminary evidence that school-based art therapy can be effective at improving anxiety and emotional and behavioural problems (Moula 2020; McDonald & StJ Drey, 2018). However, there has not been a thorough review of the grey literature on visual art interventions in schools to support student mental health.
This project is a systematic review of the effectiveness of art interventions (defined broadly as any intervention incorporating the visual arts) in treating and preventing mental health problems.
Aim 1: Identify studies that assess the effectiveness of visual arts interventions (art therapy and other visual art interventions) on reducing/preventing mental health problems in children aged 4-18 years in schools/ educational settings.
Aim 2: Narratively synthesise these studies and the effectiveness of the interventions.
Aim 3: Robustly critique the quality of included research studies.
Systematic review methodology including: running database searches, systematically hand-searching websites for grey literature, assessing articles for inclusion, robustly critiquing quality (e.g. in line with Grebosz-Haring et al., 2022) of included studies, assessing risk of bias in included studies, and narratively synthesising the included studies.
We do not anticipate it will be possible to conduct a meta-analysis as the resulting studies are likely to be clinically and methodologically heterogeneous.
Easwaran 2021. Why art matters for youth mental health: A youth led participatory insight analysis doi:10.31234/OSF.IO/AP476.
Grebosz-Haring 2022. Front. Psychol., 13:821093
McDonald 2018. Int. J. Art Ther., 23:1, 33-44
Moula 2020. Int. J. Art Ther., 25, 88–99
Dr Naomi Warne (lead), Dr Helen Bould,
Mental health conditions are common in young people and are associated with significant functional impairment and mortality. Visual art-based interventions, including art therapy and non-therapeutic engagement with the visual arts, can help young people express their feelings without the need for verbal communication. Importantly, visual art interventions can also reduce mental health symptoms in young people (Easwaran et al., 2021).
Systematic reviews have found preliminary evidence that school-based art therapy can be effective at improving anxiety and emotional and behavioural problems (Moula 2020; McDonald & StJ Drey, 2018). However, there has not been a thorough review of the grey literature on visual art interventions in schools to support student mental health.
This project is a systematic review of the effectiveness of art interventions (defined broadly as any intervention incorporating the visual arts) in treating and preventing mental health problems.
Aim 1: Identify studies that assess the effectiveness of visual arts interventions (art therapy and other visual art interventions) on reducing/preventing mental health problems in children aged 4-18 years in schools/ educational settings.
Aim 2: Narratively synthesise these studies and the effectiveness of the interventions.
Aim 3: Robustly critique the quality of included research studies.
Systematic review methodology including: running database searches, systematically hand-searching websites for grey literature, assessing articles for inclusion, robustly critiquing quality (e.g. in line with Grebosz-Haring et al., 2022) of included studies, assessing risk of bias in included studies, and narratively synthesising the included studies.
We do not anticipate it will be possible to conduct a meta-analysis as the resulting studies are likely to be clinically and methodologically heterogeneous.
Easwaran 2021. Why art matters for youth mental health: A youth led participatory insight analysis doi:10.31234/OSF.IO/AP476.
Grebosz-Haring 2022. Front. Psychol., 13:821093
McDonald 2018. Int. J. Art Ther., 23:1, 33-44
Moula 2020. Int. J. Art Ther., 25, 88–99
Dr Annie Herbert (lead), Dr Laura Howe, Dr Christine Barter
‘Selection bias’, a phenomenon that occurs when individuals in a study sample differ to that in the population of interest, is a huge problem across molecular, genetic, and life-course epidemiological studies. Most studies in this area rely on long-term cohort studies, to capture detailed prospective measurements on large samples of individuals, and for multiple generations. However, these studies often suffer from selection bias issues, first from the recruited sample being different to the general population, and then from loss-to-follow-up (drop-out or non-response) as the study continues, particularly by the time the second generation have become young adults. This selection bias is likely to bias estimates, for example, of prevalence, group differences, and intergenerational transmission, given that those who are most socioeconomically disadvantaged are less likely to be recruited and into cohort studies and are more likely to be lost.
Here, the effects of selection bias on estimates will be studied and accounted for in the established Avon Longitudinal Study of Parents & Children (ALSPAC) using an exemplar of domestic violence and abuse (‘DVA’, physical, sexual, financial, or psychological abuse, including controlling behaviours). Recent estimates in ALSPAC suggest around one-third of UK young adults experience DVA by the time they’re 21, the prevalence being higher for women than men (41% vs 29% victimised, 25% vs 20% perpetrated), and that certain groups of children who grow up around DVA (e.g. young males who grow up around physical violence) are at increased risk of violence and abuse within their own intimate relationships as they grow up (‘intergenerational transmission’). However, some estimates conflict with other literature (e.g. that perpetration is higher in women, or that there is no evidence for the detrimental impact of parental coercive control), and selection bias may play a large part in this.
This study aims to explore the extent to which estimates of prevalence and associations for DVA are sensitive to selection bias, and adjust estimates to account for it. The findings will be used to report more accurate estimates on DVA within the UK general population.
Objectives are to:
1. Compare characteristics between the general population (from general population statistics) with those of recruited mothers who did and did not report DVA during pregnancy.
2. Estimate the association between parental DVA during pregnancy and subsequent study loss-to-follow-up for mothers, their partners (where applicable), and the children.
3. Estimate the extent to which accounting for loss-to-follow-up affects statistics often reported in the DVA literature, such as DVA prevalence (including gender comparisons), and the association between parental DVA and young adult DVA (intergenerational transmission).
All analyses will be carried out in data on two generations of the Avon Longitudinal Study of Parents and Children (ALSPAC). Parent and ‘child’ (as a young adult) DVA variables have been previously defined in ALSPAC (captured at time-points from pregnancy until the young adult is age 21; Herbert et al, Wellcome Open Research 2020). Prevalence of parental and young adult DVA will be estimated (both overall and for separate DVA subtypes, such as psychological and physical), and binary logistic regressions will be fitted to estimate associations between parental and young adult DVA, adjusted for socio-economic indicators recorded during the mother’s pregnancy. These analyses will first be carried out on complete cases, and then again using i) multiple imputation to accounting for any missing data; ii) reweighting analyses using inverse probability treatment weighting. All analyses will be stratified by child gender.
By the end of the project, the student will have knowledge and skills in ALSPAC data, selection bias, regression analyses, multiple imputation, and inverse probability treatment weighting using the Stata or R programming package (depending on preference), as well as experience in interpreting study findings for public health. As the parental and young adult DVA variables have been characterised and studied in ALSPAC previously, we expect a large amount of work can be done, such that the finished work could feasibly be submitted to a relevant journal, such as Journal of Public Health, or Trauma, Violence & Abuse.
1. Marcus R Munafò, Kate Tilling, Amy E Taylor, David M Evans, George Davey Smith, Collider scope: when selection bias can substantially influence observed associations, International Journal of Epidemiology, Volume 47, Issue 1, February 2018, Pages 226–235, https://doi.org/10.1093/ije/dyx206
2. Howe LD, Tilling K, Galobardes B, Lawlor DA. Loss to follow-up in cohort studies: bias in estimates of socioeconomic inequalities. Epidemiology. 2013 Jan;24(1):1-9. doi: 10.1097/EDE.0b013e31827623b1. PMID: 23211345; PMCID: PMC5102324. DOI: 10.1097/EDE.0b013e31827623b1
3. Herbert A, Heron J, Barter C et al. Risk factors for intimate partner violence and abuse among adolescents and young adults: findings from a UK population-based cohort [version 3; peer review: 2 approved]. Wellcome Open Res 2021, 5:176 (https://doi.org/10.12688/wellcomeopenres.16106.3)
4. Haselschwerdt ML, Savasuk-Luxton R, Hlavaty K. A Methodological Review and Critique of the "Intergenerational Transmission of Violence" Literature. Trauma Violence Abuse. 2019 Apr;20(2):168-182. doi: 10.1177/1524838017692385. Epub 2017 Feb 13. PMID: 29333984.
Dr Annie Herbert (lead), Dr Laura Howe, Dr Christine Barter
‘Selection bias’, a phenomenon that occurs when individuals in a study sample differ to that in the population of interest, is a huge problem across molecular, genetic, and life-course epidemiological studies. Most studies in this area rely on long-term cohort studies, to capture detailed prospective measurements on large samples of individuals, and for multiple generations. However, these studies often suffer from selection bias issues, first from the recruited sample being different to the general population, and then from loss-to-follow-up (drop-out or non-response) as the study continues, particularly by the time the second generation have become young adults. This selection bias is likely to bias estimates, for example, of prevalence, group differences, and intergenerational transmission, given that those who are most socioeconomically disadvantaged are less likely to be recruited and into cohort studies and are more likely to be lost.
Here, the effects of selection bias on estimates will be studied and accounted for in the established Avon Longitudinal Study of Parents & Children (ALSPAC) using an exemplar of domestic violence and abuse (‘DVA’, physical, sexual, financial, or psychological abuse, including controlling behaviours). Recent estimates in ALSPAC suggest around one-third of UK young adults experience DVA by the time they’re 21, the prevalence being higher for women than men (41% vs 29% victimised, 25% vs 20% perpetrated), and that certain groups of children who grow up around DVA (e.g. young males who grow up around physical violence) are at increased risk of violence and abuse within their own intimate relationships as they grow up (‘intergenerational transmission’). However, some estimates conflict with other literature (e.g. that perpetration is higher in women, or that there is no evidence for the detrimental impact of parental coercive control), and selection bias may play a large part in this.
This study aims to explore the extent to which estimates of prevalence and associations for DVA are sensitive to selection bias, and adjust estimates to account for it. The findings will be used to report more accurate estimates on DVA within the UK general population.
Objectives are to:
1. Compare characteristics between the general population (from general population statistics) with those of recruited mothers who did and did not report DVA during pregnancy.
2. Estimate the association between parental DVA during pregnancy and subsequent study loss-to-follow-up for mothers, their partners (where applicable), and the children.
3. Estimate the extent to which accounting for loss-to-follow-up affects statistics often reported in the DVA literature, such as DVA prevalence (including gender comparisons), and the association between parental DVA and young adult DVA (intergenerational transmission).
All analyses will be carried out in data on two generations of the Avon Longitudinal Study of Parents and Children (ALSPAC). Parent and ‘child’ (as a young adult) DVA variables have been previously defined in ALSPAC (captured at time-points from pregnancy until the young adult is age 21; Herbert et al, Wellcome Open Research 2020). Prevalence of parental and young adult DVA will be estimated (both overall and for separate DVA subtypes, such as psychological and physical), and binary logistic regressions will be fitted to estimate associations between parental and young adult DVA, adjusted for socio-economic indicators recorded during the mother’s pregnancy. These analyses will first be carried out on complete cases, and then again using i) multiple imputation to accounting for any missing data; ii) reweighting analyses using inverse probability treatment weighting. All analyses will be stratified by child gender.
By the end of the project, the student will have knowledge and skills in ALSPAC data, selection bias, regression analyses, multiple imputation, and inverse probability treatment weighting using the Stata or R programming package (depending on preference), as well as experience in interpreting study findings for public health. As the parental and young adult DVA variables have been characterised and studied in ALSPAC previously, we expect a large amount of work can be done, such that the finished work could feasibly be submitted to a relevant journal, such as Journal of Public Health, or Trauma, Violence & Abuse.
