Monday  2-8 pm Berlin time

 14:00    Lecture 0  General Introduction / Overview of the course

 14:30   Lecture 1   Introduction to GWAS: Linkage disequilibrium and Linear Regression

 15:30   Lecture 2   GWAS: case studies / examples from literature

 16:30  break

 17:00    Lab 2 - part 1  basic Linux and R

 18:00   Lab 2 - part 2  Practicalities and set-up (server, github repo, conda envs, etc) and description of datasets

 18:30   Lab 2 - part 3  basic Linux and R

 19:00   Lab 3     GWAS: basic models

 19:30   Lab 3 (demonstration)GWAS: basic models (linear and logistic regression, population structure, etc.)

             
 Tuesday 2-8 pm Berlin time

 14:00    Lecture 4     EDA: theory

 14:30    Lab 4     EDA practice

 15:00    Lecture 5     data preprocessing: theory

 16:00    Lab 5    data preprocessing: practice

 17:00     break

 17:30    Lecture 6 Imputation of missing genotypes: theory

 18:30    Lab 6 - part 1  practical session on imputation (Beagle)

 19:30    Lab 7 (demonstration) KNNI imputation

             
 Wednesday   2-8 pm Berlin time

 14:00   Lecture 7  GWAS, the full model (all SNPs)

 15:30   Lab 9 (demonstration)   a few steps in the past (GenABEL)

 16:30   Lab 10    GWAS: the stand-alone script(s) for the full model

 17:00 break

 17:30   Lecture 8  GWAS: experimental design and statistical power

 18:30   Lecture 9  The multiple testing issue?

 19:30   Lab 10  revising the steps involved in GWAS
 

 Thursday 2-8 pm Berlin time

 14:00    Lecture 10 Bioinformatics pipelines: a super-elementary introduction

 15:00    Lab 11    Building a pipeline with Snakemake

 15:30    Lab 12    The GWAS pipeline for continuous phenotype

 16:00   Lab 13    The GWAS pipeline for binary phenotype

 16:30     break

 17:00    Lab 14  Introducing the exercise (+ light touch on RMarkdown)

 17:30    Collaborative exercise  Let’s build our own GWAS pipeline on new data

 19:00    Discussion  Q&A on  building pipelines for GWAS


 Friday 2-8 pm Berlin time

 14:00   Lecture 11  GWAS models for categorical traits (a primer)

 15:00   Lecture 12  GWAS models for longitudinal data (a primer)

 16:00: break

 16:30   Lecture 13  A light touch on post-GWAS analysis

 17:30   Lecture 14  A glimpse on ROH-based alternative

 18:00   Kahoot quiz  on what we learned about GWAS!

 19:00:  Wrap-up discussion  on GWAS