GENETIC EPIDEMIOLOGY MODELS AND COMPUTER SOFTWARE

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DESCRIPTION (Adapted from the Investigator's Abstract): The purpose of this grant application is to continue to develop an analytic framework, the regression models for the analysis of family data, and to facilitate the transfer of this new biostatistical methodology to genetic epidemiology through the development of the software: G.E.M.S. Many studies in the genetic epidemiology of cancer are based on families of two or more generations. Such family studies include the assessment of the role of measured risk factors taking into account specific biologic relationships, the determination of the distribution and familial correlations in age-of-onset segregation and linkage to determine the possible involvement of genes in the etiology of the disease. The desired statistical framework encompasses dependent binary and survival outcomes with regression variables, but the scope should include modeling the dependence without or with reference to genes transmitted in families according to the laws of genetics. The analytic framework is the regressive models which account for familial correlations by specifying a regression relationship between a person's phenotype and a set of explanatory variables including his genotype with respect to specific loci, the phenotypes of older relatives, and environmental and lifestyle co-variates. The appeal of this method is that it simultaneously provides for the effects resulting from important gene(s) and those resulting from complex patterns of residual familial correlations, including sib-sib, spouse-spouse, and parent-offspring phenotypic correlations, without or with reference to explicit genetic or environmental causal mechanisms. Know mechanisms are incorporated by choosing suitable parameterization. Proposed developments include broadly applicable distributions and the classifications and regression trees (CART) method for screening large numbers of markers and other risk factors. These are then adapted to do the following: Modeling and analysis of familial aggregation of disease and risk factors including genetic markers; Distribution and familial correlations in age-of-onset; Segregation and linkage analyses. The methods will be implemented in the software package G.E.M.S., which is designed to be user-friendly and portable across common computing platforms.