Multiple regression methods show great potential for rare variant association tests.

The investigation of associations between rare genetic variants and diseases or phenotypes has two goals. Firstly, the identification of which genes or genomic regions are associated, and secondly, discrimination of associated variants from background noise within each region. Over the last few year...

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Autores principales:	ChangJiang Xu, Martin Ladouceur, Zari Dastani, J Brent Richards, Antonio Ciampi, Celia M T Greenwood
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2012
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/ae4e9d724aba43d3a9fdb01059ed9d29
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spelling	oai:doaj.org-article:ae4e9d724aba43d3a9fdb01059ed9d292021-11-18T07:09:16ZMultiple regression methods show great potential for rare variant association tests.1932-620310.1371/journal.pone.0041694https://doaj.org/article/ae4e9d724aba43d3a9fdb01059ed9d292012-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22916111/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203The investigation of associations between rare genetic variants and diseases or phenotypes has two goals. Firstly, the identification of which genes or genomic regions are associated, and secondly, discrimination of associated variants from background noise within each region. Over the last few years, many new methods have been developed which associate genomic regions with phenotypes. However, classical methods for high-dimensional data have received little attention. Here we investigate whether several classical statistical methods for high-dimensional data: ridge regression (RR), principal components regression (PCR), partial least squares regression (PLS), a sparse version of PLS (SPLS), and the LASSO are able to detect associations with rare genetic variants. These approaches have been extensively used in statistics to identify the true associations in data sets containing many predictor variables. Using genetic variants identified in three genes that were Sanger sequenced in 1998 individuals, we simulated continuous phenotypes under several different models, and we show that these feature selection and feature extraction methods can substantially outperform several popular methods for rare variant analysis. Furthermore, these approaches can identify which variants are contributing most to the model fit, and therefore both goals of rare variant analysis can be achieved simultaneously with the use of regression regularization methods. These methods are briefly illustrated with an analysis of adiponectin levels and variants in the ADIPOQ gene.ChangJiang XuMartin LadouceurZari DastaniJ Brent RichardsAntonio CiampiCelia M T GreenwoodPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 7, Iss 8, p e41694 (2012)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q ChangJiang Xu Martin Ladouceur Zari Dastani J Brent Richards Antonio Ciampi Celia M T Greenwood Multiple regression methods show great potential for rare variant association tests.
description	The investigation of associations between rare genetic variants and diseases or phenotypes has two goals. Firstly, the identification of which genes or genomic regions are associated, and secondly, discrimination of associated variants from background noise within each region. Over the last few years, many new methods have been developed which associate genomic regions with phenotypes. However, classical methods for high-dimensional data have received little attention. Here we investigate whether several classical statistical methods for high-dimensional data: ridge regression (RR), principal components regression (PCR), partial least squares regression (PLS), a sparse version of PLS (SPLS), and the LASSO are able to detect associations with rare genetic variants. These approaches have been extensively used in statistics to identify the true associations in data sets containing many predictor variables. Using genetic variants identified in three genes that were Sanger sequenced in 1998 individuals, we simulated continuous phenotypes under several different models, and we show that these feature selection and feature extraction methods can substantially outperform several popular methods for rare variant analysis. Furthermore, these approaches can identify which variants are contributing most to the model fit, and therefore both goals of rare variant analysis can be achieved simultaneously with the use of regression regularization methods. These methods are briefly illustrated with an analysis of adiponectin levels and variants in the ADIPOQ gene.
format	article
author	ChangJiang Xu Martin Ladouceur Zari Dastani J Brent Richards Antonio Ciampi Celia M T Greenwood
author_facet	ChangJiang Xu Martin Ladouceur Zari Dastani J Brent Richards Antonio Ciampi Celia M T Greenwood
author_sort	ChangJiang Xu
title	Multiple regression methods show great potential for rare variant association tests.
title_short	Multiple regression methods show great potential for rare variant association tests.
title_full	Multiple regression methods show great potential for rare variant association tests.
title_fullStr	Multiple regression methods show great potential for rare variant association tests.
title_full_unstemmed	Multiple regression methods show great potential for rare variant association tests.
title_sort	multiple regression methods show great potential for rare variant association tests.
publisher	Public Library of Science (PLoS)
publishDate	2012
url	https://doaj.org/article/ae4e9d724aba43d3a9fdb01059ed9d29
work_keys_str_mv	AT changjiangxu multipleregressionmethodsshowgreatpotentialforrarevariantassociationtests AT martinladouceur multipleregressionmethodsshowgreatpotentialforrarevariantassociationtests AT zaridastani multipleregressionmethodsshowgreatpotentialforrarevariantassociationtests AT jbrentrichards multipleregressionmethodsshowgreatpotentialforrarevariantassociationtests AT antoniociampi multipleregressionmethodsshowgreatpotentialforrarevariantassociationtests AT celiamtgreenwood multipleregressionmethodsshowgreatpotentialforrarevariantassociationtests
_version_	1718423873586724864

Multiple regression methods show great potential for rare variant association tests.

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