This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. Over the last ten years, linkage analysis has been a primary tool in the effort to discover genes that predispose to common complex genetic diseases in humans. Although progress has been made, overall the results have been somewhat disappointing due to low power and lack of replicability of results. The primary impediment to successful linkage studies in complex diseases is believed to be locus heterogeneity - that is, the complexity of the underlying genetic model. Our work has as its premise the notion that most family data sets are composed of subsets, each segregating for a different set of genes. Our goal is to use additional phenotypic information, collected in the course of the study, as surrogate measures of subset membership. When included as covariates in a linkage analysis, these phenotypic measures can provide considerable power both to detect linkage and to identify phenotypic characteristics of the linked subset. We are therefore developing and studying extensions to standard linkage methods to include covariates. We are also developing multilocus models that include covariates so that the joint action of sets of loci can be studied. As part of this effort, we have recently addressed the problem of identifying the true asymptotic p values when these methods of analysis are used, by using simulation results and regression analysis. We found that the regression models suggested by our analysis provide more accurate alternative p values for model-free linkage analysis when using the conditional logistic model, and these results are currently being incorporated in the linkage program LODPAL. Ultimately, these tools will enable gene mappers to rapidly construct detailed genetic models for complex disorders, greatly facilitating the process of gene discovery.
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