robustocs: Robust optimal contribution selection

Clicks: 3
ID: 322960
2026
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Emerging

Ranked #294 of 825 articles by views in BMC Bioinformatics

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 825 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
SUMMARY: 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 favourable 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. AVAILABILITY AND IMPLEMENTATION: robustocs is implemented in Python and released, with documentation, on GitHub under the MIT license at https://github.com/Foggalong/RobustOCS . SUPPLEMENTARY INFORMATION: No supplementary information is available.
Reference Key
openalex_W7171868077 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Josh Fogg, Jaime Ortiz-Cuadros, Ivan Pocrnić, J A Julian Hall, Gregor Gorjanc
Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag569
URL
https://doi.org/10.1093/bioinformatics/btag569
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.