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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BioMed Central
2014
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| Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4143720/ |
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pubmed-4143720 |
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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 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. |
| 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 |
Wang, Shuaicheng Fang, Shurong Sha, Qiuying Zhang, Shuanglin |
| spellingShingle |
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 |
| author_facet |
Wang, Shuaicheng Fang, Shurong Sha, Qiuying Zhang, Shuanglin |
| author_sort |
Wang, Shuaicheng |
| title |
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_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_sort |
detecting association of rare and common variants by testing an optimally weighted combination of variants with longitudinal data |
| 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. |
| publisher |
BioMed Central |
| publishDate |
2014 |
| url |
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4143720/ |
| _version_ |
1613127412160135168 |