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

BMC Proceedings

Proceedings

Saurabh Ghosh¹  Indranil Mukhopadhyay¹  Sujayam Saha¹ 

[1] Human Genetics Unit, Indian Statistical Institute, 203 Barrackpore Trunk Road, 700108, Kolkata, India;

关键词: Empirical Power;    Causative SNPs;    Binary Trait;    Quantitative Phenotype;    Univariate Phenotype;   

DOI  :  10.1186/1753-6561-5-S9-S73

来源: Springer

【 摘 要 】

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.

【 授权许可】

Unknown   
© Mukhopadhyay et al; licensee BioMed Central Ltd. 2011. This article is published under license to BioMed Central Ltd. 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.