Analysis of genetic association data

Background:
The development of efficient high-throughput genotyping platforms has radically changed the approach human geneticists take to identify the genetic variants that are associated with common inherited disease and quantitative traits. Previously, in looking for the genetic predictors of disease risk, researchers would target a single polymorphism in a single candidate gene. Currently, researchers comprehensively assess the common genetic variation that exists in a candidate gene region or the entire genome by genotyping multiple single nucleotide polymorphisms (SNPs, i.e., single DNA base variants). The shift from a focus on single to multiple polymorphisms brings with it a wide range of research design and analysis questions: How does one optimally design an association study? What are the most powerful methods for combining information across markers? How does one deal with the problem of multiple testing?

Objectives:
The major goal of this course will be to introduce participants to approaches for the design and analysis of genetic association data involving multiple SNP markers, with a particular focus on any issues that arise in the analysis of Genome Wide Association Study (GWAS) data. The course will involve both didactic presentations of the methods and practical assignments that involve application of the methods presented. The course will primarily use the software program plink (https://www.cog-genomics.org/plink2) as well as other freely available software. The course will be designed around the stages of a genetic association study from study design (e.g., power analysis), to data clean-up (e.g., basic quality control procedures, testing Hardy-Weinberg, SNP imputation) through to analysis and statistical modelling (e.g. correction for multiple testing, principal component analysis, polygenic risk score analysis) and interpretation and visualization of research findings (e.g., annotation, QQ plots, Manhattan plots).

Instructors:

Matt McGue Lene Christiansen
Guest Professor Professor
Department of Epidemiology Department of Epidemiology
University of Southern Denmark University of Southern Denmark

Qihua Tan Marianne Nygaard
Professor Post-doctoral Researcher
Department of Epidemiology Department of Epidemiology
University of Southern Denmark University of Southern Denmark

Prerequisites:
The course is designed principally for those with limited backgrounds in genetic epidemiology, the analysis of association data, and GWAS. It is meant to be primarily introductory. However, optimally course participants will have: 1) a basic understanding of introductory human genetics (e.g., gene structure and function, segregation, recombination, analysis of familial resemblance); 2) applied statistics through basic multivariate analysis; and 3) elementary data processing skills. Also, course participants will be expected to have their own laptop computers, which they will use to complete lab assignments.
                                          

ECTS-points: 3

Max number of participants: 20

Course Fee:
The course is free of charge for PhD students enrolled in Universities that have joined the "Open market agreement". For other participants there is a course fee of DKK 6829,-

PhD programme: PhD Programme in Public Health