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.

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