introduction_to_gwas

Material for the Course “Introduction to genome-wide association studies (GWAS)”

Instructors: Filippo Biscarini, Oscar Gonzalez-Recio, Christian Werner

This course will introduce students, researchers and professionals to the steps needed to build an analysis pipeline for Genome-Wide Association Studies (GWAS). The course will describe all the necessary steps involved in a typical GWAS study, which will then be used to build a reusable and reproducible bioinformatics pipeline.

Each day the course will start at 14:00 and end at 20:00 (CET). As a general rule, we’ll have a longer break (30 minutes) at 16:00 and two shorter breaks (10-15 minutes) later on during the day (to be decided flexibly depending on the sessions).

Day 1

• Lecture 0 General Introduction / Overview of the Course [Filippo, Oscar, Christian]
  • General Introduction
  • GWAS Workflow (short)
• Lecture 1 GWAS Overview: Case Studies / Examples from Literature [Oscar]
  • GWAS Overview
• Lecture 2 Introduction to GWAS: Linkage Disequilibrium and Linear Regression [Oscar]
  • Introduction to GWAS
• Lab 1 (Demonstration) GWAS: Basic Models (Linear and Logistic Regression) [Oscar]
  • R code. Exercise on Simple Linear Regression
  • Rmarkdown Code. Exercise on Simple Logistic Regression
• Lab 2 - Description of Datasets [Christian]
  • Description of Datasets
• Course Manual
• GWAS Workflow

Day 2

• Lecture 3 The Multiple Testing Issue [Oscar]
  • Multiple Testing
  • R code. Exercise on multiple testing correction
• Lecture 4 Statistical Power, Population Stratification and Experimental Design [Oscar]
  • Statistical Power and Population Stratification
  • R code. Exercise on statistical power
• Lecture 5 Initial Data Analysis, Exploratory Data Analysis and Data Pre-Processing [Christian]
  • Brief Genotyping overview
  • IDA, EDA & Data Pre-Processing
• Lab 3 GWAS: a first simple exercise for you! [Christian, Filippo]
  • GWAS demonstration in R - script
  • GWAS demonstration in R - genotypes
  • GWAS demonstration in R - map
  • GWAS demonstration in R - phenotypes
  • GWAS exercise in R - genotypes
  • GWAS exercise in R - map
  • GWAS exercise in R - phenotypes

Day 3

• Lab 4 Data filtering and mean/median imputation in R [Filippo]
  • filter_genotype_data.R
  • mean_imputation.R
  • median_imputation.R
• Lab 5 GWAS: The Stand-Alone Script(s) for the Full Model [Filippo]
  • gwas_rrblup.R
  • gwas_statgengwas.R
  • gwas_sommer.R
• Lecture 6 KNN Imputation
  • KNN Imputation
• Lab 6 (Demonstration) KNNI Imputation [Filippo] [OPTIONAL]
  • knni_illustration.Rmd
  • [data_for_KNNI_illustration]
  • knni_tidymodels.R
  • [02_knni.sh] [support script]
  • [hamming.R] [support script]
  • [knni.R] [support script]
• Lecture 7 Working in the shell [Christian]
  • Linux and the Shell [OPTIONAL]
  • Common Data Types and Formats
• Lecture 8 Imputation of Missing Genotypes [Christian]
  • Imputation
• Lab 7 Imputation of Missing Genotypes using Beagle [Christian]

Day 4

• Lecture 9 Brief Intermission:
  • R code PCA & Population Structure
  • Imputed rice genotypes
• Lab 8 Revising the Steps involved in GWAS [Filippo]
  • slides
  • 1.get_data.sh
  • 2.step_filtering.sh
  • 3.step_imputation.sh
  • 4.gwas.sh
• Lab 9 Introducing the Exercise [Filippo]
  • Collaborative Exercise
• Collaborative Exercise: let’s build our own GWAS workflow on new data. Pig (Sus scrofa) data. [Filippo, Oscar, Christian]
  • Part 1: Individual/Group Break-Out Sessions to give it a try independetly
  • Part 2: Whole-Group Revision of the Exercise: step-by-step (1.get_data; 2.filter; 3.imputation; 4.GWAS)
  • exercise tips
• Bonus exercise [Optional] (Parus major data)

Day 5

• Lecture 10 A light Touch on Post-GWAS Analysis: Inferring Functionality [Oscar]
  • slides
  • R code. Exercise on R, and FUMA
• Lecture 11 GWAS Model Extensions and Applications: [Filippo, Christian, Oscar]
  • 12.1 GWAS Model Extensions_Dominance_and_other_genotype_Codifications
  • 12.2 GWAS Model Extensions_Polyploids
    • [R code GWASpoly (vignette)]
  • 12.3 GWAS Model Extensions_Trait_Types: categorical, longitudinal
    • slides
    • R code GWAS for categorical Traits
    • R code GWAS for categorical Traits - Examples
    • R code GWAS for longitudinal Traits
  • 12.4 GWAS Model Extensions Multi-Trait Multi-Locus models & software
    • slides
    • multiple-trait models
    • multiple-loci models
  • 12.5 A bioinformatic pipeline for GWAS
    • slides
  • 12.6 Additional software for GWAS
    • gemma
    • regenie
  • ROH-based and Resampling Methods as alternative approaches
  • Applications of GWAS: Mendelian Randomization
• Final Quiz on what we learned about GWAS! [Filippo, Oscar, Christian]
• Conclusions and Wrap-Up Discussion on GWAS [Filippo, Oscar, Christian]

Organization of the Code for the practical Sessions

1. the GWAS workflow in R
2. preparatory_steps: download and prepare the data
3. preprocessing: filter the data
4. imputation: imputing missing genotypes
5. gwas: run the GWAS models
6. power_and_significance: designing GWAS experiments
7. steps: identifying the individual steps involved in a GWAS study
8. pipeline: assembling the individual steps into a bioinformatics pipeline for GWAS
9. collaborative exercise: trying out what we learnt on new data