Quantitative trait loci discovery, validation, and fine mapping in porcine and bovine populations

New tools and technologies in the area of genomics and statistical genetics have become available to livestock geneticists interested in improving performance of their species of interest. Lean gain efficiency is an important trait to the economics of swine production. Twinning in cattle is a trait having been proven detrimental to many aspects of the dairy industry. Eight densely spaced microsatellite markers were genotyped over a 30 centimorgan region of Sus scrofa chromosome 4 (SSC4) in a closed population of breeding swine. A total of 1266 pigs' performance records comprising 14 half-sib sire families entered into a study to uncover quantitative trait loci (QTL) affecting variation on the component traits of lean gain efficiency. Secondarily, 921 Holstein bulls from 100 paternal half-sib families were genotyped for 435 single nucleotide polymorphism (SNP) markers targeting 14 previously identified regions across the bovine autosomes harboring putative QTL. Additional individual markers were also targeted for association with twinning rate. Twinning rate predicted transmitting abilities (PTAs) were calculated using calving records from 1994 to 1998 (Data I) and 1999 to 2006 (Data II), and the underlying liability scores from threshold model analysis were used as the analysis trait. Linkage combined with linkage disequilibrium (LLD) analysis methods, followed by likelihood ratio tests, was utilized to separate random polygenic variation from variation produced by putative QTL. Single marker association analysis was also performed on the Holstein data to identify markers for use in marker-assisted selection (MAS). Little evidence for QTL on SSC4 was uncovered in the swine LLD analysis. Linkage disequilibrium may not be extensive enough, or marker density may not be sufficient for successful identification of QTL. LLD analysis uncovered 10 significant regions in agreement between both datasets, with multiple QTL likely on BTA14. Single marker association analyses identified 71 significant associations. Stepwise backward elimination and cross-validation analyses identified 12 and 18 SNP for use in two final reduced marker panels explaining 6.3% and 9.6% of the variation in PTAs, respectively, allowing the prediction of genetic merit for twinning rate.