ArticleBMC genomics2020
Gene expression predictions and networks in natural populations supports the omnigenic theory.
Article in BMC genomics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
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Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Genome-Enabled Prediction Methods Based on Machine Learning.Methods in molecular biology (Clifton, N.J.) · 2022Pooled it
- From linear models to deep learning: statistical advances in genomic selection for animal breeding.Journal of animal science and biotechnology · 2026Review
- Flexibility in Gene Coexpression at Developmental and Evolutionary Timescales.Molecular biology and evolution · 2025Article
- Functional brain network identification in opioid use disorder using machine learning analysis of resting-state fMRI BOLD signals.Computers in biology and medicine · 2025Article
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- Transcriptome-based prediction for polygenic traits in rice using different gene subsets.BMC genomics · 2024Article
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- Contact lenses as novel tear fluid sampling vehicles for total RNA isolation, precipitation, and amplification.Scientific reports · 2024Article
- Genomic data integration tutorial, a plant case study.BMC genomics · 2024Article
- RNAseq based variant dataset in a black poplar association panel.BMC research notes · 2023Article
- A multi-level approach reveals key physiological and molecular traits in the response of two rice genotypes subjected to water deficit at the reproductive stage.Plant-environment interactions (Hoboken, N.J.) · 2023Review
- A persistent major mutation in canonical jasmonate signaling is embedded in an herbivory-elicited gene network.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Implementing Core Genes and an Omnigenic Model for Behaviour Traits Prediction in Genomics.Genes · 2023Review
- Identification of schizophrenia symptom-related gene modules by postmortem brain transcriptome analysis.Translational psychiatry · 2023Article
- Article
- eQTLs are key players in the integration of genomic and transcriptomic data for phenotype prediction.BMC genomics · 2022Article
- Improving lodgepole pine genomic evaluation using spatial correlation structure and SNP selection with single-step GBLUP.Heredity · 2022Article
- Generating Douglas-fir Breeding Value Estimates Using Airborne Laser Scanning Derived Height and Crown Metrics.Frontiers in plant science · 2022Article
- Integrating genomic information and productivity and climate-adaptability traits into a regional white spruce breeding program.PloS one · 2022Article
- Single-step genomic prediction of Eucalyptus dunnii using different identity-by-descent and identity-by-state relationship matrices.Heredity · 2021Article
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundRecent literature on the differential role of genes within networks distinguishes core from peripheral genes. If previous works have shown contrasting features between them, whether such categorization matters for phenotype prediction remains to be studied.
resultsWe measured 17 phenotypic traits for 241 cloned genotypes from a Populus nigra collection, covering growth, phenology, chemical and physical properties. We also sequenced RNA for each genotype and built co-expression networks to define core and peripheral genes. We found that cores were more differentiated between populations than peripherals while being less variable, suggesting that they have been constrained through potentially divergent selection. We also showed that while cores were overrepresented in a subset of genes statistically selected for their capacity to predict the phenotypes (by Boruta algorithm), they did not systematically predict better than peripherals or even random genes.
conclusionOur work is the first attempt to assess the importance of co-expression network connectivity in phenotype prediction. While highly connected core genes appear to be important, they do not bear enough information to systematically predict better quantitative traits than other gene sets.
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