Led by Jaime Eduardo Ortiz Cuadros Genetic gain and genetic diversity are two essential, yet often conflicting, goals in modern breeding programs. Achieving a sustainable balance between the remains major challenge in dairy cattle. In this project we are evaluating this challenge from theoretical and practical perspective. The practical perspective will be conducted in collaboration with the Norwegian Red breeding program. There are three objectives of this study. First, selection of future parents is based on the estimates of breeding values. While the accuracy of these estimates improves as more information is incorporated, a degree of uncertainty always remains. Standard optimal contribution selection is a commonly used approach to manage the trade-off between genetic gain and genetic diversity. We are evaluating how uncertainty in estimated breeding values via robust optimization can be taken into account when selecting the best individual for the next generation. Second, diversity is traditionally estimated using expected relatedness from pedigree data, whose reliability depends on the amount of records that shape pedigree depth and completeness. The growing availability of genomic data enables the computation of realised relatedness based on SNP genotypes. However, existing methods for computing and visualizing pedigree and genomic relationship matrices face computational and memory limitations. Principal Component Analysis (PCA), a common tool for visualizing high-dimensional data, has recently been applied for efficient pedigree visualization. Here, we will extend this approach by applying PCA to hybrid pedigree-genomic relatedness matrix, offering a practical tool for studying population structure and genetic diversity. Third, the potential for selection depends on the amount of additive genetic variation available for future genetic gain. Thus, analysing genetic trends and multivariate genetic variation in synergistic and antagonistic traits is essential for identifying key breeding actions and optimizing breeding programs. Temporal and genomic analyses of additive genetic variance form a framework that offers valuable insights into the dynamics of genetic (co)variation across time and genomic regions for economically important traits under selection. We will use this framework to evaluate the pattern of additive genetic (co)variation across four traits representing production, conformation, reproduction, and health. This will contribute to the sustainability of dairy cattle breeding programmes and improve our understanding of the population processes that have shaped its genetic variance. This article was published on 2025-05-16
