Comparison of SNP-based and gene-based association studies in detecting rare variants using unrelated individuals

We compare the SNP-based and gene-based association studies using 697 unrelated individuals. The Benjamini-Hochberg procedure was applied to control the false discovery rate for all the multiple comparisons. We use a linear model for the single-nucleotide polymorphism (SNP) based association study....

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Main Authors:	Tong, Liping, Tayo, Bamidele, Yang, Jie, Cooper, Richard S
Format:	Online
Language:	English
Published:	BioMed Central 2011
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3287878/

id	pubmed-3287878
recordtype	oai_dc
spelling	pubmed-32878782012-02-28 Comparison of SNP-based and gene-based association studies in detecting rare variants using unrelated individuals Tong, Liping Tayo, Bamidele Yang, Jie Cooper, Richard S Proceedings We compare the SNP-based and gene-based association studies using 697 unrelated individuals. The Benjamini-Hochberg procedure was applied to control the false discovery rate for all the multiple comparisons. We use a linear model for the single-nucleotide polymorphism (SNP) based association study. For the gene-based study, we consider three methods. The first one is based on a linear model, the second is similarity based, and the third is a new two-step procedure. The results of power using a subset of SNPs show that the SNP-based association test is more powerful than the gene-based ones. However, in some situations, a gene-based study is able to detect the associated variants that were neglected in a SNP-based study. Finally, we apply these methods to a replicate of the quantitative trait Q1 and the binary trait D (D = 1, affected; D = 0, unaffected) for a genome-wide gene search. BioMed Central 2011-11-29 /pmc/articles/PMC3287878/ /pubmed/22373242 http://dx.doi.org/10.1186/1753-6561-5-S9-S41 Text en Copyright ©2011 Tong 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	Tong, Liping Tayo, Bamidele Yang, Jie Cooper, Richard S
spellingShingle	Tong, Liping Tayo, Bamidele Yang, Jie Cooper, Richard S Comparison of SNP-based and gene-based association studies in detecting rare variants using unrelated individuals
author_facet	Tong, Liping Tayo, Bamidele Yang, Jie Cooper, Richard S
author_sort	Tong, Liping
title	Comparison of SNP-based and gene-based association studies in detecting rare variants using unrelated individuals
title_short	Comparison of SNP-based and gene-based association studies in detecting rare variants using unrelated individuals
title_full	Comparison of SNP-based and gene-based association studies in detecting rare variants using unrelated individuals
title_fullStr	Comparison of SNP-based and gene-based association studies in detecting rare variants using unrelated individuals
title_full_unstemmed	Comparison of SNP-based and gene-based association studies in detecting rare variants using unrelated individuals
title_sort	comparison of snp-based and gene-based association studies in detecting rare variants using unrelated individuals
description	We compare the SNP-based and gene-based association studies using 697 unrelated individuals. The Benjamini-Hochberg procedure was applied to control the false discovery rate for all the multiple comparisons. We use a linear model for the single-nucleotide polymorphism (SNP) based association study. For the gene-based study, we consider three methods. The first one is based on a linear model, the second is similarity based, and the third is a new two-step procedure. The results of power using a subset of SNPs show that the SNP-based association test is more powerful than the gene-based ones. However, in some situations, a gene-based study is able to detect the associated variants that were neglected in a SNP-based study. Finally, we apply these methods to a replicate of the quantitative trait Q1 and the binary trait D (D = 1, affected; D = 0, unaffected) for a genome-wide gene search.
publisher	BioMed Central
publishDate	2011
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3287878/
_version_	1611508759545249792

Comparison of SNP-based and gene-based association studies in detecting rare variants using unrelated individuals

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