robustocs: robust optimal contribution selection

doi:10.1093/bioinformatics/btag569

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robustocs: robust optimal contribution selection

2026-07-30

DOI：10.1093/bioinformatics/btag569

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Abstract

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Optimal contribution selection (OCS) is a selective breeding method that manages the conversion of genetic variation into genetic gain to facilitate short-term competitiveness and long-term sustainability of breeding programmes. Traditional approaches to OCS and truncation selection (TS) rely on estimates of breeding values and do not explicitly account for uncertainty in these estimates. Here, we use concepts from robust optimization to formulate a robust optimal contribution selection problem (ROCS) and develop two solutions based on conic optimization and sequential quadratic programming. We implemented these methods in the robustocs Python package, which leverages the Gurobi and HiGHS solvers. Our results show favorable performance when solving the ROCS problem using sequential quadratic programming with the HiGHS solver. We show that classical TS and OCS arise as special cases of the robust selection formulations (RTS and ROCS). We demonstrate the package with a small example, comparing outcomes of TS, RTS, OCS, and ROCS. Compared to TS and OCS, RTS and ROCS find contributions that reduce the uncertainty of genetic gain and group coancestry at the expense of reduced genetic gain.