In this short workshop, we will use popular software tools such as PLINK and GEMMA to generate interesting biological insights from large-scale genetic data. In particular, we will conduct a genome-wide association analysis (GWAS) to identify genetic contributors to physiological traits in mice. (We cannot use human data in this workshop due to data sharing restrictions.) This workshop is mainly intended to develop practical computing skills for researchers working with genetic data—concepts such as “genotype” and “genetic variant” will not be explained. This will be a hands-on workshop, and we will do “live coding” throughout.

Objectives: Attendees will: (1) work through the basic steps of a genome-wide association analysis (GWAS); (2) understand how phenotype and genotype data from a GWAS are encoded in computer files; (3) learn about the benefits and complications of using linear-mixed models (LMMs) for GWAS; (4) use R and command-line tools to inspect and prepare the GWAS data for analysis; (5) use PLINK and GEMMA to implement a “genome-wide” association analysis; (6) use R to visualize and interpret the results of an association analysis; and (7) learn through “live coding.”

Level: Intermediate.

Duration: 2 hours

Prerequisites: This hands-on workshop assumes participants are already familiar with R and a UNIX-like shell environment. An RCC user account is recommended, but not required. (Note that guest access to the RCC cluster will not be available if the tutorial is done remotely.) All participants must bring a laptop with a Mac, Linux, or Windows operating system that they have administrative privileges on.

Github repository: https://github.com/rcc-uchicago/genetic-data-analysis-2

Analysis of Genetic Data 2: Mapping Genome-wide Associations