Monday 23^rd – Classes from 09:30 to 17:30

 

 Module 1 – – Overview

 The purpose of this module is provide a grand view of genetic dissection of complex traits as well as the technological development which lead to GWAS. It will also set stage for later parts of the workshop.

 

Lecture 1

 

 Self-introduction

Workshop outlines

The roadmap to GWAS -- Background, study designs, implementations

GWAS catalog

 

• Linux

A rich variety of open-source software is available for system administration, database management, Internet facility and development environment including system-level commands and utilities to enable powerful high-level programming languages such as C/C++/Fortran/Python are readily available. R is built on these.

• R for reproducible research

A combination of data management, statistical analysis, graphics, programming in a unified environment, it enjoys ever-growing user-base and facilities.

 

 

Lab 1

• Linux

• R

 

Exercises

• TASKS

• Suggested reading

Khoury et al. (1993), Lange (2002), Lander & Schork (1994), Thomas (2004), Ziegler et al. (2010).

 

 

 

 

Tuesday 24^th – Classes from 09:30 to 17:30

 

Module 2 – – Elements of genetic association

The purpose of this module is to get into the basic considerations of the genetic association studies. At end of the module, you will be able to conduct the relevant analyses.

 

Lecture 2

 

Chromosomes, DNA, QC, alleles, genotypes, HWE, mode of inheritance, haplotypes and linkage disequilibrium, GxG and GxE interactions

 

Phenotype: QC, transformation

 

Study designs: case-control, case-cohort, family

 

Association models: linear, logistic, Cox regression models; R^2, AUC, Cstat

 

Meta-analysis: fixed and random effects models

 

Missing data models

 

Population stratification and genomic controls

 

 

Lab 2

• Linux

• R packages

 

 Exercises

• TASKS

basic association testing, haplotype analysis and pedigree operations.

 

 

 

Wednesday 25^th – Classes from 09:30 to 17:30

 Module 3  – –GWAS

This module focuses on main analyses for GWAS.

 

Lecture 3

 

gene chips, HapMap, 1000 genomes project

 

QC-HWE, call rates, MAF

 

Genotype imputation, imputation quality

 

Multiple testing, FDR, q-value

 

Discovery, replication studies

 

Report of results and GSEA

 

Prediction

 

Lab 3

• Linux

• R and Bioconductor packages

 

 

Exercises

• TASKS

 

 

Thursday 26^th – Classes from 09:30 to 17:30

 Module 4–Advanced topics

This module covers several areas of GWAS in more details.

 

Lecture 4

 

Rare variants

 

Longitudinal data

 

Polygenic modelling

 

Bayesian methods

 

Machine learning

 

 

Lab 4

• Linux

• R
  

 

Excercises

• TASKS

rare variant analysis, logintudinal analysis, polygeneic modeling

 

 

 

Friday 27^th – Classes from 09:30 to 17:30

Module 5 --Additional topics

The module will look further into several other areas of research in GWAS, to be followed by some case studies.

 

Lecture 5

 

Conditional/joint analysis

 

Mendelian randomization

 

Microarray, methylation, TWAS

 

Case studies

1.          A SpiroMeta call for HRC imputation data contribution
2.          A GLGC/GIANT call for rare variant analysis.
3.          A CHARGE call call for gene-lifestyle interaction analysis.