Genetic Analysis of CVD Risk Factors

项目1的目标是检测、绘制和表征导致癌症风险变化的基因。 心血管疾病（CVD）。该计划项目的重点（圣安东尼奥家庭心脏研究， SAFHS）是在不考虑疾病状况的情况下确定的墨西哥裔美国人大家庭。中的每 在8 cmorgan图谱中，对~ 1400个家族成员进行了>400个微卫星标记的基因分型。在 在本研究期间，已经获得了影响HDL-C和其他指标的QTL的重要证据， 脂蛋白测量;与代谢综合征、糖尿病和胰岛素相关的表型;肥胖相关 特征;与血压相关的表型及其随年龄的变化;以及几种新的CVD相关 表型，包括肺炎衣原体感染的风险。鉴定功能等位基因， 在项目2和3中，将对这些基因中的一些最佳特征进行研究， 将在项目4中确定基因。方案一利用广布基因型资源， 在过去15年中创建的表型数据以及大量新生成的 表达数据和550 K SNP面板目前正在其他资金下进行基因分型，我们将 (1)利用纵向数据调查表型变化的遗传基础，特别是， 询问通过横截面数据分析确定的QTL是否也影响随时间的变化。 (2)对新获得的转录本数据进行多变量分析， 表达表型，以及一组精心选择的CVD风险因素和环境变量， 产生关于基因作用的假说。 (3)使用550 K SNP面板进行全基因组关联筛选，以获得临床重要性状， 在SAFHS中尚未定位QTL的显著遗传力。 (4)使用与从550 K SNP面板中提取的一组密集SNP的关联，以随访提示 从目前的赠款期间的联系高峰。 因此，项目1将致力于跟踪本赠款期内有趣的发现; 新数据分析中的新QTL;风险因素中年龄相关变化的遗传分析;以及 新的风险因素的遗传分析，包括20，000+转录水平的信息。

The objective of Project 1 is to detect, map and characterize genes that contribute to variation in risk of cardiovascular disease (CVD). The focus in this Program Project (the San Antonio Family Heart Study, SAFHS) is on extended Mexican American families ascertained without regard to disease status. Each of the -1400 family members has been genotyped for >400 microsatellite markers in an 8 centimorgan map. In the current grant period significant evidence has been obtained for QTLs that influence HDL-C and other lipoprotein measures; phenotypes related to metabolic syndrome, diabetes, and insulin; obesity-related traits; phenotypes related to blood pressure and its change with age; and several novel CVD-related phenotypes, including risk of infection with Chlamydia pneumoniae. Identification of the functional alleles for a few of the best characterized of these genes will be pursued in Projects 2 and 3, and novel candidate genes will be identified in Project 4. In Project 1, taking advantage of the resource of extensive genotypic and phenotypic data that has been created in the past 15 years as well as a wealth of newly generated expression data and a 550K SNP panel currently being genotyped under other funding, we will (1) utilize the longitudinal data to investigate the genetic basis of phenotypic change, and in particular, to ask whether QTLs identified through analyses of cross-sectional data also influence change over time. (2) perform multivariate analyses of newly available transcript data that yield more than 20,000 quantitative expression phenotypes, and a set of carefully selected CVD risk factors and environmental variables to generate hypotheses concerning gene action. (3) conduct genome-wide association screens with a 550K SNP panel for clinically important traits with significant heritabilities for which no QTLs have yet been localized in the SAFHS. (4) use association with a dense set of SNPs, drawn from the 550K SNP panel, to follow-up suggestive linkage peaks from the current grant period. Thus, Project 1 will be devoted to follow-up of intriguing findings from the current grant period; detection of new QTLs in analyses of newly-available data; genetic analyses of age-related changes in risk factors; and genetic analysis of new risk factors, including information on levels of 20,000+ transcripts.