1. Marcus R Munafò, Kate Tilling, Amy E Taylor, David M Evans, George Davey Smith, Collider scope: when selection bias can substantially influence observed associations, International Journal of Epidemiology, Volume 47, Issue 1, February 2018, Pages 226–235, https://doi.org/10.1093/ije/dyx206
2. Howe LD, Tilling K, Galobardes B, Lawlor DA. Loss to follow-up in cohort studies: bias in estimates of socioeconomic inequalities. Epidemiology. 2013 Jan;24(1):1-9. doi: 10.1097/EDE.0b013e31827623b1. PMID: 23211345; PMCID: PMC5102324. DOI: 10.1097/EDE.0b013e31827623b1
3. Herbert A, Heron J, Barter C et al. Risk factors for intimate partner violence and abuse among adolescents and young adults: findings from a UK population-based cohort [version 3; peer review: 2 approved]. Wellcome Open Res 2021, 5:176 (https://doi.org/10.12688/wellcomeopenres.16106.3)
4. Haselschwerdt ML, Savasuk-Luxton R, Hlavaty K. A Methodological Review and Critique of the "Intergenerational Transmission of Violence" Literature. Trauma Violence Abuse. 2019 Apr;20(2):168-182. doi: 10.1177/1524838017692385. Epub 2017 Feb 13. PMID: 29333984.
Dr Clare French (lead), Professor Deborah Caldwell, Louise Letley, UK Health Security Agency Dr Julie Yates, UK Health Security Agency
Ensuring high and equitable uptake of vaccinations is a cornerstone of public health. Socially excluded groups such as Gypsy, Roma and Traveller communities, vulnerable migrants, the homeless, and those with drug dependence face particular barriers to accessing vaccination(1).
A recent systematic review on the effectiveness of interventions to increase vaccine uptake among socially excluded groups(2) found limited evidence from randomised controlled trials and other robust non-randomised study designs. In particular, the review identified no eligible studies among vulnerable migrants or Gypsy, Roma and Traveller communities. To reduce inequalities in vaccination it is crucial that the available evidence for these populations is identified and summarised, and can thus inform practice and policy.
This project could be undertaken in either 3 or 4/4.5 months.
Conduct an evidence review to identify effective or promising strategies (e.g. reminders, education, ‘pop up’ / mobile clinics etc) to improve vaccine uptake among vulnerable migrants and Gypsy, Roma and Traveller communities in high and upper-middle income countries.
Prepare a manuscript for potential publication in a peer-reviewed journal.
• Prepare a brief review protocol.
• Identify studies for inclusion in the review by:
(i) Updating and screening an existing database of potentially eligible studies to identify non-randomised studies on the populations of interest (e.g. cohort studies, uncontrolled before-and-after studies, descriptive studies).
(ii) Conducting additional targeted database searches, as appropriate.
(iii) Identifying and searching grey literature sources such as key websites and other online sources for information on ‘what works’ from best practice examples.
• Develop a data extraction database (e.g. in Excel) and extract data from eligible studies.
• Conduct risk of bias assessments on included studies, as appropriate.
• Synthesise the evidence. This will likely largely involve a narrative synthesis, but may also utilise ‘Synthesis Without Meta-analysis’ (SWiM) approaches, and possibly meta-analysis, depending on the types of studies/evidence identified.
1. Ekezie W et al. Access to Vaccination among Disadvantaged, Isolated and Difficult-to-Reach Communities in the WHO European Region: A Systematic Review. Vaccines 2022;10:1038.
2. Sidebotham E et al. Interventions to increase vaccine-uptake amongst socially excluded groups: a systematic review. PROSPERO 2023 CRD42023448594.
Dr Clare French (lead), Professor Deborah Caldwell, Louise Letley, UK Health Security Agency Dr Julie Yates, UK Health Security Agency
Ensuring high and equitable uptake of vaccinations is a cornerstone of public health. Socially excluded groups such as Gypsy, Roma and Traveller communities, vulnerable migrants, the homeless, and those with drug dependence face particular barriers to accessing vaccination(1).
A recent systematic review on the effectiveness of interventions to increase vaccine uptake among socially excluded groups(2) found limited evidence from randomised controlled trials and other robust non-randomised study designs. In particular, the review identified no eligible studies among vulnerable migrants or Gypsy, Roma and Traveller communities. To reduce inequalities in vaccination it is crucial that the available evidence for these populations is identified and summarised, and can thus inform practice and policy.
This project could be undertaken in either 3 or 4/4.5 months.
Conduct an evidence review to identify effective or promising strategies (e.g. reminders, education, ‘pop up’ / mobile clinics etc) to improve vaccine uptake among vulnerable migrants and Gypsy, Roma and Traveller communities in high and upper-middle income countries.
Prepare a manuscript for potential publication in a peer-reviewed journal.
• Prepare a brief review protocol.
• Identify studies for inclusion in the review by:
(i) Updating and screening an existing database of potentially eligible studies to identify non-randomised studies on the populations of interest (e.g. cohort studies, uncontrolled before-and-after studies, descriptive studies).
(ii) Conducting additional targeted database searches, as appropriate.
(iii) Identifying and searching grey literature sources such as key websites and other online sources for information on ‘what works’ from best practice examples.
• Develop a data extraction database (e.g. in Excel) and extract data from eligible studies.
• Conduct risk of bias assessments on included studies, as appropriate.
• Synthesise the evidence. This will likely largely involve a narrative synthesis, but may also utilise ‘Synthesis Without Meta-analysis’ (SWiM) approaches, and possibly meta-analysis, depending on the types of studies/evidence identified.
1. Ekezie W et al. Access to Vaccination among Disadvantaged, Isolated and Difficult-to-Reach Communities in the WHO European Region: A Systematic Review. Vaccines 2022;10:1038.
2. Sidebotham E et al. Interventions to increase vaccine-uptake amongst socially excluded groups: a systematic review. PROSPERO 2023 CRD42023448594.
Nancy McBride (lead), Dr Carolina Borges, Dr Kate Birchenall Prof Deborah Lawlor
Our knowledge of the mechanisms that underpin gestational age (GA), and related pregnancy disorders, such as pre term birth (PTB – births < 37 weeks gestation), and very preterm birth (vPTB, births < 34 weeks gestation), is still poor (1).
PTB affects around 15 million births worldwide each year, and is the largest contributor to fetal mortality and morbidity (2). While we know there are clinical factors and other co-morbid disorders of pregnancy that increase risk of PTB, we still do not know what a woman’s gestational duration will be when she first attends clinic, and often until she signs symptoms of spontaneous pre term labour.
Metabolomics may help us elucidate more about these mechanisms. The NMR platform quantifies metabolic traits. The targeted metabolic traits measured by the platform represent a broad molecular signature of metabolism including routine lipids, lipoprotein subclass profiling, fatty acid composition and several low-molecular metabolites, including amino acids, ketone bodies and gluconeogenesis-related metabolites (3).
Metabolomics can improve our understanding of pregnancy-related disorders because metabolite levels are known to change markedly during pregnancy and may reflect underlying pathophysiology (4-6). Changes in metabolite profiles have been previously associated with other pregnancy-related disorders as well as adverse cardio-metabolic outcomes that associate with pregnancy related disorders (4, 7).
We have access to pregnancy and birth cohorts of mothers and offspring within which this can be explored, within the MR-PREG consortium. In a previous study of 7,440 pregnant participants of the Born in Bradford cohort, maternal dyslipidaemia in the second trimester was associated with a shorter GA at birth (8). These associations may be explained by residual confounding and reverse causality. Mendelian randomization (MR) is a causal inference technique which can help to elucidate whether these associations are causal (9). We hope to identify the metabolic changes underlying the physiological onset of labour to help develop new clinically useful strategies and identify potential drug targets (1).
The aim of this study is to explore the causal effect of maternal metabolites on the risk of PTB, it’s subtypes, and gestational age, using MR.
This study will be undertaken within the MR-PREG collaboration, which aims to explore causes and consequences of different pregnancy and perinatal outcomes. The PhD candidate will have access to the largest genome-wide association studies (GWAS) currently available for NMR-profiled metabolites (using data from ~300,000 UK Biobank individuals) and large meta-analyses conducted in European cohorts of GA/PTB (births <37 weeks, and subtypes such as very-pre-term birth, <34 weeks). We will use these to identify genetic instruments for metabolites and PTB / gestational age, and use two-sample Mendelian randomisation to probe the causal role of metabolites on gestational age and PTB.
1. Following Spontaneous Labour at Term in Humans Using Untargeted Metabolomics Analysis: A Pilot Study. LID - 10.3390/ijerph16091527 [doi] LID - 1527. (1660-4601 (Electronic)).
2. Stock SJ, Horne M, Bruijn M, White H, Boyd KA, Heggie R, et al. Development and validation of a risk prediction model of preterm birth for women with preterm labour symptoms (the QUIDS study): A prospective cohort study and individual participant data meta-analysis. PLOS Medicine. 2021;18(7):e1003686.
3. Dunn WB, Bailey NJC, Johnson HE. Measuring the metabolome: current analytical technologies. Analyst. 2005;130(5):606-25.
4. McBride N, White SL, Farrar D, Poston L, Sattar N, Nelson SM, et al. Do nuclear magnetic resonance (NMR)-based metabolomics improve the prediction of pregnancy-related disorders? medRxiv. 2020:2020.06.22.20134650.
5. Souza RT, Galvão RB, Leite DA-O, Passini R, Jr., Baker P, Cecatti JA-O. Use of metabolomics for predicting spontaneous preterm birth in asymptomatic pregnant women: protocol for a systematic review and meta-analysis. (2044-6055 (Electronic)).
6. Considine EC, Khashan AS, Kenny LC. Screening for Preterm Birth: Potential for a Metabolomics Biomarker Panel. Metabolites. 2019;9(5):90.
7. White SL, Pasupathy D, Sattar N, Nelson SM, Lawlor DA, Briley AL, et al. Metabolic profiling of gestational diabetes in obese women during pregnancy. (1432-0428 (Electronic)).
8. Birchenall KA. Investigating the trigger for human parturition using metabolomic and phosphoproteomic techniques within case-control and cohort studies: University of Bristol; 2020.
9. Skrivankova VW, Richmond RC, Woolf BAR, Davies NM, Swanson SA, VanderWeele TJ, et al. Strengthening the reporting of observational studies in epidemiology using mendelian randomisation (STROBE-MR): explanation and elaboration. BMJ. 2021;375:n2233.
Nancy McBride (lead), Dr Carolina Borges, Dr Kate Birchenall Prof Deborah Lawlor
Our knowledge of the mechanisms that underpin gestational age (GA), and related pregnancy disorders, such as pre term birth (PTB – births < 37 weeks gestation), and very preterm birth (vPTB, births < 34 weeks gestation), is still poor (1).
PTB affects around 15 million births worldwide each year, and is the largest contributor to fetal mortality and morbidity (2). While we know there are clinical factors and other co-morbid disorders of pregnancy that increase risk of PTB, we still do not know what a woman’s gestational duration will be when she first attends clinic, and often until she signs symptoms of spontaneous pre term labour.
Metabolomics may help us elucidate more about these mechanisms. The NMR platform quantifies metabolic traits. The targeted metabolic traits measured by the platform represent a broad molecular signature of metabolism including routine lipids, lipoprotein subclass profiling, fatty acid composition and several low-molecular metabolites, including amino acids, ketone bodies and gluconeogenesis-related metabolites (3).
Metabolomics can improve our understanding of pregnancy-related disorders because metabolite levels are known to change markedly during pregnancy and may reflect underlying pathophysiology (4-6). Changes in metabolite profiles have been previously associated with other pregnancy-related disorders as well as adverse cardio-metabolic outcomes that associate with pregnancy related disorders (4, 7).
We have access to pregnancy and birth cohorts of mothers and offspring within which this can be explored, within the MR-PREG consortium. In a previous study of 7,440 pregnant participants of the Born in Bradford cohort, maternal dyslipidaemia in the second trimester was associated with a shorter GA at birth (8). These associations may be explained by residual confounding and reverse causality. Mendelian randomization (MR) is a causal inference technique which can help to elucidate whether these associations are causal (9). We hope to identify the metabolic changes underlying the physiological onset of labour to help develop new clinically useful strategies and identify potential drug targets (1).
