Hulsman, Lauren Lorene (2013-05). Investigation of Genomic Estimated Breeding Values and Association Methodologies using Bayesian Inference in a Nellore-Angus Crossbred Population for Two Traits. Doctoral Dissertation.
Thesis
The objectives of this study were to 1) evaluate marker associations for genomic regions of interest and significant ontology terms, 2) evaluate and compare 4 models for their efficacy in predicting genetic merit, 3) evaluate and compare the impact of using breed-of-origin genotypes in a Bayesian prediction model, and 4) evaluate the effects of data partitioning using family structure on predictions. Nellore-Angus F2, F3 and half-sibling calves were used with records for overall temperament at weaning (OTW; a subjective scoring system; n = 769) and Warner-Bratzler shear force (WBSF; a measure of tenderness; n = 389). After filtering, 34,913 markers were available for use. Bayesian methods employed were BayesB (using ̂) and BayesC (using π = 0 and ̂) in GenSel software, where, after estimation, π ̂ = 0.995 or 0.997 for WBSF or OTW, respectively. No regions associated with either trait were found using π ̂, but when π = 0 associated regions were identified (37 and 147 regions for OTW and WBSF, respectively). Comparison of genomic estimated breeding values from these 3 Bayesian models to an animal model showed that BayesC procedures (using ̂) had the highest accuracy for both traits, but that BayesB had the lowest indication of bias in either case. Using a subset of the population (n = 440), genotypes based on the breed in which the alleles originated from (i.e., breed-of-origin genotypes) were assigned to markers mapped to autosomes (n = 34,449), and incorporated into prediction analyses using BayesB (π ̂ = 0.997) with or without nucleotide-based genotypes. In either case, there was an increase in accuracy when breed-of-origin genotypes were incorporated into prediction analyses. Data partitions based on family structure resulted in 13 distinct training and validations groups. Relationship of individuals in the training with validation individuals did have an impact in some cases, but not all. There was poor prediction of genomic estimated breeding values for individuals in the validation population using BayesB methods, but performed better in all cases than breeding values generated using an animal model. Future studies incorporating breed-of-origin genotypes are of interest to determine if accuracy is improved in these groups.
