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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Citation

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

Hauser

Elizabeth Rebecca Hauser

Professor of Biostatistics & Bioinformatics

The incorporation of personalized medicine to all areas of human health represents a turning point for human genetics studies, a point at which the discoveries made have real implications for clinical medicine.  It is important for students to gain experience in how human genetics studies are conducted and how results of those studies may be used.  As a statistical geneticist and biostatistician my research interests are focused on developing and applying statistical methods to search for genes causing common human diseases.  My research programs combine development and application of statistical methods for genetic studies, with a particular emphasis on understanding the joint effects of genes and environment. 

These studies I work on cover diverse areas in biomedicine but are always collaborative, with the goal of bringing robust data science and statistical methods to the project.  Collaborative studies include genetic and ‘omics studies of cardiovascular disease, health effects of air pollution, genetic analysis of adherence to an exercise program, genetic analysis in evaluating colon cancer risk, genetic analysis of suicide, and systems biology analysis of Gulf War Illness.

Keywords: human genetics, genetic association, gene mapping, genetic epidemiology, statistical genetics, biostatistics, cardiovascular disease, computational biology, diabetes, aging, colon cancer, colon polyps, kidney disease, Gulf War Illness, exercise behavior, suicide




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.
Kraus

Virginia Byers Kraus

Mary Bernheim Distinguished Professor of Medicine

Virginia Byers Kraus, MD, PhD, is the Mary Bernheim Distinguished Professor of Medicine, Professor of Orthopaedic Surgery, Professor of Pathology and a faculty member of the Duke Molecular Physiology Institute in the Duke University School of Medicine. She is a practicing Rheumatologist with over 30 years’ experience in translational musculoskeletal research focusing on osteoarthritis, the most common of all arthritides. She trained at Brown University (ScB 1979), Duke University (MD 1982, PhD 1993) and the Duke University School of Medicine (Residency in Internal Medicine and Fellowship in Rheumatology). Her career has focused on elucidating osteoarthritis pathogenesis and translational research into the discovery and validation of biomarkers for early osteoarthritis detection, prediction of progression, monitoring of disease status, and facilitation of therapeutic developments. She is co-PI of the Foundation for NIH Biomarkers Consortium Osteoarthritis project. Trained as a molecular biologist and a Rheumatologist, she endeavors to study disease from bedside to bench.

Shah

Svati Hasmukh Shah

Ursula Geller Distinguished Professor of Research in Cardiovascular Diseases

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