The aim of this study is to explore the causal effect of maternal metabolites on the risk of PTB, it’s subtypes, and gestational age, using MR.
This study will be undertaken within the MR-PREG collaboration, which aims to explore causes and consequences of different pregnancy and perinatal outcomes. The PhD candidate will have access to the largest genome-wide association studies (GWAS) currently available for NMR-profiled metabolites (using data from ~300,000 UK Biobank individuals) and large meta-analyses conducted in European cohorts of GA/PTB (births <37 weeks, and subtypes such as very-pre-term birth, <34 weeks). We will use these to identify genetic instruments for metabolites and PTB / gestational age, and use two-sample Mendelian randomisation to probe the causal role of metabolites on gestational age and PTB.
1. Following Spontaneous Labour at Term in Humans Using Untargeted Metabolomics Analysis: A Pilot Study. LID - 10.3390/ijerph16091527 [doi] LID - 1527. (1660-4601 (Electronic)).
2. Stock SJ, Horne M, Bruijn M, White H, Boyd KA, Heggie R, et al. Development and validation of a risk prediction model of preterm birth for women with preterm labour symptoms (the QUIDS study): A prospective cohort study and individual participant data meta-analysis. PLOS Medicine. 2021;18(7):e1003686.
3. Dunn WB, Bailey NJC, Johnson HE. Measuring the metabolome: current analytical technologies. Analyst. 2005;130(5):606-25.
4. McBride N, White SL, Farrar D, Poston L, Sattar N, Nelson SM, et al. Do nuclear magnetic resonance (NMR)-based metabolomics improve the prediction of pregnancy-related disorders? medRxiv. 2020:2020.06.22.20134650.
5. Souza RT, Galvão RB, Leite DA-O, Passini R, Jr., Baker P, Cecatti JA-O. Use of metabolomics for predicting spontaneous preterm birth in asymptomatic pregnant women: protocol for a systematic review and meta-analysis. (2044-6055 (Electronic)).
6. Considine EC, Khashan AS, Kenny LC. Screening for Preterm Birth: Potential for a Metabolomics Biomarker Panel. Metabolites. 2019;9(5):90.
7. White SL, Pasupathy D, Sattar N, Nelson SM, Lawlor DA, Briley AL, et al. Metabolic profiling of gestational diabetes in obese women during pregnancy. (1432-0428 (Electronic)).
8. Birchenall KA. Investigating the trigger for human parturition using metabolomic and phosphoproteomic techniques within case-control and cohort studies: University of Bristol; 2020.
9. Skrivankova VW, Richmond RC, Woolf BAR, Davies NM, Swanson SA, VanderWeele TJ, et al. Strengthening the reporting of observational studies in epidemiology using mendelian randomisation (STROBE-MR): explanation and elaboration. BMJ. 2021;375:n2233.
Dr Hayley Wragg (lead), Dr Louise Millard,
The climate is at a breaking point with over 11,000 scientists declaring a climate emergency in 2020 [1]. The MRC Integrative Epidemiology Unit (IEU) has been monitoring and improving its carbon footprint, but currently lacks data on the carbon cost of computing activities undertaken here. Establishing this footprint will help identify ways to reduce it.
We have identified packages to quantify the carbon cost of pieces of code (Green Algorithms, Code Carbon), but we need to find a way to easily utilize these across users and summarize the output. Green Algorithms can be used across programming languages but must be installed by HPC administrators. Code Carbon is a python specific package but does not require administrative rights. The student will evaluate these packages, and any alternatives they identify, for easily gathering carbon cost data across the IEU.
[1] Ripple, W.J., Wolf, C., Newsome, T.M., Barnard, P., Moomaw, W.R. and Grandcolas, P., 2019. World scientists' warning of a climate emergency. BioScience.
The primary aim of the project is to develop a mechanism for users to automatically record their computing usage and submit this information to a central database. This will then be translated into the carbon cost for each piece of code, which can be combined to provide the collective carbon cost of computing in the IEU.
Possible tools used: Git, Bash, Python, R, Jupyter notebooks
Method:
Explore approaches to capture the amount of compute resource used by (a) trying these approaches with example analysis scripts and (b) talking with researchers to understand requirements.
Develop a central repository containing scripts that researchers can use to capture their compute usage data and submit these data to a central database. Compute usage data will include details such as when the code was run, and technical details of the machine used (e.g. BlueCrystal or other server) in addition to CPU usage, as this information is needed to estimate carbon cost.
The student will develop skills in:
- Data science: data-handling, analyzing, forecasting, presenting data.
- Code development and version control.
- Insight into a breadth of research methods used within the IEU.
1 Code Carbon at https://codecarbon.io/
2 Green Algorithms at https://www.green-algorithms.org/
3 Meeting green computing challenges doi: 10.1109/EPTC.2008.4763421
4 Assessing the impact of green computing practices doi: 10.1109/PICMET.2009.5261969
5 The climate impact of ICT: A review of estimates, trends and regulations arXiv:2102.02622
Dr Hayley Wragg (lead), Dr Louise Millard,
The climate is at a breaking point with over 11,000 scientists declaring a climate emergency in 2020 [1]. The MRC Integrative Epidemiology Unit (IEU) has been monitoring and improving its carbon footprint, but currently lacks data on the carbon cost of computing activities undertaken here. Establishing this footprint will help identify ways to reduce it.
We have identified packages to quantify the carbon cost of pieces of code (Green Algorithms, Code Carbon), but we need to find a way to easily utilize these across users and summarize the output. Green Algorithms can be used across programming languages but must be installed by HPC administrators. Code Carbon is a python specific package but does not require administrative rights. The student will evaluate these packages, and any alternatives they identify, for easily gathering carbon cost data across the IEU.
[1] Ripple, W.J., Wolf, C., Newsome, T.M., Barnard, P., Moomaw, W.R. and Grandcolas, P., 2019. World scientists' warning of a climate emergency. BioScience.
The primary aim of the project is to develop a mechanism for users to automatically record their computing usage and submit this information to a central database. This will then be translated into the carbon cost for each piece of code, which can be combined to provide the collective carbon cost of computing in the IEU.
Possible tools used: Git, Bash, Python, R, Jupyter notebooks
Method:
Explore approaches to capture the amount of compute resource used by (a) trying these approaches with example analysis scripts and (b) talking with researchers to understand requirements.
Develop a central repository containing scripts that researchers can use to capture their compute usage data and submit these data to a central database. Compute usage data will include details such as when the code was run, and technical details of the machine used (e.g. BlueCrystal or other server) in addition to CPU usage, as this information is needed to estimate carbon cost.
The student will develop skills in:
- Data science: data-handling, analyzing, forecasting, presenting data.
- Code development and version control.
- Insight into a breadth of research methods used within the IEU.
1 Code Carbon at https://codecarbon.io/
2 Green Algorithms at https://www.green-algorithms.org/
3 Meeting green computing challenges doi: 10.1109/EPTC.2008.4763421
4 Assessing the impact of green computing practices doi: 10.1109/PICMET.2009.5261969
5 The climate impact of ICT: A review of estimates, trends and regulations arXiv:2102.02622
Dr Charlotte Archer (lead), Professor Nicola Wiles, Professor Debbi Caldwell
EMDR has been established as an effective treatment for post-traumatic stress disorder (PTSD). However, PTSD has been re-categorised as a trauma/stressor-related disorder instead of anxiety. A meta-analysis to evaluate the effectiveness of EMDR on reducing symptoms of anxiety reports RCTs published up to 2018 (Faretta & Del Farra, 2019; Yunitri et al, 2020). However, further studies have been published (Inci Izmir et al, 2023; Azimisefat et al, 2022).
To assess the effectiveness eye movement desensitisation and reprocessing for anxiety disorders.
Systematic review, meta-analysis if applicable.
Azimisefat et al, 2022. (2022). Efficacy of virtual reality exposure therapy and eye movement desensitization and reprocessing therapy on symptoms of acrophobia and anxiety sensitivity in adolescent girls: A randomized controlled trial. Frontiers in Psychology. 13, doi: 10.3389/fpsyg.2022.919148.
Inci Izmir, SB., Korkmazlar, Ü., Ercan, ES. (2023) Eye Movement Desensitization and Reprocessing Therapy in Adolescents With Panic Disorder: A Twelve-Week Follow-Up Study. Clinical Child Psychology and Psychiatry. 2023;0(0). doi:10.1177/13591045231184757
Faretta, E., & Dal Farra, M. (2019) Efficacy of EMDR Therapy for Anxiety Disorders. Journal of EMDR Practice and Research, 13(4), DOI: 10.1891/1933-3196.13.4.325.
Yunitri et al, 2020. The effectiveness of eye movement desensitization and reprocessing toward anxiety disorder: A meta-analysis of randomized controlled trials. Journal of Psychiatric Research, 123, p102-113.
Dr Charlotte Archer (lead), Professor Nicola Wiles, Professor Debbi Caldwell
EMDR has been established as an effective treatment for post-traumatic stress disorder (PTSD). However, PTSD has been re-categorised as a trauma/stressor-related disorder instead of anxiety. A meta-analysis to evaluate the effectiveness of EMDR on reducing symptoms of anxiety reports RCTs published up to 2018 (Faretta & Del Farra, 2019; Yunitri et al, 2020). However, further studies have been published (Inci Izmir et al, 2023; Azimisefat et al, 2022).
To assess the effectiveness eye movement desensitisation and reprocessing for anxiety disorders.
Systematic review, meta-analysis if applicable.
Azimisefat et al, 2022. (2022). Efficacy of virtual reality exposure therapy and eye movement desensitization and reprocessing therapy on symptoms of acrophobia and anxiety sensitivity in adolescent girls: A randomized controlled trial. Frontiers in Psychology. 13, doi: 10.3389/fpsyg.2022.919148.
Inci Izmir, SB., Korkmazlar, Ü., Ercan, ES. (2023) Eye Movement Desensitization and Reprocessing Therapy in Adolescents With Panic Disorder: A Twelve-Week Follow-Up Study. Clinical Child Psychology and Psychiatry. 2023;0(0). doi:10.1177/13591045231184757
Faretta, E., & Dal Farra, M. (2019) Efficacy of EMDR Therapy for Anxiety Disorders. Journal of EMDR Practice and Research, 13(4), DOI: 10.1891/1933-3196.13.4.325.
Yunitri et al, 2020. The effectiveness of eye movement desensitization and reprocessing toward anxiety disorder: A meta-analysis of randomized controlled trials. Journal of Psychiatric Research, 123, p102-113.
Dr Hannah Elliott (lead), Dr Rebecca Richmond,
DNA methylation is strongly associated with smoking status at multiple sites throughout the genome. Studies have been largely restricted to European origin individuals, yet the greatest increase in contemporary smoking rates is occurring in low income countries such as the Indian subcontinent. The Southall And Brent Revisited cohort have sought to identify health in ageing and improve understanding of the reasons underlying ethnic group differences in health (1). Previous work in this cohort identified ethnic differences in smoking related loci but sample numbers were small resulting in limited interpretation of findings (2). The SABRE DNA methylation dataset has now been vastly expanded, allowing more comprehensive analysis of smoking in relation to DNA methylation and ethnic group in this cohort.
This project aims to:
i) Test the association between DNA methylation and smoking in ~2000 individuals from the SABRE cohort.
ii) Estimate the genetic and environmental contribution to differences observed between ethnic groups and explore drivers (eg differences in smoking behaviour)
iii) identify if methylation differences are likely to have functional or tissue specific signatures using external reference sets.
This project will utilise genome-wide epigenetic and survey data collected from the Southall And Brent Revisited (SABRE) study (1). We will test the association between smoking status and DNA methylation profile using EWAS and identify differences between self-reported ethnic groups. Statistical modelling will be used to quantify genetic (mQTL) and environmental contributions to ethnic differences observed. Resources such as GTEX (3) and eFORGE (4) will be used to explore functional relevance of findings.
