GENETIC-EPIDEMIOLOGIC STUDY OF HD RISK FACTORS

建议在以前收集的9个数据集中进行检查 美国人口的家族相似性模式和 用一种新的统计方法研究基因与环境的相互作用 探索性数据分析(SEDA)。要分析的数据包括 血压、血脂和脂蛋白的生理学变量 胆固醇浓度和体重，以及各种文化和 环境变量包括饮食、压力、体力活动、吸烟 行为，以及与以下方面相关的知识、态度和意见的衡量标准 心脏病，在无关的个人、配偶和核心人群中调查 家庭套装。要检查的问题包括父母的影响 互动、手足互动、共同家庭环境、 亲子传递、遗传方式、遗传特征 性状分布，种群异质性，代际差异， 家庭结构、人口结构对家庭互动的影响。在……里面 此外，为了研究基因与环境的相互作用，我们建议使用 分层和加权程序的层次结构，将 一种或多种环境因素的不同水平对SEDA的影响 继承统计模式。其他具体目标是比较 家族相似性模式与基因-环境交互作用：(1)交叉 地理上不同的人口；(2)随机选择的家庭之间 和通过高血脂先证者确定的家族，以及(Iii)在家族中 随着时间的推移反复测量。我们的目标是区分 影响家庭成员的各种影响的相对强弱和形式 聚集心血管危险因素的最终目的是 提供最有效地处理其修改的策略。 将考察四类SEDA统计量：SEDA泛函， SEDA指数、关联数组和各种权重以及 SEDA泛函和SEDA指数的分层。多变量 将研究所有四类统计的扩展。这个 这些SEDA统计数据的重要性将由以下几个方面确定 有选择地对性状值进行洗牌的排列技术 家人。这一过程系统地改变了某些家庭结构 在保持其他家庭关系完好无损的同时，建立良好的家庭关系。

It is proposed to examine in previously collected data sets from nine United States populations patterns of familial similarity and gene-environment interactions by a new statistical methodology, Structured Exploratory Data Analysis (SEDA). The data to be analyzed include physiologic variables of blood pressure, plasma lipid and lipoprotein cholesterol concentrations and weight, and a variety of cultural and environmental variables including diet, stress, physical activity, smoking behavior, and measures of knowledge, attitude and opinions that relate to heart disease, surveyed in unrelated individuals, spouse-pairs, and nuclear family sets. Among the issues to be examined are the effects of parental interaction, sibship interaction, common family environment, parent-offspring transmission, mode of inheritance, characteristics of the trait distribution, population heterogeneity, generational differences, family structure, and population structure on familial interactions. In addition, to investigate gene-environment interactions we propose to employ a hierarchy of stratifications and weighting procedures that contrast the effect of different levels of one or more environmental factors on SEDA mode of inheritance statistics. Other specific aims are to compare familial similarity patterns and gene-environment interactions: (1) across geographically diverse populations, (ii) between randomly selected families and families ascertained via a high lipid proband, and (iii) in families measured repeatedly over time. Our objective is to distinguish the relative strengths and forms of various influences that affect the familial aggregation of cardiovascular risk factors for the purpose of eventually offering strategies to most effectively approach their modification. Four classes of SEDA-statistics are to be examined: SEDA-functionals, SEDA-indices, association arrays, and various weightings and stratifications of the SEDA-functionals and SEDA-indices. Multivariate extensions of all four classes of statistics will be studied. The significance of these SEDA-statistics will be determined by a spectrum of permutation techniques that selectively shuffle the trait values across families. The process systematically alters certain family structure relationships while keeping other familial relationships intact.