GENETIC-EPIDEMIOLOGIC STUDY OF HD RISK FACTORS

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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.