This 3-day course will be online and consist of live sessions. Practical sessions will use the R programming language via Posit Cloud, consequently attendees do not need to install R on their computers.

A short introduction to molecular epidemiology and to R will be provided prior to the start of the course.

By the end of the course participants should be able to:

• discuss the utility of high-throughput measurements of molecular phenotypes such as DNA methylation, metabolites, gene expression, protein abundance and genotype in epidemiology and medicine;
• design molecular studies using sound epidemiological study design principles and justify their choice of design;
• choose and apply appropriate statistical methods for common analyses of molecular data;
• interpret findings of molecular studies;
• derive and evaluate the performance of molecular biomarkers for indexing exposure and predicting health outcomes;
• apply methods to strengthen causal inference of molecular phenotypes; and
• critically appraise molecular epidemiology literature.

This course is intended for individuals engaged in population-based studies who wish to incorporate molecular measures of epigenetic marks, gene expression, metabolite presence, protein abundance or genotype into their research. A basic knowledge of epidemiology is required, and some understanding of genetics terminology would be advantageous. Some practical knowledge of R would be helpful. The course includes information on laboratory-based methods, but this will be aimed at the non-specialist (i.e. those without first-hand lab experience).

More advanced participants may be interested in the Machine Learning with Omics Data short course offered by the same team, which builds on many concepts introduced here.

This course will cover:

1. the various uses of high-throughput molecular data in epidemiology and medicine (including as an exposure, outcome, mediator, indicator and predictor);
2. key considerations in the design of molecular studies (including choosing appropriate technologies and statistical analyses);
3. practical analysis of molecular data;
4. interpreting the biological function some of the most popular molecular data types (including DNA methylation, metabolite abundance, gene expression, protein abundance and genetics);
5. methods for deriving and evaluating the performance of molecular biomarkers for indexing exposure and predicting health outcomes;
6. causality of molecular phenotypes (including the importance of establishing causality to address certain research questions, examples of causal inference techniques, applying Mendelian randomization); and
7. critical appraisal of the molecular epidemiological literature.

Dr Paul Yousefi is a data scientist who applies emerging methods in machine learning and statistical prediction to develop multi-dimensional genomic biomarkers of health risk factors, patterns of exposure, and emerging disease phenotypes.

Dr Matthew Suderman is a bioinformatician who specialises in the handling and integrated analysis of large molecular datasets for the discovery of biomarkers of disease risk and outcomes.

Dr Hannah Elliott is an epigenetic epidemiologist who has been working with high throughput molecular data for over a decade.

To make sure the course is suitable for you and you will benefit from attending, please ensure you meet the following prerequisites before booking:

Before booking this course, please make sure you read the information provided above about the target audience and prerequisites. It is important that you have access to the relevant IT resources needed for the course and meet the knowledge prerequisites to ensure you can get the most from the course.

Bookings are taken via our online booking system, for which you must register an account. To check if you are eligible for free or discounted courses please see our fees and voucher packs page. All bookings are subject to our terms & conditions, which can be read in full here.

For help and support with booking a course refer to our booking information page, FAQs or feel free to contact us directly. For available payment options please see: How to pay your short course fees.

Participants are granted access to our virtual learning platform (Blackboard) 1 to 2 weeks in advance of the course. This allows time for any pre-course work to be completed and to familiarise with the platform.

To gain the most from the course, we recommend that you attend in full and participate in all interactive components. We endeavour to record all live lecture sessions and upload these to the online learning environment within 24 hours. This allows course participants to review these sessions at leisure and revisit them multiple times. Please note that we do not record breakout sessions.

All course participants retain access to the online learning materials and recordings for 3 months after the course. 

University of Bristol staff and postgraduate students who do not wish to attend the full course may instead register for access to the 'Materials & Recordings' version of this course: Further information and bookings.

100% of attendees recommend this course*.
*Attendee feedback from 2022-2023.

Here is a sample of feedback from the last run of the course:

"Practicals were excellent! Having the tutors going through the tutorials was very helpful as it gave time to think about the method rather than spending time coding and trying to debug".
Course feedback, May 2023

"I really appreciate the pace of all of the presenters, particularly Matt. Each lecture was a manageable length, and the content was concise and succinct. This made it so much easier to keep up and take additional notes/think of questions".
Course feedback, May 2023

"Really well organised course and well developed materials that clearly related to the course objectives. Good continuity between the lectures and interactive sessions. The tutors were excellent - extremely knowledgeable and friendly".
Course feedback, May 2023