Gene Size Matters

Alexandra Mirina; Gil Atzmon; Kenny Ye; Aviv Bergman

doi:10.1371/journal.pone.0049093

Gene Size Matters

Alexandra Mirina, Gil Atzmon, Kenny Ye, Aviv Bergman

Research output: Contribution to journal › Article › peer-review

17 Scopus citations

Abstract

In this work we show that in genome-wide association studies (GWAS) there is a strong bias favoring of genes covered by larger numbers of SNPs. Thus, we state here that there is a need for correction for such bias when performing downstream gene-level analysis, e.g. pathway analysis and gene-set analysis. We investigate several methods of obtaining gene level statistical significance in GWAS, and compare their effectiveness in correcting such bias. We also propose a simple algorithm based on first order statistic that corrects such bias.

Original language	English (US)
Article number	e49093
Journal	PloS one
Volume	7
Issue number	11
DOIs	https://doi.org/10.1371/journal.pone.0049093
State	Published - Nov 9 2012

ASJC Scopus subject areas

General Biochemistry, Genetics and Molecular Biology
General Agricultural and Biological Sciences
General

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10.1371/journal.pone.0049093

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AB - In this work we show that in genome-wide association studies (GWAS) there is a strong bias favoring of genes covered by larger numbers of SNPs. Thus, we state here that there is a need for correction for such bias when performing downstream gene-level analysis, e.g. pathway analysis and gene-set analysis. We investigate several methods of obtaining gene level statistical significance in GWAS, and compare their effectiveness in correcting such bias. We also propose a simple algorithm based on first order statistic that corrects such bias.

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Gene Size Matters

Abstract

ASJC Scopus subject areas

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