This renewal application of HL39107 will establish a research paradigm for defining the genetic architecture of an intermediate quantitative trait that translates genomic variation into variation in risk of a common chronic disease. Genetic architecture is defined by the number of genes that influence a trait, the number of alleles for each gene and their frequencies and the contribution of variation in each gene, and combinations of genes, to interindividual phenotypic variation in the population at large. We have selected plasma Apolipoprotein E (ApoE) as a model trait for developing strategies for researching the genetic architecture of an intermediate trait because the common E2, E3 and E4 alleles of the structural ApoE gene on chromosome (chr) 1 9q have consistently been identified as significant predictors of interindividual variation in risk of coronary artery disease and Alzheimer's disease, but they explain only 20 percent of the genetic variability in plasma ApoE levels. The proposed research will use the completed genome-wide linkage analyses carried out by this project in the past four years to identify gene variations that explain the remaining 80 percent of the genetic variability in plasma ApoE levels. Detailed analyses identified three regions with convincing evidence for the presence of a gene that influences plasma ApoE. Therefore, the SPECIFIC AIMS of this renewal application will carry out further analyses of the ApoE gene itself (Region 1), detailed analyses of an additional positional candidate gene, the low density lipoprotein receptor (LDLR) gene, on chr l9p (Region 2) and identification of a novel gene, or genes, influencing plasma ApoE levels on chr 13q (Region 3).
These AIMS will be accomplished in a sample of 552 three-generation pedigrees randomly ascertained without regard to health containing 3941 examined individuals from Rochester, MN. We will develop a web-based resource to provide a structured approach to guide other researchers in identifying genes that influence disease risk, in measuring DNA sequence variations in those genes and in establishing which combinations of DNA sequence variations in those genes are predictors of variation of disease risk in which individuals and which environmental strata within a particular population.
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