Dataset Information

Cgmisc: enhanced genome-wide association analyses and visualization.

ABSTRACT:

Unlabelled

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/cgmisc

Contact

marcin.kierczak@imbim.uu.se.

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Kierczak M

PROVIDER: S-EPMC4653382 | biostudies-literature | 2015 Dec

REPOSITORIES: biostudies-literature

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Publications

cgmisc: enhanced genome-wide association analyses and visualization.

Kierczak Marcin M Jabłońska Jagoda J Forsberg Simon K G SK Bianchi Matteo M Tengvall Katarina K Pettersson Mats M Scholz Veronika V Meadows Jennifer R S JR Jern Patric P Carlborg Örjan Ö Lindblad-Toh Kerstin K

Bioinformatics (Oxford, England) 20150806 23

<h4>Unlabelled</h4>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 ...[more]

PMID: 26249815

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