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Detecting association of rare and common variants by testing an optimally weighted combination of variants with longitudinal data

Increasing evidence shows that complex diseases are caused by both common and rare variants. Recently, several statistical methods for detecting associations of rare variants have been developed, including the test for testing the effect of an optimally weighted combination of variants (TOW) develop...

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Detalles Bibliográficos
Autores principales: Wang, Shuaicheng, Fang, Shurong, Sha, Qiuying, Zhang, Shuanglin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4143720/
https://www.ncbi.nlm.nih.gov/pubmed/25519418
http://dx.doi.org/10.1186/1753-6561-8-S1-S91
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author Wang, Shuaicheng
Fang, Shurong
Sha, Qiuying
Zhang, Shuanglin
author_facet Wang, Shuaicheng
Fang, Shurong
Sha, Qiuying
Zhang, Shuanglin
author_sort Wang, Shuaicheng
collection PubMed
description Increasing evidence shows that complex diseases are caused by both common and rare variants. Recently, several statistical methods for detecting associations of rare variants have been developed, including the test for testing the effect of an optimally weighted combination of variants (TOW) developed by our group in 2012. These methodologies consider phenotype measurement at only one time point. Because many sequence data have been developed on population cohorts that contain phenotype measurements at multiple time points, such as the data set provided in the Genetic Analysis Workshop 18 (GAW18), we extend TOW from phenotype measurement at one time point to phenotype measurements at multiple time points. We then apply the newly proposed method to the GAW18 data set and compare the power of the new method with TOW using only one phenotype measurement. The application results show that the newly proposed method jointly modeling phenotype measurements at all time points has increased power over TOW.
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spelling pubmed-41437202014-09-02 Detecting association of rare and common variants by testing an optimally weighted combination of variants with longitudinal data Wang, Shuaicheng Fang, Shurong Sha, Qiuying Zhang, Shuanglin BMC Proc Proceedings Increasing evidence shows that complex diseases are caused by both common and rare variants. Recently, several statistical methods for detecting associations of rare variants have been developed, including the test for testing the effect of an optimally weighted combination of variants (TOW) developed by our group in 2012. These methodologies consider phenotype measurement at only one time point. Because many sequence data have been developed on population cohorts that contain phenotype measurements at multiple time points, such as the data set provided in the Genetic Analysis Workshop 18 (GAW18), we extend TOW from phenotype measurement at one time point to phenotype measurements at multiple time points. We then apply the newly proposed method to the GAW18 data set and compare the power of the new method with TOW using only one phenotype measurement. The application results show that the newly proposed method jointly modeling phenotype measurements at all time points has increased power over TOW. BioMed Central 2014-06-17 /pmc/articles/PMC4143720/ /pubmed/25519418 http://dx.doi.org/10.1186/1753-6561-8-S1-S91 Text en Copyright © 2014 Wang 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
Wang, Shuaicheng
Fang, Shurong
Sha, Qiuying
Zhang, Shuanglin
Detecting association of rare and common variants by testing an optimally weighted combination of variants with longitudinal data
title Detecting association of rare and common variants by testing an optimally weighted combination of variants with longitudinal data
title_full Detecting association of rare and common variants by testing an optimally weighted combination of variants with longitudinal data
title_fullStr Detecting association of rare and common variants by testing an optimally weighted combination of variants with longitudinal data
title_full_unstemmed Detecting association of rare and common variants by testing an optimally weighted combination of variants with longitudinal data
title_short Detecting association of rare and common variants by testing an optimally weighted combination of variants with longitudinal data
title_sort detecting association of rare and common variants by testing an optimally weighted combination of variants with longitudinal data
topic Proceedings
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4143720/
https://www.ncbi.nlm.nih.gov/pubmed/25519418
http://dx.doi.org/10.1186/1753-6561-8-S1-S91
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