Tian Liu, from the Genome Institute of Singapore, is invited by Dr. Wu and will give a talk on the PHS Seminar on July 9, 2009.
The following part is the abstract and the details of her talk.
Interactions between alleles at different loci, called epistasis, have been increasingly recognized to be of paramount importance in the pathogenesis of most common human diseases, such as cancer or cardiovascular disease, and patientsf responsiveness to a medicine. The most common approaches for detecting genome-wide epistasis are based on genetic mapping that associates phenotypic variation of a trait with a linkage or linkage disequilibrium map constructed by polymorphic markers. Integrating the principle of quantitative genetics, we here propose a statistical model for dissecting a complex disease into its genetic action and interaction components composed of causal single nucleotide polymorphisms (SNPs) in a simple case-control association study. The model can discern four different kinds of epistasis, additive ~ additive, additive ~ dominant, dominant ~ additive, and dominant ~ dominant interactions. To test each kind of epistasis, a ?2 test statistic was computed for a two by two contingency table derived from combined zygotic genotypes in both the case and control groups. We derived an analytical approach for estimating the asymptotic distribution of the ?2 test statistic under the null hypothesis, with the result being consistent with that from Monte Carlo simulations. Computer simulations show that the model is more powerful and informative than existing approaches. The new model was used to analyze a case-control data set for candidate gene studies of stroke, leading to the identification of several signficant interactions between causal SNPs on this disease.