Family-based association test is one way of mapping disease susceptibility genes by testing for association between marker genotypes and disease phenotypes in family data. Missing data usually exist in real data sets. We have proposed the Monte Carlo pedigree disequilibrium test (MCPDT) to test for association using general pedigree data with missing genotypes. It generates a Monte Carlo sample of missing genotypes conditioning on observed genotypes and calculates test statistics based on the MC sample. Since MCPDT uses population information, we are also trying to take into account population substructure in a Bayesian framework. An MCMC algorithm is used to estimate population substructure from pedigree data with multiple unlinked markers and the estimates are then used in MCPDT. Simulation studies have been done to evaluate the performance of these methods.

Meet the speaker in Room 212 Cockins Hall at 4:30 p.m. Refreshments will be served.