This project will provide training and practical experience in R, use of the HPC, epigenetics analysis and analysis in the context of diverse data. It expected that this work will lead to a publication as a research paper.
1. Tillin T, Forouhi NG, McKeigue PM, Chaturvedi N, Group SS. Southall And Brent REvisited: Cohort profile of SABRE, a UK population-based comparison of cardiovascular disease and diabetes in people of European, Indian Asian and African Caribbean origins. Int J Epidemiol. 2012;41(1):33-42.
2. Elliott HR, Tillin T, McArdle WL, Ho K, Duggirala A, Frayling TM, et al. Differences in smoking associated DNA methylation patterns in South Asians and Europeans. Clin Epigenetics. 2014;6(1):4.
3. Pierce BL, Tong L, Argos M, Demanelis K, Jasmine F, Rakibuz-Zaman M, et al. Co-occurring expression and methylation QTLs allow detection of common causal variants and shared biological mechanisms. Nat Commun. 2018;9(1):804.
4. Breeze CE. Cell Type-Specific Signal Analysis in Epigenome-Wide Association Studies. Methods Mol Biol. 2022;2432:57-71.
Dr Hannah Elliott (lead), Dr Rebecca Richmond,
DNA methylation is strongly associated with smoking status at multiple sites throughout the genome. Studies have been largely restricted to European origin individuals, yet the greatest increase in contemporary smoking rates is occurring in low income countries such as the Indian subcontinent. The Southall And Brent Revisited cohort have sought to identify health in ageing and improve understanding of the reasons underlying ethnic group differences in health (1). Previous work in this cohort identified ethnic differences in smoking related loci but sample numbers were small resulting in limited interpretation of findings (2). The SABRE DNA methylation dataset has now been vastly expanded, allowing more comprehensive analysis of smoking in relation to DNA methylation and ethnic group in this cohort.
This project aims to:
i) Test the association between DNA methylation and smoking in ~2000 individuals from the SABRE cohort.
ii) Estimate the genetic and environmental contribution to differences observed between ethnic groups and explore drivers (eg differences in smoking behaviour)
iii) identify if methylation differences are likely to have functional or tissue specific signatures using external reference sets.
This project will utilise genome-wide epigenetic and survey data collected from the Southall And Brent Revisited (SABRE) study (1). We will test the association between smoking status and DNA methylation profile using EWAS and identify differences between self-reported ethnic groups. Statistical modelling will be used to quantify genetic (mQTL) and environmental contributions to ethnic differences observed. Resources such as GTEX (3) and eFORGE (4) will be used to explore functional relevance of findings.
This project will provide training and practical experience in R, use of the HPC, epigenetics analysis and analysis in the context of diverse data. It expected that this work will lead to a publication as a research paper.
1. Tillin T, Forouhi NG, McKeigue PM, Chaturvedi N, Group SS. Southall And Brent REvisited: Cohort profile of SABRE, a UK population-based comparison of cardiovascular disease and diabetes in people of European, Indian Asian and African Caribbean origins. Int J Epidemiol. 2012;41(1):33-42.
2. Elliott HR, Tillin T, McArdle WL, Ho K, Duggirala A, Frayling TM, et al. Differences in smoking associated DNA methylation patterns in South Asians and Europeans. Clin Epigenetics. 2014;6(1):4.
3. Pierce BL, Tong L, Argos M, Demanelis K, Jasmine F, Rakibuz-Zaman M, et al. Co-occurring expression and methylation QTLs allow detection of common causal variants and shared biological mechanisms. Nat Commun. 2018;9(1):804.
4. Breeze CE. Cell Type-Specific Signal Analysis in Epigenome-Wide Association Studies. Methods Mol Biol. 2022;2432:57-71.
Dr Naomi Warne (lead), Dr Helen Bould,
Prevention programmes are vital to reduce rates of anxiety and depression in young people. Schools can do this at scale but current programmes are often tied to research funding and not sustained long-term (Werner-Seidler et al., 2021).
To make school prevention programmes sustainable, we need to work with school staff and students, building on existing expertise in schools. In this project, we are working to understand how art teachers and mental health professionals could support using creativity to build resilience to anxiety and depression.
Firstly, we are consulting stakeholders to understand what would and wouldn’t work for an art and wellbeing programme in schools (i.e. the “barriers” and “facilitators”).
Results from this project will directly inform co-development of an art and wellbeing programme for Year 7 students (aged 11-12 years old) in schools.
Aim 1: Explore the potential barriers and facilitators to an art and wellbeing programme aimed at preventing anxiety and depression in schools. Explore how barriers can be overcome/avoided and how facilitators can be further promoted.
Aim 2: Investigate any potential differences in the barriers and facilitators between stakeholder groups and/or between schools.
We will have focus group data with groups of 1) students, 2) parents/guardians, 3) school teaching and support staff, 4) school policymakers/leaders, and 5) creative therapists and other mental health professionals working with schools.
The PhD researcher will conduct Reflexive Thematic Analysis (RTA; Braun & Clarke, 2021; Clarke & Braun, 2021) on the transcripts and photos of creative work from the different stakeholder groups.
RTA is a qualitative data analysis method that can be used to identify interesting and relevant patterns, or themes, that occur across groups. RTA includes: (1) data familiarisation and writing familiarisation notes; 2) systematic data coding; 3) generating initial themes from coded and collated data; 4) developing and reviewing themes; 5) refining, defining and naming themes; and 6) writing the report.
Braun & Clarke (2006) Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa
Clarke & Braun (2021) Thematic analysis: a practical guide. SAGE Publications Ltd.
Werner-Seidler, et al. (2021) Clinical Psychology Review, 89. https://doi.org/10.1016/J.CPR.2021.102079
Dr Naomi Warne (lead), Dr Helen Bould,
Prevention programmes are vital to reduce rates of anxiety and depression in young people. Schools can do this at scale but current programmes are often tied to research funding and not sustained long-term (Werner-Seidler et al., 2021).
To make school prevention programmes sustainable, we need to work with school staff and students, building on existing expertise in schools. In this project, we are working to understand how art teachers and mental health professionals could support using creativity to build resilience to anxiety and depression.
Firstly, we are consulting stakeholders to understand what would and wouldn’t work for an art and wellbeing programme in schools (i.e. the “barriers” and “facilitators”).
Results from this project will directly inform co-development of an art and wellbeing programme for Year 7 students (aged 11-12 years old) in schools.
Aim 1: Explore the potential barriers and facilitators to an art and wellbeing programme aimed at preventing anxiety and depression in schools. Explore how barriers can be overcome/avoided and how facilitators can be further promoted.
Aim 2: Investigate any potential differences in the barriers and facilitators between stakeholder groups and/or between schools.
We will have focus group data with groups of 1) students, 2) parents/guardians, 3) school teaching and support staff, 4) school policymakers/leaders, and 5) creative therapists and other mental health professionals working with schools.
The PhD researcher will conduct Reflexive Thematic Analysis (RTA; Braun & Clarke, 2021; Clarke & Braun, 2021) on the transcripts and photos of creative work from the different stakeholder groups.
RTA is a qualitative data analysis method that can be used to identify interesting and relevant patterns, or themes, that occur across groups. RTA includes: (1) data familiarisation and writing familiarisation notes; 2) systematic data coding; 3) generating initial themes from coded and collated data; 4) developing and reviewing themes; 5) refining, defining and naming themes; and 6) writing the report.
Braun & Clarke (2006) Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa
Clarke & Braun (2021) Thematic analysis: a practical guide. SAGE Publications Ltd.
Werner-Seidler, et al. (2021) Clinical Psychology Review, 89. https://doi.org/10.1016/J.CPR.2021.102079
Dr Naomi Warne (lead), Dr Helen Bould,
Mental health conditions are common in young people and are associated with significant functional impairment and mortality. Visual art-based interventions, including art therapy and non-therapeutic engagement with the visual arts, can help young people express their feelings without the need for verbal communication. Importantly, visual art interventions can also reduce mental health symptoms in young people (Easwaran et al., 2021).
Systematic reviews have found preliminary evidence that school-based art therapy can be effective at improving anxiety and emotional and behavioural problems (Moula 2020; McDonald & StJ Drey, 2018). However, there has not been a thorough review of the grey literature on visual art interventions in schools to support student mental health.
This project is a systematic review of the effectiveness of art interventions (defined broadly as any intervention incorporating the visual arts) in treating and preventing mental health problems.
Aim 1: Identify studies that assess the effectiveness of visual arts interventions (art therapy and other visual art interventions) on reducing/preventing mental health problems in children aged 4-18 years in schools/ educational settings.
Aim 2: Narratively synthesise these studies and the effectiveness of the interventions.
Aim 3: Robustly critique the quality of included research studies.
Systematic review methodology including: running database searches, systematically hand-searching websites for grey literature, assessing articles for inclusion, robustly critiquing quality (e.g. in line with Grebosz-Haring et al., 2022) of included studies, assessing risk of bias in included studies, and narratively synthesising the included studies.
We do not anticipate it will be possible to conduct a meta-analysis as the resulting studies are likely to be clinically and methodologically heterogeneous.
Easwaran 2021. Why art matters for youth mental health: A youth led participatory insight analysis doi:10.31234/OSF.IO/AP476.
Grebosz-Haring 2022. Front. Psychol., 13:821093
McDonald 2018. Int. J. Art Ther., 23:1, 33-44
Moula 2020. Int. J. Art Ther., 25, 88–99
Dr Naomi Warne (lead), Dr Helen Bould,
Mental health conditions are common in young people and are associated with significant functional impairment and mortality. Visual art-based interventions, including art therapy and non-therapeutic engagement with the visual arts, can help young people express their feelings without the need for verbal communication. Importantly, visual art interventions can also reduce mental health symptoms in young people (Easwaran et al., 2021).
Systematic reviews have found preliminary evidence that school-based art therapy can be effective at improving anxiety and emotional and behavioural problems (Moula 2020; McDonald & StJ Drey, 2018). However, there has not been a thorough review of the grey literature on visual art interventions in schools to support student mental health.
This project is a systematic review of the effectiveness of art interventions (defined broadly as any intervention incorporating the visual arts) in treating and preventing mental health problems.
Aim 1: Identify studies that assess the effectiveness of visual arts interventions (art therapy and other visual art interventions) on reducing/preventing mental health problems in children aged 4-18 years in schools/ educational settings.
Aim 2: Narratively synthesise these studies and the effectiveness of the interventions.
Aim 3: Robustly critique the quality of included research studies.
Systematic review methodology including: running database searches, systematically hand-searching websites for grey literature, assessing articles for inclusion, robustly critiquing quality (e.g. in line with Grebosz-Haring et al., 2022) of included studies, assessing risk of bias in included studies, and narratively synthesising the included studies.
We do not anticipate it will be possible to conduct a meta-analysis as the resulting studies are likely to be clinically and methodologically heterogeneous.
Easwaran 2021. Why art matters for youth mental health: A youth led participatory insight analysis doi:10.31234/OSF.IO/AP476.
Grebosz-Haring 2022. Front. Psychol., 13:821093
McDonald 2018. Int. J. Art Ther., 23:1, 33-44
Moula 2020. Int. J. Art Ther., 25, 88–99
Dr Hannah Elliott (lead), Dr Paul Yousefi,
DNA methylation is an epigenetic molecular mark which attaches to DNA and participates in control of transcription. The study of DNA methylation in epidemiological health studies is an established research area, although most data analysed to date has poor diversity amongst study participants (1).
Disease prediction using epigenetic data is a quickly growing research area that offers the prospect of risk stratification and early intervention. However, training data used to build epigenetic disease predictors have also relied on individuals of European genetic ancestry (2).
Cohorts such as the Southall And Brent Revisited (SABRE) cohort have sought to identify health in ageing and improve understanding of the reasons underlying ethnic group differences in health (3).
