Research Areas

Developing the PHESANT software package for conducting phenome scans (pheWAS, MR-pheWAS) in UK Biobank. PHESANT can also be used to generate a cleaned version of UK Biobank phenotype data for large scale analyses.
MR-pheWAS studies, searching for the causal effect of an exposure of interest on many outcomes.
Derive clean datasets for large-scale analyses
Time-series data analysis of digital health data
The dynamic relationship between genotype and phenotype, and how it varies over time and space.
Genome-wide analysis of high-dimensional multi-omic data using machine learning approaches specifically designed for “large-p small-n" data, these include methods for stable feature selection and addressing multi-collinearity.

Understand how we can use novel digital footprint data to study human behaviour and real-life outcomes, such as health.

Linking digital footprints data into cohorts (developing ground truth in smart data research)
Transaction data, specifically loyalty and banking cards, and working on realising the value of using these data to improve population health.
Using machine learning to analyse and make predictions about fertility using intra-vaginal measurements of basal body temperature.
Ethical and legal issues around linking data
Research in domain of reproductive health, diet, pain management with shopping data
Using shopping data for decision-making research
Deriving novel phenotypes from digital health data, including accelerometers and continuous glucose monitoring data
Molecular measurements in peripheral tissues (e.g. blood and saliva) for non-invasive risk stratification and early detection of cancer.
Molecular epidemiology, Statistical prediction and development of genomic biomarkers.

Our Methodologies

PHESANT – for conducting phenome scans (pheWAS, MR-pheWAS) in UK Biobank. PHESANT can also be used to generate a cleaned version of UK Biobank phenotype data for large scale analyses (GITHUB)
GLU – for analysing continuous glucose monitoring data (GITHUB)

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