A Robust GWSS Method to Simultaneously Detect Rare and Common Variants for Complex Disease

The rapid advances in sequencing technologies and the resulting next-generation sequencing data provide the opportunity to detect disease-associated variants with a better solution, in particular for low-frequency variants. Although both common and rare variants might exert their independent effects...

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Main Authors:	Kao, Chung-Feng, Liu, Jia-Rou, Hung, Hung, Kuo, Po-Hsiu
Format:	Online
Language:	English
Published:	Public Library of Science 2015
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4399906/

id	pubmed-4399906
recordtype	oai_dc
spelling	pubmed-43999062015-04-21 A Robust GWSS Method to Simultaneously Detect Rare and Common Variants for Complex Disease Kao, Chung-Feng Liu, Jia-Rou Hung, Hung Kuo, Po-Hsiu Research Article The rapid advances in sequencing technologies and the resulting next-generation sequencing data provide the opportunity to detect disease-associated variants with a better solution, in particular for low-frequency variants. Although both common and rare variants might exert their independent effects on the risk for the trait of interest, previous methods to detect the association effects rarely consider them simultaneously. We proposed a class of test statistics, the generalized weighted-sum statistic (GWSS), to detect disease associations in the presence of common and rare variants with a case-control study design. Information of rare variants was aggregated using a weighted sum method, while signal directions and strength of the variants were considered at the same time. Permutations were performed to obtain the empirical p-values of the test statistics. Our simulation showed that, compared to the existing methods, the GWSS method had better performance in most of the scenarios. The GWSS (in particular VDWSS-t) method is particularly robust for opposite association directions, association strength, and varying distributions of minor-allele frequencies. It is therefore promising for detecting disease-associated loci. For empirical data application, we also applied our GWSS method to the Genetic Analysis Workshop 17 data, and the results were consistent with the simulation, suggesting good performance of our method. As re-sequencing studies become more popular to identify putative disease loci, we recommend the use of this newly developed GWSS to detect associations with both common and rare variants. Public Library of Science 2015-04-16 /pmc/articles/PMC4399906/ /pubmed/25880329 http://dx.doi.org/10.1371/journal.pone.0120873 Text en © 2015 Kao 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.
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	Kao, Chung-Feng Liu, Jia-Rou Hung, Hung Kuo, Po-Hsiu
spellingShingle	Kao, Chung-Feng Liu, Jia-Rou Hung, Hung Kuo, Po-Hsiu A Robust GWSS Method to Simultaneously Detect Rare and Common Variants for Complex Disease
author_facet	Kao, Chung-Feng Liu, Jia-Rou Hung, Hung Kuo, Po-Hsiu
author_sort	Kao, Chung-Feng
title	A Robust GWSS Method to Simultaneously Detect Rare and Common Variants for Complex Disease
title_short	A Robust GWSS Method to Simultaneously Detect Rare and Common Variants for Complex Disease
title_full	A Robust GWSS Method to Simultaneously Detect Rare and Common Variants for Complex Disease
title_fullStr	A Robust GWSS Method to Simultaneously Detect Rare and Common Variants for Complex Disease
title_full_unstemmed	A Robust GWSS Method to Simultaneously Detect Rare and Common Variants for Complex Disease
title_sort	robust gwss method to simultaneously detect rare and common variants for complex disease
description	The rapid advances in sequencing technologies and the resulting next-generation sequencing data provide the opportunity to detect disease-associated variants with a better solution, in particular for low-frequency variants. Although both common and rare variants might exert their independent effects on the risk for the trait of interest, previous methods to detect the association effects rarely consider them simultaneously. We proposed a class of test statistics, the generalized weighted-sum statistic (GWSS), to detect disease associations in the presence of common and rare variants with a case-control study design. Information of rare variants was aggregated using a weighted sum method, while signal directions and strength of the variants were considered at the same time. Permutations were performed to obtain the empirical p-values of the test statistics. Our simulation showed that, compared to the existing methods, the GWSS method had better performance in most of the scenarios. The GWSS (in particular VDWSS-t) method is particularly robust for opposite association directions, association strength, and varying distributions of minor-allele frequencies. It is therefore promising for detecting disease-associated loci. For empirical data application, we also applied our GWSS method to the Genetic Analysis Workshop 17 data, and the results were consistent with the simulation, suggesting good performance of our method. As re-sequencing studies become more popular to identify putative disease loci, we recommend the use of this newly developed GWSS to detect associations with both common and rare variants.
publisher	Public Library of Science
publishDate	2015
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4399906/
_version_	1613212496479387648

A Robust GWSS Method to Simultaneously Detect Rare and Common Variants for Complex Disease

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