Gene Expression Quantitative Trait (eQTL) Mapping

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General Information

Analyzing genome variants through cell or tissue gene expression is known as expression quantitative trait locus (eQTL) analysis. eQTL analysis identifies loci that explain variation in transcript abundance. Specifically, genetic variants underlying eQTL explain variation in gene expression levels. eQTL analysis can reveal the architecture of quantitative traits, connect DNA sequence variation to phenotypic variation, and shed light on transcriptional regulation and regulatory variation. Traditional analytic techniques like linkage and association mapping can be applied to thousands of gene expression traits (transcripts) in eQTL analysis, such that gene expression can be mapped in much the same way as a physiological phenotype like blood pressure or heart rate. Joining gene expression and physiological phenotypes with genetic variation can uncover genes with variants affecting disease phenotypes. This lesson teaches eQTL analysis using qtl2, a R package for analyzing quantitative phenotypes and genetic data from complex crosses like the Diversity Outbred (DO). The course includes a published analytical workflow from a study of DO mice.

Who: The course is aimed at researchers who want to employ eQTL analysis to identify, quantify, and characterize expression quantitative trait loci. Prerequisites: Some R programming skills are required for successful participation. If you can manipulate R data structures (e.g. lists, matrices, data frames) you are ready for this course. You will also need to know QTL mapping with qtl2, which you can learn from from the Quantitative Trait Mapping lesson and from Karl Broman'sqtl2 user guide and other documentation. Knowledge of genetics and statistics will also help you gain the most from this course. To ensure that all participants receive the support that they need during training, remote participation will not be made available. You must attend in person and on-site.

Where: 600 Main Street, Bar Harbor, Maine. Get directions with OpenStreetMap or Google Maps.

When: Oct 7, 2022. Add to your Google Calendar.

Requirements: Participants must bring a laptop with a Mac, Linux, or Windows operating system (not a tablet, Chromebook, etc.). They should have a few specific software packages installed (listed below).

Accessibility: We are committed to making this workshop accessible to everybody. For workshops at a physical location, the workshop organizers have checked that:

• The room is wheelchair / scooter accessible.
• Accessible restrooms are available.

Materials will be provided in advance of the workshop and large-print handouts are available if needed by notifying the organizers in advance. If we can help making learning easier for you (e.g. sign-language interpreters, lactation facilities) please get in touch (using contact details below) and we will attempt to provide them.

Contact: Please email susan.mcclatchy@jax.org for more information.

Roles: To learn more about the roles at the workshop (who will be doing what), refer to our Workshop FAQ.

Collaborative Notes

We will use this collaborative document for chatting, taking notes, and sharing URLs and bits of code.

Surveys

Please be sure to complete these surveys before and after the workshop.

Pre-workshop Survey

Post-workshop Survey

Schedule

Setup

To participate in a workshop, you will need access to software as described below. In addition, you will need an up-to-date web browser.

We maintain a list of common issues that occur during installation as a reference for instructors that may be useful on the Configuration Problems and Solutions wiki page.

qtl2

The qtl2 package contains code for haplotype reconstruction, QTL mapping and plotting.

Install R/qtl2 from https://cran.r-project.org/:

install.packages("qtl2")

Data files and project organization

1. Make a new folder in your Desktop called mapping. Move into this new folder.

2. Create a data folder to hold the data, a scripts folder to house your scripts, and a results folder to hold results.

Alternatively, you can use the R console to run the following commands for steps 1 and 2.

setwd("~/Desktop")
dir.create("./mapping")
setwd("~/Desktop/mapping")
dir.create("./data")
dir.create("./scripts")
dir.create("./results")

1. Please download the following large files before the workshop, and place them in your data folder. You can download the files from the URLs below and move the files the same way that you would for downloading and moving any other kind of data.

• cc_variants.sqlite doi:10.6084/m9.figshare.5280229.v2, variants in the Collaborative Cross founders (3 GB)
• mouse_genes.sqlite doi:10.6084/m9.figshare.5280238.v4 full set of mouse gene annotations (677 MB)
• mouse_genes_mgi.sqlite doi:10.6084/m9.figshare.5286019.v5 just the MGI mouse gene annotations (11 MB)
• DO QTL data Dan’s DO data from the benzene study

Alternatively, you can copy and paste the following into the R console to download the data.

setwd("~/Desktop/mapping")
download.file("https://ndownloader.figshare.com/files/9746485", "./data/cc_variants.sqlite")
download.file("https://ndownloader.figshare.com/files/9746458", "./data/mouse_genes.sqlite")
download.file("https://ndownloader.figshare.com/files/9746452", "./data/mouse_genes_mgi.sqlite")
download.file("ftp://ftp.jax.org/dgatti/qtl2_workshop/qtl2_demo.Rdata", "./data/qtl2_demo.Rdata")

You will need these for the final lesson episodes on SNP association mapping and QTL analysis in Diversity Outbred mice.