Improving understanding of whether epigenetic predictors are generalisable across diverse individuals is an important step in reducing health equalities in the UK and beyond.
This project aims to:
i) Test the generalisability of epigenetic disease predictors in a UK based cohort which includes first generation south Asian migrants
ii) Contribute to addressing issues around lack of diversity and health inequality in molecular epidemiology and population health
iii) Provide training and practical experience in R, use of the high performance computing (HPC), epigenetics analysis and prediction in the context of diverse data. It expected that this work will lead to a publication as a research paper
This project will utilise genome-wide epigenetic and clinical data collected from the Southall And Brent Revisited (SABRE) study (3). We will assess DNA methylation predictors for diabetes and cardiovascular disease developed in external cohorts including Generation Scotland (4, 5).
We will first assess generalisability of predictors in SABRE stratified by self-reported ethnic group. Prediction error assessments will be reported. For predictors with moderate to high prediction error across ethnic groups, we will attempt to improve predictor generalisability using feature removal.
If appropriate, we will also explore re-training of predictors and compare performance with existing European derived predictors.
1. Breeze CE, Wong JYY, Beck S, Berndt SI, Franceschini N. Diversity in EWAS: current state, challenges, and solutions. Genome Med. 2022;14(1):71.
2. Yousefi PD, Suderman M, Langdon R, Whitehurst O, Davey Smith G, Relton CL. DNA methylation-based predictors of health: applications and statistical considerations. Nat Rev Genet. 2022;23(6):369-83.
3. Tillin T, Forouhi NG, McKeigue PM, Chaturvedi N, Group SS. Southall And Brent REvisited: Cohort profile of SABRE, a UK population-based comparison of cardiovascular disease and diabetes in people of European, Indian Asian and African Caribbean origins. Int J Epidemiol. 2012;41(1):33-42.
4. Cheng Y, Gadd DA, Gieger C, Monterrubio-Gomez K, Zhang Y, Berta I, et al. Development and validation of DNA methylation scores in two European cohorts augment 10-year risk prediction of type 2 diabetes. Nat Aging. 2023;3(4):450-8.
5. Gadd DA, Hillary RF, McCartney DL, Zaghlool SB, Stevenson AJ, Cheng Y, et al. Epigenetic scores for the circulating proteome as tools for disease prediction. Elife. 2022;11.
Dr Hannah Elliott (lead), Dr Paul Yousefi,
DNA methylation is an epigenetic molecular mark which attaches to DNA and participates in control of transcription. The study of DNA methylation in epidemiological health studies is an established research area, although most data analysed to date has poor diversity amongst study participants (1).
Disease prediction using epigenetic data is a quickly growing research area that offers the prospect of risk stratification and early intervention. However, training data used to build epigenetic disease predictors have also relied on individuals of European genetic ancestry (2).
Cohorts such as the Southall And Brent Revisited (SABRE) cohort have sought to identify health in ageing and improve understanding of the reasons underlying ethnic group differences in health (3).
Improving understanding of whether epigenetic predictors are generalisable across diverse individuals is an important step in reducing health equalities in the UK and beyond.
This project aims to:
i) Test the generalisability of epigenetic disease predictors in a UK based cohort which includes first generation south Asian migrants
ii) Contribute to addressing issues around lack of diversity and health inequality in molecular epidemiology and population health
iii) Provide training and practical experience in R, use of the high performance computing (HPC), epigenetics analysis and prediction in the context of diverse data. It expected that this work will lead to a publication as a research paper
This project will utilise genome-wide epigenetic and clinical data collected from the Southall And Brent Revisited (SABRE) study (3). We will assess DNA methylation predictors for diabetes and cardiovascular disease developed in external cohorts including Generation Scotland (4, 5).
We will first assess generalisability of predictors in SABRE stratified by self-reported ethnic group. Prediction error assessments will be reported. For predictors with moderate to high prediction error across ethnic groups, we will attempt to improve predictor generalisability using feature removal.
If appropriate, we will also explore re-training of predictors and compare performance with existing European derived predictors.
1. Breeze CE, Wong JYY, Beck S, Berndt SI, Franceschini N. Diversity in EWAS: current state, challenges, and solutions. Genome Med. 2022;14(1):71.
2. Yousefi PD, Suderman M, Langdon R, Whitehurst O, Davey Smith G, Relton CL. DNA methylation-based predictors of health: applications and statistical considerations. Nat Rev Genet. 2022;23(6):369-83.
3. Tillin T, Forouhi NG, McKeigue PM, Chaturvedi N, Group SS. Southall And Brent REvisited: Cohort profile of SABRE, a UK population-based comparison of cardiovascular disease and diabetes in people of European, Indian Asian and African Caribbean origins. Int J Epidemiol. 2012;41(1):33-42.
4. Cheng Y, Gadd DA, Gieger C, Monterrubio-Gomez K, Zhang Y, Berta I, et al. Development and validation of DNA methylation scores in two European cohorts augment 10-year risk prediction of type 2 diabetes. Nat Aging. 2023;3(4):450-8.
5. Gadd DA, Hillary RF, McCartney DL, Zaghlool SB, Stevenson AJ, Cheng Y, et al. Epigenetic scores for the circulating proteome as tools for disease prediction. Elife. 2022;11.
Beck Richardson (lead), Sarah Lewis, Kevin Thiessen
Orofacial clefting (OFC) is a common form of congenital disorder affecting 1 in 700 live births [1]. A large proportion of OFCs, particularly syndromic forms, are caused by genetic changes [1]. Potential links between the genetic changes that cause OFCs and the development of other abnormalities have been observed [1]. Cardiovascular abnormalities, particularly congenital heart diseases (CHD) are particularly prevalent in patients with OFCs [1,2] although the precise relationship between the causes of these disease states is not well understood. Mutations in the transcription factor Interferon Regulatory Factor 6 (IRF6) cause the most common form of syndromic orofacial clefting, Van Der Woude syndrome (VWS) [3]. Patients with VWS can also exhibit congenital heart defects [4]. Although, the cellular role of IRF6 in orofacial clefting is well defined, precise mechanisms of action in the heart have not been investigated. Our preliminary data using zebrafish animal models indicates strong expression of Irf6 in the epicardium of the adult zebrafish heart. In addition, other cleft genes may increase susceptibility to cardiovascular disease.
The main aim of this project is to use a range of different approaches to investigate the potential genetic correlation between OFC and heart disease.
Objectives:
(1) Perform a literature review to identify potential genes that could be linked to OFC and cardiovascular disease.
(2) Use summary statistic data from existing genome wide association studies (GWAS) to identify genetic correlations between cleft and heart disease.
(3) Assess cardiac phenotypes in irf6-/- zebrafish mutants to determine the cellular mechanisms that contribute to cardiac abnormalities.
A systematic literature review will be performed to identify a list of genes associated with OFC and cardiovascular changes. Candidate cellular mechanisms will then be identified for each gene as will zebrafish orthologues to produce a final shortlist of genes that could be investigated further in our animal model. LD score regression and Mendelian randomization analyses will be performed under the guidance of the co-supervisors to determine the genetic overlap between cleft and heart disease and whether cleft predisposes to cardiovascular disease. Lightsheet imaging and echocardiography will be used to visualise the whole heart of live irf6-/- mutants and wildtype siblings. Measurements of atrium/ventricle shape will be taken from these images and functional parameters such as ejection fraction, heart rate and stroke volume calculated.
References
[1] - Setó-Salvia N, Stanier P. Genetics of cleft lip and/or cleft palate: association with other common anomalies. Eur J Med Genet. 2014; 57(8):381-93. doi: 10.1016/j.ejmg.2014.04.003.
[2] - Gisele C.P. et al., Cardiovascular abnormalities in patients with oral cleft: a clinical-electrocardiographic-echocardiographic study. Clinics 2018; 73: e108. https://doi.org/10.6061/clinics/2018/e108.
[3] - Kondo, S., et al., Mutations in IRF6 cause Van der Woude and popliteal pterygium syndromes. Nat Genet, 2002. 32(2): p. 285-9.
[4] – Rizos, M., Spyropoulos, MN., Van der Woude syndrome: a review. Cardinal signs, epidemiology, associated features, differential diagnosis, expressivity, genetic counselling and treatment. Eur J Orthod, 2004. 26(1): p. 17-24.
Beck Richardson (lead), Sarah Lewis, Kevin Thiessen
Orofacial clefting (OFC) is a common form of congenital disorder affecting 1 in 700 live births [1]. A large proportion of OFCs, particularly syndromic forms, are caused by genetic changes [1]. Potential links between the genetic changes that cause OFCs and the development of other abnormalities have been observed [1]. Cardiovascular abnormalities, particularly congenital heart diseases (CHD) are particularly prevalent in patients with OFCs [1,2] although the precise relationship between the causes of these disease states is not well understood. Mutations in the transcription factor Interferon Regulatory Factor 6 (IRF6) cause the most common form of syndromic orofacial clefting, Van Der Woude syndrome (VWS) [3]. Patients with VWS can also exhibit congenital heart defects [4]. Although, the cellular role of IRF6 in orofacial clefting is well defined, precise mechanisms of action in the heart have not been investigated. Our preliminary data using zebrafish animal models indicates strong expression of Irf6 in the epicardium of the adult zebrafish heart. In addition, other cleft genes may increase susceptibility to cardiovascular disease.
The main aim of this project is to use a range of different approaches to investigate the potential genetic correlation between OFC and heart disease.
Objectives:
(1) Perform a literature review to identify potential genes that could be linked to OFC and cardiovascular disease.
(2) Use summary statistic data from existing genome wide association studies (GWAS) to identify genetic correlations between cleft and heart disease.
(3) Assess cardiac phenotypes in irf6-/- zebrafish mutants to determine the cellular mechanisms that contribute to cardiac abnormalities.
A systematic literature review will be performed to identify a list of genes associated with OFC and cardiovascular changes. Candidate cellular mechanisms will then be identified for each gene as will zebrafish orthologues to produce a final shortlist of genes that could be investigated further in our animal model. LD score regression and Mendelian randomization analyses will be performed under the guidance of the co-supervisors to determine the genetic overlap between cleft and heart disease and whether cleft predisposes to cardiovascular disease. Lightsheet imaging and echocardiography will be used to visualise the whole heart of live irf6-/- mutants and wildtype siblings. Measurements of atrium/ventricle shape will be taken from these images and functional parameters such as ejection fraction, heart rate and stroke volume calculated.
References
[1] - Setó-Salvia N, Stanier P. Genetics of cleft lip and/or cleft palate: association with other common anomalies. Eur J Med Genet. 2014; 57(8):381-93. doi: 10.1016/j.ejmg.2014.04.003.
[2] - Gisele C.P. et al., Cardiovascular abnormalities in patients with oral cleft: a clinical-electrocardiographic-echocardiographic study. Clinics 2018; 73: e108. https://doi.org/10.6061/clinics/2018/e108.
[3] - Kondo, S., et al., Mutations in IRF6 cause Van der Woude and popliteal pterygium syndromes. Nat Genet, 2002. 32(2): p. 285-9.
[4] – Rizos, M., Spyropoulos, MN., Van der Woude syndrome: a review. Cardinal signs, epidemiology, associated features, differential diagnosis, expressivity, genetic counselling and treatment. Eur J Orthod, 2004. 26(1): p. 17-24.
Dr Anna Hurley-Wallace (lead), Dr Katie Whale,
Chronic musculoskeletal pain is a common issue experienced by adolescents and young adults (AYAs) [1,2,3], which can have a significant impact on their wellbeing [4,5]. Conditions include rheumatoid arthritis, hypermobility disorders, and Ehlers-Danlos Syndromes. A key challenge for AYAs is accessing, and subsequently engaging with appropriate multidisciplinary healthcare services for chronic musculoskeletal pain [6]. Musculoskeletal pain management programmes usually focus on physical or occupational therapy [7,8], with adjuvant psychological therapy and medications as appropriate. Barriers preventing AYAs from accessing and engaging with musculoskeletal pain management programmes remain unclear. By understanding these barriers, interventions and service improvements can be implemented to improve programme engagement.
