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Integrating binary traits with quantitative phenotypes for association mapping of multivariate phenotypes

Clinical binary end-point traits are often governed by quantitative precursors. Hence it may be a prudent strategy to analyze a clinical end-point trait by considering a multivariate phenotype vector, possibly including both quantitative and qualitative phenotypes. A major statistical challenge lies...

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Detalles Bibliográficos
Autores principales: Mukhopadhyay, Indranil, Saha, Sujayam, Ghosh, Saurabh
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3287913/
https://www.ncbi.nlm.nih.gov/pubmed/22373144
http://dx.doi.org/10.1186/1753-6561-5-S9-S73
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author Mukhopadhyay, Indranil
Saha, Sujayam
Ghosh, Saurabh
author_facet Mukhopadhyay, Indranil
Saha, Sujayam
Ghosh, Saurabh
author_sort Mukhopadhyay, Indranil
collection PubMed
description Clinical binary end-point traits are often governed by quantitative precursors. Hence it may be a prudent strategy to analyze a clinical end-point trait by considering a multivariate phenotype vector, possibly including both quantitative and qualitative phenotypes. A major statistical challenge lies in integrating the constituent phenotypes into a reduced univariate phenotype for association analyses. We assess the performances of certain reduced phenotypes using analysis of variance and a model-free quantile-based approach. We find that analysis of variance is more powerful than the quantile-based approach in detecting association, particularly for rare variants. We also find that using a principal component of the quantitative phenotypes and the residual of a logistic regression of the binary phenotype on the quantitative phenotypes may be an optimal method for integrating a binary phenotype with quantitative phenotypes to define a reduced univariate phenotype.
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spelling pubmed-32879132012-02-28 Integrating binary traits with quantitative phenotypes for association mapping of multivariate phenotypes Mukhopadhyay, Indranil Saha, Sujayam Ghosh, Saurabh BMC Proc Proceedings Clinical binary end-point traits are often governed by quantitative precursors. Hence it may be a prudent strategy to analyze a clinical end-point trait by considering a multivariate phenotype vector, possibly including both quantitative and qualitative phenotypes. A major statistical challenge lies in integrating the constituent phenotypes into a reduced univariate phenotype for association analyses. We assess the performances of certain reduced phenotypes using analysis of variance and a model-free quantile-based approach. We find that analysis of variance is more powerful than the quantile-based approach in detecting association, particularly for rare variants. We also find that using a principal component of the quantitative phenotypes and the residual of a logistic regression of the binary phenotype on the quantitative phenotypes may be an optimal method for integrating a binary phenotype with quantitative phenotypes to define a reduced univariate phenotype. BioMed Central 2011-11-29 /pmc/articles/PMC3287913/ /pubmed/22373144 http://dx.doi.org/10.1186/1753-6561-5-S9-S73 Text en Copyright ©2011 Mukhopadhyay 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.
spellingShingle Proceedings
Mukhopadhyay, Indranil
Saha, Sujayam
Ghosh, Saurabh
Integrating binary traits with quantitative phenotypes for association mapping of multivariate phenotypes
title Integrating binary traits with quantitative phenotypes for association mapping of multivariate phenotypes
title_full Integrating binary traits with quantitative phenotypes for association mapping of multivariate phenotypes
title_fullStr Integrating binary traits with quantitative phenotypes for association mapping of multivariate phenotypes
title_full_unstemmed Integrating binary traits with quantitative phenotypes for association mapping of multivariate phenotypes
title_short Integrating binary traits with quantitative phenotypes for association mapping of multivariate phenotypes
title_sort integrating binary traits with quantitative phenotypes for association mapping of multivariate phenotypes
topic Proceedings
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3287913/
https://www.ncbi.nlm.nih.gov/pubmed/22373144
http://dx.doi.org/10.1186/1753-6561-5-S9-S73
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