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.

94% of attendees recommend this course*.
*Attendee feedback from 2024.

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

"I liked that the materials were there before the course started so that you could prepare in advance, go through the materials and prepare questions" - Course feedback, May 2024

"The course tutors were engaging and very knowledgeable about the subject area. The breakout groups were interesting and there was plenty of discussion. There was a good balance between methods, data collection and application" - Course feedback, May 2024

"Covered a range of 'omics and made them all interesting and relevant. Good mix of lecture and interactive sessions" - Course feedback, May 2024

"Moderators were engaging and the pace of the lectures was good. It was helpful to be able to review some of the materials before the lecture to help understanding" - Course feedback, May 2024

The grant review at the end was really helpful to consolidate what we learned over the past few days" - Course feedback, May 2024

"The lectures and the hands-on practical session were amazing. It is a very welcoming environment, and tutors ask questions" - Course feedback, May 2024

"Knowledgeable and passionate faculty. The platform was fit for purpose and the choice of sessions were well thought out" - Course feedback, May 2024

"The lectures were well delivered, practical sessions were brilliant" - Course feedback, May 2024

"Platform connection and access to material was easily and quickly accessible. The quality of the material and the broad scope of omic topic was very well presented" - Course feedback, May 2024

"A wonderful course! I highly recommend" - Course feedback, May 2024

"It has given me a really good overview of a topic that intimidated me. I fell that I have a much better understanding of how these methods can be used to interrogate the data and the strengths/weaknesses to various approaches. I am already using the ideas to work into my next funding application" - Course feedback, May 2024

"The MR lecture and multi-omics integration was really informative and helpful. I plan to use concepts from both of these in future work" - Course feedback, May 2024

"It was great to work through real-world examples of a paper and grants to increase critical appraisal skills in this area. I also learned a lot from small group and overall group discussions. The course will help me to interpret the literature on molecular epidemiology topics, and has better equipped me to engage more with molecular epi researchers which will encourage future research collaborations" - Course feedback, May 2024

"It's great to have such a practical course that really helps with grant writing" - Course feedback, May 2024