Browsing by Person "Wellmann, Robin"

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Improving the accuracy of multi-breed prediction in admixed populations by accounting for the breed origin of haplotype segments
(2022) Schmid, Markus; Stock, Joana; Bennewitz, Jörn; Wellmann, Robin
Numerically small breeds have often been upgraded with mainstream breeds. This historic introgression predisposes the breeds for joint genomic evaluations with mainstream breeds. The linkage disequilibrium structure differs between breeds. The marker effects of a haplotype segment may, therefore, depend on the breed from which the haplotype segment originates. An appropriate method for genomic evaluation would account for this dependency. This study proposes a method for the computation of genomic breeding values for small admixed breeds that incorporate phenotypic and genomic information from large introgressed breeds by considering the breed origin of alleles (BOA) in the evaluation. The proposed BOA model classifies haplotype segments according to their origins and assumes different but correlated SNP effects for the different origins. The BOA model was compared in a simulation study to conventional within-breed genomic best linear unbiased prediction (GBLUP) and conventional multi-breed GBLUP models. The BOA model outperformed within-breed GBLUP as well as multi-breed GBLUP in most cases.
Selection index theory for populations under directional and stabilizing selection
(2023) Wellmann, Robin; Wellmann, Robin; Department of Animal Genetics and Breeding, University of Hohenheim, Stuttgart, Germany
Background: The purpose of a selection index is that its use to select animals for breeding maximizes the profit of a breed in future generations. The profit of a breed is in general a quantity that predicts the satisfaction of future owners with their breed, and the satisfaction of the consumers with the products that are produced by the breed. Many traits, such as conformation traits and product quality traits have intermediate optima. Traditional selection index theory applies only to directional selection and cannot achieve any further improvement once the trait means have reached their optima. A well-founded theory is needed that extends the established selection index theory to cover directional as well as stabilizing selection as limiting cases, and that can be applied to maximize the profit of a breed in both situations. Results: The optimum selection index shifts the trait means towards the optima and, in the case of stabilizing selection, decreases the phenotypic variance, which causes the phenotypes to be closer to the optimum. The optimum index depends not only on the breeding values, but also on the squared breeding values, the allele contents of major quantitative trait loci (QTL), the QTL heterozygosities, the inbreeding coefficient of the animal, and the kinship of the animal with the population. Conclusion: The optimum selection index drives the alleles of major QTL to fixation when the trait mean approaches the optimum because decreasing the phenotypic variance shifts the trait values closer to the optimum, which increases the profit of the breed. The index weight on the kinship coefficient balances the increased genetic gain that can be achieved in future generations by outcrossing, and the increased genetic gain that can be achieved under stabilizing selection by reducing the phenotypic variance. In a model with dominance variance, it can also account for the effect of inbreeding depression. The combining ability between potential mating partners, which predicts the total merit of their offspring, could become an important parameter for mate allocation that could be used to further shift the phenotypes towards their optimum values.
Toward a resilience selection index with indicator traits in German Holstein dairy cattle
(2025) Keßler, Franziska; Wellmann, Robin; Chagunda, Mizeck G. G.; Benenwitz, Jörn
Resilience expresses the ability of an individual to cope with short-term disturbances and to recover quickly by returning to the original level of performance. It can be measured by variance-based parameters and by the autocorrelation of daily milk yields in dairy cows. The design of resilience indicator traits and their heritabilities and genetic correlations have been studied in detail in recent years. There is a need to combine different resilience indicators in an index. The relevance of resilience indicator traits for incorporation into selection indices arises from their correlations with health traits and longevity. The correlations of diverse resilience indicator traits with health traits and longevity were analyzed. The resilience indicator traits were identified that would lead to the highest correlated selection response in the German selection index for health, and appropriate weights of the resilience indicator traits in a selection index for resilience were derived. Certain variance-based indicators were significantly positively correlated with most of the established health and functional traits, whereas the autocorrelation had a negligible correlation with these traits. A resilience selection index composed of 2 different variance-based resilience indicator traits was most likely to be recommended. Its correlation with overall performance was positive but moderately small. Incorporating more than 2 resilience indicator traits into the index improved the correlated response in health traits only slightly.