Primary aim: Explore and summarise young people’s (16 to 24-years) experiences of accessing and engaging with musculoskeletal chronic pain programmes offered in secondary care. Objectives: (i) identify barriers to accessing musculoskeletal pain programmes, (ii) identify barriers and facilitators to successful engagement with musculoskeletal pain programmes, (iii) explore contextual factors that may be contributing to AYA levels of engagement with musculoskeletal pain programmes, (iv) draft a set of key recommendations for a co-intervention to improve AYA’s engagement with musculoskeletal pain programmes, focusing on engagement with the physical therapy component.
A secondary analysis of qualitative interview data, which was originally collected to capture young people’s experiences of online resources for chronic pain management[9]. The aim for this analysis is to explore and summarise experiences of accessing and engaging with pain management programmes within the musculoskeletal pain participant sub-set (n = 8), with potential expansion to the post-injury pain sub-set (n = 3). Thematic analysis will be conducted using NVivo [10]. Results should present the themes and suggest key recommendations for a co-intervention to improve AYA’s engagement with musculoskeletal pain programmes. Original transcripts and field notes are available.
1. King, S., Chambers, C. T., Huguet, A., MacNevin, R. C., McGrath, P. J., Parker, L., & MacDonald, A. J. (2011). The epidemiology of chronic pain in children and adolescents revisited: a systematic review. Pain, 152(12), 2729-2738. https://doi.org/10.1016/j.pain.2011.07.016
2. Kastelein, M., Luijsterburg, P. A. J., Heintjes, E. M., van Middelkoop, M., Verhaar, J. A. N., Koes, B. W., & Bierma-Zeinstra, S. M. A. (2015). The 6-year trajectory of non-traumatic knee symptoms (including patellofemoral pain) in adolescents and young adults in general practice: a study of clinical predictors. British Journal of Sports Medicine, 49(6), 400-405. http://dx.doi.org/10.1136/bjsports-2014-093557
3. Hanvold, T. N., Veiersted, K. B., & Wærsted, M. (2010). A prospective study of neck, shoulder, and upper back pain among technical school students entering working life. Journal of Adolescent Health, 46(5), 488-494. https://doi.org/10.1016/j.jadohealth.2009.11.200
4. Myrtveit, S. M., Sivertsen, B., Skogen, J. C., Frostholm, L., Stormark, K. M., & Hysing, M. (2014). Adolescent neck and shoulder pain—the association with depression, physical activity, screen-based activities, and use of health care services. Journal of Adolescent Health, 55(3), 366-372. https://doi.org/10.1016/j.jadohealth.2014.02.016
5. Rathleff, M. S., Holden, S., Straszek, C. L., Olesen, J. L., Jensen, M. B., & Roos, E. M. (2019). Five-year prognosis and impact of adolescent knee pain: a prospective population-based cohort study of 504 adolescents in Denmark. BMJ open, 9(5), e024113. http://dx.doi.org/10.1136/bmjopen-2018-024113
6. Slater, H., Jordan, J. E., Chua, J., Schütze, R., Wark, J. D., & Briggs, A. M. (2016). Young people's experiences of persistent musculoskeletal pain, needs, gaps and perceptions about the role of digital technologies to support their co-care: a qualitative study. BMJ open, 6(12), e014007. https://doi.org/10.1136/bmjopen-2016-014007
7. Caes, L., Fisher, E., Clinch, J., & Eccleston, C. (2018). Current evidence-based interdisciplinary treatment options for pediatric musculoskeletal pain. Current Treatment Options in Rheumatology, 4, 223-234. https://doi.org/10.1007/s40674-018-0101-7
8. Van Meulenbroek, T., Conijn, A. E. A., Huijnen, I. P. J., Engelbert, R. H. H., & Verbunt, J. A. (2020). Multidisciplinary Treatment for Hypermobile Adolescents with Chronic Musculoskeletal Pain. Journal of rehabilitation medicine. Clinical communications, 3, 1000033. https://doi.org/10.2340/20030711-1000033
9. Hurley-Wallace, A., Kirby, S., & Bishop, F. (2022). Trusting in the online 'community': An interview study exploring internet use in young people with chronic pain. British journal of pain, 16(3), 341–353. https://doi.org/10.1177/20494637211061970
10. Clarke, V., Braun, V., & Hayfield, N. (2015). Thematic analysis. Qualitative psychology: A practical guide to research methods, 3, 222-248.
Dr Anna Hurley-Wallace (lead), Dr Katie Whale,
Chronic musculoskeletal pain is a common issue experienced by adolescents and young adults (AYAs) [1,2,3], which can have a significant impact on their wellbeing [4,5]. Conditions include rheumatoid arthritis, hypermobility disorders, and Ehlers-Danlos Syndromes. A key challenge for AYAs is accessing, and subsequently engaging with appropriate multidisciplinary healthcare services for chronic musculoskeletal pain [6]. Musculoskeletal pain management programmes usually focus on physical or occupational therapy [7,8], with adjuvant psychological therapy and medications as appropriate. Barriers preventing AYAs from accessing and engaging with musculoskeletal pain management programmes remain unclear. By understanding these barriers, interventions and service improvements can be implemented to improve programme engagement.
Primary aim: Explore and summarise young people’s (16 to 24-years) experiences of accessing and engaging with musculoskeletal chronic pain programmes offered in secondary care. Objectives: (i) identify barriers to accessing musculoskeletal pain programmes, (ii) identify barriers and facilitators to successful engagement with musculoskeletal pain programmes, (iii) explore contextual factors that may be contributing to AYA levels of engagement with musculoskeletal pain programmes, (iv) draft a set of key recommendations for a co-intervention to improve AYA’s engagement with musculoskeletal pain programmes, focusing on engagement with the physical therapy component.
A secondary analysis of qualitative interview data, which was originally collected to capture young people’s experiences of online resources for chronic pain management[9]. The aim for this analysis is to explore and summarise experiences of accessing and engaging with pain management programmes within the musculoskeletal pain participant sub-set (n = 8), with potential expansion to the post-injury pain sub-set (n = 3). Thematic analysis will be conducted using NVivo [10]. Results should present the themes and suggest key recommendations for a co-intervention to improve AYA’s engagement with musculoskeletal pain programmes. Original transcripts and field notes are available.
1. King, S., Chambers, C. T., Huguet, A., MacNevin, R. C., McGrath, P. J., Parker, L., & MacDonald, A. J. (2011). The epidemiology of chronic pain in children and adolescents revisited: a systematic review. Pain, 152(12), 2729-2738. https://doi.org/10.1016/j.pain.2011.07.016
2. Kastelein, M., Luijsterburg, P. A. J., Heintjes, E. M., van Middelkoop, M., Verhaar, J. A. N., Koes, B. W., & Bierma-Zeinstra, S. M. A. (2015). The 6-year trajectory of non-traumatic knee symptoms (including patellofemoral pain) in adolescents and young adults in general practice: a study of clinical predictors. British Journal of Sports Medicine, 49(6), 400-405. http://dx.doi.org/10.1136/bjsports-2014-093557
3. Hanvold, T. N., Veiersted, K. B., & Wærsted, M. (2010). A prospective study of neck, shoulder, and upper back pain among technical school students entering working life. Journal of Adolescent Health, 46(5), 488-494. https://doi.org/10.1016/j.jadohealth.2009.11.200
4. Myrtveit, S. M., Sivertsen, B., Skogen, J. C., Frostholm, L., Stormark, K. M., & Hysing, M. (2014). Adolescent neck and shoulder pain—the association with depression, physical activity, screen-based activities, and use of health care services. Journal of Adolescent Health, 55(3), 366-372. https://doi.org/10.1016/j.jadohealth.2014.02.016
5. Rathleff, M. S., Holden, S., Straszek, C. L., Olesen, J. L., Jensen, M. B., & Roos, E. M. (2019). Five-year prognosis and impact of adolescent knee pain: a prospective population-based cohort study of 504 adolescents in Denmark. BMJ open, 9(5), e024113. http://dx.doi.org/10.1136/bmjopen-2018-024113
6. Slater, H., Jordan, J. E., Chua, J., Schütze, R., Wark, J. D., & Briggs, A. M. (2016). Young people's experiences of persistent musculoskeletal pain, needs, gaps and perceptions about the role of digital technologies to support their co-care: a qualitative study. BMJ open, 6(12), e014007. https://doi.org/10.1136/bmjopen-2016-014007
7. Caes, L., Fisher, E., Clinch, J., & Eccleston, C. (2018). Current evidence-based interdisciplinary treatment options for pediatric musculoskeletal pain. Current Treatment Options in Rheumatology, 4, 223-234. https://doi.org/10.1007/s40674-018-0101-7
8. Van Meulenbroek, T., Conijn, A. E. A., Huijnen, I. P. J., Engelbert, R. H. H., & Verbunt, J. A. (2020). Multidisciplinary Treatment for Hypermobile Adolescents with Chronic Musculoskeletal Pain. Journal of rehabilitation medicine. Clinical communications, 3, 1000033. https://doi.org/10.2340/20030711-1000033
9. Hurley-Wallace, A., Kirby, S., & Bishop, F. (2022). Trusting in the online 'community': An interview study exploring internet use in young people with chronic pain. British journal of pain, 16(3), 341–353. https://doi.org/10.1177/20494637211061970
10. Clarke, V., Braun, V., & Hayfield, N. (2015). Thematic analysis. Qualitative psychology: A practical guide to research methods, 3, 222-248.
Dr Martha Elwenspoek (lead), Prof Penny Whiting, Katie Charlwood
Patients with long term conditions (LTC) such as hypertension require regular blood tests to monitor and manage their condition. However, there is a lack of evidence on what tests are useful in monitoring these conditions, and how often these require testing. This has led to a considerable variation in monitoring strategies used in primary care. Over testing can be harmful, as it can cause unnecessary follow-up testing, increased workload for clinicians, costs for NHS, and anxiety for patients.
This project is part of the Optimal Testing project, a 5-year NIHR funded project which aims to develop strategies, based on existing and new evidence, that can be recommended for use to monitor long term conditions within primary care.
to emulate a clinical trial in routine data in order to investigate the benefit to hypertensive patients of having their renal function (eGFR) monitored regularly.
Routinely collected primary care data (CPRD) will be used to emulate the target trial. The student will learn to apply epidemiological methods, data management skills, working with large datasets, and using statistical software (R).
Jones T, Patel R, Elwenspoek MMC, Watson JC, Mann E, Alsop K, Whiting PF. Variation in laboratory testing for patients with long-term conditions: a longitudinal cohort study in UK primary care. BJGP Open. 2023
Elwenspoek MMC, Mann E, Alsop K, Clark H, Patel R, Watson JC, Whiting P. GP's perspectives on laboratory test use for monitoring long-term conditions: an audit of current testing practice. BMC Fam Pract. 2020
Martha Elwenspoek, Rita Patel, Jessica Watson, Ed Mann, Katharine Alsop and Penny Whiting. What is the evidence behind guideline recommendations to monitor chronic diseases in UK primary care?
British Journal of General Practice. 2019
Elwenspoek MMC, Patel R, Watson JC, Whiting P. Are guidelines for monitoring chronic disease in primary care evidence based? BMJ. 2019
Dr Martha Elwenspoek (lead), Prof Penny Whiting, Katie Charlwood
Patients with long term conditions (LTC) such as hypertension require regular blood tests to monitor and manage their condition. However, there is a lack of evidence on what tests are useful in monitoring these conditions, and how often these require testing. This has led to a considerable variation in monitoring strategies used in primary care. Over testing can be harmful, as it can cause unnecessary follow-up testing, increased workload for clinicians, costs for NHS, and anxiety for patients.
