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Detecting epistatic effects in association studies at a genomic level based on an ensemble approach

Motivation: Most complex diseases involve multiple genes and their interactions. Although genome-wide association studies (GWAS) have shown some success for identifying genetic variants underlying complex diseases, most existing studies are based on limited single-locus approaches, which detect sing...

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Detalles Bibliográficos
Autores principales: Li, Jing, Horstman, Benjamin, Chen, Yixuan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3117367/
https://www.ncbi.nlm.nih.gov/pubmed/21685074
http://dx.doi.org/10.1093/bioinformatics/btr227
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author Li, Jing
Horstman, Benjamin
Chen, Yixuan
author_facet Li, Jing
Horstman, Benjamin
Chen, Yixuan
author_sort Li, Jing
collection PubMed
description Motivation: Most complex diseases involve multiple genes and their interactions. Although genome-wide association studies (GWAS) have shown some success for identifying genetic variants underlying complex diseases, most existing studies are based on limited single-locus approaches, which detect single nucleotide polymorphisms (SNPs) essentially based on their marginal associations with phenotypes. Results: In this article, we propose an ensemble approach based on boosting to study gene–gene interactions. We extend the basic AdaBoost algorithm by incorporating an intuitive importance score based on Gini impurity to select candidate SNPs. Permutation tests are used to control the statistical significance. We have performed extensive simulation studies using three interaction models to evaluate the efficacy of our approach at realistic GWAS sizes, and have compared it with existing epistatic detection algorithms. Our results indicate that our approach is valid, efficient for GWAS and on disease models with epistasis has more power than existing programs. Contact: jingli@case.edu
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spelling pubmed-31173672011-06-17 Detecting epistatic effects in association studies at a genomic level based on an ensemble approach Li, Jing Horstman, Benjamin Chen, Yixuan Bioinformatics Ismb/Eccb 2011 Proceedings Papers Committee July 17 to July 19, 2011, Vienna, Austria Motivation: Most complex diseases involve multiple genes and their interactions. Although genome-wide association studies (GWAS) have shown some success for identifying genetic variants underlying complex diseases, most existing studies are based on limited single-locus approaches, which detect single nucleotide polymorphisms (SNPs) essentially based on their marginal associations with phenotypes. Results: In this article, we propose an ensemble approach based on boosting to study gene–gene interactions. We extend the basic AdaBoost algorithm by incorporating an intuitive importance score based on Gini impurity to select candidate SNPs. Permutation tests are used to control the statistical significance. We have performed extensive simulation studies using three interaction models to evaluate the efficacy of our approach at realistic GWAS sizes, and have compared it with existing epistatic detection algorithms. Our results indicate that our approach is valid, efficient for GWAS and on disease models with epistasis has more power than existing programs. Contact: jingli@case.edu Oxford University Press 2011-07-01 2011-06-14 /pmc/articles/PMC3117367/ /pubmed/21685074 http://dx.doi.org/10.1093/bioinformatics/btr227 Text en © The Author(s) 2011. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/2.5 This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.5), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Ismb/Eccb 2011 Proceedings Papers Committee July 17 to July 19, 2011, Vienna, Austria
Li, Jing
Horstman, Benjamin
Chen, Yixuan
Detecting epistatic effects in association studies at a genomic level based on an ensemble approach
title Detecting epistatic effects in association studies at a genomic level based on an ensemble approach
title_full Detecting epistatic effects in association studies at a genomic level based on an ensemble approach
title_fullStr Detecting epistatic effects in association studies at a genomic level based on an ensemble approach
title_full_unstemmed Detecting epistatic effects in association studies at a genomic level based on an ensemble approach
title_short Detecting epistatic effects in association studies at a genomic level based on an ensemble approach
title_sort detecting epistatic effects in association studies at a genomic level based on an ensemble approach
topic Ismb/Eccb 2011 Proceedings Papers Committee July 17 to July 19, 2011, Vienna, Austria
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3117367/
https://www.ncbi.nlm.nih.gov/pubmed/21685074
http://dx.doi.org/10.1093/bioinformatics/btr227
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