ArticleMolecular breeding : new strategies in plant improvement2021
Diversifying maize genomic selection models.
Article in Molecular breeding : new strategies in plant improvement, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Breeding climate-resilient maize for food security in semi-arid sub-Saharan Africa: strategies and future directions.Frontiers in plant science · 2026Review
- Artificial intelligence: the human response to approach the complexity of big data in biology.GigaScience · 2025Review
- Breeding for resistance to maize streak virus: challenges, progress and future directions: a review.Frontiers in plant science · 2025Review
- Maximizing efficiency in sunflower breeding through historical data optimization.Plant methods · 2024Article
- Regularized multi-trait multi-locus linear mixed models for genome-wide association studies and genomic selection in crops.BMC bioinformatics · 2023Article
- Genome-Wide Association Study and Genomic Prediction of Fusarium Wilt Resistance in Common Bean Core Collection.International journal of molecular sciences · 2023Article
- Breeding crops for drought-affected environments and improved climate resilience.The Plant cell · 2023Review
- Genomic versus phenotypic selection to improve corn borer resistance and grain yield in maize.Frontiers in plant science · 2023Article
- Can biochemical traits bridge the gap between genomics and plant performance? A study in rice under drought.Plant physiology · 2022Article
- Maize genetics, genomics, and sustainable improvement.Molecular breeding : new strategies in plant improvement · 2022Article
Corrections and comments
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Authors and funding
2 authors.
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Abstract
Genomic selection (GS) is one of the most powerful tools available for maize breeding. Its use of genome-wide marker data to estimate breeding values translates to increased genetic gains with fewer breeding cycles. In this review, we cover the history of GS and highlight particular milestones during its adaptation to maize breeding. We discuss how GS can be applied to developing superior maize inbreds and hybrids. Additionally, we characterize refinements in GS models that could enable the encapsulation of non-additive genetic effects, genotype by environment interactions, and multiple levels of the biological hierarchy, all of which could ultimately result in more accurate predictions of breeding values. Finally, we suggest the stages in a maize breeding program where it would be beneficial to apply GS. Given the current sophistication of high-throughput phenotypic, genotypic, and other -omic level data currently available to the maize community, now is the time to explore the implications of their incorporation into GS models and thus ensure that genetic gains are being achieved as quickly and efficiently as possible.
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