Optimal Utilization of Genomic Information for Dissecting Complex Traits

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The University of California at Riverside is awarded a grant to start research into the theoretical development of new statistical methods for optimal utilization of established genomic databases for genetically dissecting complex traits. The statistical method to be used is the Bayesian method, which will be implemented via the Markov chain Monte Carlo (MCMC) algorithm. Specific areas to be studied include development of optimal statistical methods and computational algorithms for mapping quantitative trait loci (QTL) with epistatic effects (interaction between loci) using markers of the entire genome. The methods are clearly in contrast to the conventional methods of genome scanning in that the latter are one-dimensional searches that often result in low power of gene detection and difficulty in interpreting the results. These problems can be avoided by using a single unified multiple effects model.