Gene-based tests of association.

Genome-wide association studies (GWAS) are now used routinely to identify SNPs associated with complex human phenotypes. In several cases, multiple variants within a gene contribute independently to disease risk. Here we introduce a novel Gene-Wide Significance (GWiS) test that uses greedy Bayesian...

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Autores principales:	Hailiang Huang, Pritam Chanda, Alvaro Alonso, Joel S Bader, Dan E Arking
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2011
Materias:	Genetics QH426-470
Acceso en línea:	https://doaj.org/article/54690885618f4c6b97de466b5a371a4b
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id	oai:doaj.org-article:54690885618f4c6b97de466b5a371a4b
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spelling	oai:doaj.org-article:54690885618f4c6b97de466b5a371a4b2021-11-18T06:17:11ZGene-based tests of association.1553-73901553-740410.1371/journal.pgen.1002177https://doaj.org/article/54690885618f4c6b97de466b5a371a4b2011-07-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/21829371/pdf/?tool=EBIhttps://doaj.org/toc/1553-7390https://doaj.org/toc/1553-7404Genome-wide association studies (GWAS) are now used routinely to identify SNPs associated with complex human phenotypes. In several cases, multiple variants within a gene contribute independently to disease risk. Here we introduce a novel Gene-Wide Significance (GWiS) test that uses greedy Bayesian model selection to identify the independent effects within a gene, which are combined to generate a stronger statistical signal. Permutation tests provide p-values that correct for the number of independent tests genome-wide and within each genetic locus. When applied to a dataset comprising 2.5 million SNPs in up to 8,000 individuals measured for various electrocardiography (ECG) parameters, this method identifies more validated associations than conventional GWAS approaches. The method also provides, for the first time, systematic assessments of the number of independent effects within a gene and the fraction of disease-associated genes housing multiple independent effects, observed at 35%-50% of loci in our study. This method can be generalized to other study designs, retains power for low-frequency alleles, and provides gene-based p-values that are directly compatible for pathway-based meta-analysis.Hailiang HuangPritam ChandaAlvaro AlonsoJoel S BaderDan E ArkingPublic Library of Science (PLoS)articleGeneticsQH426-470ENPLoS Genetics, Vol 7, Iss 7, p e1002177 (2011)
institution	DOAJ
collection	DOAJ
language	EN
topic	Genetics QH426-470
spellingShingle	Genetics QH426-470 Hailiang Huang Pritam Chanda Alvaro Alonso Joel S Bader Dan E Arking Gene-based tests of association.
description	Genome-wide association studies (GWAS) are now used routinely to identify SNPs associated with complex human phenotypes. In several cases, multiple variants within a gene contribute independently to disease risk. Here we introduce a novel Gene-Wide Significance (GWiS) test that uses greedy Bayesian model selection to identify the independent effects within a gene, which are combined to generate a stronger statistical signal. Permutation tests provide p-values that correct for the number of independent tests genome-wide and within each genetic locus. When applied to a dataset comprising 2.5 million SNPs in up to 8,000 individuals measured for various electrocardiography (ECG) parameters, this method identifies more validated associations than conventional GWAS approaches. The method also provides, for the first time, systematic assessments of the number of independent effects within a gene and the fraction of disease-associated genes housing multiple independent effects, observed at 35%-50% of loci in our study. This method can be generalized to other study designs, retains power for low-frequency alleles, and provides gene-based p-values that are directly compatible for pathway-based meta-analysis.
format	article
author	Hailiang Huang Pritam Chanda Alvaro Alonso Joel S Bader Dan E Arking
author_facet	Hailiang Huang Pritam Chanda Alvaro Alonso Joel S Bader Dan E Arking
author_sort	Hailiang Huang
title	Gene-based tests of association.
title_short	Gene-based tests of association.
title_full	Gene-based tests of association.
title_fullStr	Gene-based tests of association.
title_full_unstemmed	Gene-based tests of association.
title_sort	gene-based tests of association.
publisher	Public Library of Science (PLoS)
publishDate	2011
url	https://doaj.org/article/54690885618f4c6b97de466b5a371a4b
work_keys_str_mv	AT hailianghuang genebasedtestsofassociation AT pritamchanda genebasedtestsofassociation AT alvaroalonso genebasedtestsofassociation AT joelsbader genebasedtestsofassociation AT danearking genebasedtestsofassociation
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Gene-based tests of association.

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