Association mapping of blood pressure levels in a longitudinal framework using binomial regression

BMC Proceedings

Proceedings

Arunabha Majumdar¹  Saurabh Ghosh¹  Indranil Mukhopadhyay¹ 

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

关键词: Association Analysis;    Blood Pressure Level;    Expectation Maximization Algorithm;    Genetic Analysis Workshop;    Systolic Blood Pressure Level;   

DOI  :  10.1186/1753-6561-8-S1-S74

来源: Springer

【 摘 要 】

Heritable quantitative characters underline complex genetic traits. However, a single quantitative phenotype may not be a suitably good surrogate for a clinical end point trait.It may be more optimal to use a multivariate phenotype vector correlated with the end point trait to carry out an association analysis. Existing methods, such as variance components and principal components, suffer from inherent limitations, such as lack of robustness or difficulty in biological interpretation of association findings. In an effort to circumvent these limitations, we propose a novel regression approach based on a conditional binomial model to detect association between a single-nucleotide polymorphism and a multivariate phenotype vector. We use our proposed method to analyze data on systolic and diastolic blood pressure levels provided in Genetic Analysis Workshop 18. We find that the bivariate analysis of the two phenotypes yields more promising results in terms of lower p-values compared to univariate analyses.

【 授权许可】

Unknown   
© Majumdar 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.