Principal-component-based multivariate regression for genetic association studies of metabolic syndrome components
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.
Type
Other articleSubject
quantitative trait lociinsulin-resistance syndrome
adiponectin gene
obesity
polymorphism
complexes
variables
genetics & heredity
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https://hdl.handle.net/10161/4343Published Version (Please cite this version)
10.1186/1471-2156-11-100Citation
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.
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