AbstractsBiology & Animal Science

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 genotypes usually exist in real datasets. We proposed the Monte Carlo pedigree disequilibrium test (MCPDT) to test for association using general pedigree data with missing genotypes. It generates Monte Carlo samples of missing genotypes conditioned on observed genotypes and then calculates test statistics with the Monte Carlo samples. In a simulation study, it achieved better performance than other family-based association test methods. Since MCPDT uses estimates of population marker allele frequencies in the generation of Monte Carlo samples, population structure may generate bias in MCPDT statistics. To adjust for population structure in MCPDT, a Markov chain Monte Carlo algorithm was designed to infer the structure from pedigree data with multiple null markers and the inferred structure was then used in MCPDT. Simulation studies were done to evaluate the performance of this method.