25th Summer Institute in Statistical Genetics (SISG)

Module 14: Association Mapping: GWAS and Sequencing Data

Mon, July 27 to Wed, July 29

Module dates/times: Monday, July 27; Tuesday, July 28, and Wednesday, July 29. Live sessions will start no earlier than 8 a.m. Pacific and end no later than 2:30 p.m. Pacific, except for Wednesdays. For modules that end on Wednesday, live sessions will end by 11 a.m. Pacific. For modules that start on Wednesday, live sessions will begin no earlier than 11:30 a.m.

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 12 and 16.

Access 2019 course materials.

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.