09:00-10:30 Welcome, Introduction to GWAS

The first session will cover the basic background of GWAS for non-experts.

• Study designs: GWAS versus linkage mapping.
• Preprocessing: imputation, quality control
• Introduction to basic statistics, p-values, multiple-testing correction

Practical component:

• Data loading in R / python / plink
• Exploration of genotype data, biases, quality filtering
• First example GWAS results: human & Arabidopsis

10:30-11:00 coffee
11:00-12:30 Introduction to linear models I

• Parameter inference in linear models
• Statistical testing with linear models
• Linear mixed models to correct for confounding variation
• Population structure

Practical component:

• Example GWAS using linear models. Comparison of F-test and alternatives.
• Evaluation of linear fits, pitfalls due to overfitting.
• Empirical evaluation of the importance of population structure correction.

12:30-13:30 lunch
13:30-15:00 Introduction to linear models II

• Multi-locus models
• LASSO penalized models

Practical component:

• Illustration of complex genetic models, involving multiple SNPs.
• LASSO versus single-locus linear models

15:00-15:30 coffee
15:30-17:00 GWAS for high-dimensional phenotypes

• Accounting for hidden confounding in high-dimensional studies.
• Joint testing of multiple related traits at once.

Practical component:

• Example GWAS on gene expression, stress/non-stress condition.