ArticleGenetics2018
Beyond Genomic Prediction: Combining Different Types of
Article in Genetics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 98 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
98 citing papers in PubMed, 195 citations in OpenAlex.
- Research progress on genomic selection breeding technology for crops.Protoplasma · 2026Review
- Climate change and crop resilience: harnessing metabolomics for predicting stress tolerance.The New phytologist · 2026Review
- Additive and Partially Dominant Effects from Genomic Variation Contribute to Rice Heterosis.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Methylome differences among barley inbreds and their association with genomic, transcriptomic, and phenotypic variation.Journal of experimental botany · 2026Article
- Optimizing biomass partitioning in wheat using UAV-based hyperspectral phenomic and genomic prediction: kernel-based and machine learning approaches.Frontiers in plant science · 2026Article
- AI-integrated digital breeding for crop improvement.Frontiers in plant science · 2026Review
- Genomic selection and reproducibility: are complex models distracting us from true scientific validity in the presence of genotype-by-environment interaction?G3 (Bethesda, Md.) · 2025Article
- Quantitative Insights into the Genetic Mechanisms of Crop Heterosis.Advanced genetics (Hoboken, N.J.) · 2025Review
- Comparing statistical 'phenomic prediction' models for remote-sensing-based phenotyping of maize susceptibility to common rust.Plant phenomics (Washington, D.C.) · 2025Article
- Predicting natural variation in the yeast phenotypic landscape with machine learning.Molecular systems biology · 2025Article
- Assessment of genomic prediction capabilities of transcriptome data in a barley multi-parent RIL population.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
- Interspecies predictions of growth traits from quantitative transcriptome data acquired during fruit development.Journal of experimental botany · 2025Article
- Unlocking gene regulatory networks for crop resilience and sustainable agriculture.Nature biotechnology · 2025Review
- Integrating multi-omics and machine learning for disease resistance prediction in legumes.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Review
- Genomic selection: Essence, applications, and prospects.The plant genome · 2025Review
- SPDC-HG: An accelerator of genomic hybrid breeding in maize.Plant biotechnology journal · 2025Article
- Advances in multi-trait genomic prediction approaches: classification, comparative analysis, and perspectives.Briefings in bioinformatics · 2025Review
- Metabolic marker-assisted genomic prediction improves hybrid breeding.Plant communications · 2025Article
- Genomic prediction for yield and malting traits in barley using metabolomic and near-infrared spectra.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
- Incorporating gene expression and environment for genomic prediction in wheat.Frontiers in plant science · 2025Article
38 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The ability to predict the agronomic performance of single-crosses with high precision is essential for selecting superior candidates for hybrid breeding. With recent technological advances, thousands of new parent lines, and, consequently, millions of new hybrid combinations are possible in each breeding cycle, yet only a few hundred can be produced and phenotyped in multi-environment yield trials. Well established prediction approaches such as best linear unbiased prediction (BLUP) using pedigree data and whole-genome prediction using genomic data are limited in capturing epistasis and interactions occurring within and among downstream biological strata such as transcriptome and metabolome. Because mRNA and small RNA (sRNA) sequences are involved in transcriptional, translational and post-translational processes, we expect them to provide information influencing several biological strata. However, using sRNA data of parent lines to predict hybrid performance has not yet been addressed. Here, we gathered genomic, transcriptomic (mRNA and sRNA) and metabolomic data of parent lines to evaluate the ability of the data to predict the performance of untested hybrids for important agronomic traits in grain maize. We found a considerable interaction for predictive ability between predictor and trait, with mRNA data being a superior predictor for grain yield and genomic data for grain dry matter content, while sRNA performed relatively poorly for both traits. Combining mRNA and genomic data as predictors resulted in high predictive abilities across both traits and combining other predictors improved prediction over that of the individual predictors alone. We conclude that downstream "omics" can complement genomics for hybrid prediction, and, thereby, contribute to more efficient selection of hybrid candidates.
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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.