GENETIC MAPPING OF ATHEROSCLEROSIS PHENOTYPES

项目1的目标是绘制基因， 与发育相关的数量表型 圣安东尼奥墨西哥裔美国家庭的动脉粥样硬化 家庭心脏研究 基因组搜索，利用10厘摩 391个短串联重复序列标记分布在整个 基因组，将进行确定染色体位置 在目前的资助期内检测到的六个主要基因（基因 影响HDL-C、LDL-C、apoAI、apoB、SHBG和DHEAS）。 对于脂蛋白表型，尚未发现主要基因， 如果检测到，将寻找主要基因效应的证据。 的 基因组搜索将集中在染色体区域， 基于亲缘关系的变异中的联系的初步证据 项目2中的组件筛选测试。 如有必要，分析 将扩展到包括这些区域中的额外DNA标记。 基因对颈动脉和纤维蛋白溶解表型的影响将是 使用750名家庭成员的回忆数据进行量化。 充分 谱系方差成分分析以及基于 方法将用于基因组搜索，以确定 影响这些表型的基因的染色体位置。 对于提供连锁证据的DNA标记， 分析将扩大到包括这些额外的标志物， 区域，利用多点方法。 为了改进遗传模型， 加强证据衔接，减少虚假数量 阳性，gxE相互作用和多效性和上位性效应 主要基因将被量化。 寡基因分析将加强 链接信号，并进一步减少误报，以及 不平衡分析将改善基因定位 影响定量CVD危险因素。基因定位于 项目1将指导候选基因的选择， 研究项目2。 这些分析将为今后的工作奠定基础。 努力识别主要基因并了解其功能。

The objective of Project 1 is to map genes that influence quantitative phenotypes related to the development of atherosclerosis in Mexican American families in the San Antonio Family Heart Study. A genomic search, utilizing a 10 centimorgan map of 391 short tandem repeat markers distributed throughout the genome, will be conducted to determine the chromosomal locations of six major genes detected during the current grant period (gene influencing HDL-C,LDL-C, apoAI, apoB, SHBG, and DHEAS). For lipoprotein phenotypes for which no major genes have yet been detected, evidence for major gene effects will be sought. The genomic search will focus on chromosomal regions that show preliminary evidence for linkage in sibship based variance component screening tests in Project 2. If warranted, the analyses will be expand to include additional DNA markers in these regions. Genetic effects on carotid and fibrinolysis phenotypes will be quantified using data from a recall of 750 family members. Full pedigree variance component analysis as well as penetrance-based methods will be used in a genomic search to determine the chromosomal locations of genes that influence these phenotypes. For DNA markers that give suggestive evidence of linkage, analyses will be expanded to include additional markers in these regions, utilizing multipoint methods. To improve genetic models, strengthen evidence for linkage, and reduce the number of false positives, gxE interactions and pleiotropic and epistatic effects of major genes will be quantified. Oligogenic analysis will strengthen linkage signals and further reduce false positives, and disequilibrium analysis will improve localization of genes influencing quantitative CVD risk factors. Genes localized in Project 1 will guide the selection of candidate genes for molecular studies in Project 2. These analyses will set the stage for future efforts to identify the major genes and understand their functions.