Genome-wide linkage analysis of cardiovascular disease biomarkers in a large, multigenerational family.

Abstract

Given the importance of cardiovascular disease (CVD) to public health and the demonstrated heritability of both disease status and its related risk factors, identifying the genetic variation underlying these susceptibilities is a critical step in understanding the pathogenesis of CVD and informing prevention and treatment strategies. Although one can look for genetic variation underlying susceptibility to CVD per se, it can be difficult to define the disease phenotype for such a qualitative analysis and CVD itself represents a convergence of diverse etiologic pathways. Alternatively, one can study the genetics of intermediate traits that are known risk factors for CVD, which can be measured quantitatively. Using the latter strategy, we have measured 21 cardiovascular-related biomarkers in an extended multigenerational pedigree, the CARRIAGE family (Carolinas Region Interaction of Aging, Genes, and Environment). These biomarkers belong to inflammatory and immune, connective tissue, lipid, and hemostasis pathways. Of these, 18 met our quality control standards. Using the pedigree and biomarker data, we have estimated the broad sense heritability (H2) of each biomarker (ranging from 0.09-0.56). A genome-wide panel of 6,015 SNPs was used subsequently to map these biomarkers as quantitative traits. Four showed noteworthy evidence for linkage in multipoint analysis (LOD score ≥ 2.6): paraoxonase (chromosome 8p11, 21), the chemokine RANTES (22q13.33), matrix metalloproteinase 3 (MMP3, 17p13.3), and granulocyte colony stimulating factor (GCSF, 8q22.1). Identifying the causal variation underlying each linkage score will help to unravel the genetic architecture of these quantitative traits and, by extension, the genetic architecture of cardiovascular risk.

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Published Version (Please cite this version)

10.1371/journal.pone.0071779

Publication Info

Nolan, Daniel, William E Kraus, Elizabeth Hauser, Yi-Ju Li, Dana K Thompson, Jessica Johnson, Hsiang-Cheng Chen, Sarah Nelson, et al. (2013). Genome-wide linkage analysis of cardiovascular disease biomarkers in a large, multigenerational family. PLoS One, 8(8). p. e71779. 10.1371/journal.pone.0071779 Retrieved from https://hdl.handle.net/10161/10873.

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Scholars@Duke

Li

Yi-Ju Li

Professor of Biostatistics & Bioinformatics

My research interest is in statistical genetics, including statistical method development and its application for understanding the genetic predisposition of human complex diseases. Here is the list of research topics:

  • Statistical genetics: development of family-based association methods for quantitative traits with or without censoring and for detecting X-linked genes for disease risk.  With the availability of next generation sequencing data, we have ongoing projects to develop the association methods for testing rare variants for different phenotypic measures.  
  • Genetics of Alzheimer's disease (AD) and Fuchs endothelial corneal dystrophy (FECD).
  • Genetic basis of age-at-onset of Alzheimer disease. 
  • Peri-operative genomic studies. Investigate the genetic risk factors for postoperative outcomes of patients underwent non-emergent coronary artery bypass grafting with cardiopulmonary bypass.

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