A novel transmission-based test of association for multivariate phenotypes: an application to systolic and diastolic blood pressure levels

BMC Proceedings

Proceedings

Saurabh Ghosh¹  Tanushree Haldar¹  Indranil Mukhopadhyay¹ 

[1] Human Genetics Unit, Indian Statistical Institute, 203, B.T. Road, 700108, Kolkata, India;

关键词: Expectation Maximization Algorithm;    Genetic Analysis Workshop;    Transmission Disequilibrium;    Heterozygous Parent;    Systolic Blood Pressure Level;   

DOI  :  10.1186/1753-6561-8-S1-S71

来源: Springer

【 摘 要 】

Unlike case-control studies, family-based tests for association are protected against population stratification. Complex genetic traits are often governed by quantitative precursors and it has been argued that it may be a more powerful strategy to analyze these quantitative precursors instead of the clinical end point trait. Although methods have been developed for family-based association tests for single quantitative traits, it is of interest to develop such methods for multivariate phenotypes. We propose a novel transmission-based approach based on a trio design using a simple logistic regression to test for association with a multivariate phenotype. We use our proposed method to analyze data on systolic and diastolic blood pressure levels provided in Genetic Analysis Workshop 18. However, we find that the bivariate analysis of the two phenotypes did not provide more promising results compared to univariate analyses, suggesting a possibility of a different set of major genetic variants modulating the two phenotypes.

【 授权许可】

Unknown   
© Haldar et al.; licensee BioMed Central Ltd. 2014. 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. 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.