The proposed research develops and applies appropriate methodology for the detection of major loci and the resolution of genetic and familial environmental effects on several phenotypes of importance to coronary heart disease (CHD), including plasma lipids and lipoprotein concentrations. Family data collected at five of the Lipid Research Clinics (LRCs) established by the National Heart, Lung, and Blood Institute (NHLBI) will be analyzed. New statistical methods will be developed for the families ascertained through probands with elevated lipid levels (non-random samples), and analyses will be performed on all data in a unified manner. Modes of inheritance will be investigated for two familial dyslipoproteinemias of considerable clinical significance: familial combined hyperlipidemia and familial primary hypoalphalipoproteinemia, two conditions known to be associated with premature CHD. Using both randomly and non-randomly ascertained data, we will especially investigate the possible major gene effects on these conditions. Specific hypotheses will be formulated and tested on the cultural and biological effects, heterogeneity among the LRCs (identifying the specific sources of such heterogeneity), familial basis for associations among the multiple risk factors, and temporal trends in family resemblance (i.e., age-dependent variation in heritabilities). They will be formulated using appropriate path models. The proposed research is expected to improve significantly our knowledge of the familial associations and interactions among the phenotypes, and to provide a systematic assessment of the familial environmental effects in diverse populations. The two unique features of this proposal are the innovative handling of both the random and non-random samples of family data, and the use of state-of-the-art models and statistical methods. It should be clarified that this proposal only complements, and does not duplicate, the ongoing efforts at the Central Patient Registry and Coordinating Center for the LRCs in Chapel Hill. It is believed that this cost-effective Public Health Research will expand understanding of a huge, expensive, resourceful, and previously collected data set.
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