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Genetic association analysis based on a joint model of gene expression and blood pressure

Recent work on genetic association studies suggests that much of the heritable variation in complex traits is unexplained, which indicates a need for using more biologically meaningful modeling approaches and appropriate statistical methods. In this study, we propose a biological framework and a cor...

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Autores principales: Konigorski, Stefan, Yilmaz, Yildiz E., Pischon, Tobias
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5133480/
https://www.ncbi.nlm.nih.gov/pubmed/27980651
http://dx.doi.org/10.1186/s12919-016-0045-6
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author Konigorski, Stefan
Yilmaz, Yildiz E.
Pischon, Tobias
author_facet Konigorski, Stefan
Yilmaz, Yildiz E.
Pischon, Tobias
author_sort Konigorski, Stefan
collection PubMed
description Recent work on genetic association studies suggests that much of the heritable variation in complex traits is unexplained, which indicates a need for using more biologically meaningful modeling approaches and appropriate statistical methods. In this study, we propose a biological framework and a corresponding statistical model incorporating multilevel biological measures, and illustrate it in the analysis of the real data provided by the Genetic Analysis Workshop (GAW) 19, which contains whole genome sequence (WGS), gene expression (GE), and blood pressure (BP) data. We investigate the direct effect of single-nucleotide variants (SNVs) on BP and GE, while considering the non-directional dependence between BP and GE, by using copula functions to jointly model BP and GE conditional on SNVs. We implement the method for analysis on a genome-wide scale, and illustrate it within an association analysis of 68,727 SNVs on chromosome 19 that lie in or around genes with available GE measures. Although there is no indication for inflated type I errors under the proposed method, our results show that the association tests have smaller p values than tests under univariate models for common and rare variants using single-variant tests and gene-based multimarker tests. Hence, considering multilevel biological measures and modeling the dependence structure between these measures by using a plausible graphical approach may lead to more informative findings than standard univariate tests of common variants and well-recognized gene-based rare variant tests.
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spelling pubmed-51334802016-12-15 Genetic association analysis based on a joint model of gene expression and blood pressure Konigorski, Stefan Yilmaz, Yildiz E. Pischon, Tobias BMC Proc Proceedings Recent work on genetic association studies suggests that much of the heritable variation in complex traits is unexplained, which indicates a need for using more biologically meaningful modeling approaches and appropriate statistical methods. In this study, we propose a biological framework and a corresponding statistical model incorporating multilevel biological measures, and illustrate it in the analysis of the real data provided by the Genetic Analysis Workshop (GAW) 19, which contains whole genome sequence (WGS), gene expression (GE), and blood pressure (BP) data. We investigate the direct effect of single-nucleotide variants (SNVs) on BP and GE, while considering the non-directional dependence between BP and GE, by using copula functions to jointly model BP and GE conditional on SNVs. We implement the method for analysis on a genome-wide scale, and illustrate it within an association analysis of 68,727 SNVs on chromosome 19 that lie in or around genes with available GE measures. Although there is no indication for inflated type I errors under the proposed method, our results show that the association tests have smaller p values than tests under univariate models for common and rare variants using single-variant tests and gene-based multimarker tests. Hence, considering multilevel biological measures and modeling the dependence structure between these measures by using a plausible graphical approach may lead to more informative findings than standard univariate tests of common variants and well-recognized gene-based rare variant tests. BioMed Central 2016-10-18 /pmc/articles/PMC5133480/ /pubmed/27980651 http://dx.doi.org/10.1186/s12919-016-0045-6 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
Konigorski, Stefan
Yilmaz, Yildiz E.
Pischon, Tobias
Genetic association analysis based on a joint model of gene expression and blood pressure
title Genetic association analysis based on a joint model of gene expression and blood pressure
title_full Genetic association analysis based on a joint model of gene expression and blood pressure
title_fullStr Genetic association analysis based on a joint model of gene expression and blood pressure
title_full_unstemmed Genetic association analysis based on a joint model of gene expression and blood pressure
title_short Genetic association analysis based on a joint model of gene expression and blood pressure
title_sort genetic association analysis based on a joint model of gene expression and blood pressure
topic Proceedings
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5133480/
https://www.ncbi.nlm.nih.gov/pubmed/27980651
http://dx.doi.org/10.1186/s12919-016-0045-6
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