Modeling DNA Diversity in Cardiovascular Health/Disease

Study Overview
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Brief Summary
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To identify and measure DNA sequence variation in 13 genes that play a central role in key physiological functions involved in the development of cardiovascular disease, that is, genes involved in lipid metabolism, carbohydrate metabolism, and blood pressure regulation.

Detailed Description
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BACKGROUND: One of the most complex and challenging problems in human biology and medicine is defining the relationship between DNA sequence variation and interindividual variation in quantitative risk factors for complex diseases having a multifactorial etiology. As the knowledge about the basic human DNA sequence increases, so will the need to define the range of natural variation in human populations and to explore the relationship between nucleotide diversity and phenotype variation in measures of human health. DESIGN NARRATIVE: The study was a collaborative one involving Dr. Deborah Nickerson at the University of Washington, Dr. Kenneth Weiss at Pennsylvania State University, and Dr. Charles Sing of the University of Michigan. Dr. Nickerson identified and measured DNA sequence variation in 13 genes that play a central role in key physiological functions involved in the development of cardiovascular disease. She applied state-of-the-art automated fluorescence-based sequencing and high-throughput DNA genotyping methods to uncover and assess DNA sequence variation in three human populations: non-Hispanic Whites from Rochester, MN (lowCVD risk), African-Americans from Jackson, MS (intermediate CVD risk) and non-Hispanic Whites from North Karelia,Finland (high CVD risk). Dr. Weiss used the theoretical and statistical approaches of molecular population genetics to characterize the cumulative effects of population history on the amount, distribution, and structure of extant variation in 13 candidate CVD susceptibility genes in three populations. His tests of linkage equilibrium and of homogeneity of the variation across several levels of stratification (among individuals, demographic variables, parts of genes, populations, and among genes) further sharpened the understanding of the nature of human genetic variation, particularly with respect to candidate CVD susceptibility genes. He made inferences about functional constraints from gene trees and from patterns of divergence among human populations and between humans and the chimpanzee. Dr. Weiss's project provided the inferential engine that drove the sampling design and sample selection in Dr. Nickerson's project and provided the demographic/historical background necessary for genotype-phenotype inferences of Dr. Sing's project. Dr. Sing developed models for the relationships between the DNA sequence variation in the 13 candidate CVD susceptibility genes identified, measured, and characterized in Dr. Nickerson's and Dr. Weiss's projects and variation in established quantitative risk factors for CVD, including total plasma cholesterol, HDL cholesterol, and triglycerides, and systolic and diastolic blood pressure collected from the 1,500 individuals. His project established which subset of DNA sequence variations in which candidate genes were associated with variation in CVD risk factors in which subset of individuals and in which of three populations. His elucidation of the relationships between DNA sequence variations and variations in intermediate biological risk factor traits revealed opportunities for intervention to alter the risk of CVD and establish whether such efforts should be directed across populations, within a population, or at an individual. The study completion date listed in this record was obtained from the End Date entered in the old format Protocol Registration and Results System (PRS).

Conditions
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Cardiovascular Diseases, Heart Diseases

Participation Criteria
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Eligibility Criteria
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No eligibility criteria

Ages Eligible for Study
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Maximum Age: 100 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
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