ArticleFrontiers in genetics2020
Prior Biological Knowledge Improves Genomic Prediction of Growth-Related Traits in
Article in Frontiers in genetics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
What it found
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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.
The trial behind it
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it, 16 citations in OpenAlex.
- Meta-analysis across Nellore cattle populations identifies common metabolic mechanisms that regulate feed efficiency-related traits.BMC genomics · 2022Pooled it
- The potential of considering photosynthesis parameters in crop yield breeding by genomic prediction.Journal of experimental botany · 2026Article
- Advances in genomics-driven genetic decoding and genomic design breeding in tomato.Horticulture research · 2026Article
- Highly Polygenic Control of Photosynthetic Responses to Nighttime Temperature Studied by Genomic Prediction.Plant, cell & environment · 2026Article
- Genomic selection: Essence, applications, and prospects.The plant genome · 2025Review
- Improvement in genomic prediction of maize with prior gene ontology information depends on traits and environmental conditions.The plant genome · 2025Article
- Enhancing genomic prediction inFrontiers in bioinformatics · 2025Article
- Improving the accuracy of genomic prediction for meat quality traits using whole genome sequence data in pigs.Journal of animal science and biotechnology · 2023Article
- The pursuit of genetic gain in agricultural crops through the application of machine-learning to genomic prediction.Frontiers in genetics · 2023Article
- Genomic prediction in plants: opportunities for ensemble machine learning based approaches.F1000Research · 2022Article
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Prediction of growth-related complex traits is highly important for crop breeding. Photosynthesis efficiency and biomass are direct indicators of overall plant performance and therefore even minor improvements in these traits can result in significant breeding gains. Crop breeding for complex traits has been revolutionized by technological developments in genomics and phenomics. Capitalizing on the growing availability of genomics data, genome-wide marker-based prediction models allow for efficient selection of the best parents for the next generation without the need for phenotypic information. Until now such models mostly predict the phenotype directly from the genotype and fail to make use of relevant biological knowledge. It is an open question to what extent the use of such biological knowledge is beneficial for improving genomic prediction accuracy and reliability. In this study, we explored the use of publicly available biological information for genomic prediction of photosynthetic light use efficiency (Φ
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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.