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

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Τίτλος: Uncertainty-aware breeding decisions: MCMC-based optimum contribution selection increases breeding decision robustness.
Συγγραφείς: Ahlinder J; Department of Tree Breeding, The Forest Research Institute of Sweden (Skogforsk), Tomterna 1, Sävar, SE 91833, Sweden., Waldmann P; Mathematical Sciences, University of Oulu, Paavo Havaksen tie 3, Oulu, FI 90570, Finland.
Πηγή: Genetics [Genetics] 2026 Oct 07; Vol. 234 (2).
Τύπος έκδοσης: Journal Article
Γλώσσα: English
Στοιχεία περιοδικού: Publisher: Oxford University Press Country of Publication: United States NLM ID: 0374636 Publication Model: Print Cited Medium: Internet ISSN: 1943-2631 (Electronic) Linking ISSN: 00166731 NLM ISO Abbreviation: Genetics Subsets: MEDLINE
Imprint Name(s): Publication: 2021- : [Oxford] : Oxford University Press
Original Publication: Austin, Tex. [etc.]
Ιατρικοί όροι (MeSH): Plant Breeding*/methods , Models, Genetic* , Selection, Genetic* , Breeding*, Picea/genetics ; Uncertainty ; Quantitative Trait Loci ; Markov Chains
Περίληψη: 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=3,226), enabling direct comparison to an oracle solution, and on Norway spruce (Picea abies  n=5,525) and Loblolly pine (Pinus taeda  n=926) forest tree breeding progeny trials, with consistent tail-gain protection observed across all three datasets. On the simulated dataset, point-estimate MAP-OCS recovered only 78% of the genetic gain achieved by an oracle solution with access to true breeding values, illustrating the cost of ignoring predictive uncertainty; CVaR-OCS attained comparable expected gain while improving tail-gain security (CVaR95+0.69%) and recovering an additional oracle-optimal individual. 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.
(© The Author(s) 2026. Published by Oxford University Press on behalf of The Genetics Society of America.)
Competing Interests: Conflicts of interest: The authors declare that they have no conflict of interest.
References: J Hered. 2020 Dec 31;111(7):646-651. (PMID: 33249481)
Genetics. 1999 Oct;153(2):1009-20. (PMID: 10511574)
BMC Genomics. 2025 Jan 6;26(1):6. (PMID: 39762732)
Genet Res. 2004 Feb;83(1):55-64. (PMID: 15125067)
Genet Sel Evol. 2013 Oct 30;45:44. (PMID: 24171942)
Theor Appl Genet. 2009 Apr;118(6):1133-42. (PMID: 19183858)
J Dairy Sci. 2009 Sep;92(9):4656-63. (PMID: 19700729)
Genet Sel Evol. 2019 Nov 15;51(1):64. (PMID: 31730478)
J Dairy Sci. 2022 May;105(5):4314-4323. (PMID: 35307183)
J Dairy Sci. 2008 Nov;91(11):4414-23. (PMID: 18946147)
Front Genet. 2021 Dec 03;12:761728. (PMID: 34925454)
J Anim Sci. 1997 Apr;75(4):934-40. (PMID: 9110204)
Biometrics. 1975 Jun;31(2):423-47. (PMID: 1174616)
Bioinformatics. 2023 Jul 1;39(7):. (PMID: 37471595)
Genet Sel Evol. 2023 Jun 8;55(1):38. (PMID: 37291496)
Genet Sel Evol. 2019 Jul 8;51(1):39. (PMID: 31286868)
Genet Sel Evol. 2006 Mar-Apr;38(2):127-46. (PMID: 16492371)
Front Genet. 2020 Aug 13;11:880. (PMID: 32903415)
Animal. 2012 Oct;6(10):1565-71. (PMID: 22717310)
Heredity (Edinb). 2024 Feb;132(2):98-105. (PMID: 38172577)
Genetics. 1943 Nov;28(6):476-90. (PMID: 17247099)
J Dairy Sci. 2001 Dec;84(12):2789-95. (PMID: 11814036)
BMC Proc. 2011 May 27;5 Suppl 3:S3. (PMID: 21624173)
Genetics. 2012 Apr;190(4):1503-10. (PMID: 22271763)
Nat Rev Genet. 2009 Nov;10(11):783-96. (PMID: 19834483)
BMC Bioinformatics. 2019 Jan 14;20(1):25. (PMID: 30642239)
Bioinform Adv. 2025 Oct 22;5(1):vbaf259. (PMID: 41250674)
J Anim Breed Genet. 2012 Oct;129(5):345-58. (PMID: 22963356)
J Anim Breed Genet. 2015 Apr;132(2):89-99. (PMID: 25823835)
Theor Appl Genet. 1983 Mar;64(4):339-44. (PMID: 24265083)
Genet Sel Evol. 2007 Jan-Feb;39(1):3-25. (PMID: 17212945)
Plants (Basel). 2023 Jan 13;12(2):. (PMID: 36679096)
Genet Sel Evol. 2023 Jul 17;55(1):48. (PMID: 37460999)
BMC Genomics. 2018 Dec 18;19(1):946. (PMID: 30563448)
Genet Sel Evol. 2017 May 12;49(1):45. (PMID: 28499352)
