Genetic Evaluation and Parameter Estimation Using Marker and Trait Information

Abstract

Genetic evaluation by BLUP using marker and trait information requires knowledge of genetic parameters, such as the recombination rate ( r) between a marker locus and a marked QTL. Maximum likelihood methods are widely used to estimate genetic parameters. This thesis presents a new approximation to the likelihood for a pedigree with loops, based on cutting all loops and extending the pedigree at the cuts. An optimum strategy to cut loops and an iterative extension technique are presented. The likelihood for a pedigree with loops is then approximated by the conditional likelihood for the entire cut-extended pedigree given the extended part. The approximation is efficient for large pedigrees with complex loops in terms of computing speed and memory requirements.