Analysis and Interpretation of Genome-wide Association Studie

Graduate Programme in Public Health

Aim and contents
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. During the past decades, genetic association analysis has shifted from single variant and candidate gene studies to genome-wide association studies (GWAS) on millions of single nucleotide polymorphisms (SNPs) and genetic variants (including structural variants) across the genome, enabling comprehensive assessment of the common and rare genetic variations in gene regions or the entire genome. The current popularity of GWAS and the reduced cost for SNP genotyping and whole genome sequencing (WGS) are producing large scale genomic big data for statistical analysis and bioinformatics interpretation. Meanwhile statistical methods and computational packages have been developed to cope with analytical challenges.

Objectives: The major goal of this course will be to introduce participants to approaches for the design and analysis of genetic association data, with a particular focus on any issues that arise in the analysis and interpretation 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 packages. 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 modeling (e.g. correction for multiple testing, principal component analysis, polygenic risk score analysis and prediction model building) and visualization of research findings (e.g., annotation, QQ plots, Manhattan plots), followed by bioinformatics interpretation of GWAS results. Cutting edge GWAS methods will also be presented as lectures. 

Course leaders: 
Associate Professor Jonas Mengel-From

Max number of Phd students: 30 students

ECTS : 3

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 5146,02,-