Large scale analysis of phenotype-pathway relationships based on GWAS results.

The widely used pathway-based approach for interpreting Genome Wide Association Studies (GWAS), assumes that since function is executed through the interactions of multiple genes, different perturbations of the same pathway would result in a similar phenotype. This assumption, however, was not syste...

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Autores principales:	Aharon Brodie, Oholi Tovia-Brodie, Yanay Ofran
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2014
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/ae9e9a719c58409da7081a85b7ae2ea4
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spelling	oai:doaj.org-article:ae9e9a719c58409da7081a85b7ae2ea42021-11-25T06:09:08ZLarge scale analysis of phenotype-pathway relationships based on GWAS results.1932-620310.1371/journal.pone.0100887https://doaj.org/article/ae9e9a719c58409da7081a85b7ae2ea42014-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/25007247/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203The widely used pathway-based approach for interpreting Genome Wide Association Studies (GWAS), assumes that since function is executed through the interactions of multiple genes, different perturbations of the same pathway would result in a similar phenotype. This assumption, however, was not systemically assessed on a large scale. To determine whether SNPs associated with a given complex phenotype affect the same pathways more than expected by chance, we analyzed 368 phenotypes that were studied in >5000 GWAS. We found 216 significant phenotype-pathway associations between 70 of the phenotypes we analyzed and known pathways. We also report 391 strong phenotype-phenotype associations between phenotypes that are affected by the same pathways. While some of these associations confirm previously reported connections, others are new and could shed light on the molecular basis of these diseases. Our findings confirm that phenotype-associated SNPs cluster into pathways much more than expected by chance. However, this is true for <20% (70/368) of the phenotypes. Different types of phenotypes show markedly different tendencies: Virtually all autoimmune phenotypes show strong clustering of SNPs into pathways, while most cancers and metabolic conditions, and all electrophysiological phenotypes, could not be significantly associated with any pathway despite being significantly associated with a large number of SNPs. While this may be due to missing data, it may also suggest that these phenotypes could result only from perturbations of specific genes and not from other perturbations of the same pathway. Further analysis of pathway-associated versus gene-associated phenotypes is, therefore, needed in order to understand disease etiology and in order to promote better drug target selection.Aharon BrodieOholi Tovia-BrodieYanay OfranPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 9, Iss 7, p e100887 (2014)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Aharon Brodie Oholi Tovia-Brodie Yanay Ofran Large scale analysis of phenotype-pathway relationships based on GWAS results.
description	The widely used pathway-based approach for interpreting Genome Wide Association Studies (GWAS), assumes that since function is executed through the interactions of multiple genes, different perturbations of the same pathway would result in a similar phenotype. This assumption, however, was not systemically assessed on a large scale. To determine whether SNPs associated with a given complex phenotype affect the same pathways more than expected by chance, we analyzed 368 phenotypes that were studied in >5000 GWAS. We found 216 significant phenotype-pathway associations between 70 of the phenotypes we analyzed and known pathways. We also report 391 strong phenotype-phenotype associations between phenotypes that are affected by the same pathways. While some of these associations confirm previously reported connections, others are new and could shed light on the molecular basis of these diseases. Our findings confirm that phenotype-associated SNPs cluster into pathways much more than expected by chance. However, this is true for <20% (70/368) of the phenotypes. Different types of phenotypes show markedly different tendencies: Virtually all autoimmune phenotypes show strong clustering of SNPs into pathways, while most cancers and metabolic conditions, and all electrophysiological phenotypes, could not be significantly associated with any pathway despite being significantly associated with a large number of SNPs. While this may be due to missing data, it may also suggest that these phenotypes could result only from perturbations of specific genes and not from other perturbations of the same pathway. Further analysis of pathway-associated versus gene-associated phenotypes is, therefore, needed in order to understand disease etiology and in order to promote better drug target selection.
format	article
author	Aharon Brodie Oholi Tovia-Brodie Yanay Ofran
author_facet	Aharon Brodie Oholi Tovia-Brodie Yanay Ofran
author_sort	Aharon Brodie
title	Large scale analysis of phenotype-pathway relationships based on GWAS results.
title_short	Large scale analysis of phenotype-pathway relationships based on GWAS results.
title_full	Large scale analysis of phenotype-pathway relationships based on GWAS results.
title_fullStr	Large scale analysis of phenotype-pathway relationships based on GWAS results.
title_full_unstemmed	Large scale analysis of phenotype-pathway relationships based on GWAS results.
title_sort	large scale analysis of phenotype-pathway relationships based on gwas results.
publisher	Public Library of Science (PLoS)
publishDate	2014
url	https://doaj.org/article/ae9e9a719c58409da7081a85b7ae2ea4
work_keys_str_mv	AT aharonbrodie largescaleanalysisofphenotypepathwayrelationshipsbasedongwasresults AT oholitoviabrodie largescaleanalysisofphenotypepathwayrelationshipsbasedongwasresults AT yanayofran largescaleanalysisofphenotypepathwayrelationshipsbasedongwasresults
_version_	1718414124082266112

Large scale analysis of phenotype-pathway relationships based on GWAS results.

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