Comparison of statistical approaches to rare variant analysis for quantitative traits

With recent advances in technology, deep sequencing data will be widely used to further the understanding of genetic influence on traits of interest. Therefore not only common variants but also rare variants need to be better used to exploit the new information provided by deep sequencing data. Rece...

Full description

Bibliographic Details
Main Authors:	Chen, Han, Hendricks, Audrey E, Cheng, Yansong, Cupples, Adrienne L, Dupuis, Josée, Liu, Ching-Ti
Format:	Online
Language:	English
Published:	BioMed Central 2011
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3287837/

id	pubmed-3287837
recordtype	oai_dc
spelling	pubmed-32878372012-02-28 Comparison of statistical approaches to rare variant analysis for quantitative traits Chen, Han Hendricks, Audrey E Cheng, Yansong Cupples, Adrienne L Dupuis, Josée Liu, Ching-Ti Proceedings With recent advances in technology, deep sequencing data will be widely used to further the understanding of genetic influence on traits of interest. Therefore not only common variants but also rare variants need to be better used to exploit the new information provided by deep sequencing data. Recently, statistical approaches for analyzing rare variants in genetic association studies have been proposed, but many of them were designed only for dichotomous outcomes. We compare the type I error and power of several statistical approaches applicable to quantitative traits for collapsing and analyzing rare variant data within a defined gene region. In addition to comparing methods that consider only rare variants, such as indicator, count, and data-adaptive collapsing methods, we also compare methods that incorporate the analysis of common variants along with rare variants, such as CMC and LASSO regression. We find that the three methods used to collapse rare variants perform similarly in this simulation setting where all risk variants were simulated to have effects in the same direction. Further, we find that incorporating common variants is beneficial and using a LASSO regression to choose which common variants to include is most useful when there is are few common risk variants compared to the total number of risk variants. BioMed Central 2011-11-29 /pmc/articles/PMC3287837/ /pubmed/22373209 http://dx.doi.org/10.1186/1753-6561-5-S9-S113 Text en Copyright ©2011 Chen et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Chen, Han Hendricks, Audrey E Cheng, Yansong Cupples, Adrienne L Dupuis, Josée Liu, Ching-Ti
spellingShingle	Chen, Han Hendricks, Audrey E Cheng, Yansong Cupples, Adrienne L Dupuis, Josée Liu, Ching-Ti Comparison of statistical approaches to rare variant analysis for quantitative traits
author_facet	Chen, Han Hendricks, Audrey E Cheng, Yansong Cupples, Adrienne L Dupuis, Josée Liu, Ching-Ti
author_sort	Chen, Han
title	Comparison of statistical approaches to rare variant analysis for quantitative traits
title_short	Comparison of statistical approaches to rare variant analysis for quantitative traits
title_full	Comparison of statistical approaches to rare variant analysis for quantitative traits
title_fullStr	Comparison of statistical approaches to rare variant analysis for quantitative traits
title_full_unstemmed	Comparison of statistical approaches to rare variant analysis for quantitative traits
title_sort	comparison of statistical approaches to rare variant analysis for quantitative traits
description	With recent advances in technology, deep sequencing data will be widely used to further the understanding of genetic influence on traits of interest. Therefore not only common variants but also rare variants need to be better used to exploit the new information provided by deep sequencing data. Recently, statistical approaches for analyzing rare variants in genetic association studies have been proposed, but many of them were designed only for dichotomous outcomes. We compare the type I error and power of several statistical approaches applicable to quantitative traits for collapsing and analyzing rare variant data within a defined gene region. In addition to comparing methods that consider only rare variants, such as indicator, count, and data-adaptive collapsing methods, we also compare methods that incorporate the analysis of common variants along with rare variants, such as CMC and LASSO regression. We find that the three methods used to collapse rare variants perform similarly in this simulation setting where all risk variants were simulated to have effects in the same direction. Further, we find that incorporating common variants is beneficial and using a LASSO regression to choose which common variants to include is most useful when there is are few common risk variants compared to the total number of risk variants.
publisher	BioMed Central
publishDate	2011
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3287837/
_version_	1611508735281201152

Comparison of statistical approaches to rare variant analysis for quantitative traits

Similar Items