Linkage analysis and classical association analysis of case-control data have identified many disease susceptibility genes, and these discoveries will lead to important public health benefits. However, recent evidence suggests that there are substantial barriers to using these methods for further gene discovery. There also is considerable uncertainty at present about optimal designs for characterizing the effects of disease susceptibility genes. The goals of this research are to develop new and improved ways to identify and characterize such genes in complex situations, and to classify individuals with respect to their disease risk. To accomplish these goals, the investigators will build on more than ten years of previous work. Specifically, in 1988 the National Cancer Institute (NCI) awarded an Outstanding Investigator Grant (OIG) to the Principal Investigator for the development and application of new and improved statistical methods for use in epidemiological research. In 1995 this grant was renewed until NCI terminated the OIG Program in 2001. The present application requests funding to continue this statistical research. Its objectives are to develop better ways to design and analyze studies of genetic predisposition and lifestyle characteristics as contributors to familial aggregation of site-specific cancers, particularly cancers of the ovary, breast and prostate.
The specific aims are twofold. One is to develop and evaluate improved methods in four problem areas: a) estimating penetrance of mutations of identified genes and modification of such penetrance by nongenetic factors; b) assessing genetic association in family data; c) identifying genes in the presence of genetic heterogeneity; and d) estimating individual indices of genetic admixture. A second specific aim is to validate these methods by application to data from epidemiological studies. These include existing data from the Cooperative Family Registry for Breast Cancer Studies (CFRBCS), new data from the Familial Registry for Ovarian Cancer (FROC), and existing data from three studies of prostate cancer. A major thrust of the work will be the development of user-friendly software to allow epidemiologists to apply the methods to their data.
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