J Anim Sci. 2006 Dec;84(12):3212-8. (PMID: 17093213)
Front Genet. 2016 Nov 29;7:210. (PMID: 27965707)
PLoS Comput Biol. 2023 Sep 11;19(9):e1010290. (PMID: 37695766)
Genetics. 2018 May;209(1):89-103. (PMID: 29514861)
Genet Res (Camb). 2011 Feb;93(1):47-64. (PMID: 21226974)
Genet Sel Evol. 2018 May 10;50(1):24. (PMID: 29747576)
Grant Information: Föreningen Skogsträdsförädling
Contributed Indexing: Keywords: Bayesian genetic evaluation; Norway spruce; conditional value at risk; estimated breeding value uncertainty; posterior uncertainty; selection optimization
Entry Date(s): Date Created: 20260803 Date Completed: 20261007 Latest Revision: 20261009
Update Code: 20261009
PubMed Central ID: PMC13643769
DOI: 10.1093/genetics/iyag205
PMID: 42546165
Βάση Δεδομένων: MEDLINE
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  Data: Uncertainty-aware breeding decisions: MCMC-based optimum contribution selection increases breeding decision robustness.
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  Data: <searchLink fieldCode="AU" term="%22Ahlinder+J%22">Ahlinder J</searchLink>; Department of Tree Breeding, The Forest Research Institute of Sweden (Skogforsk), Tomterna 1, Sävar, SE 91833, Sweden.<br /><searchLink fieldCode="AU" term="%22Waldmann+P%22">Waldmann P</searchLink>; Mathematical Sciences, University of Oulu, Paavo Havaksen tie 3, Oulu, FI 90570, Finland.
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  Data: 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=3,226), enabling direct comparison to an oracle solution, and on Norway spruce (Picea abies  n=5,525) and Loblolly pine (Pinus taeda  n=926) forest tree breeding progeny trials, with consistent tail-gain protection observed across all three datasets. On the simulated dataset, point-estimate MAP-OCS recovered only 78% of the genetic gain achieved by an oracle solution with access to true breeding values, illustrating the cost of ignoring predictive uncertainty; CVaR-OCS attained comparable expected gain while improving tail-gain security (CVaR95+0.69%) and recovering an additional oracle-optimal individual. 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.<br /> (© The Author(s) 2026. Published by Oxford University Press on behalf of The Genetics Society of America.)
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  Data: Conflicts of interest: The authors declare that they have no conflict of interest.
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  Data: J Hered. 2020 Dec 31;111(7):646-651. (PMID: <searchLink fieldCode="PM" term="%2233249481%22">33249481)</searchLink><br />Genetics. 1999 Oct;153(2):1009-20. (PMID: <searchLink fieldCode="PM" term="%2210511574%22">10511574)</searchLink><br />BMC Genomics. 2025 Jan 6;26(1):6. (PMID: <searchLink fieldCode="PM" term="%2239762732%22">39762732)</searchLink><br />Genet Res. 2004 Feb;83(1):55-64. (PMID: <searchLink fieldCode="PM" term="%2215125067%22">15125067)</searchLink><br />Genet Sel Evol. 2013 Oct 30;45:44. (PMID: <searchLink fieldCode="PM" term="%2224171942%22">24171942)</searchLink><br />Theor Appl Genet. 2009 Apr;118(6):1133-42. (PMID: <searchLink fieldCode="PM" term="%2219183858%22">19183858)</searchLink><br />J Dairy Sci. 2009 Sep;92(9):4656-63. (PMID: <searchLink fieldCode="PM" term="%2219700729%22">19700729)</searchLink><br />Genet Sel Evol. 2019 Nov 15;51(1):64. (PMID: <searchLink fieldCode="PM" term="%2231730478%22">31730478)</searchLink><br />J Dairy Sci. 2022 May;105(5):4314-4323. (PMID: <searchLink fieldCode="PM" term="%2235307183%22">35307183)</searchLink><br />J Dairy Sci. 2008 Nov;91(11):4414-23. (PMID: <searchLink fieldCode="PM" term="%2218946147%22">18946147)</searchLink><br />Front Genet. 2021 Dec 03;12:761728. (PMID: <searchLink fieldCode="PM" term="%2234925454%22">34925454)</searchLink><br />J Anim Sci. 1997 Apr;75(4):934-40. (PMID: <searchLink fieldCode="PM" term="%229110204%22">9110204)</searchLink><br />Biometrics. 1975 Jun;31(2):423-47. (PMID: <searchLink fieldCode="PM" term="%221174616%22">1174616)</searchLink><br />Bioinformatics. 