Principal-component-based multivariate regression for genetic association studies of metabolic syndrome components

BMC Genetics

Methodology Article

Canddy Yau¹  Andrew Dellinger²  Hao Mei³  Jiang He³  Meng Wang⁴  Sathanur R Srinivasan⁵  Gerald S Berenson⁵  Wei Chen⁵ 

[1] Biostatistics Department, School of Public Health and Tropical Medicine, Tulane University, New Orleans, USA;Center for Human Genetics, Duke University, Durham, NC, USA;Epidemiology Department, School of Public Health and Tropical Medicine, Tulane University, New Orleans, USA;School of Life Science, Nanjing University, Nanjing, PR China;Tulane Center for Cardiovascular Health, Tulane University Health Sciences Center, New Orleans, USA;

关键词: Quantitative Trait Locus;    Pleiotropic Effect;    Canonical Variable;    Recessive Model;    Multiple Trait;   

DOI  :  10.1186/1471-2156-11-100

 received in 2009-12-07, accepted in 2010-11-09,  发布年份 2010

来源: Springer

【 摘 要 】

BackgroundQuantitative traits often underlie risk for complex diseases. For example, weight and body mass index (BMI) underlie the human abdominal obesity-metabolic syndrome. Many attempts have been made to identify quantitative trait loci (QTL) over the past decade, including association studies. However, a single QTL is often capable of affecting multiple traits, a quality known as gene pleiotropy. Gene pleiotropy may therefore cause a loss of power in association studies focused only on a single trait, whether based on single or multiple markers.ResultsWe propose using principal-component-based multivariate regression (PCBMR) to test for gene pleiotropy with comprehensive evaluation. This method generates one or more independent canonical variables based on the principal components of original traits and conducts a multivariate regression to test for association with these new variables. Systematic simulation studies have shown that PCBMR has great power. PCBMR-based pleiotropic association studies of abdominal obesity-metabolic syndrome and its possible linkage to chromosomal band 3q27 identified 11 susceptibility genes with significant associations. Whereas some of these genes had been previously reported to be associated with metabolic traits, others had never been identified as metabolism-associated genes.ConclusionsPCBMR is a computationally efficient and powerful test for gene pleiotropy. Application of PCBMR to abdominal obesity-metabolic syndrome indicated the existence of gene pleiotropy affecting this syndrome.

【 授权许可】

Unknown   
© Mei et al; licensee BioMed Central Ltd. 2010. 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.