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
- Data loading in R / python / plink
- Exploration of genotype data, biases, quality filtering
- First example GWAS results: human & Arabidopsis
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
- 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.
13:30-15:00 Introduction to linear models II
- Multi-locus models
- LASSO penalized models
- Illustration of complex genetic models, involving multiple SNPs.
- LASSO versus single-locus linear models
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.
- Example GWAS on gene expression, stress/non-stress condition.