ArticleGenetics2020
Multi-trait Genomic Selection Methods for Crop Improvement.
Article in Genetics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed.
- Genetic dissection of cold tolerance across developmental stages in sorghum reveals stage-specific haplotypes and potential trade-offs.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Article
- Research progress on genomic selection breeding technology for crops.Protoplasma · 2026Review
- Index- and Pareto-based multi-trait selection identifies dual-purpose sorghum and pearl millet cultivars for arid agro-ecosystems of Saudi Arabia.Frontiers in plant science · 2026Article
- Longitudinal genomic prediction of cacao disease resilience identifies robust witches' broom disease targets.Frontiers in plant science · 2026Article
- Leveraging the multi-trait donor Pusa Srijan for biofortification and yield improvement in bread wheat.Scientific reports · 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
- MtCro: multi-task deep learning framework improves multi-trait genomic prediction of crops.Plant methods · 2025Article
- Comparative Study of Single-Trait and Multi-Trait Genomic Prediction Models.Animals : an open access journal from MDPI · 2024Article
- PyBrOpS: a Python package for breeding program simulation and optimization for multi-objective breeding.G3 (Bethesda, Md.) · 2024Article
- Genomic estimated selection criteria and parental contributions in parent selection increase genetic gain of maternal haploid inducers in maize.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2024Article
- Hybrid Prediction in Horticulture Crop Breeding: Progress and Challenges.Plants (Basel, Switzerland) · 2024Review
- Article
- Genetic Gain and Inbreeding in Different Simulated Genomic Selection Schemes for Grain Yield and Oil Content in Safflower.Plants (Basel, Switzerland) · 2024Article
- Linking genetic markers and crop model parameters using neural networks to enhance genomic prediction of integrative traits.Frontiers in plant science · 2024Article
- SCAG: A Stratified, Clustered, and Growing-Based Algorithm for Soybean Branch Angle Extraction and Ideal Plant Architecture Evaluation.Plant phenomics (Washington, D.C.) · 2024Article
- Maintenance of UK bread baking quality: Trends in wheat quality traits over 50 years of breeding and potential for future application of genomic-assisted selection.The plant genome · 2023Article
- The 2020 derecho revealed limited overlap between maize genes associated with root lodging and root system architecture.Plant physiology · 2023Article
- New insights into trait introgression with the look-ahead intercrossing strategy.G3 (Bethesda, Md.) · 2023Article
- Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Plant breeders make selection decisions based on multiple traits, such as yield, plant height, flowering time, and disease resistance. A commonly used approach in multi-trait genomic selection is index selection, which assigns weights to different traits relative to their economic importance. However, classical index selection only optimizes genetic gain in the next generation, requires some experimentation to find weights that lead to desired outcomes, and has difficulty optimizing nonlinear breeding objectives. Multi-objective optimization has also been used to identify the Pareto frontier of selection decisions, which represents different trade-offs across multiple traits. We propose a new approach, which maximizes certain traits while keeping others within desirable ranges. Optimal selection decisions are made using a new version of the look-ahead selection (LAS) algorithm, which was recently proposed for single-trait genomic selection, and achieved superior performance with respect to other state-of-the-art selection methods. To demonstrate the effectiveness of the new method, a case study is developed using a realistic data set where our method is compared with conventional index selection. Results suggest that the multi-trait LAS is more effective at balancing multiple traits compared with index selection.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.