Show simple item record Dellinger, Andrew en_US 2011-06-21T17:29:32Z 2011-06-21T17:29:32Z 2010 en_US
dc.identifier.citation Mei,Hao;Chen,Wei;Dellinger,Andrew;He,Jiang;Wang,Meng;Yau,Canddy;Srinivasan,Sathanur R.;Berenson,Gerald S.. 2010. Principal-component-based multivariate regression for genetic association studies of metabolic syndrome components. Bmc Genetics 11( ): 100-100. en_US
dc.identifier.issn 1471-2156 en_US
dc.description.abstract Background: Quantitative 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. Results: We 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. Conclusions: PCBMR 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. en_US
dc.language.iso en_US en_US
dc.publisher BIOMED CENTRAL LTD en_US
dc.relation.isversionof doi:10.1186/1471-2156-11-100 en_US
dc.subject quantitative trait loci en_US
dc.subject insulin-resistance syndrome en_US
dc.subject adiponectin gene en_US
dc.subject obesity en_US
dc.subject polymorphism en_US
dc.subject complexes en_US
dc.subject variables en_US
dc.subject genetics & heredity en_US
dc.title Principal-component-based multivariate regression for genetic association studies of metabolic syndrome components en_US
dc.title.alternative en_US
dc.description.version Version of Record en_US 2010-11-9 en_US
duke.description.endpage 100 en_US
duke.description.issue en_US
duke.description.startpage 100 en_US
duke.description.volume 11 en_US
dc.relation.journal Bmc Genetics en_US

Files in this item

This item appears in the following Collection(s)

Show simple item record