Genetic Epidemiology of CVD Risk Factors

描述（由申请人提供）：了解常见的多因素疾病如心血管疾病（CVD）的遗传基础仍然是一个难以捉摸的目标，但近年来分子遗传技术、统计遗传方法和CVD危险因素的表型评估的巨大进步促进了更复杂的心脏病风险遗传研究。本研究的总体目标是阐明遗传因素影响CVD危险因素的作用，最终确定影响CVD风险年龄相关进展的特定基因。这一目标将通过一项新的创新合作研究项目来实现，该项目由赖特州立大学医学院和西南生物医学研究基金会协调的R01拨款组成。这项研究以25年前开始调查的5个多代大家庭（4个白人和1个非裔美国人）中的764个人为中心。从原始参与者收集的数据包括数百种生化、医学、生理、行为、生理、心理、遗传和人口统计学特征。不过，从某种程度上说，最初的研究是超前的，因为成本效益高的全基因组图谱和有效分析来自大量亲属的家族数据的统计遗传方法还需要10年或20年的时间。

DESCRIPTION (provided by applicant): Understanding the genetic basis of common multifactorial diseases such as cardiovascular disease (CVD) remains an elusive goal, but the great advances in molecular genetic technology, statistical genetic methods, and phenotypic assessment of CVD risk factors in recent years have facilitated more sophisticated genetic studies of risks for heart disease. The overall goal of this study is to elucidate the role of genetic factors influencing risk factors for CVD, ultimately identifying specific genes influencing the age-related progression of CVD risks. This goal will be pursued through a new and innovative collaborative research project consisting of coordinated R01 grants to Wright State University School of Medicine and the southwest Foundation for Biomedical Research. The study population centers on 764 individuals in five large, multigenerational, extended families (four white and one African-American) originally examined 25 years ago. Data collected from the original participants includes hundreds of biochemical, medical, physiological, behavioral, physical, psychological, genetic and demographic traits. To some extent, though, the original study was ahead of its time in that cost-effective whole genome mapping and statistical genetic methods for effective analysis of familial data from large extended kindred's were a decade or two away. The proposed study consists of four specific aims: 1) Collect 25-year follow-up data from approximately 500 of the original participants, and new data from approximately 500 of their relatives not examined in the original study. The CVD risk factor phenotypes to be collected include hemodynamic measures, carotid intima-media thickness, and measures of cardiopulmonary function. 2) Obtain DNA samples from these 1,000 individuals and use modern high-throughput molecular genotyping methods to create a 10 cM genetic marker map. 3) Quantify and characterize the nature of genetic influences on CVD risk factors using quantitative genetic methods suited for cross-sectional and serial (follow-up) data from relatives in large extended families. 4) Conduct linkage analyses to identify chromosomal regions (QTLs) harboring genes that influence individual variation in CVD risk factors. Following these linkage analyses, we will examine more closely our strongest linkage signals with fine mapping linkage analysis in order to narrow chromosomal regions of interest.