title: Integrating binary traits with quantitative phenotypes for association mapping of multivariate phenotypes.
abstract: 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.
authors.affiliation: Human Genetics Unit, Indian Statistical Institute, 203 Barrackpore Trunk Road, Kolkata 700108, India. saurabh@isical.ac.in.
authors.forename: Indranil
authors.initials: I
authors.lastname: Mukhopadhyay
authors.forename: Sujayam
authors.initials: S
authors.lastname: Saha
authors.forename: Saurabh
authors.initials: S
authors.lastname: Ghosh