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Insights from GWAS: emerging landscape of mechanisms underlying complex trait disease

BACKGROUND: There are now over 2000 loci in the human genome where genome wide association studies (GWAS) have found one or more SNPs to be associated with altered risk of a complex trait disease. At each of these loci, there must be some molecular level mechanism relevant to the disease. What are t...

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Autores principales: Pal, Lipika R, Yu, Chen-Hsin, Mount, Stephen M, Moult, John
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4480957/
https://www.ncbi.nlm.nih.gov/pubmed/26110739
http://dx.doi.org/10.1186/1471-2164-16-S8-S4
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author Pal, Lipika R
Yu, Chen-Hsin
Mount, Stephen M
Moult, John
author_facet Pal, Lipika R
Yu, Chen-Hsin
Mount, Stephen M
Moult, John
author_sort Pal, Lipika R
collection PubMed
description BACKGROUND: There are now over 2000 loci in the human genome where genome wide association studies (GWAS) have found one or more SNPs to be associated with altered risk of a complex trait disease. At each of these loci, there must be some molecular level mechanism relevant to the disease. What are these mechanisms and how do they contribute to disease? RESULTS: Here we consider the roles of three primary mechanism classes: changes that directly alter protein function (missense SNPs), changes that alter transcript abundance as a consequence of variants close-by in sequence, and changes that affect splicing. Missense SNPs are divided into those predicted to have a high impact on in vivo protein function, and those with a low impact. Splicing is divided into SNPs with a direct impact on splice sites, and those with a predicted effect on auxiliary splicing signals. The analysis was based on associations found for seven complex trait diseases in the classic Wellcome Trust Case Control Consortium (WTCCC1) GWA study and subsequent studies and meta-analyses, collected from the GWAS catalog. Linkage disequilibrium information was used to identify possible candidate SNPs for involvement in disease mechanism in each of the 356 loci associated with these seven diseases. With the parameters used, we find that 76% of loci have at least of these mechanisms. Overall, except for the low incidence of direct impact on splice sites, the mechanisms are found at similar frequencies, with changes in transcript abundance the most common. But the distribution of mechanisms over diseases varies markedly, as does the fraction of loci with assigned mechanisms. Many of the implicated proteins have previously been suggested as relevant, but the specific mechanism assignments are new. In addition, a number of new disease relevant proteins are proposed. CONCLUSIONS: The high fraction of GWAS loci with proposed mechanisms suggests that these classes of mechanism play a major role. Other mechanism types, such as variants affecting expression of genes remote in the DNA sequence, will contribute in other loci. Each of the identified putative mechanisms provides a hypothesis for further investigation.
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spelling pubmed-44809572015-07-10 Insights from GWAS: emerging landscape of mechanisms underlying complex trait disease Pal, Lipika R Yu, Chen-Hsin Mount, Stephen M Moult, John BMC Genomics Research BACKGROUND: There are now over 2000 loci in the human genome where genome wide association studies (GWAS) have found one or more SNPs to be associated with altered risk of a complex trait disease. At each of these loci, there must be some molecular level mechanism relevant to the disease. What are these mechanisms and how do they contribute to disease? RESULTS: Here we consider the roles of three primary mechanism classes: changes that directly alter protein function (missense SNPs), changes that alter transcript abundance as a consequence of variants close-by in sequence, and changes that affect splicing. Missense SNPs are divided into those predicted to have a high impact on in vivo protein function, and those with a low impact. Splicing is divided into SNPs with a direct impact on splice sites, and those with a predicted effect on auxiliary splicing signals. The analysis was based on associations found for seven complex trait diseases in the classic Wellcome Trust Case Control Consortium (WTCCC1) GWA study and subsequent studies and meta-analyses, collected from the GWAS catalog. Linkage disequilibrium information was used to identify possible candidate SNPs for involvement in disease mechanism in each of the 356 loci associated with these seven diseases. With the parameters used, we find that 76% of loci have at least of these mechanisms. Overall, except for the low incidence of direct impact on splice sites, the mechanisms are found at similar frequencies, with changes in transcript abundance the most common. But the distribution of mechanisms over diseases varies markedly, as does the fraction of loci with assigned mechanisms. Many of the implicated proteins have previously been suggested as relevant, but the specific mechanism assignments are new. In addition, a number of new disease relevant proteins are proposed. CONCLUSIONS: The high fraction of GWAS loci with proposed mechanisms suggests that these classes of mechanism play a major role. Other mechanism types, such as variants affecting expression of genes remote in the DNA sequence, will contribute in other loci. Each of the identified putative mechanisms provides a hypothesis for further investigation. BioMed Central 2015-06-18 /pmc/articles/PMC4480957/ /pubmed/26110739 http://dx.doi.org/10.1186/1471-2164-16-S8-S4 Text en Copyright © 2015 Pal et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/4.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Pal, Lipika R
Yu, Chen-Hsin
Mount, Stephen M
Moult, John
Insights from GWAS: emerging landscape of mechanisms underlying complex trait disease
title Insights from GWAS: emerging landscape of mechanisms underlying complex trait disease
title_full Insights from GWAS: emerging landscape of mechanisms underlying complex trait disease
title_fullStr Insights from GWAS: emerging landscape of mechanisms underlying complex trait disease
title_full_unstemmed Insights from GWAS: emerging landscape of mechanisms underlying complex trait disease
title_short Insights from GWAS: emerging landscape of mechanisms underlying complex trait disease
title_sort insights from gwas: emerging landscape of mechanisms underlying complex trait disease
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4480957/
https://www.ncbi.nlm.nih.gov/pubmed/26110739
http://dx.doi.org/10.1186/1471-2164-16-S8-S4
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