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Modeling of multivariate longitudinal phenotypes in family genetic studies with Bayesian multiplicity adjustment

Genetic studies often collect data on multiple traits. Most genetic association analyses, however, consider traits separately and ignore potential correlation among traits, partially because of difficulties in statistical modeling of multivariate outcomes. When multiple traits are measured in a pedi...

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Autores principales: Ding, Lili, Kurowski, Brad G, He, Hua, Alexander, Eileen S, Mersha, Tesfaye B, Fardo, David W, Zhang, Xue, Pilipenko, Valentina V, Kottyan, Leah, Martin, Lisa J
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4143665/
https://www.ncbi.nlm.nih.gov/pubmed/25519340
http://dx.doi.org/10.1186/1753-6561-8-S1-S69
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author Ding, Lili
Kurowski, Brad G
He, Hua
Alexander, Eileen S
Mersha, Tesfaye B
Fardo, David W
Zhang, Xue
Pilipenko, Valentina V
Kottyan, Leah
Martin, Lisa J
author_facet Ding, Lili
Kurowski, Brad G
He, Hua
Alexander, Eileen S
Mersha, Tesfaye B
Fardo, David W
Zhang, Xue
Pilipenko, Valentina V
Kottyan, Leah
Martin, Lisa J
author_sort Ding, Lili
collection PubMed
description Genetic studies often collect data on multiple traits. Most genetic association analyses, however, consider traits separately and ignore potential correlation among traits, partially because of difficulties in statistical modeling of multivariate outcomes. When multiple traits are measured in a pedigree longitudinally, additional challenges arise because in addition to correlation between traits, a trait is often correlated with its own measures over time and with measurements of other family members. We developed a Bayesian model for analysis of bivariate quantitative traits measured longitudinally in family genetic studies. For a given trait, family-specific and subject-specific random effects account for correlation among family members and repeated measures, respectively. Correlation between traits is introduced by incorporating multivariate random effects and allowing time-specific trait residuals to correlate as in seemingly unrelated regressions. The proposed model can examine multiple single-nucleotide variations simultaneously, as well as incorporate familyspecific, subject-specific, or time-varying covariates. Bayesian multiplicity technique is used to effectively control false positives. Genetic Analysis Workshop 18 simulated data illustrate the proposed approach's applicability in modeling longitudinal multivariate outcomes in family genetic association studies.
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spelling pubmed-41436652014-09-02 Modeling of multivariate longitudinal phenotypes in family genetic studies with Bayesian multiplicity adjustment Ding, Lili Kurowski, Brad G He, Hua Alexander, Eileen S Mersha, Tesfaye B Fardo, David W Zhang, Xue Pilipenko, Valentina V Kottyan, Leah Martin, Lisa J BMC Proc Proceedings Genetic studies often collect data on multiple traits. Most genetic association analyses, however, consider traits separately and ignore potential correlation among traits, partially because of difficulties in statistical modeling of multivariate outcomes. When multiple traits are measured in a pedigree longitudinally, additional challenges arise because in addition to correlation between traits, a trait is often correlated with its own measures over time and with measurements of other family members. We developed a Bayesian model for analysis of bivariate quantitative traits measured longitudinally in family genetic studies. For a given trait, family-specific and subject-specific random effects account for correlation among family members and repeated measures, respectively. Correlation between traits is introduced by incorporating multivariate random effects and allowing time-specific trait residuals to correlate as in seemingly unrelated regressions. The proposed model can examine multiple single-nucleotide variations simultaneously, as well as incorporate familyspecific, subject-specific, or time-varying covariates. Bayesian multiplicity technique is used to effectively control false positives. Genetic Analysis Workshop 18 simulated data illustrate the proposed approach's applicability in modeling longitudinal multivariate outcomes in family genetic association studies. BioMed Central 2014-06-17 /pmc/articles/PMC4143665/ /pubmed/25519340 http://dx.doi.org/10.1186/1753-6561-8-S1-S69 Text en Copyright © 2014 Ding 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
Ding, Lili
Kurowski, Brad G
He, Hua
Alexander, Eileen S
Mersha, Tesfaye B
Fardo, David W
Zhang, Xue
Pilipenko, Valentina V
Kottyan, Leah
Martin, Lisa J
Modeling of multivariate longitudinal phenotypes in family genetic studies with Bayesian multiplicity adjustment
title Modeling of multivariate longitudinal phenotypes in family genetic studies with Bayesian multiplicity adjustment
title_full Modeling of multivariate longitudinal phenotypes in family genetic studies with Bayesian multiplicity adjustment
title_fullStr Modeling of multivariate longitudinal phenotypes in family genetic studies with Bayesian multiplicity adjustment
title_full_unstemmed Modeling of multivariate longitudinal phenotypes in family genetic studies with Bayesian multiplicity adjustment
title_short Modeling of multivariate longitudinal phenotypes in family genetic studies with Bayesian multiplicity adjustment
title_sort modeling of multivariate longitudinal phenotypes in family genetic studies with bayesian multiplicity adjustment
topic Proceedings
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4143665/
https://www.ncbi.nlm.nih.gov/pubmed/25519340
http://dx.doi.org/10.1186/1753-6561-8-S1-S69
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