Tutors

Dr Doug Speed and Professor David Balding

Cost: Free, but advance Registration is REQUIRED

Register here

 
Background

In recent years, there has been a massive increase in the amount of genetic data available. Through resources such as the UK and China-Kadoorie Biobanks, researchers can now access data for thousands of phenotypes and hundreds of thousands of individuals. There has also been great progress developing genome-wide statistical tools for detecting causal variants, constructing prediction models and better understanding the genetic architecture of complex traits.

Course outline

We will cover a wide range of GWAS analyses. We will begin with basic analyses, such as quality control, single-SNP regression and classical polygenic risk scores. However, the main focus will be more advanced analysis. These will include both methods that use individual-level data (e.g., mixed-model association analysis, G-REML, gene-based association tests, using tools such as Bolt-LMM, GCTA and LDAK), and methods that use summary statistics (e.g., heritability and genetic correlation analyses, using tools such as LDSC and SumHer). We will explain the principals underlying these methods, highlighting both their common elements and differences in underlying modelling assumptions.

The course will compose of two lectures and two practicals. The practicals will provide step-by-step details for analysing genetic data, starting either with individual-level data (e.g., PLINK files or the output from IMPUTE2) or summary statistics (p-values from a GWAS). There will be a selection of worked examples; to take part in the practicals, participants should bring a laptop (ideally running either MAC or LINUX).

Prerequisites

This course is designed for researchers who have at least some experience of GWAS. Participants should have a basic understanding of statistics, and would ideally be familiar with the idea of a shrinkage regression. Knowledge of SNP genotypes and linkage disequilibrium will be assumed. Computer scripts and output will be discussed that assume some familiarity with scientific computing using Linux. Experience with PLINK would be helpful but is not essential. 

Provisional timetable

09:30 - 12:00: Lecture 1 followed by Practical 1
Introduction to analysing GWAS data, including quality control, single-SNP analysis, polygenic risk scores, mixed-model analysis and gene-based analysis

12:00 - 13:00: Lunch break - participants can bring a lunchbox, or purchase food at the cafe next door

13:00 - 15.30: Lecture 2 followed by Practical 2
Estimating heritability, confounding bias, bivariate correlations and enrichment of functional categories

Tea, coffee, juice and biscuits will be provided throughout the day

Contact

Dr Doug Speed, AIAS Fellow, Aarhus Institute of Advanced Studies, Aarhus University

E-mail: doug@aias.au.dk

Høegh-Guldbergs Gade 6B
8000 Aarhus C
Denmark