This project is part of the Optimal Testing project, a 5-year NIHR funded project which aims to develop strategies, based on existing and new evidence, that can be recommended for use to monitor long term conditions within primary care.
to emulate a clinical trial in routine data in order to investigate the benefit to hypertensive patients of having their renal function (eGFR) monitored regularly.
Routinely collected primary care data (CPRD) will be used to emulate the target trial. The student will learn to apply epidemiological methods, data management skills, working with large datasets, and using statistical software (R).
Jones T, Patel R, Elwenspoek MMC, Watson JC, Mann E, Alsop K, Whiting PF. Variation in laboratory testing for patients with long-term conditions: a longitudinal cohort study in UK primary care. BJGP Open. 2023
Elwenspoek MMC, Mann E, Alsop K, Clark H, Patel R, Watson JC, Whiting P. GP's perspectives on laboratory test use for monitoring long-term conditions: an audit of current testing practice. BMC Fam Pract. 2020
Martha Elwenspoek, Rita Patel, Jessica Watson, Ed Mann, Katharine Alsop and Penny Whiting. What is the evidence behind guideline recommendations to monitor chronic diseases in UK primary care?
British Journal of General Practice. 2019
Elwenspoek MMC, Patel R, Watson JC, Whiting P. Are guidelines for monitoring chronic disease in primary care evidence based? BMJ. 2019
Apostolos Gkatzionis (lead), Kate Tilling, Rosie Cornish
Selection bias is a common concern in epidemiologic studies. Inverse Probability Weighting (IPW) is often used to adjust for selection bias by modelling selection into the study in terms of observed covariates. Applied scientists often implement IPW using a logistic model with only main effects to model selection into the study. This is typically done for convenience but is not a flexible modelling strategy. At the same time, evidence has emerged that a log-additive model may be more relevant, and that it is important to include interactions between covariates in the selection model.
The aim of this project will be to conduct a simulation study to compare the performance of IPW when implemented with a logistic model against a log-additive model, as well as to investigate the benefits of including interaction terms in either model. Other, more flexible models may also be considered for comparison.
IPW with a logistic or log-additive selection model. The comparison between the different selection models will be done using simulated data in R or Stata.
Gkatzionis A., Seaman, S. R., Hughes, R. A., Tilling K. (2023). Relationship between Collider Bias and Interactions on the Log-Additive Scale. arXiv:2308.00568.
Jiang, Z. and Ding, P. (2017). The directions of selection bias. Statistics and Probability Letters, 125, 104-109.
Bartlett, J. W., Harel, O., Carpenter, J. R. (2015). Asymptotically Unbiased Estimation of Exposure Odds Ratios in Complete Records Logistic Regression. American Journal of Epidemiology, 182(8), 730–736.
Apostolos Gkatzionis (lead), Kate Tilling, Rosie Cornish
Selection bias is a common concern in epidemiologic studies. Inverse Probability Weighting (IPW) is often used to adjust for selection bias by modelling selection into the study in terms of observed covariates. Applied scientists often implement IPW using a logistic model with only main effects to model selection into the study. This is typically done for convenience but is not a flexible modelling strategy. At the same time, evidence has emerged that a log-additive model may be more relevant, and that it is important to include interactions between covariates in the selection model.
The aim of this project will be to conduct a simulation study to compare the performance of IPW when implemented with a logistic model against a log-additive model, as well as to investigate the benefits of including interaction terms in either model. Other, more flexible models may also be considered for comparison.
IPW with a logistic or log-additive selection model. The comparison between the different selection models will be done using simulated data in R or Stata.
Gkatzionis A., Seaman, S. R., Hughes, R. A., Tilling K. (2023). Relationship between Collider Bias and Interactions on the Log-Additive Scale. arXiv:2308.00568.
Jiang, Z. and Ding, P. (2017). The directions of selection bias. Statistics and Probability Letters, 125, 104-109.
Bartlett, J. W., Harel, O., Carpenter, J. R. (2015). Asymptotically Unbiased Estimation of Exposure Odds Ratios in Complete Records Logistic Regression. American Journal of Epidemiology, 182(8), 730–736.
Dr Amanda Chong (lead), Professor Tom Gaunt,
In the United Kingdom, studies have shown that stroke disproportionately affects minority demographic groups with individuals of South Asian and African ethnicity having an age-adjusted stroke incidence approximately 1.5 and 2.2 times higher than individuals of White European ethnicity, respectively(1, 2). In addition, in a UK Government report, individuals of African and south Asian ethnicity were associated with a worse stroke mortality outcome (measured by years of life lost) compared to individuals of White European ethnicity(3). This report also highlighted that individuals from these minority ethnic groups were living in the most deprived areas(3).
Studies have however argued that as ethnicity is a complex social construct, it is not well represented by genetics(4). Therefore, genetic ancestry can be used to understand ethnic differences as it is a better proxy for genetic diversity and is correlated with ethnicity but is not constrained by sociocultural factors(4). Accordingly, we will use genetic ancestry in this project to disentangle the socioenvironmental and genetic factors that contribute to the ethnic differences in stroke risk.
1. Investigate the effect of SED on stroke risk.
2. Investigate the effect of genetic ancestry on stroke risk.
3. Evaluate the interaction of SED and genetic ancestry on stroke risk.
Multivariable regression analyses will be performed using data from UK Biobank. Extracted data will include: Age at baseline, sex, Townsend deprivation index (as a measure of socioeconomic deprivation), self-reported ethnic background, stroke subtypes identified using the ICD-10 codes, and genetic principal components. In addition, inference of genetic ancestry will be based on previous analyses conducted by Constantinescu et al. 2022(5).
1. Ramadan H, Patterson C, Maguire S, Melvin I, Kain K, Teale E, et al. Incidence of first stroke and ethnic differences in stroke pattern in Bradford, UK: Bradford Stroke Study. International Journal of Stroke. 2018;13(4):374-8.
2. Markus HS, Khan U, Birns J, Evans A, Kalra L, Rudd AG, et al. Differences in stroke subtypes between black and white patients with stroke: the South London Ethnicity and Stroke Study. Circulation. 2007;116(19):2157-64.
3. Ali R, Chowdhury A, Forouhi N, Wareham N. Ethnic disparities in the major causes of mortality and their risk factors – a rapid review. 2021.
4. Lewis AC, Molina SJ, Appelbaum PS, Dauda B, Di Rienzo A, Fuentes A, et al. Getting genetic ancestry right for science and society. Science. 2022;376(6590):250-2.
5. Constantinescu A-E, Mitchell RE, Zheng J, Bull CJ, Timpson NJ, Amulic B, et al. A framework for research into continental ancestry groups of the UK Biobank. Human genomics. 2022;16(1):1-14.
Dr Amanda Chong (lead), Professor Tom Gaunt,
In the United Kingdom, studies have shown that stroke disproportionately affects minority demographic groups with individuals of South Asian and African ethnicity having an age-adjusted stroke incidence approximately 1.5 and 2.2 times higher than individuals of White European ethnicity, respectively(1, 2). In addition, in a UK Government report, individuals of African and south Asian ethnicity were associated with a worse stroke mortality outcome (measured by years of life lost) compared to individuals of White European ethnicity(3). This report also highlighted that individuals from these minority ethnic groups were living in the most deprived areas(3).
Studies have however argued that as ethnicity is a complex social construct, it is not well represented by genetics(4). Therefore, genetic ancestry can be used to understand ethnic differences as it is a better proxy for genetic diversity and is correlated with ethnicity but is not constrained by sociocultural factors(4). Accordingly, we will use genetic ancestry in this project to disentangle the socioenvironmental and genetic factors that contribute to the ethnic differences in stroke risk.
1. Investigate the effect of SED on stroke risk.
2. Investigate the effect of genetic ancestry on stroke risk.
3. Evaluate the interaction of SED and genetic ancestry on stroke risk.
Multivariable regression analyses will be performed using data from UK Biobank. Extracted data will include: Age at baseline, sex, Townsend deprivation index (as a measure of socioeconomic deprivation), self-reported ethnic background, stroke subtypes identified using the ICD-10 codes, and genetic principal components. In addition, inference of genetic ancestry will be based on previous analyses conducted by Constantinescu et al. 2022(5).
1. Ramadan H, Patterson C, Maguire S, Melvin I, Kain K, Teale E, et al. Incidence of first stroke and ethnic differences in stroke pattern in Bradford, UK: Bradford Stroke Study. International Journal of Stroke. 2018;13(4):374-8.
2. Markus HS, Khan U, Birns J, Evans A, Kalra L, Rudd AG, et al. Differences in stroke subtypes between black and white patients with stroke: the South London Ethnicity and Stroke Study. Circulation. 2007;116(19):2157-64.
3. Ali R, Chowdhury A, Forouhi N, Wareham N. Ethnic disparities in the major causes of mortality and their risk factors – a rapid review. 2021.
4. Lewis AC, Molina SJ, Appelbaum PS, Dauda B, Di Rienzo A, Fuentes A, et al. Getting genetic ancestry right for science and society. Science. 2022;376(6590):250-2.
5. Constantinescu A-E, Mitchell RE, Zheng J, Bull CJ, Timpson NJ, Amulic B, et al. A framework for research into continental ancestry groups of the UK Biobank. Human genomics. 2022;16(1):1-14.
Dr Rebecca Richmond (lead), Dr Eleanor Sanderson, Dr Jess Tyrrell
Insomnia has previously been shown to have a causal effect on glycaemic traits and diabetes through univariable Mendelian randomization (MR), although the mechanisms underlying this effect are unclear. Insomnia is correlated with depression and may have a shared genetic aetiology or there may be a causal effect between these traits. MR uses genetic variants to estimate the causal effect of an exposure on an outcome of interest in a way that aims to overcome bias from unobserved confounding. However, MR is biased if the genetic variants are associated with the outcome through a pathway that does not include the exposure, such as if genetic variants associated with insomnia are also associated with depression. Multivariable MR is an extension of MR that can include multiple exposures and so can account for such pathways. Multivariable MR can also be used to establish the extent to which depression mediates or confounders the effect of insomnia on glycaemic traits and type 2 diabetes.
The aim of this project is to estimate to what extent the effect of insomnia on glycaemic traits and type 2 diabetes can be explained by depression.
This project will use individual level data from UK Biobank and apply multivariable MR method to estimate the extent to which the effect of insomnia on glycaemic traits and type 2 diabetes is explained by depression. Multivariable MR can also be biased by pathways from the genetic instruments to the outcome that do not act through the exposure and therefore this project will apply a novel multivariable MR that estimates which genetic variants are likely to have pleiotropic effects and obtains effect estimates that are robust to that pleiotropy.
Liu, James, et al. “Assessing the causal role of sleep traits on glycated hemoglobin: a Mendelian Randomization study”. Diabetes Care 45.4 (2022): 772-781.
Sanderson, Eleanor, et al. "An examination of multivariable Mendelian randomization in the single-sample and two-sample summary data settings." International journal of epidemiology 48.3 (2019): 713-727.
Dr Rebecca Richmond (lead), Dr Eleanor Sanderson, Dr Jess Tyrrell
Insomnia has previously been shown to have a causal effect on glycaemic traits and diabetes through univariable Mendelian randomization (MR), although the mechanisms underlying this effect are unclear. Insomnia is correlated with depression and may have a shared genetic aetiology or there may be a causal effect between these traits. MR uses genetic variants to estimate the causal effect of an exposure on an outcome of interest in a way that aims to overcome bias from unobserved confounding. However, MR is biased if the genetic variants are associated with the outcome through a pathway that does not include the exposure, such as if genetic variants associated with insomnia are also associated with depression. Multivariable MR is an extension of MR that can include multiple exposures and so can account for such pathways. Multivariable MR can also be used to establish the extent to which depression mediates or confounders the effect of insomnia on glycaemic traits and type 2 diabetes.
