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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4399906/ https://www.ncbi.nlm.nih.gov/pubmed/25880329 http://dx.doi.org/10.1371/journal.pone.0120873 |
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author | Kao, Chung-Feng Liu, Jia-Rou Hung, Hung Kuo, Po-Hsiu |
author_facet | Kao, Chung-Feng Liu, Jia-Rou Hung, Hung Kuo, Po-Hsiu |
author_sort | Kao, Chung-Feng |
collection | PubMed |
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. |
format | Online Article Text |
id | pubmed-4399906 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
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 PLoS One 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. |
spellingShingle | Research Article 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 |
title | 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_short | 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 |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4399906/ https://www.ncbi.nlm.nih.gov/pubmed/25880329 http://dx.doi.org/10.1371/journal.pone.0120873 |
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