ReviewFrontiers in genetics2022
Genomic Selection: A Tool for Accelerating the Efficiency of Molecular Breeding for Development of Climate-Resilient Crops.
Review in Frontiers in genetics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 85 papers, 2 of them syntheses that pooled it.
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Who cites it
85 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Acetylation as a dynamic regulatory interface between plant stress memory, cross-tolerance, and crop resilience design.Frontiers in plant science · 2026Pooled it
- Meta-analysis and co-expression analysis revealed stable QTL and candidate genes conferring resistances to Fusarium and Gibberella ear rots while reducing mycotoxin contamination in maize.Frontiers in plant science · 2022Pooled it
- Heat stress responses of plants during adaptation to high temperature environments: A review of recent advances and insights.Plant signaling & behavior · 2026Review
- Genetic dissection of southern corn leaf blight resistance in sweet corn through genome-wide association studies and genomic selection.The plant genome · 2026Article
- Review
- Insights from farming Macrocystis pyrifera offshore: phenotypic analysis, genome-wide association studies, genomic selection.Heredity · 2026Article
- Genomic prediction in dolichos bean (Lablab purpureus L. Sweet) breeding: choice of the type of training population and selection generation.BMC plant biology · 2026Article
- Missing comparability: When genomic selection faces field variability. A case study in soybeans.The plant genome · 2026Article
- 2NPLGBM: a genomic model that merges the strengths of classical and machine learning methods in genomic prediction.Plant methods · 2026Article
- Water stress tolerance, genomic selection and identification of genomic regions in a MAGIC population of eggplant.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Article
- Leveraging AI and integrated genomic-enviromic prediction for intelligent sugarcane breeding.Plant communications · 2026Review
- CLCNet: a contrastive learning and chromosome-aware network for genomic prediction in plants.Briefings in bioinformatics · 2026Article
- An updated molecular toolkit for genomics-assisted breeding of waxy sorghum [Sorghum bicolor (L.) Moench].Journal of applied genetics · 2026Article
- A Quantitative Trait Nucleotide-Based Genomic Selection Strategy for Seed Oil and Protein Content in Soybean.Plants (Basel, Switzerland) · 2026Article
- Review
- Genomic Selection for Lodging-Related Traits in Double-Cropping Rice.Plants (Basel, Switzerland) · 2026Article
- Crop phenotype prediction using SNP context and whole-genome feature embedding based on DNABERT-2.Plant methods · 2026Article
- Haplotype applications in genomic selection.Genome biology · 2026Review
- Optimizing resource allocation inFrontiers in plant science · 2026Article
- Breeding climate-resilient maize for food security in semi-arid sub-Saharan Africa: strategies and future directions.Frontiers in plant science · 2026Review
25 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Since the inception of the theory and conceptual framework of genomic selection (GS), extensive research has been done on evaluating its efficiency for utilization in crop improvement. Though, the marker-assisted selection has proven its potential for improvement of qualitative traits controlled by one to few genes with large effects. Its role in improving quantitative traits controlled by several genes with small effects is limited. In this regard, GS that utilizes genomic-estimated breeding values of individuals obtained from genome-wide markers to choose candidates for the next breeding cycle is a powerful approach to improve quantitative traits. In the last two decades, GS has been widely adopted in animal breeding programs globally because of its potential to improve selection accuracy, minimize phenotyping, reduce cycle time, and increase genetic gains. In addition, given the promising initial evaluation outcomes of GS for the improvement of yield, biotic and abiotic stress tolerance, and quality in cereal crops like wheat, maize, and rice, prospects of integrating it in breeding crops are also being explored. Improved statistical models that leverage the genomic information to increase the prediction accuracies are critical for the effectiveness of GS-enabled breeding programs. Study on genetic architecture under drought and heat stress helps in developing production markers that can significantly accelerate the development of stress-resilient crop varieties through GS. This review focuses on the transition from traditional selection methods to GS, underlying statistical methods and tools used for this purpose, current status of GS studies in crop plants, and perspectives for its successful implementation in the development of climate-resilient crops.
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