Exploiting SNP Correlations within Random Forest for Genome-Wide Association Studies
The primary goal of genome-wide association studies (GWAS) is to discover variants that could lead, in isolation or in combination, to a particular trait or disease. Standard approaches to GWAS, however, are usually based on univariate hypothesis tests and therefore can account neither for correlati...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3973686/ https://www.ncbi.nlm.nih.gov/pubmed/24695491 http://dx.doi.org/10.1371/journal.pone.0093379 |
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author | Botta, Vincent Louppe, Gilles Geurts, Pierre Wehenkel, Louis |
author_facet | Botta, Vincent Louppe, Gilles Geurts, Pierre Wehenkel, Louis |
author_sort | Botta, Vincent |
collection | PubMed |
description | The primary goal of genome-wide association studies (GWAS) is to discover variants that could lead, in isolation or in combination, to a particular trait or disease. Standard approaches to GWAS, however, are usually based on univariate hypothesis tests and therefore can account neither for correlations due to linkage disequilibrium nor for combinations of several markers. To discover and leverage such potential multivariate interactions, we propose in this work an extension of the Random Forest algorithm tailored for structured GWAS data. In terms of risk prediction, we show empirically on several GWAS datasets that the proposed T-Trees method significantly outperforms both the original Random Forest algorithm and standard linear models, thereby suggesting the actual existence of multivariate non-linear effects due to the combinations of several SNPs. We also demonstrate that variable importances as derived from our method can help identify relevant loci. Finally, we highlight the strong impact that quality control procedures may have, both in terms of predictive power and loci identification. Variable importance results and T-Trees source code are all available at www.montefiore.ulg.ac.be/~botta/ttrees/ and github.com/0asa/TTree-source respectively. |
format | Online Article Text |
id | pubmed-3973686 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-39736862014-04-04 Exploiting SNP Correlations within Random Forest for Genome-Wide Association Studies Botta, Vincent Louppe, Gilles Geurts, Pierre Wehenkel, Louis PLoS One Research Article The primary goal of genome-wide association studies (GWAS) is to discover variants that could lead, in isolation or in combination, to a particular trait or disease. Standard approaches to GWAS, however, are usually based on univariate hypothesis tests and therefore can account neither for correlations due to linkage disequilibrium nor for combinations of several markers. To discover and leverage such potential multivariate interactions, we propose in this work an extension of the Random Forest algorithm tailored for structured GWAS data. In terms of risk prediction, we show empirically on several GWAS datasets that the proposed T-Trees method significantly outperforms both the original Random Forest algorithm and standard linear models, thereby suggesting the actual existence of multivariate non-linear effects due to the combinations of several SNPs. We also demonstrate that variable importances as derived from our method can help identify relevant loci. Finally, we highlight the strong impact that quality control procedures may have, both in terms of predictive power and loci identification. Variable importance results and T-Trees source code are all available at www.montefiore.ulg.ac.be/~botta/ttrees/ and github.com/0asa/TTree-source respectively. Public Library of Science 2014-04-02 /pmc/articles/PMC3973686/ /pubmed/24695491 http://dx.doi.org/10.1371/journal.pone.0093379 Text en © 2014 Botta et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Botta, Vincent Louppe, Gilles Geurts, Pierre Wehenkel, Louis Exploiting SNP Correlations within Random Forest for Genome-Wide Association Studies |
title | Exploiting SNP Correlations within Random Forest for Genome-Wide Association Studies |
title_full | Exploiting SNP Correlations within Random Forest for Genome-Wide Association Studies |
title_fullStr | Exploiting SNP Correlations within Random Forest for Genome-Wide Association Studies |
title_full_unstemmed | Exploiting SNP Correlations within Random Forest for Genome-Wide Association Studies |
title_short | Exploiting SNP Correlations within Random Forest for Genome-Wide Association Studies |
title_sort | exploiting snp correlations within random forest for genome-wide association studies |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3973686/ https://www.ncbi.nlm.nih.gov/pubmed/24695491 http://dx.doi.org/10.1371/journal.pone.0093379 |
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