Genomic Predictors of Arteriosclerosis in Hypertensives

描述(由申请人提供)： 心、脑、肾和外周动脉的动脉硬化(即动脉硬化和动脉硬化)会导致靶器官损害和临床后遗症，如心脏病发作和衰竭、中风和痴呆、慢性肾脏疾病和跛行。由于多种致病途径，大量基因的变异可能影响高血压患者靶器官损害的易感性。动脉病遗传流行病学网络(Genoa)成立于1995年，旨在利用同胞间的连锁分析研究高血压及其靶器官并发症的遗传学。作为正在进行的热那亚研究的一部分，已经收集了动脉硬化的量化指标，包括冠状动脉钙化的动脉硬化/大血管指标、主动脉根部直径、踝臂血压指数，以及缺血性脑损伤(皮质下白质高信号、脑萎缩和脑室容量)和慢性肾脏疾病(血清肌酐和蛋白尿)的动脉硬化/微血管指标。在这项应用中，我们建议对动脉硬化的这些表型指标进行全基因组关联研究，利用来自热那亚队列的1418名非洲裔美国人和1095名非西班牙裔白人的500,000个单核苷酸多态。具体目标如下：目标1：利用500,000个SNPs，在1418名非洲裔美国人和1095名热那亚非西班牙裔白人参与者中，识别包含疾病易感基因座证据的基因组区域，用于动脉硬化的定量测量。我们将在这两个样本中使用最先进的单变量和多变量方法进行全基因组关联分析，以确定特征特定的基因座，以及对多种遗传相关的动脉硬化指标具有多效性影响的遗传基因座。利用我们数据的高血压同胞对结构，我们将创建两个高血压患者的复制集合(一个同胞在不相关的集合1中，第二个同胞在不相关的集合2中)来执行复制的全基因组关联。SNP关联将根据多次测试进行调整，使用交叉验证评估其预测效用，并比较重复证据以减少假阳性。目的2：为了研究背景相关遗传效应在这些队列中靶器官疾病分布中的作用，我们将检验SNP-协变量(年龄、性别、吸烟、肥胖、酒精、体力活动、教育、血压药物治疗)和SNP-SNP相互作用对目标1中研究的动脉硬化的相同定量指标的影响。SNP关联将针对多重测试进行调整，使用交叉验证评估其预测效用，并比较重复证据以减少假阳性。我们还将使用监督模式识别方法(分类和回归树、随机森林、按状态的基因组身份方法)来建立这些临床表型的预测模型，以识别高危个体。(摘要结束)

DESCRIPTION (provided by applicant): Arteriosclerosis (i.e., atherosclerosis and arteriolosclerosis) of the cardiac, cerebral, renal, and peripheral arteries leads to target organ damage and clinical sequelae such as heart attack and failure, stroke and dementia, chronic kidney disease, and claudication. Because of multiple etiologic pathways, variations in a large number of genes are likely to influence susceptibility to target organ damage in hypertensive individuals. The Genetic Epidemiology Network of Arteriopathy (GENOA) was initiated in 1995 to study the genetics of hypertension and its target organ complications using linkage analysis in sibships. As part of the ongoing GENOA study, quantitative measures of Arteriosclerosis including both atherosclerotic/ macrovascular measures of coronary artery calcification, aortic root diameter, ankle-brachial BP index, and arteriolosclerotic /microvascular measures of ischemic brain injury (subcortical white matter hyperintensities, brain atrophy, and ventricular volume) and chronic kidney disease (serum creatinine and albuminuria) have been collected. In this application, we propose to conduct a genome-wide association study of these phenotypic measures of arteriosclerosis, utilizing 500,000 single nucleotide polymorphisms measured on 1418 African-Americans and 1095 non-Hispanic Whites from the GENOA cohort. With the following specific aims: Aim 1: To identify genomic regions containing evidence of disease susceptibility loci for quantitative measures of Arteriosclerosis in 1418 African-American and 1095 non-Hispanic white GENOA participants using 500,000 SNPs. We will perform genome-wide association analysis in these two samples using state-of-the-art univariate and multivariate approaches to identify trait-specific loci, as well as genetic loci with pleiotropic effects on multiple genetically correlated measures of arteriosclerosis. Capitalizing on the hypertensive sibpair structure of our data, we will create two replicate sets of hypertensives (one sib in unrelated Set 1, second sib in unrelated Set 2) to perform replicate genome-wide associations. SNP associations will be adjusted for multiple testing, assessed for their predictive utility using cross-validation, and compared for replicate evidence to reduce false positives. Aim 2: To investigate the role of context dependent genetic effects on the distribution of target organ disease in these cohorts, we will test for effects of SNP-covariate interactions (age, sex, smoking, obesity, alcohol, physical activity, education, blood pressure drug treatment) and SNP-SNP interactions on the same quantitative measures of Arteriosclerosis investigated in Aim 1. SNP associations will be adjusted for multiple testing, assessed for their predictive utility using cross-validation, and compared for replicate evidence to reduce false positives. We will also use supervised pattern recognition methods (classification and regression trees, random forests, genomic identity-by-state methods) to build predictive models of these clinical phenotypes to identify at-risk individuals. (End of Abstract)