Mixture Models in Quantitative Trait Loci (QTL) Mapping
Department of Biostatistics
University of North Carolina - Chapel Hill
In a QTL study, the putative QTL position is often unknown, which results in unknown QTL genotypes. Due to the unknown QTL genotypes, phenotype data therefore arise from mixtures of distributions under standard interval mapping procedures. Previous approaches to estimation involve modeling the distributions parametrically. In this talk, we will introduce several semi-parametric and non-parametric QTL mapping methods. Further, accurately estimating the QTL position is one of the major goals of any QTL study. Traditionally, the position corresponding to the peak of the profile LOD score from interval mapping is used to estimate the QTL position and often referred as the MLE estimate of QTL position. Is the MLE estimate truly optimal? Several alternative estimates will help us to answer this question.