Registration for this module closes July 12. 

Live session timeframe (exact schedule with live sessions will be posted by module instructors prior to the start of the module): Monday: 8 a.m. – 2:30 p.m. Pacific (11 a.m. – 5:30 p.m. Eastern); Tuesday: 8 a.m. – 2:30 p.m. Pacific (11 a.m. – 5:30 p.m. Eastern); Wednesday: 8 a.m. – 11 a.m. Pacific (11 a.m. – 2 p.m. Eastern).

This module will provide students with the basic tools to carry out genetic association analysis within the context of genome wide association studies (GWAS) and next-generation sequencing studies with considerable emphasis on hands-on learning.

Topics covered include: case-control (disease) association testing; quantitative trait analysis; quality control processes in GWAS; multi-locus testing using gene and pathway information; population structure and ancestry inference; association testing in the presence of population structure and/or relatedness; gene-environment and gene-gene interactions; basic rare variant association analysis in sequencing studies; advanced rare variant methods; sequence kernel association tests (SKAT); meta analysis; design considerations; and other emerging topics.

An important component of this module is in-class software exercises which will provide students with hands-on experience analyzing real data using state-of-the-art analysis tools for GWAS and next generation sequencing data.

Assumes basic familiarity with R. Other public domain software that will be used includes PLINK.

Suggested pairing: Modules 14 or 15.

Learning Objectives: After attending this module, participants will be able to:

1. Perform SNP-based association testing with adjustment for covariates in R.
2. Run a GWAS with PLINK.
3. Create Manhattan plots and quantile-quantile plots in R from PLINK GWAS results.
4. Perform multi-loci association testing in PLINK using gene and pathway information.
5. Perform principal components analysis (PCA)  for population structure inference and correction in a GWAS.
6. Perform a linear mixed model (LMM) for GWAS with relatedness and/or population structure.
7. Test gene-gene and gene-environment interactions.
8. Run sequence kernel association tests and other advanced methods for rare variant association methods.
9. Perform a meta-analysis.