A novel approach for multi-SNP GWAS and its application in Alzheimer's disease

doi:10.1186/s12859-016-1093-7

A novel approach for multi-SNP GWAS and its application in Alzheimer's disease

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Abstract

Background: Genome-wide association studies (GWAS) have effectively identified genetic factors for many diseases. Many diseases, including Alzheimer's disease (AD), have epistatic causes, requiring more sophisticated analyses to identify groups of variants which together affect phenotype. Results: Based on the GWAS statistical model, we developed a multi-SNP GWAS analysis to identify pairs of variants whose common occurrence signaled the Alzheimer's disease phenotype. Conclusions: Despite not having sufficient data to demonstrate significance, our preliminary experimentation identified a high correlation between GRIA3 and HLA-DRB5 (an AD gene). GRIA3 has not been previously reported in association with AD, but is known to play a role in learning and memory.

Original language	English
Article number	268
Journal	BMC Bioinformatics
Volume	17
DOIs	https://doi.org/10.1186/s12859-016-1093-7
State	Published - Jul 25 2016

Bibliographical note

Funding Information:
Publication of this article was funded by the Department of Biology and the College of Life Sciences at Brigham Young University. This article has been published as part of BMC Bioinformatics Volume 17 Supplement 7, 2016: Selected articles from the 12th Annual Biotechnology and Bioinformatics Symposium: bioinformatics. The full contents of the supplement are available online at https://bmcbioinformatics.biomedcentral. com/articles/supplements/volume-17-supplement-7.

Publisher Copyright:
© 2016 Bodily et al.

Keywords

Alzheimer's disease
Epistasis
GWAS
Multi-SNP GWAS

ASJC Scopus subject areas

Structural Biology
Biochemistry
Molecular Biology
Computer Science Applications
Applied Mathematics

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10.1186/s12859-016-1093-7

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title = "A novel approach for multi-SNP GWAS and its application in Alzheimer's disease",

abstract = "Background: Genome-wide association studies (GWAS) have effectively identified genetic factors for many diseases. Many diseases, including Alzheimer's disease (AD), have epistatic causes, requiring more sophisticated analyses to identify groups of variants which together affect phenotype. Results: Based on the GWAS statistical model, we developed a multi-SNP GWAS analysis to identify pairs of variants whose common occurrence signaled the Alzheimer's disease phenotype. Conclusions: Despite not having sufficient data to demonstrate significance, our preliminary experimentation identified a high correlation between GRIA3 and HLA-DRB5 (an AD gene). GRIA3 has not been previously reported in association with AD, but is known to play a role in learning and memory.",

keywords = "Alzheimer's disease, Epistasis, GWAS, Multi-SNP GWAS",

author = "Bodily, {Paul M.} and Fujimoto, {M. Stanley} and Page, {Justin T.} and Clement, {Mark J.} and Ebbert, {Mark T.W.} and Ridge, {Perry G.}",

note = "Funding Information: Publication of this article was funded by the Department of Biology and the College of Life Sciences at Brigham Young University. This article has been published as part of BMC Bioinformatics Volume 17 Supplement 7, 2016: Selected articles from the 12th Annual Biotechnology and Bioinformatics Symposium: bioinformatics. The full contents of the supplement are available online at https://bmcbioinformatics.biomedcentral. com/articles/supplements/volume-17-supplement-7. Publisher Copyright: {\textcopyright} 2016 Bodily et al.",

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AU - Bodily, Paul M.

AU - Fujimoto, M. Stanley

AU - Page, Justin T.

AU - Clement, Mark J.

AU - Ebbert, Mark T.W.

AU - Ridge, Perry G.

N1 - Funding Information: Publication of this article was funded by the Department of Biology and the College of Life Sciences at Brigham Young University. This article has been published as part of BMC Bioinformatics Volume 17 Supplement 7, 2016: Selected articles from the 12th Annual Biotechnology and Bioinformatics Symposium: bioinformatics. The full contents of the supplement are available online at https://bmcbioinformatics.biomedcentral. com/articles/supplements/volume-17-supplement-7. Publisher Copyright: © 2016 Bodily et al.

PY - 2016/7/25

Y1 - 2016/7/25

N2 - Background: Genome-wide association studies (GWAS) have effectively identified genetic factors for many diseases. Many diseases, including Alzheimer's disease (AD), have epistatic causes, requiring more sophisticated analyses to identify groups of variants which together affect phenotype. Results: Based on the GWAS statistical model, we developed a multi-SNP GWAS analysis to identify pairs of variants whose common occurrence signaled the Alzheimer's disease phenotype. Conclusions: Despite not having sufficient data to demonstrate significance, our preliminary experimentation identified a high correlation between GRIA3 and HLA-DRB5 (an AD gene). GRIA3 has not been previously reported in association with AD, but is known to play a role in learning and memory.

AB - Background: Genome-wide association studies (GWAS) have effectively identified genetic factors for many diseases. Many diseases, including Alzheimer's disease (AD), have epistatic causes, requiring more sophisticated analyses to identify groups of variants which together affect phenotype. Results: Based on the GWAS statistical model, we developed a multi-SNP GWAS analysis to identify pairs of variants whose common occurrence signaled the Alzheimer's disease phenotype. Conclusions: Despite not having sufficient data to demonstrate significance, our preliminary experimentation identified a high correlation between GRIA3 and HLA-DRB5 (an AD gene). GRIA3 has not been previously reported in association with AD, but is known to play a role in learning and memory.

KW - Alzheimer's disease

KW - Epistasis

KW - GWAS

KW - Multi-SNP GWAS

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A novel approach for multi-SNP GWAS and its application in Alzheimer's disease

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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