A statistical framework for quantitative trait mapping

We describe a general statistical framework for the genetic analysis of qua ntitative trait data in inbred line crosses. Our main result is based on th e observation that, by conditioning on the unobserved QTL genotypes, the pr oblem can be split into two statistically independent and manageable parts. The first part involves only the relationship between the QTL and the phen otype. The second part involves only the location of the QTL in the genome. We developed a simple Monte Carlo algorithm to implement Bayesian QTL anal ysis. This algorithm simulates multiple versions of complete genotype infor mation on a genomewide grid of locations using information in the marker ge notype data. Weights are assigned to the simulated genotypes to capture inf ormation in the phenotype data. The weighted complete genotypes are used to approximate quantities needed for statistical inference of QTL locations a nd effect sizes. One advantage of this approach is that only, the weights a re recomputed as the analyst considers different candidate models. This dev ice allows the analyst to focus on modeling and model comparisons. The prop osed framework can accommodate multiple interacting QTL, nonnormal and mult ivariate phenotypes, covariates, missing genotype YP data, and genotyping e rrors in any type of inbred line cross. A software tool implementing this p rocedure is available. We demonstrate our approach to QTL analysis using da ta from a mouse backcross population that is segregating multiple interacti ng QTL associated with salt-induced hypertension.