Abstract

This thesis presents and discusses the use of various genetic models, high performance computing, global optimization algorithms and statistical methods for mapping Quantitative Trait Loci (QTL). The aim of the work has been to develop statistically powerful and computationally efficient methods to detect genomic loci affecting multifactorial traits, and use the methods use to analyse experimental data. Imprinting is an epigenetic phenomena which causes differential expression of alleles based on their parental origin. A genetic model handling imprinting was used during QTL mapping in an experimental Wild Boar x Large White intercross. The analyses revealed a paternally imprinted QTL with large effect on the development of muscle mass. Parallel computing algorithms for interval mapping and randomization testing in QTL mapping are described. New randomization testing schemes are now computationally feasible due to these algorithms. Selection of appropriate kernel algorithms for solving least squares type problems in QTL mapping is discussed. The importance of optimization of QTL mapping software is also illustrated. A genetic algorithm was shown to be efficient in a multidimensional search for interacting QTL. The genetic algorithm significantly decreases the computational demand when employing simultaneous mapping of multiple QTL, and makes randomization testing based on multidimensional searches computationally feasible. A new randomization testing scheme based on simultaneous mapping of epistatic QTL was also proposed and evaluated. A simulation study showed that the method increases the power to detect epistatic QTL. A large intercross was derived between Red junglefowl and White Leghorn chickens. A number of QTL affecting growth was revealed using the newly developed method for simultaneous mapping of epistatic QTL pairs. In total, 21 QTL were identified, and eleven of these were only detected by the new simultaneous mapping method. Epistasis was shown to be an important component in the genetic regulation of the growth process.