ArticleHeredity2017
Prediction and association mapping of agronomic traits in maize using multiple omic data.
Article in Heredity, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 56 papers.
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Who cites it
56 citing papers in PubMed.
- Candidate genes with antagonistic roles in stomatal development are associated with population-wide variation in apple.Horticulture research · 2026Article
- From linear models to deep learning: statistical advances in genomic selection for animal breeding.Journal of animal science and biotechnology · 2026Review
- Prediction of disease resilience of pigs using multi-omics data.Journal of animal science and biotechnology · 2026Article
- ReaGP: integrating residual units and attention mechanisms in convolution neural network for genomic prediction.Genetics, selection, evolution : GSE · 2026Article
- Breeding climate-resilient maize for food security in semi-arid sub-Saharan Africa: strategies and future directions.Frontiers in plant science · 2026Review
- Integrating 3D imaging, GWAS, and single-cell transcriptome approaches to elucidate root system architecture in Populus.Plant physiology · 2025Article
- Integrating gene expression data via weighted multiple kernel ridge regression improved accuracy of genomic prediction.Genetics, selection, evolution : GSE · 2025Article
- Genomic selection: Essence, applications, and prospects.The plant genome · 2025Review
- Leveraging Automated Machine Learning for Environmental Data-Driven Genetic Analysis and Genomic Prediction in Maize Hybrids.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Metabolic marker-assisted genomic prediction improves hybrid breeding.Plant communications · 2025Article
- Incorporating gene expression and environment for genomic prediction in wheat.Frontiers in plant science · 2025Article
- Genome-Wide Association Study for Resistance toPlants (Basel, Switzerland) · 2024Article
- Climate Change-The Rise of Climate-Resilient Crops.Plants (Basel, Switzerland) · 2024Review
- Multi-omics assists genomic prediction of maize yield with machine learning approaches.Molecular breeding : new strategies in plant improvement · 2024Article
- Integrating genome-wide association study into genomic selection for the prediction of agronomic traits in rice (Molecular breeding : new strategies in plant improvement · 2023Article
- Article
- Evaluating metabolic and genomic data for predicting grain traits under high night temperature stress in rice.G3 (Bethesda, Md.) · 2023Article
- Haplotype-tagged SNPs improve genomic prediction accuracy for Fusarium head blight resistance and yield-related traits in wheat.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2023Article
- Association mapping reveals novel genes and genomic regions controlling grain size architecture in mini core accessions of Indian National Genebank wheat germplasm collection.Frontiers in plant science · 2023Article
- Genome wide association analysis for yield related traits in maize.BMC plant biology · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Genomic selection holds a great promise to accelerate plant breeding via early selection before phenotypes are measured, and it offers major advantages over marker-assisted selection for highly polygenic traits. In addition to genomic data, metabolome and transcriptome are increasingly receiving attention as new data sources for phenotype prediction. We used data available from maize as a model to compare the predictive abilities of three different omic data sources using eight representative methods for six traits. We found that the best linear unbiased prediction overall performs better than other methods across different traits and different omic data, and genomic prediction performs better than transcriptomic and metabolomic predictions. For the same maize data, we also conducted genome-wide association study, transcriptome-wide association studies and metabolome-wide association studies for the six agronomic traits using both the genome-wide efficient mixed model association (GEMMA) method and a modified least absolute shrinkage and selection operator (LASSO) method. The new LASSO method has the ability to perform statistical tests. Simulation studies show that the modified LASSO performs better than GEMMA in terms of high power and low Type 1 error.
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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.