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Ahlinder, J.

Publications and source records attributed to Ahlinder, J..

2 recordsLinked to original sources

Uncertainty-aware breeding decisions: MCMC-based optimum contribution selection increases breeding decision robustness

Optimum contribution selection (OCS) balances genetic gain and inbreeding by optimizing parental contributions to the next generation, but current implementations rely on point estimates of breeding values that discard the uncertainty inherent in genetic evaluations. We introduce CVaR-OCS, a novel formulation that incorporates the full posterior distribution of estimated breeding values (EBVs) directly into the OCS objective via Conditional Value at Risk (CVaR) (a coherent risk measure from financial portfolio theory) allowing a single optimization to simultaneously maximize expected genetic gain and protect against worst-case outcomes driven by EBV uncertainty. We evaluate CVaR-OCS on a simulated multi-generation genomic selection dataset with known true breeding values (QTL-MAS 2010; n = 900 candidates), enabling direct comparison to an oracle solution, and on Norway spruce (Picea abies n = 5,525) forest tree breeding progeny trials. On the simulated dataset, MAP-OCS overestimated its own expected genetic gain by 16.7% due to EBV uncertainty, a bias eliminated by CVaR-OCS by construction, while CVaR-OCS also recovered one additional oracle-optimal individual and reduced gain distribution variance by 4.8%. In Norway spruce, the recommended CVaR-OCS operating point improved tail-gain security by 6.60% and broadened the selection base from 145 to 159 individuals at a genetic gain cost of only 0.70%. Complementary MCMC-based robustness scores revealed that 25 MAP-OCS selections in Norway spruce were unstable across the posterior distribution; post-hoc exclusion of these individuals failed to improve tail-gain security, motivating the principled CVaR-OCS approach. CVaR-OCS provides breeders with a principled, computationally efficient tool for uncertainty-aware selection decisions, and multi-generation simulation studies are needed to fully characterize its long-term effects on genetic gain trajectories and inbreeding accumulation.

genetics↗

Principal component analysis revisited: fast multi-trait genetic evaluations with smooth convergence

A cornerstone in breeding and population genetics is the genetic evaluation procedure, needed to make important decisions on population management. Multivariate mixed model analysis, in which many traits is considered jointly, utilizes genetic and environmental correlations between traits to improve the accuracy. However, the number of parameters in the multi-trait model grows exponentially with the number of traits which reduces its scalability. Here, we suggest using principal component analysis (PCA) to reduce the dimensions of the response variables, and then using the computed principal components (PC) as separate responses in the genetic evaluation analysis. As PCs are orthogonal to each other, multivariate analysis is no longer needed and separate univariate analyses can be performed instead. We compared the approach to traditional multivariate analysis in terms of computational requirement and rank lists according to predicted genetic merit on two forest tree datasets with 22 and 27 measured traits respectively. Obtained rank lists of the top 50 individuals were in good agreement. Interestingly, the required computational time of the approach only took a few seconds without convergence issues, unlike the traditional approach which required considerably more time to run (seven and ten hours respectively). Our approach can easily handle missing data and can be used with all available linear mixed models software as it does not require any specific implementation. The approach can help to mitigate difficulties with multi-trait genetic analysis in both breeding and wild populations.

genetics↗