The advent of high resolution genetic maps of highly informative short tandem repeat polymorphisms (STRPs) has made the mapping of genes which confer increased susceptibility to common and/or complex diseases technically feasible. However, the statistical and computational methodology to efficiently utilize this technology is lagging behind the requisite laboratory methods. The overall goal of this proposal is to develop the statistical and computational tools to effectively take maximal advantage of these dense genetic maps. We have previously developed a method (MIM) using multiple marker loci to partition genetic variance of a human quantitative trait to loci located in specific chromosomal regions. In this proposal we plan to continue and extend our work on the MIM methodology and incorporate these new theoretical developments in an expanded version of our previously developed computer software. We will examine the utility of a Monte-Carlo method for multipoint linkage analysis of complex traits on large extended pedigrees with substantial amounts of missing data. We plan to adapt this method to several non-parametric or model-free linkage analysis methods to increase their power in general pedigrees. Lastly, we will apply the methods derived in the above specific aims to several human traits of varying complexity using previously gathered genome-wide genotypic data from our ongoing linkage studies.
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