2023 Jul 1;39(7):. (PMID: <searchLink fieldCode="PM" term="%2237471595%22">37471595)</searchLink><br />Genet Sel Evol. 2023 Jun 8;55(1):38. (PMID: <searchLink fieldCode="PM" term="%2237291496%22">37291496)</searchLink><br />Genet Sel Evol. 2019 Jul 8;51(1):39. (PMID: <searchLink fieldCode="PM" term="%2231286868%22">31286868)</searchLink><br />Genet Sel Evol. 2006 Mar-Apr;38(2):127-46. (PMID: <searchLink fieldCode="PM" term="%2216492371%22">16492371)</searchLink><br />Front Genet. 2020 Aug 13;11:880. (PMID: <searchLink fieldCode="PM" term="%2232903415%22">32903415)</searchLink><br />Animal. 2012 Oct;6(10):1565-71. (PMID: <searchLink fieldCode="PM" term="%2222717310%22">22717310)</searchLink><br />Heredity (Edinb). 2024 Feb;132(2):98-105. (PMID: <searchLink fieldCode="PM" term="%2238172577%22">38172577)</searchLink><br />Genetics. 1943 Nov;28(6):476-90. (PMID: <searchLink fieldCode="PM" term="%2217247099%22">17247099)</searchLink><br />J Dairy Sci. 2001 Dec;84(12):2789-95. (PMID: <searchLink fieldCode="PM" term="%2211814036%22">11814036)</searchLink><br />BMC Proc. 2011 May 27;5 Suppl 3:S3. (PMID: <searchLink fieldCode="PM" term="%2221624173%22">21624173)</searchLink><br />Genetics. 2012 Apr;190(4):1503-10. (PMID: <searchLink fieldCode="PM" term="%2222271763%22">22271763)</searchLink><br />Nat Rev Genet. 2009 Nov;10(11):783-96. (PMID: <searchLink fieldCode="PM" term="%2219834483%22">19834483)</searchLink><br />BMC Bioinformatics. 2019 Jan 14;20(1):25. (PMID: <searchLink fieldCode="PM" term="%2230642239%22">30642239)</searchLink><br />Bioinform Adv. 2025 Oct 22;5(1):vbaf259. (PMID: <searchLink fieldCode="PM" term="%2241250674%22">41250674)</searchLink><br />J Anim Breed Genet. 2012 Oct;129(5):345-58. (PMID: <searchLink fieldCode="PM" term="%2222963356%22">22963356)</searchLink><br />J Anim Breed Genet. 2015 Apr;132(2):89-99. (PMID: <searchLink fieldCode="PM" term="%2225823835%22">25823835)</searchLink><br />Theor Appl Genet. 1983 Mar;64(4):339-44. (PMID: <searchLink fieldCode="PM" term="%2224265083%22">24265083)</searchLink><br />Genet Sel Evol. 2007 Jan-Feb;39(1):3-25. (PMID: <searchLink fieldCode="PM" term="%2217212945%22">17212945)</searchLink><br />Plants (Basel). 2023 Jan 13;12(2):. (PMID: <searchLink fieldCode="PM" term="%2236679096%22">36679096)</searchLink><br />Genet Sel Evol. 2023 Jul 17;55(1):48. (PMID: <searchLink fieldCode="PM" term="%2237460999%22">37460999)</searchLink><br />BMC Genomics. 2018 Dec 18;19(1):946. (PMID: <searchLink fieldCode="PM" term="%2230563448%22">30563448)</searchLink><br />Genet Sel Evol. 2017 May 12;49(1):45. (PMID: <searchLink fieldCode="PM" term="%2228499352%22">28499352)</searchLink><br />J Anim Sci. 2006 Dec;84(12):3212-8. (PMID: <searchLink fieldCode="PM" term="%2217093213%22">17093213)</searchLink><br />Front Genet. 2016 Nov 29;7:210. (PMID: <searchLink fieldCode="PM" term="%2227965707%22">27965707)</searchLink><br />PLoS Comput Biol. 2023 Sep 11;19(9):e1010290. (PMID: <searchLink fieldCode="PM" term="%2237695766%22">37695766)</searchLink><br />Genetics. 2018 May;209(1):89-103. (PMID: <searchLink fieldCode="PM" term="%2229514861%22">29514861)</searchLink><br />Genet Res (Camb). 2011 Feb;93(1):47-64. (PMID: <searchLink fieldCode="PM" term="%2221226974%22">21226974)</searchLink><br />Genet Sel Evol. 2018 May 10;50(1):24. (PMID: <searchLink fieldCode="PM" term="%2229747576%22">29747576)</searchLink>
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  Data: <i>Keywords: </i>Bayesian genetic evaluation; Norway spruce; conditional value at risk; estimated breeding value uncertainty; posterior uncertainty; selection optimization
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        Text: English
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        Type: general
      – SubjectFull: Uncertainty
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      – SubjectFull: Quantitative Trait Loci
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