In this short workshop, we will use popular software tools such as 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, so please bring your laptop!

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) appreciate some of 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 GEMMA to implement a "genome-wide" association analysis; (6) use R to visualize and interpret the results of an association analysis; (7) learn through "live coding."

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. Guest access to the RCC cluster will be available in class to those with no RCC account. All participants must bring a laptop with a Mac, Linux, or Windows operating system that they have administrative privileges on.

• CFW_measures.txt contains measurements of 200 phenotypes collected from 2,117 CFW mice. These data accompany the Nature Genetics paper, "Genome-wide association of multiple complex traits in outbred mice by ultra-low-coverage sequencing," by Nicod et al. This file was downloaded from https://wp.cs.ucl.ac.uk/outbredmice.

• CFW_covariates.txt contains covariate data (e.g., sex, age, batch) recorded for 2,117 CFW mice. This file was downloaded from the same source as CFW_measures.txt.

• list_of_1934_mice_used_for_analysis.txt gives the ids of the 1,934 mice retained for the genome-wide association analyses in Nicod et al (2016). This list was obtained from file List_of_1934_mice_used_for_analysis.RData downloaded from http://mtweb.cs.ucl.ac.uk/dosages. The ids were adjusted to match the ids in the phenotype table.

• This workshop attempts to apply elements of the Software Carpentry approach. See also this article. Please also take a look at the Code of Conduct, and the license information.

• To generate PDFs of the slides from the R Markdown source, run make slides.pdf in the docs directory. For this to work, you will need to to install the rmarkdown package in R, as well as the packages used in slides.Rmd. For more details, see the Makefile.

These materials were developed by Peter Carbonetto at the University of Chicago. Thank you to Matthew Stephens for his support and guidance.