Identifying optimal sets of individuals under a pairwise genomic relationship threshold for breeding and conservation.
Maximizing genetic response to selection while constraining inbreeding is a central challenge in breeding and conservation. Classic optimal contribution selection methods address this by managing average population coancestry. However, this often results in complex, nonlinear optimization problems t...
| Publicado en: | Genetics Vol. 233; no. 2; pp. 1 - 12 |
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| Autores principales: | , , , , |
| Formato: | equations & formulas tables/charts Journal Article |
| Publicado: |
Oxford University Press / USA
Jun2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=194728466&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 194728466 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00166731 GNT jtl: Genetics issn: 00166731 maglogo: N pubinfo: dt: Jun2026 vid: 233 iid: 2 pid: 622 pub: Oxford University Press / USA artinfo: ui: 194728466 194728466 194728466 10.1093/genetics/iyag095 194728466 ppf: 1 ppct: 11 formats: tig: atl: Identifying optimal sets of individuals under a pairwise genomic relationship threshold for breeding and conservation. aug: au: Lstibůrek, Milan Bittner, Václav Solvin, Thomas Mørtvedt Korecký, Jiří Steffenrem, Arne affil: Faculty of Forestry and Wood Sciences, Czech University of Life Sciences Prague, Kamýcká 129, Prague 6 165 00, Czech Republic sug: subj: Genomics Genetic Variation Reproduction Techniques Conservation of Natural Resources Models, Biological Norway Genotype Phenotype Cattle Software Computer Simulation Models, Theoretical Bioinformatics Plants Genetics ab: Maximizing genetic response to selection while constraining inbreeding is a central challenge in breeding and conservation. Classic optimal contribution selection methods address this by managing average population coancestry. However, this often results in complex, nonlinear optimization problems that cannot be guaranteed to reach a global optimum. Furthermore, many applications require a stricter pairwise constraint to avoid immediate inbreeding in offspring. Here, we present a binary integer linear programming formulation to select an optimal subset of individuals under a strict maximum tolerable pairwise genomic relationship threshold. We construct a binary matrix indicating whether each pair exceeds this threshold. This reformulation transforms the problem from a complex nonlinear program into a binary integer linear program. While this formulation remains NP-hard, the linearity allows modern solvers to efficiently navigate the solution space and, when convergence is achieved within the imposed runtime and tolerance settings, certify global optimality, a key advantage over heuristic approaches. We demonstrate the method using two distinct datasets: a large Norway spruce breeding population and a conservation population of German Black Pied cattle. We explore the trade-offs between the selection response, the relationship threshold, and the maximum number of individuals that can be selected under the threshold. Although large, dense problem instances remain computationally demanding, our results show that typical applications can often be solved to proven global optimality in seconds, whereas denser instances may terminate with a remaining optimality gap. This method is a practical solution for breeders and conservation geneticists to select optimal subsets under a strict relationship threshold, enabling applications from maximizing gain in breeding populations to establishing genetic reserves for endangered species. pubtype: Academic Journal doctype: equations & formulas tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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