As genomics reshapes the future of medicine, the ability to analyse and interpret large-scale genetic data has become critical for health researchers and data scientists. The Statistical Genetics & Pharmacogenomics module offers a comprehensive introduction to statistical methods in genetics and pharmacogenomics—equipping students with the tools needed to uncover how genetic variation influences disease risk and treatment response.
Through a blend of weekly lectures, intensive computer practicals, and expert guest seminars, students will develop key skills in working with genome-wide data. You will learn about genotype quality control, imputation of missing variants, detection of population structure, and methods for testing associations between both common and rare genetic variants and a wide range of outcome types. From binary disease states to time-to-event outcomes, the focus is on applying the right statistical models to real-world genetic questions.
The module assumes no prior experience with genomic data. You will begin by learning core genetic terminology and data formats, and gain hands-on experience using powerful analysis tools within a high-performance computing environment. Research-connected teaching ensures relevance to current scientific practice, and the use of authentic datasets helps to build your confidence and digital fluency.
Assessment is entirely coursework-based and mirrors real-world analysis tasks. Assessment 1 (25%) involves quality control of a genetic dataset, requiring students to report exclusions, plots, and code. Assessment 2 (75%) challenges students to test genetic associations using a range of analytical methods, interpreting their results in the context of disease or drug response outcomes.
Whether you are aiming for a career in biomedical research, statistical genetics, or the pharmaceutical industry, this module provides the foundation needed to engage with the genomic data revolution.