Molecular Epidemiology

This 3-day course will be online and will consist of live seminars and practical sessions. Practical sessions will use the R programming language via Posit Cloud. Attendees do not need to install R on their computers in advance of the course, and complete scripts are provided for those who are less familiar with R.

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 Hannah Elliott is an epigenetic epidemiologist who has been working with high throughput molecular data for over a decade. Her interests are molecular mechanisms of disease (particularly cardio-metabolic disease) and increasing population diversity in molecular epidemiology studies.

Dr Neil Goulding has a background in mathematics and statistics and has been working in epidemiology for 7 years on projects looking at metabolomic and proteomic associations with various health outcomes.

Dr Nancy McBride is an epidemiologist interested in using omics data to improve the prediction and understanding of adverse pregnancy outcomes.

Dr Rebecca Richmond (she/her) is a molecular epidemiologist whose research aims to 1) highlight the relative importance and inter-relationships of health behaviours for prioritization in disease prevention strategies, particularly cancer, and 2) to identify molecular pathways which could serve as therapeutic targets for intervention.  

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 Kaitlin Wade (she/her) is an epidemiologist interested in integrating human genetics with population health data to understand the causal role played by the human gut microbiome and various health outcomes.

Dr Sarah Watkins is an epigenetic epidemiologist interested in how the environment we live in affects our biology and risk of disease.

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.

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 Ultra) 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 5 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 2026.

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

“Each lecture was delivered at a great pace and questions were welcomed throughout. The practicals went well and it was nice to have the option to both code along or just watch if uncertain." - Course feedback, April 2026

“I would recommend it to anyone post MsC level - great overview refresher of all the omics with crucial multi omic and mediation methods to compliment it, great for inspiring young scientists like myself." - Course feedback, April 2026

“Lectures and content were very useful -allowing me to get a better understanding of different omics and concepts. There was a good mix of background, methods and analysis. I enjoyed the praticals, helped consolidate concepts and learn about analysis methods. I think the walk through format worked well. The grant review session was quite fun and informative too. Plenty of chances for questions." - Course feedback, April 2026

“Overall, this was a very high-quality short course. The programme was well organized, academically rigorous, and highly relevant for researchers working with complex biomedical data. I appreciated the balance between conceptual teaching, methodological depth, and practical application." - Course feedback, April 2026

“The course was excellent overall. The content was highly detailed, up to date, and pitched at a high academic level. The data examples, papers, and supporting materials were particularly strong and helped connect the theoretical concepts with practical research applications." - Course feedback, April 2026

“The materials were presented in an easily accessible way, the instructors were really helpful, clear, and knowledgeable, so that it was easy to follow the content. Making available the recordings is extremely helpful as some of the subject matter is quite complex, so revisiting it will be helpful." - Course feedback, April 2026

“The sessions were very informative and the instructors were very responsive. I could see all the hard work put in. I am grateful for the access to a ton of study material, recordings and r-code." - Course feedback, April 2026

“This module provides a clear overview of omics data and how it’s applied in practice. The practical sessions are especially helpful for understanding the statistical methods used across different molecular biomarkers." - Course feedback, April 2026

“This was a very comprehensive course, starting from the basics, covering data analysis and also critiquing grants. It was very useful specially for someone learning molecular epi for the first time." - Course feedback, April 2026