Literature DB >> 17384015

GenABEL: an R library for genome-wide association analysis.

Yurii S Aulchenko1, Stephan Ripke, Aaron Isaacs, Cornelia M van Duijn.   

Abstract

UNLABELLED: Here we describe an R library for genome-wide association (GWA) analysis. It implements effective storage and handling of GWA data, fast procedures for genetic data quality control, testing of association of single nucleotide polymorphisms with binary or quantitative traits, visualization of results and also provides easy interfaces to standard statistical and graphical procedures implemented in base R and special R libraries for genetic analysis. We evaluated GenABEL using one simulated and two real data sets. We conclude that GenABEL enables the analysis of GWA data on desktop computers. AVAILABILITY: http://cran.r-project.org.

Mesh:

Year:  2007        PMID: 17384015     DOI: 10.1093/bioinformatics/btm108

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  893 in total

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Journal:  Am J Hum Genet       Date:  2012-06-14       Impact factor: 11.025

3.  Genome-wide association study identifies novel loci for plasma levels of protein C: the ARIC study.

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Review 4.  Genome-wide association studies--data generation, storage, interpretation, and bioinformatics.

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Review 5.  Overview of techniques to account for confounding due to population stratification and cryptic relatedness in genomic data association analyses.

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Journal:  Heredity (Edinb)       Date:  2010-07-14       Impact factor: 3.821

6.  GCTA: a tool for genome-wide complex trait analysis.

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8.  Identification of additional loci associated with antibody response to Mycobacterium avium ssp. Paratuberculosis in cattle by GSEA-SNP analysis.

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9.  Discordant association of the CREBRF rs373863828 A allele with increased BMI and protection from type 2 diabetes in Māori and Pacific (Polynesian) people living in Aotearoa/New Zealand.

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10.  Single-nucleotide polymorphisms of stemness genes predicted to regulate RNA splicing, microRNA and oncogenic signaling are associated with prostate cancer survival.

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