Overview
A Statistical Framework for Quntative Trait Mapping
QTL Archive
A Statistical Framework for Quantitative Trait Mapping
Abstract: We describe a general statistical framework for the genetic analysis of quantitative trait data in inbred line crosses. The framework makes it computationally feasible to implement important generalizations of the single-QTL model based on normally-distributed phenotypes. This includes considering multiple interacting QTL, non-normal and multivariate phenotypes, covariates, missing genotype data and genotyping errors in any type of inbred line cross. Our main result is based on the observation that if the QTL genotypes were known, the problem could be split into two statistically independent and manageable parts. The first part involves only the relationship between the QTLs and the phenotype. The second part involves only the location of the QTLs in the genome. We have developed a simple Monte Carlo algorithm to implement Bayesian QTL analysis. This algorithm simulates multiple versions of complete genotype information on a genome-wide grid of locations using information in the marker genotype data. Weights are assigned to the simulated genotypes to capture information in the phenotype data. The weighted complete genotypes are used to approximate quantities needed for statistical inference of QTL locations and effect sizes. One advantage of this approach is that only the weights are recomputed as the analyst considers different candidate models. A software tool implementing this procedure is available. We demonstrate our approach to QTL analysis using data from a mouse backcross population that is segregating multiple interacting QTL associated with salt--induced hypertension.
