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Application of noncollapsing methods to the gene-based association test: a comparison study using Genetic Analysis Workshop 18 data
Rare variants have been proposed to play a significant role in the onset and development of common diseases. However, traditional analysis methods have difficulties in detecting association signals for rare causal variants because of a lack of statistical power. We propose a two-stage, gene-based me...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4143635/ https://www.ncbi.nlm.nih.gov/pubmed/25519333 http://dx.doi.org/10.1186/1753-6561-8-S1-S53 |
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author | Zhang, Tian-Xiao Xie, Yi-Ran Rice, John P |
author_facet | Zhang, Tian-Xiao Xie, Yi-Ran Rice, John P |
author_sort | Zhang, Tian-Xiao |
collection | PubMed |
description | Rare variants have been proposed to play a significant role in the onset and development of common diseases. However, traditional analysis methods have difficulties in detecting association signals for rare causal variants because of a lack of statistical power. We propose a two-stage, gene-based method for association mapping of rare variants by applying four different noncollapsing algorithms. Using the Genome Analysis Workshop18 whole genome sequencing data set of simulated blood pressure phenotypes, we studied and contrasted the false-positive rate of each algorithm using receiver operating characteristic curves. The statistical power of these methods was also evaluated and compared through the analysis of 200 simulated replications in a smaller genotype data set. We showed that the Fisher's method was superior to the other 3 noncollapsing methods, but was no better than the standard method implemented with famSKAT. Further investigation is needed to explore the potential statistical properties of these approaches. |
format | Online Article Text |
id | pubmed-4143635 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-41436352014-09-02 Application of noncollapsing methods to the gene-based association test: a comparison study using Genetic Analysis Workshop 18 data Zhang, Tian-Xiao Xie, Yi-Ran Rice, John P BMC Proc Proceedings Rare variants have been proposed to play a significant role in the onset and development of common diseases. However, traditional analysis methods have difficulties in detecting association signals for rare causal variants because of a lack of statistical power. We propose a two-stage, gene-based method for association mapping of rare variants by applying four different noncollapsing algorithms. Using the Genome Analysis Workshop18 whole genome sequencing data set of simulated blood pressure phenotypes, we studied and contrasted the false-positive rate of each algorithm using receiver operating characteristic curves. The statistical power of these methods was also evaluated and compared through the analysis of 200 simulated replications in a smaller genotype data set. We showed that the Fisher's method was superior to the other 3 noncollapsing methods, but was no better than the standard method implemented with famSKAT. Further investigation is needed to explore the potential statistical properties of these approaches. BioMed Central 2014-06-17 /pmc/articles/PMC4143635/ /pubmed/25519333 http://dx.doi.org/10.1186/1753-6561-8-S1-S53 Text en Copyright © 2014 Zhang 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. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Proceedings Zhang, Tian-Xiao Xie, Yi-Ran Rice, John P Application of noncollapsing methods to the gene-based association test: a comparison study using Genetic Analysis Workshop 18 data |
title | Application of noncollapsing methods to the gene-based association test: a comparison study using Genetic Analysis Workshop 18 data |
title_full | Application of noncollapsing methods to the gene-based association test: a comparison study using Genetic Analysis Workshop 18 data |
title_fullStr | Application of noncollapsing methods to the gene-based association test: a comparison study using Genetic Analysis Workshop 18 data |
title_full_unstemmed | Application of noncollapsing methods to the gene-based association test: a comparison study using Genetic Analysis Workshop 18 data |
title_short | Application of noncollapsing methods to the gene-based association test: a comparison study using Genetic Analysis Workshop 18 data |
title_sort | application of noncollapsing methods to the gene-based association test: a comparison study using genetic analysis workshop 18 data |
topic | Proceedings |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4143635/ https://www.ncbi.nlm.nih.gov/pubmed/25519333 http://dx.doi.org/10.1186/1753-6561-8-S1-S53 |
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