DOQTL: QTL Mapping for Diversity Outbred Mice

DOQTL: QTL Mapping for Diversity Outbred Mice

Description 

The Diversity Outbred (DO) is a heterogeneous stock derived from the same eight inbred founder strains: A/J, C57BL/6J, 129S1/SvImJ, NOD/ShiLtJ, NZO/HlLtJ, CAST/EiJ, PWK/PhJ, and WSB/EiJ. Analysis of DO mice involves several challenges and DOQTL is specialized software that aids in the analysis of DO mice.

DOQTL can:

1. Read in data from the Mouse Universal Genotyping Arrays (MUGA and MegaMUGA),

2. Reconstruct DO genomes in terms of the eight founder haplotypes,

3. Perform linkage mapping using the founder haplotype content,

4. Impute the founder variants onto DO genomes to perform association mapping.

DOQTL is written in the R language has several functions to create publication quality graphics.

We have tested DOQTL in one other model organism and with other mouse genotyping arrays. The analysis pipeline is designed to be flexible but we encourage you to contact us if you want to try another organism. We will make an effort to assist you in making DOQTL work for other MAGIC populations.

DOQTL is currently in beta-testing. If you find an issue, please contact Daniel Gatti.

Installation

There are several dependencies that must be installed before installation of DOQTL. Run the code below in R before installing DOQTL.

source("http://bioconductor.org/biocLite.R")
biocLite(c("annotate", "annotationTools", "biomaRt", "Biobase", "corpcor", "GenomicRanges", "hwriter", "MASS", "mclust", "org.Hs.eg.db", "org.Mm.eg.db", "QTLRel", "Rsamtools", "XML"))

Downloads

The binaries were compiled under R 3.0.2.

Source Code

Windows Binary

Mac Binary

Vignettes

Running the vignettes also requires the following data package: MUGAExampleData

Reading_MUGA_Data.pdf : A guide to reading in MUGA or MegaMUGA data.

Genotyping_DO_Mice.pdf: A guide to running the haplotype reconstruction pipeline.

QTL_Mapping_DO_Mice.pdf : A guide to QTL mapping using the DO haplotype reconstructions.

Probe specific quantile normalization of MUGA genotyping data