Genome-wide linkage analysis of cardiovascular disease biomarkers in a large, multigenerational family.
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2013
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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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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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William Erle Kraus
My training, expertise and research interests range from human integrative physiology and genetics to animal exercise models to cell culture models of skeletal muscle adaptation to mechanical stretch. I am trained clinically as an internist and preventive cardiologist, with particular expertise in preventive cardiology and cardiac rehabilitation. My research training spans molecular biology and cell culture, molecular genetics, and integrative human exercise physiology and metabolism. I practice as a preventive cardiologist with a focus on cardiometabolic risk and exercise physiology for older athletes. My research space has both a basic wet laboratory component and a human integrative physiology one.
One focus of our work is an integrative physiologic examination of exercise effects in human subjects in clinical studies of exercise training in normal individuals, in individuals at risk of disease (such as pre-diabetes and metabolic syndrome; STRRIDE), and in individuals with disease (such as coronary heart disease, congestive heart failure and cancer).
A second focus of my research group is exploration of genetic determinates of disease risk in human subjects. We conduct studies of early onset cardiovascular disease (GENECARD; CATHGEN), congestive heart failure (HF-ACTION), peripheral arterial disease (AMNESTI), and metabolic syndrome. We are exploring analytic models of predicting disease risk using established and innovative statistical methodology.
A third focus of my group’s work is to understand the cellular signaling mechanisms underlying the normal adaptive responses of skeletal muscle to physiologic stimuli, such as occur in exercise conditioning, and to understand the abnormal maladaptive responses that occur in response to pathophysiologic stimuli, such as occur in congestive heart failure, aging and prolonged exposure to microgravity.
Recently we have begun to investigate interactions of genes and lifestyle interventions on cardiometabolic outcomes. We have experience with clinical lifestyle intervention studies, particularly the contributions of genetic variants to interventions responses. We call this Lifestyle Medicopharmacogenetics.
KEY WORDS:
exercise, skeletal muscle, energy metabolism, cell signaling, gene expression, cell stretch, heart failure, aging, spaceflight, human genetics, early onset cardiovascular disease, lifestyle medicine
Elizabeth Rebecca Hauser
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
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