Literature DB >> 18435473

Enhanced detection of genetic association of hypertensive heart disease by analysis of latent phenotypes.

C Charles Gu1, Hubert R Flores, Lisa de las Fuentes, Victor G Dávila-Román.   

Abstract

Hypertension and hypertensive heart disease (HHD) are inter-related phenotypes frequently observed with other comorbidities such as diabetes, obesity, and dyslipidemia, which probably reflect the complex gene-gene and/or gene-environment interactions resulting in HHD. The complexity of HHD led us to examine intermediate phenotypes (e.g., echocardiographically-derived measures) for simpler clues to the genetic underpinnings of the disease. We applied the method of independent component analysis to a prospective study of the metabolic predictors of left ventricular hypertrophy and extracted latent traits of HHD from panels of multi-dimensional anthropomorphic, hemodynamic echocardiographic and metabolic data. Based on the latent trait values, classification of subjects into different risk groups for HHD captured meaningful subtypes of the disease as reflected in the distributions of primary clinical indicators. Furthermore, we detected genetic associations of the latent HHD traits with single nucleotide polymorphisms in three candidate genes in the peroxisome proliferator-activated receptors complex, for which no significant association was found with the original clinical indicators of HHD. Consensus analysis of the results from repeated independent component analysis runs showed satisfactory robustness and estimated about 3-4 separate unseen sources for the observed HHD-related outcomes. (c) 2008 Wiley-Liss, Inc.

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Year:  2008        PMID: 18435473     DOI: 10.1002/gepi.20326

Source DB:  PubMed          Journal:  Genet Epidemiol        ISSN: 0741-0395            Impact factor:   2.135


  6 in total

1.  Association and interaction of PPAR-complex gene variants with latent traits of left ventricular diastolic function.

Authors:  Jyh-Ming Jimmy Juang; Lisa de Las Fuentes; Alan D Waggoner; C Charles Gu; Víctor G Dávila-Román
Journal:  BMC Med Genet       Date:  2010-04-28       Impact factor: 2.103

2.  A custom correlation coefficient (CCC) approach for fast identification of multi-SNP association patterns in genome-wide SNPs data.

Authors:  Sharlee Climer; Wei Yang; Lisa de las Fuentes; Victor G Dávila-Román; C Charles Gu
Journal:  Genet Epidemiol       Date:  2014-08-28       Impact factor: 2.135

3.  Comparison of Heritability Estimation and Linkage Analysis for Multiple Traits Using Principal Component Analyses.

Authors:  Jingjing Liang; Brian E Cade; Heming Wang; Han Chen; Kevin J Gleason; Emma K Larkin; Richa Saxena; Xihong Lin; Susan Redline; Xiaofeng Zhu
Journal:  Genet Epidemiol       Date:  2016-04       Impact factor: 2.135

4.  Aggregate blood pressure responses to serial dietary sodium and potassium intervention: defining responses using independent component analysis.

Authors:  Gengsheng Chen; Lisa de las Fuentes; Chi C Gu; Jiang He; Dongfeng Gu; Tanika Kelly; James Hixson; Cashell Jacquish; D C Rao; Treva K Rice
Journal:  BMC Genet       Date:  2015-06-20       Impact factor: 2.797

5.  The St. Louis African American health-heart study: methodology for the study of cardiovascular disease and depression in young-old African Americans.

Authors:  Robin R Bruchas; Lisa de Las Fuentes; Robert M Carney; Joann L Reagan; Carlos Bernal-Mizrachi; Amy E Riek; Chi Charles Gu; Andrew Bierhals; Mario Schootman; Theodore K Malmstrom; Thomas E Burroughs; Phyllis K Stein; Douglas K Miller; Victor G Dávila-Román
Journal:  BMC Cardiovasc Disord       Date:  2013-09-08       Impact factor: 2.298

6.  Genetic association analysis of coronary heart disease by profiling gene-environment interaction based on latent components in longitudinal endophenotypes.

Authors:  C Charles Gu; Wei Will Yang; Aldi T Kraja; Lisa de Las Fuentes; Victor G Dávila-Román
Journal:  BMC Proc       Date:  2009-12-15
  6 in total

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