ArticleThe plant genome2026
Randomization across breeding cohorts improves the accuracy of conventional and genomic selection.
Article in The plant genome, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Optimizing multi-trait selection for profitability in Illinois wheat using a factor analytic mixed model and an economic selection index.The plant genome · 2026Article
- Randomization across breeding cohorts improves the accuracy of conventional and genomic selection.The plant genome · 2026Article
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Authors and funding
2 authors.
Funding
Abstract
Breeding programs conventionally evaluate cohorts in separate trials; however, environmental differences across testing areas can be confounded with genetic differences between cohorts, potentially reducing the accuracy of breeding value estimation. We test whether the conventional approach of restricting randomization of cohorts to within trials reduces genomic and conventional selection accuracy when compared to the complete randomization of all cohorts across a trial, using in silico simulation with marker data from University of Illinois winter wheat breeding lines. We evaluated selection accuracy for conventional best linear unbiased prediction (BLUP), genomic BLUP (GBLUP), and genomic-enabled sparse testing across a comprehensive simulation space spanning narrow-sense heritabilities of 0.2-0.8, genetic correlations between testing areas from 0.2 to 1.0, and three replication levels. Difference-in-differences (DiD) analysis established causal inference by comparing design performance as conditions deteriorated from an optimal baseline where both designs performed equivalently. Complete randomization improved BLUP accuracy by 11.7%, reaching 15.7% under low replication and low genetic correlation between areas. Genomic data largely eliminated this design effect, with GBLUP showing no significant DiD interaction effect. However, genomic-enabled sparse testing revealed a significant DiD effect and an improvement in selection accuracy of 1.5% that increased to a 5.5% advantage under challenging conditions. While heritability had the strongest main effect on selection accuracy, genetic correlation between areas showed the largest interaction with randomization scheme, with design performance diverging significantly only as this parameter decreased. Programs with genomic data and balanced phenotypic data can use either restricted or complete randomization, but those with other circumstances can benefit from complete randomization.
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