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Joint analysis of multiple blood pressure phenotypes in GAW19 data by using a multivariate rare-variant association test
INTRODUCTION: Large-scale sequencing studies often measure many related phenotypes in addition to the genetic variants. Joint analysis of multiple phenotypes in genetic association studies may increase power to detect disease-associated loci. METHODS: We apply a recently developed multivariate rare-...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5133485/ https://www.ncbi.nlm.nih.gov/pubmed/27980654 http://dx.doi.org/10.1186/s12919-016-0048-3 |
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author | Sun, Jianping Bhatnagar, Sahir R. Oualkacha, Karim Ciampi, Antonio Greenwood, Celia M. T. |
author_facet | Sun, Jianping Bhatnagar, Sahir R. Oualkacha, Karim Ciampi, Antonio Greenwood, Celia M. T. |
author_sort | Sun, Jianping |
collection | PubMed |
description | INTRODUCTION: Large-scale sequencing studies often measure many related phenotypes in addition to the genetic variants. Joint analysis of multiple phenotypes in genetic association studies may increase power to detect disease-associated loci. METHODS: We apply a recently developed multivariate rare-variant association test to the Genetic Analysis Workshop 19 data in order to test associations between genetic variants and multiple blood pressure phenotypes simultaneously. We also compare this multivariate test with a widely used univariate test that analyzes phenotypes separately. RESULTS: The multivariate test identified 2 genetic variants that have been previously reported as associated with hypertension or coronary artery disease. In addition, our region-based analyses also show that the multivariate test tends to give smaller p values than the univariate test. CONCLUSIONS: Hence, the multivariate test has potential to improve test power, especially when multiple phenotypes are correlated. |
format | Online Article Text |
id | pubmed-5133485 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-51334852016-12-15 Joint analysis of multiple blood pressure phenotypes in GAW19 data by using a multivariate rare-variant association test Sun, Jianping Bhatnagar, Sahir R. Oualkacha, Karim Ciampi, Antonio Greenwood, Celia M. T. BMC Proc Proceedings INTRODUCTION: Large-scale sequencing studies often measure many related phenotypes in addition to the genetic variants. Joint analysis of multiple phenotypes in genetic association studies may increase power to detect disease-associated loci. METHODS: We apply a recently developed multivariate rare-variant association test to the Genetic Analysis Workshop 19 data in order to test associations between genetic variants and multiple blood pressure phenotypes simultaneously. We also compare this multivariate test with a widely used univariate test that analyzes phenotypes separately. RESULTS: The multivariate test identified 2 genetic variants that have been previously reported as associated with hypertension or coronary artery disease. In addition, our region-based analyses also show that the multivariate test tends to give smaller p values than the univariate test. CONCLUSIONS: Hence, the multivariate test has potential to improve test power, especially when multiple phenotypes are correlated. BioMed Central 2016-10-18 /pmc/articles/PMC5133485/ /pubmed/27980654 http://dx.doi.org/10.1186/s12919-016-0048-3 Text en © The Author(s). 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. 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 Sun, Jianping Bhatnagar, Sahir R. Oualkacha, Karim Ciampi, Antonio Greenwood, Celia M. T. Joint analysis of multiple blood pressure phenotypes in GAW19 data by using a multivariate rare-variant association test |
title | Joint analysis of multiple blood pressure phenotypes in GAW19 data by using a multivariate rare-variant association test |
title_full | Joint analysis of multiple blood pressure phenotypes in GAW19 data by using a multivariate rare-variant association test |
title_fullStr | Joint analysis of multiple blood pressure phenotypes in GAW19 data by using a multivariate rare-variant association test |
title_full_unstemmed | Joint analysis of multiple blood pressure phenotypes in GAW19 data by using a multivariate rare-variant association test |
title_short | Joint analysis of multiple blood pressure phenotypes in GAW19 data by using a multivariate rare-variant association test |
title_sort | joint analysis of multiple blood pressure phenotypes in gaw19 data by using a multivariate rare-variant association test |
topic | Proceedings |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5133485/ https://www.ncbi.nlm.nih.gov/pubmed/27980654 http://dx.doi.org/10.1186/s12919-016-0048-3 |
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