The aim of this project is to estimate to what extent the effect of insomnia on glycaemic traits and type 2 diabetes can be explained by depression.
This project will use individual level data from UK Biobank and apply multivariable MR method to estimate the extent to which the effect of insomnia on glycaemic traits and type 2 diabetes is explained by depression. Multivariable MR can also be biased by pathways from the genetic instruments to the outcome that do not act through the exposure and therefore this project will apply a novel multivariable MR that estimates which genetic variants are likely to have pleiotropic effects and obtains effect estimates that are robust to that pleiotropy.
Liu, James, et al. “Assessing the causal role of sleep traits on glycated hemoglobin: a Mendelian Randomization study”. Diabetes Care 45.4 (2022): 772-781.
Sanderson, Eleanor, et al. "An examination of multivariable Mendelian randomization in the single-sample and two-sample summary data settings." International journal of epidemiology 48.3 (2019): 713-727.
Dr Rebecca Richmond (lead), Dr Jasmine Khouja, Prof Marcus Munafò Ms Stephanie Page
Caffeine is a widely-consumed psychoactive substance found in coffee, tea, soft drinks and energy drinks. Its effect on sleep is well documented in laboratory-based studies, although the extent to which regular caffeine consumption affects sleep in the general population is not fully understood. There is likely to be a bi-directional relationship between poor sleep and caffeine intake, whereby caffeine before bedtime has disruptive effects on sleep, while poor sleep could lead to increases in caffeine consumption the next day as self-medication against fatigue. However, the relationship between caffeine intake and sleep is likely to be complicated by wide inter-individual variation in metabolism. Investigating the causal interplay between caffeine consumption, caffeine metabolism and sleep measures may provide useful insights.
Mendelian randomization (MR) is a method which uses genetic variation to establish and orient the causal direction of effect between an exposure and outcome. Treur et al. (2018) conducted bi-directional, two-sample MR analysis which found limited evidence for causal effects of caffeine intake on sleep, or vice versa. This was aside from weak evidence that higher plasma caffeine levels causally decrease the odds of being a morning person. The researchers concluded that although caffeine may affect sleep when taken shortly before bedtime, there was no evidence for a detrimental impct of sustained patterns of high caffeine on poorer sleep. However, this study did not specifically dissect the role of caffeine consumption from caffeine metabolism. The relationships between caffeine and objective sleep measures have also yet to be evaluated using an MR approach.
1) To establish the bi-directional effects between caffeine intake and sleep traits (insomnia, sleep duration, chronotype) and compare estimates with those previously reported
2) To investigate the bi-directional relationship between caffeine intake and other sleep traits (e.g. daytime sleepiness, napping and sleep-disordered breathing), as well as objective sleep measures (e.g. sleep duration, timing, fragmentation, efficiency)
3) To evaluate the potential role caffeine metabolism plays in underlying the effects of caffeine on sleep behaviour using genetic variation associated with the caffeine metabolism.
i) Two-sample Mendelian randomization
ii) Generation of polygenic risk scores for sleep and caffeine measures
iii) Multivariable Mendelian randomization
iv) Sensitivity analyses to test MR assumptions
Treur, Jorien L., et al. "Investigating genetic correlations and causal effects between caffeine consumption and sleep behaviours." Journal of Sleep Research 27.5 (2018): e12695.
Woolf, Benjamin, et al. "Comparison of caffeine consumption behavior with plasma caffeine levels as exposures in drug-target Mendelian randomization and implications for interpreting effects on obesity." medRxiv (2023): 2023-05.
Dr Rebecca Richmond (lead), Dr Jasmine Khouja, Prof Marcus Munafò Ms Stephanie Page
Caffeine is a widely-consumed psychoactive substance found in coffee, tea, soft drinks and energy drinks. Its effect on sleep is well documented in laboratory-based studies, although the extent to which regular caffeine consumption affects sleep in the general population is not fully understood. There is likely to be a bi-directional relationship between poor sleep and caffeine intake, whereby caffeine before bedtime has disruptive effects on sleep, while poor sleep could lead to increases in caffeine consumption the next day as self-medication against fatigue. However, the relationship between caffeine intake and sleep is likely to be complicated by wide inter-individual variation in metabolism. Investigating the causal interplay between caffeine consumption, caffeine metabolism and sleep measures may provide useful insights.
Mendelian randomization (MR) is a method which uses genetic variation to establish and orient the causal direction of effect between an exposure and outcome. Treur et al. (2018) conducted bi-directional, two-sample MR analysis which found limited evidence for causal effects of caffeine intake on sleep, or vice versa. This was aside from weak evidence that higher plasma caffeine levels causally decrease the odds of being a morning person. The researchers concluded that although caffeine may affect sleep when taken shortly before bedtime, there was no evidence for a detrimental impct of sustained patterns of high caffeine on poorer sleep. However, this study did not specifically dissect the role of caffeine consumption from caffeine metabolism. The relationships between caffeine and objective sleep measures have also yet to be evaluated using an MR approach.
1) To establish the bi-directional effects between caffeine intake and sleep traits (insomnia, sleep duration, chronotype) and compare estimates with those previously reported
2) To investigate the bi-directional relationship between caffeine intake and other sleep traits (e.g. daytime sleepiness, napping and sleep-disordered breathing), as well as objective sleep measures (e.g. sleep duration, timing, fragmentation, efficiency)
3) To evaluate the potential role caffeine metabolism plays in underlying the effects of caffeine on sleep behaviour using genetic variation associated with the caffeine metabolism.
i) Two-sample Mendelian randomization
ii) Generation of polygenic risk scores for sleep and caffeine measures
iii) Multivariable Mendelian randomization
iv) Sensitivity analyses to test MR assumptions
Treur, Jorien L., et al. "Investigating genetic correlations and causal effects between caffeine consumption and sleep behaviours." Journal of Sleep Research 27.5 (2018): e12695.
Woolf, Benjamin, et al. "Comparison of caffeine consumption behavior with plasma caffeine levels as exposures in drug-target Mendelian randomization and implications for interpreting effects on obesity." medRxiv (2023): 2023-05.
Rebecca Wilson (lead), Kate Birnie, Theresa Redaniel
Traumatic brain injuries can cause long- term adversities for patients, including post-concussion syndrome (PCS), where symptoms (including cognitive, physical and behavioural problems) can last for several months. Approximately 33% children with concussion experience PCS. It is important to identify risk factors for developing PCS, one potential risk factor is low socioeconomic position.
Using primary care data, this project will explore whether socioeconomic group is associated with developing post-concussion syndrome after a traumatic brain injury in children aged 1-17 years as the primary objective. The secondary objective will explore whether this effect is modified by child’s age and sex.
Univariable analysis including measures of socioeconomic status and post-concussion syndrome and multivariable logistic regression including potential confounders. Multiple imputation will be used to manage missing data.
Dr Katharine Looker (lead), Ms Eleanor Walsh (e.walsh@bristol.ac.uk),
Since March 2020 there have been many research groups set up to investigate health and associated outcomes of the SARS-CoV-2 pandemic. Latterly, these studies have used longitudinal data to investigate long-COVID beyond the acute phase (>4 weeks). Prevalence estimates of long-COVID have been difficult to interpret due to heterogenous study designs. Whilst the limitations of pooled estimates due to study heterogeneity have been acknowledged, the specifics of these study differences have yet to be described and directly compared. A direct comparison of study designs and populations would be useful to illustrate the similarities and differences in study characteristics to aid the interpretation of long-covid estimates in adults, children and young people.
Review to describe and compare the design characteristics of long-COVID studies to aid interpretation of long-COVID estimates across UK populations, for example:
- Characteristics of population (age, gender, location)
- Recruitment strategy & sample size
- Exposure and outcome measures (survey, biological samples, electronic health records)
- Long-COVID definitions
- Compare estimates of long-COVID by age across adults, children and young people.
- Appropriate search strategies (e.g., MeSH terms, citations) and publication databases to identify relevant scientific outputs
- Summarise and describe study designs and methodologies appropriate for comparison
- Calculate pooled estimates using meta-analysis/meta-regression, where appropriate
- Interpret and critically appraise summarised descriptions considering current literature, context and methodologies.
- Software: R or Stata; Endnote
Chua PEY, et al. Epidemiological and Clinical Characteristics of Non-Severe and Severe Pediatric and Adult COVID-19 Patients across Different Geographical Regions in the Early Phase of Pandemic: A Systematic Review and Meta-Analysis of Observational Studies. 2021. doi: 10.1136/jim-2021-001858
Lopez-Leon S, et al. Long-COVID in children and adolescents: a systematic review and meta-analyses. 2022. doi: 10.1038/s41598-022-13495-5
Al-Aly Z, et al. High-dimensional characterization of post-acute sequelae of COVID-19. 2021. doi: 10.1038/s41586-021-03553-9
Dr Katharine Looker (lead), Ms Eleanor Walsh (e.walsh@bristol.ac.uk),
Respiratory tract infections (RTIs) in children and young people are common in both primary care and hospital settings. Fever is common as a presenting symptom in children and can be without apparent cause or identifiable infection. Yet infections and fever at presentation to healthcare differ by age and disease severity between clinical settings. A review of current evidence would be a unique way of quantifying these differences in infections diagnosed in children and young people presenting to healthcare and the community.
Review of studies of the characteristics of children who present with RTIs with or without associated fever in primary care and secondary care and national surveillance data in the UK before and during the COVID-19 era. To investigate potential differences in
i) baseline exposure characteristics (e.g., age, gender, co-morbidities) and
ii) outcomes (e.g. diagnoses, no. of consultations/duration of hospital stay, prescriptions).
- Appropriate search strategies (e.g., MeSH terms, citations) and publication databases to identify relevant scientific outputs
- Calculate pooled estimates using meta-analysis/meta-regression, where appropriate
- Interpret and critically appraise summarised descriptions considering current literature, context and methodologies.
- Software: R or Stata; Endnote
Whitburn S, et al. The frequency distribution of presenting symptoms in children aged six months to six years to primary care. 2011. doi: 10.1017/s146342361000040x
Macfarlane J, et al. Prospective study of the incidence, aetiology and outcome of adult lower respiratory tract illness in the community. 2001. doi: 10.1136/thorax.56.2.109
Bleeker SE, et al. Predicting serious bacterial infection in young children with fever without apparent source. 2001. doi: 10.1080/080352501317130236
Professor Carol Joinson (lead), Dr Christina Dardani,
Observational studies have found evidence that depression and anxiety are prospectively associated with lower urinary tract symptoms (LUTS) in women. Although prospective studies provide evidence of the direction of observed associations, they are limited by unmeasured and residual confounding. Observational studies that rely on self-report questionnaires to assess depression and anxiety exposures are also limited by measurement error. Polygenic risk scores (PRS) can be used estimate an individual’s underlying genetic liability to complex traits such as depression and anxiety. PRS should not be associated with genetic or environmental confounders at a population level, which can bias observational studies.
This project, based on data from the ALSPAC mothers’ cohort, will examine the relationship between depression, anxiety and LUTS using PRS for depression and anxiety derived from genetic variants identified in published genome wide association studies. The analysis will examine evidence for differential associations between the depression/anxiety PRS and LUTS including different subtypes of urinary incontinence (UI) (stress UI, urgency UI, mixed UI), urinary urgency, and nocturia.
Logistic regression analysis will be used to examine the associations between the PRS for depression/anxiety and binary variables for the LUTS phenotypes. Analyses will be adjusted for the first ten principal components of the ALSPAC genotype data.
Als TD, et al. Nat Med. 2023;29(7):1832-1844.
Purves KL, et al. Molecular Psychiatry, 2020. 25(12): p. 3292-3303.
Felde G, et al. Neurourol Urodyn. 2017;36(2):322-328.