ArticleG3 (Bethesda, Md.)2019
Optimizing Selection and Mating in Genomic Selection with a Look-Ahead Approach: An Operations Research Framework.
Article in G3 (Bethesda, Md.), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
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Who cites it
38 citing papers in PubMed.
- Optimization of family sizes in sets of crosses with a greedy allocation strategy based on automatic differentiation.Frontiers in plant science · 2026Article
- A Similarity Matrix for Preserving Haplotype Diversity Amongst Parents in Genomic Selection.Journal of animal breeding and genetics = Zeitschrift fur Tierzuchtung und Zuchtungsbiologie · 2025Article
- DiffBreed: automatic differentiation enables efficient gradient-based optimization of breeding strategies.Bioinformatics (Oxford, England) · 2025Article
- Applying Genomic Offsets to Breeding Programmes: Bridging Evolutionary Insights With Practical Applications.Evolutionary applications · 2025Article
- Genomic selection: Essence, applications, and prospects.The plant genome · 2025Review
- Integer programming as a powerful tool for polyclonal selection in ancient grapevine varieties.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
- Optimization of breeding program design through stochastic simulation with evolutionary algorithms.G3 (Bethesda, Md.) · 2025Article
- Optimizing the selection of quantitative traits in plant breeding using simulation.Frontiers in plant science · 2025Review
- Cross potential selection: a proposal for optimizing crossing combinations in recurrent selection using the usefulness criterion of future inbred lines.G3 (Bethesda, Md.) · 2024Article
- Genomic-inferred cross-selection methods for multi-trait improvement in a recurrent selection breeding program.Plant methods · 2024Article
- Article
- Improving selection decisions with mating information by accounting for Mendelian sampling variances looking two generations ahead.Genetics, selection, evolution : GSE · 2024Article
- AI-assisted selection of mating pairs through simulation-based optimized progeny allocation strategies in plant breeding.Frontiers in plant science · 2024Article
- Optimization of breeding program design through stochastic simulation with kernel regression.G3 (Bethesda, Md.) · 2023Article
- A genetic tradeoff for tolerance to moderate and severe heat stress in US hybrid maize.PLoS genetics · 2023Article
- Clonal diploid and autopolyploid breeding strategies to harness heterosis: insights from stochastic simulation.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2023Article
- Genomic selection performs as effectively as phenotypic selection for increasing seed yield in soybean.The plant genome · 2023Article
- A simulation framework for reciprocal recurrent selection-based hybrid breeding under transparent and opaque simulators.Frontiers in plant science · 2023Article
- A look-ahead approach to maximizing present value of genetic gains in genomic selection.G3 (Bethesda, Md.) · 2022Article
- Winter wheat yield prediction using convolutional neural networks from environmental and phenological data.Scientific reports · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
New genotyping technologies have made large amounts of genotypic data available for plant breeders to use in their efforts to accelerate the rate of genetic gain. Genomic selection (GS) techniques allow breeders to use genotypic data to identify and select, for example, plants predicted to exhibit drought tolerance, thereby saving expensive and limited field-testing resources relative to phenotyping all plants within a population. A major limitation of existing GS approaches is the trade-off between short-term genetic gain and long-term potential. Some approaches focus on achieving short-term genetic gain at the cost of reduced genetic diversity necessary for long-term gains. In contrast, others compromise short-term progress to preserve long-term potential without consideration of the time and resources required to achieve it. Our contribution is to define a new "look-ahead" metric for assessing selection decisions, which evaluates the probability of achieving high genetic gains by a specific time with limited resources. Moreover, we propose a heuristic algorithm to identify optimal selection decisions that maximize the look-ahead metric. Simulation results demonstrate that look-ahead selection outperforms other published selection methods.
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