ArticleFrontiers in plant science2021
Genetic Dissection of Grain Yield of Maize and Yield-Related Traits Through Association Mapping and Genomic Prediction.
Article in Frontiers in plant science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 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
- Genetic Architecture and Meta-QTL Identification of Yield Traits in Maize (Plants (Basel, Switzerland) · 2025Article
- Climate-Resilient Crops: Integrating AI, Multi-Omics, and Advanced Phenotyping to Address Global Agricultural and Societal Challenges.Plants (Basel, Switzerland) · 2025Review
- Unraveling the genetic basis of maize ear diameter in a multi-parent RIL population derived from tropical and temperate germplasms.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
- Dissecting the genetic architecture of yield-related traits by QTL mapping in maize.Frontiers in plant science · 2025Article
- Phenotype identification and genome-wide association study of ear-internode vascular bundles in maize (Zea mays).Journal of plant research · 2024Article
- Advancements and Prospects of Genome-Wide Association Studies (GWAS) in Maize.International journal of molecular sciences · 2024Review
- Linkage mapping and genomic prediction of grain quality traits in tropical maize (Frontiers in genetics · 2024Article
- Genetic architecture of ear traits based on association mapping and co-expression networks in maize inbred lines and hybrids.Molecular breeding : new strategies in plant improvement · 2023Article
- Integrated Genomic Selection for Accelerating Breeding Programs of Climate-Smart Cereals.Genes · 2023Review
- Article
- Article
- QTL mapping of maize (Physiology and molecular biology of plants : an international journal of functional plant biology · 2023Article
- Multi-trait and multi-environment genomic prediction for flowering traits in maize: a deep learning approach.Frontiers in plant science · 2023Article
- Genomic prediction and association mapping of maize grain yield in multi-environment trials based on reaction norm models.Frontiers in genetics · 2023Article
- Unravelling the genetic framework associated with grain quality and yield-related traits in maize (Frontiers in genetics · 2023Article
- Machine learning-assisted analysis for agronomic dataset of 49 Balangu (Lallemantia iberica L.) ecotypes from different regions of Iran.Scientific reports · 2022Article
- Genome-wide association study identifies candidate genes and favorable haplotypes for seed yield inMolecular breeding : new strategies in plant improvement · 2022Article
- Genome wide association analysis for yield related traits in maize.BMC plant biology · 2022Article
- Combining datasets for maize root seedling traits increases the power of GWAS and genomic prediction accuracies.Journal of experimental botany · 2022Article
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Abstract
High yield is the primary objective of maize breeding. Genomic dissection of grain yield and yield-related traits contribute to understanding the yield formation and improving the yield of maize. In this study, two genome-wide association study (GWAS) methods and genomic prediction were made on an association panel of 309 inbred lines. GWAS analyses revealed 22 significant trait-marker associations for grain yield per plant (GYP) and yield-related traits. Genomic prediction analyses showed that reproducing kernel Hilbert space (RKHS) outperformed the other four models based on GWAS-derived markers for GYP, ear weight, kernel number per ear and row, ear length, and ear diameter, whereas genomic best linear unbiased prediction (GBLUP) showed a slight superiority over other modes in most subsets of the trait-associated marker (TAM) for thousand kernel weight and kernel row number. The prediction accuracy could be improved when significant single-nucleotide polymorphisms were fitted as the fixed effects. Integrating information on population structure into the fixed model did not improve the prediction performance. For GYP, the prediction accuracy of TAMs derived from fixed and random model Circulating Probability Unification (FarmCPU) was comparable to that of the compressed mixed linear model (CMLM). For yield-related traits, CMLM-derived markers provided better accuracies than FarmCPU-derived markers in most scenarios. Compared with all markers, TAMs could effectively improve the prediction accuracies for GYP and yield-related traits. For eight traits, moderate- and high-prediction accuracies were achieved using TAMs. Taken together, genomic prediction incorporating prior information detected by GWAS could be a promising strategy to improve the grain yield of maize.
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