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This article is part of the supplement: Genetic Analysis Workshop 17: Unraveling Human Exome Data

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

Indranil Mukhopadhyay, Sujayam Saha and Saurabh Ghosh*

Author Affiliations

Human Genetics Unit, Indian Statistical Institute, 203 Barrackpore Trunk Road, Kolkata 700108, India

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BMC Proceedings 2011, 5(Suppl 9):S73  doi:10.1186/1753-6561-5-S9-S73

Published: 29 November 2011


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.