Zugl.: Bremen, Universiẗat, Diss., 2007.

Zugl.: Köln, Universiẗat, Diss., 2004.

Four F2:4 populations of peanut (Arachis hypogaea L.) resulting from the complex cross Tamrun 96 X Tx901639-3 X Sun Oleic 95R were grown in three disease nurseries over a 2 year period. Three separate selection techniques were applied to determine which technique would provide the most effective method for selecting a multiple disease resistant, runner-type peanut. Technique I involved selection at a tomato spotted wilt virus nursery during the first cycle of selection and transferring the selections to a Sclerotinia minor (Jagger) nursery for a second cycle of selection in year two. Technique II was the reciprocal of Technique I. Technique III involved selection of the populations at a multiple disease nursery for two consecutive years. Selections were based on disease ratings, growth habits, pod and seed characteristics, and oleic/linoleic acid ratios. Disease ratings were scored as percentage infection on a scale of 0 (0% plot infected) to 10 (100% plot infected). Disease severity was also rated on a scale of 1 (symptoms noted, but no yield effects) to 10 (plant death, no yield). There were two final selections for each population using each selection technique that were yield tested over a 2 year period to determine which technique was superior. The yield tests were conducted using completely randomized block design at all three disease nurseries with an additional disease-free site included. Data for disease ratings, yield, grade, and value per hectare were combined within locations across years. All three selection techniques provided lines with more disease resistance than the parents; however, there was no difference detected between the effectiveness of the three techniques in terms of disease resistance, yield, grade, or value per hectare.

The overall goal of this thesis was to improve the accuracy of genomic prediction in dairy and beef cattle by developing, evaluating and enhancing novel or existent models and approaches for genomic selection. Four studies were conducted to fulfill this goal. In the first study, the impact of using genotypes imputed from low density panels for genomic prediction was evaluated and compared between a Bayesian mixture model and the Genomic Best Linear Unbiased Prediction (GBLUP) method. Results showed that for traits affected by a few large QTL, the Bayesian mixture model resulted in greater reduction in accuracy of genomic prediction, compared to GBLUP. However, for all SNP panels, scenarios and all traits studied, the Bayesian mixture model produced greater or similar accuracy, compared to the GBLUP method. In the second study, a new computing algorithm, called right-hand side updating strategy (RHSU), was proposed and compared to the conventional Gauss-Seidel residual update algorithm (GSRU) for genomic prediction. Results showed that RHSU would outperform GSRU once the sample size exceeded a fraction of the number of the SNPs. As the sample size continued to grow, the RHSU algorithm became more efficient than GSRU. In the third study, three different strategies of forming a training population for genomic prediction, within-breed, across-breed and pooling data from different breeds, were evaluated in Angus and Charolais steers using phenotypes on residual feed intake (RFI) and genotypes on the Illumina BovineSNP50 Beadchip (50k). Results suggested that using the 50k SNP panel, within-breed genomic prediction was a safe strategy; across-breed prediction resulted in the lowest accuracy; pooling data from different breeds had a potential to improve the accuracy but should be conducted with caution due to possible loss of accuracy. In the last study, a multi-task Bayesian learning model was proposed for multi-population genomic prediction. The performance of the multi-task model was evaluated in Holstein and Ayrshire dairy breeds. Results showed that the multi-task Bayesian learning model is effective and could be beneficial to smaller populations where only a limited number of training animals are available.

Frankfurt (Main), University, Diss., 2008. / Erscheinungsjahr an der Haupttitelstelle: 2007.

Thesis (Ph.D.)--University of Pennsylvania, 1924. / Reproduction of original from Yale Law School Library. Bibliography: p. 104-107.

Thesis (doctoral)--Wiss. Hochsch. für Unternehmensführung, Vallendar, 2004.

Zugl.: Frankfurt (Main), Hochsch. für Bankwirtschaft, Diplomarbeit, 1997.

Zugl.: Berlin, Techn. Universiẗat, Diss., 1996.

Kaiserslautern, Techn. University, Diss., 2003.