Abstract
Summary: High-throughput genotyping and sequencing technologies facilitate studies of complex genetic traits and provide new research opportunities. The increasing popularity of genome-wide association studies (GWAS) leads to the discovery of new associated loci and a better understanding of the genetic architecture underlying not only diseases, but also other monogenic and complex phenotypes. Several softwares are available for performing GWAS analyses, R environment being one of them.Results: We present cgmisc, an R package that enables enhanced data analysis and visualization of results from GWAS. The package contains several utilities and modules that complement and enhance the functionality of the existing software. It also provides several tools for advanced visualization of genomic data and utilizes the power of the R language to aid in preparation of publication-quality figures. Some of the package functions are specific for the domestic dog (Canis familiaris) data.Availability and implementation: The package is operating system-independent and is available from: https://github.com/cgmisc-team/cgmiscContact: marcin.kierczak@imbim.uu.seSupplementary information: Supplementary data are available at Bioinformatics online.
Highlights
High-throughput genotyping and sequencing has opened several new research opportunities to study complex genetic traits and genome-wide association studies (GWAS) is a popular way to analyse genotyping data from segregating populations
We developed a number of algorithms and solutions to several common GWAS tasks
A few functions in the package are tailored for working with data from the domestic dog (Canis familiaris) but are easy to adjust for analysing other species
Summary
High-throughput genotyping and sequencing has opened several new research opportunities to study complex genetic traits and genome-wide association studies (GWAS) is a popular way to analyse genotyping data from segregating populations. One advantage of the GenABEL package, that often makes it the software of choice, is that in addition to GWAS-specific functionalities, it provides access to the R (R Development Core Team, 2008) language and community-contributed packages. 0 3.15 8.7 14.26 21.66 29.06 36.46 43.87 51.27 58.67 66.07 73.47 80.88 88.28 Mbp present these and more functions in the form of the documented and supported R package cgmisc.
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