From SNPs to genes: disease association at the gene level.

Interpreting Genome-Wide Association Studies (GWAS) at a gene level is an important step towards understanding the molecular processes that lead to disease. In order to incorporate prior biological knowledge such as pathways and protein interactions in the analysis of GWAS data it is necessary to de...

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Main Authors: Benjamin Lehne, Cathryn M Lewis, Thomas Schlitt
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2011-01-01
Series:PLoS ONE
Online Access:http://europepmc.org/articles/PMC3128073?pdf=render
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author Benjamin Lehne
Cathryn M Lewis
Thomas Schlitt
author_facet Benjamin Lehne
Cathryn M Lewis
Thomas Schlitt
author_sort Benjamin Lehne
collection DOAJ
description Interpreting Genome-Wide Association Studies (GWAS) at a gene level is an important step towards understanding the molecular processes that lead to disease. In order to incorporate prior biological knowledge such as pathways and protein interactions in the analysis of GWAS data it is necessary to derive one measure of association for each gene. We compare three different methods to obtain gene-wide test statistics from Single Nucleotide Polymorphism (SNP) based association data: choosing the test statistic from the most significant SNP; the mean test statistics of all SNPs; and the mean of the top quartile of all test statistics. We demonstrate that the gene-wide test statistics can be controlled for the number of SNPs within each gene and show that all three methods perform considerably better than expected by chance at identifying genes with confirmed associations. By applying each method to GWAS data for Crohn's Disease and Type 1 Diabetes we identified new potential disease genes.
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spelling doaj.art-8e3732e1b8804caea61b9af470f72fae2022-12-22T00:09:41ZengPublic Library of Science (PLoS)PLoS ONE1932-62032011-01-0166e2013310.1371/journal.pone.0020133From SNPs to genes: disease association at the gene level.Benjamin LehneCathryn M LewisThomas SchlittInterpreting Genome-Wide Association Studies (GWAS) at a gene level is an important step towards understanding the molecular processes that lead to disease. In order to incorporate prior biological knowledge such as pathways and protein interactions in the analysis of GWAS data it is necessary to derive one measure of association for each gene. We compare three different methods to obtain gene-wide test statistics from Single Nucleotide Polymorphism (SNP) based association data: choosing the test statistic from the most significant SNP; the mean test statistics of all SNPs; and the mean of the top quartile of all test statistics. We demonstrate that the gene-wide test statistics can be controlled for the number of SNPs within each gene and show that all three methods perform considerably better than expected by chance at identifying genes with confirmed associations. By applying each method to GWAS data for Crohn's Disease and Type 1 Diabetes we identified new potential disease genes.http://europepmc.org/articles/PMC3128073?pdf=render
spellingShingle Benjamin Lehne
Cathryn M Lewis
Thomas Schlitt
From SNPs to genes: disease association at the gene level.
PLoS ONE
title From SNPs to genes: disease association at the gene level.
title_full From SNPs to genes: disease association at the gene level.
title_fullStr From SNPs to genes: disease association at the gene level.
title_full_unstemmed From SNPs to genes: disease association at the gene level.
title_short From SNPs to genes: disease association at the gene level.
title_sort from snps to genes disease association at the gene level
url http://europepmc.org/articles/PMC3128073?pdf=render
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