Introductory description

The module runs in Term 2 and will introduce students to probabilistic models and statistical methods used in the analysis of genetic data.

This module is available for students on a course where it is a listed option and as an Unusual Option to students who have completed the prerequisite modules.

Pre-requisites:
Statistics Students: ST218 Mathematical Statistics A AND ST219 Mathematical Statistics B
Non-Statistics Students: ST220 Introduction to Mathematical Statistics

Module web page

Module aims

Outline syllabus

This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.

Discrete-time reproductive models - Wright-Fisher, Moran. Coalescent limit. Properties of the standard coalescent, models of mutation, summary statistics and tests for neutrality, e.g. Tajima's D. Hardy-Weinberg laws, recombination, linkage mapping, linkage disequilibrium, association testing, an overview of genome-wide association studies.

Learning outcomes

By the end of the module, students should be able to:

• Describe simple discrete-time reproductive models and use them to derive the standard coalescent model
• Apply the predictions of coalescent theory to test for departures from neutrality, using simple datasets of DNA sequence variation
• Use simple datasets to evaluate the evidence for associations between genetic loci and Mendelian diseases
• Discuss the practical issues involved in scaling up association testing to whole genomes.

Indicative reading list

View reading list on Talis Aspire

Subject specific skills

Formulation of models for population genetic data, model-based predictions of genetic diversity, parameter estimation and hypothesis testing from DNA sequence data, genome-wide association studies

Transferable skills

Inference from data, assessment of uncertainty, evaluation of evidence, academic writing, computation