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A Nonparametric Regression Approach to Control for Population Stratification in Rare Variant Association Studies
Recently, there is increasing interest to detect associations between rare variants and complex traits. Rare variant association studies usually need large sample sizes due to the rarity of the variants, and large sample sizes typically require combining information from different geographic locatio...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5114546/ https://www.ncbi.nlm.nih.gov/pubmed/27857226 http://dx.doi.org/10.1038/srep37444 |
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author | Sha, Qiuying Zhang, Kui Zhang, Shuanglin |
author_facet | Sha, Qiuying Zhang, Kui Zhang, Shuanglin |
author_sort | Sha, Qiuying |
collection | PubMed |
description | Recently, there is increasing interest to detect associations between rare variants and complex traits. Rare variant association studies usually need large sample sizes due to the rarity of the variants, and large sample sizes typically require combining information from different geographic locations within and across countries. Although several statistical methods have been developed to control for population stratification in common variant association studies, these methods are not necessarily controlling for population stratification in rare variant association studies. Thus, new statistical methods that can control for population stratification in rare variant association studies are needed. In this article, we propose a principal component based nonparametric regression (PC-nonp) approach to control for population stratification in rare variant association studies. Our simulations show that the proposed PC-nonp can control for population stratification well in all scenarios, while existing methods cannot control for population stratification at least in some scenarios. Simulations also show that PC-nonp’s robustness to population stratification will not reduce power. Furthermore, we illustrate our proposed method by using whole genome sequencing data from genetic analysis workshop 18 (GAW18). |
format | Online Article Text |
id | pubmed-5114546 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Nature Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-51145462016-11-25 A Nonparametric Regression Approach to Control for Population Stratification in Rare Variant Association Studies Sha, Qiuying Zhang, Kui Zhang, Shuanglin Sci Rep Article Recently, there is increasing interest to detect associations between rare variants and complex traits. Rare variant association studies usually need large sample sizes due to the rarity of the variants, and large sample sizes typically require combining information from different geographic locations within and across countries. Although several statistical methods have been developed to control for population stratification in common variant association studies, these methods are not necessarily controlling for population stratification in rare variant association studies. Thus, new statistical methods that can control for population stratification in rare variant association studies are needed. In this article, we propose a principal component based nonparametric regression (PC-nonp) approach to control for population stratification in rare variant association studies. Our simulations show that the proposed PC-nonp can control for population stratification well in all scenarios, while existing methods cannot control for population stratification at least in some scenarios. Simulations also show that PC-nonp’s robustness to population stratification will not reduce power. Furthermore, we illustrate our proposed method by using whole genome sequencing data from genetic analysis workshop 18 (GAW18). Nature Publishing Group 2016-11-18 /pmc/articles/PMC5114546/ /pubmed/27857226 http://dx.doi.org/10.1038/srep37444 Text en Copyright © 2016, The Author(s) http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ |
spellingShingle | Article Sha, Qiuying Zhang, Kui Zhang, Shuanglin A Nonparametric Regression Approach to Control for Population Stratification in Rare Variant Association Studies |
title | A Nonparametric Regression Approach to Control for Population Stratification in Rare Variant Association Studies |
title_full | A Nonparametric Regression Approach to Control for Population Stratification in Rare Variant Association Studies |
title_fullStr | A Nonparametric Regression Approach to Control for Population Stratification in Rare Variant Association Studies |
title_full_unstemmed | A Nonparametric Regression Approach to Control for Population Stratification in Rare Variant Association Studies |
title_short | A Nonparametric Regression Approach to Control for Population Stratification in Rare Variant Association Studies |
title_sort | nonparametric regression approach to control for population stratification in rare variant association studies |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5114546/ https://www.ncbi.nlm.nih.gov/pubmed/27857226 http://dx.doi.org/10.1038/srep37444 |
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