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

Mei H, Chen W, Dellinger A, He J, Wang M, Yau C, Srinivasan SR, Berenson GS - BMC Genet. (2010)

Bottom Line: 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.Application of PCBMR to abdominal obesity-metabolic syndrome indicated the existence of gene pleiotropy affecting this syndrome.

View Article: PubMed Central - HTML - PubMed

Affiliation: Epidemiology Department, Tulane University, New Orleans, USA.

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.

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Related in: MedlinePlus

Pleiotropic association study of WEIGHT, HIP, BMI and WAIST based on general model by PCMBA on the candidate region, 182-227cM of Chromosome 3. There are 4769 total SNPs. The x axis is the SNP position and y axis is negative logarithm of p-value, i.e. -log (P).
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Figure 1: Pleiotropic association study of WEIGHT, HIP, BMI and WAIST based on general model by PCMBA on the candidate region, 182-227cM of Chromosome 3. There are 4769 total SNPs. The x axis is the SNP position and y axis is negative logarithm of p-value, i.e. -log (P).

Mentions: The results of the PCBMR pleiotropic association studies based on the GEN model are presented in Figures 1 and 2. Markers with significant p-values (≤1e-5) are summarized in Tables 7 and 8. For these markers, analyses based on recessive, dominant and additive models were conducted, and the best genetic model and its p-value were documented.


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

Mei H, Chen W, Dellinger A, He J, Wang M, Yau C, Srinivasan SR, Berenson GS - BMC Genet. (2010)

Pleiotropic association study of WEIGHT, HIP, BMI and WAIST based on general model by PCMBA on the candidate region, 182-227cM of Chromosome 3. There are 4769 total SNPs. The x axis is the SNP position and y axis is negative logarithm of p-value, i.e. -log (P).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2991276&req=5

Figure 1: Pleiotropic association study of WEIGHT, HIP, BMI and WAIST based on general model by PCMBA on the candidate region, 182-227cM of Chromosome 3. There are 4769 total SNPs. The x axis is the SNP position and y axis is negative logarithm of p-value, i.e. -log (P).
Mentions: The results of the PCBMR pleiotropic association studies based on the GEN model are presented in Figures 1 and 2. Markers with significant p-values (≤1e-5) are summarized in Tables 7 and 8. For these markers, analyses based on recessive, dominant and additive models were conducted, and the best genetic model and its p-value were documented.

Bottom Line: 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.Application of PCBMR to abdominal obesity-metabolic syndrome indicated the existence of gene pleiotropy affecting this syndrome.

View Article: PubMed Central - HTML - PubMed

Affiliation: Epidemiology Department, Tulane University, New Orleans, USA.

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.

Show MeSH
Related in: MedlinePlus