ArticleGenetics2015
Prediction of Plant Height in Arabidopsis thaliana Using DNA Methylation Data.
Article in Genetics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 1 of them a synthesis that pooled it.
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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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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
33 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
- Mul-PheG2P: decoupled learning and prediction-space fusion enables robust and interpretable multi-phenotype genomic prediction.The New phytologist · 2026Article
- Unveiling Plant Metabolic Diversity: Integrating Metabolomics with Multi-Omics Approaches for Crop Improvement.Plants (Basel, Switzerland) · 2026Review
- Methylome differences among barley inbreds and their association with genomic, transcriptomic, and phenotypic variation.Journal of experimental botany · 2026Article
- Role of multi-omics in aquaculture genetics and breeding: current status and future perspective.Science China. Life sciences · 2025Review
- Article
- Prediction of plant complex traits via integration of multi-omics data.Nature communications · 2024Article
- Exploring Genetic and Epigenetic Changes in Lingonberry Using Molecular Markers: Implications for Clonal Propagation.Current issues in molecular biology · 2023Article
- Epigenomics in stress tolerance of plants under the climate change.Molecular biology reports · 2023Review
- Epigenetic Changes Occurring in Plant Inbreeding.International journal of molecular sciences · 2023Review
- Relative importance of genotype, gene expression, and DNA methylation on complex traits in perennial ryegrass.The plant genome · 2022Article
- Integration of DNA Methylation and Transcriptome Data Improves Complex Trait Prediction inPlants (Basel, Switzerland) · 2022Article
- Decoding the sorghum methylome: understanding epigenetic contributions to agronomic traits.Biochemical Society transactions · 2022Article
- Assessment of natural DNA methylation variation and its association with economically important traits in dolichos bean (Lablab purpureus L. Var. Lignosus) using AMP-PCR assay.Journal of applied genetics · 2021Article
- Review
- Diversifying maize genomic selection models.Molecular breeding : new strategies in plant improvement · 2021Article
- Integrating genomic and infrared spectral data improves the prediction of milk protein composition in dairy cattle.Genetics, selection, evolution : GSE · 2021Article
- Exploiting Epigenetic Variations for Crop Disease Resistance Improvement.Frontiers in plant science · 2021Review
- Epigenetics and epigenomics: underlying mechanisms, relevance, and implications in crop improvement.Functional & integrative genomics · 2020Review
- Epigenetics: possible applications in climate-smart crop breeding.Journal of experimental botany · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Prediction of complex traits using molecular genetic information is an active area in quantitative genetics research. In the postgenomic era, many types of -omic (e.g., transcriptomic, epigenomic, methylomic, and proteomic) data are becoming increasingly available. Therefore, evaluating the utility of this massive amount of information in prediction of complex traits is of interest. DNA methylation, the covalent change of a DNA molecule without affecting its underlying sequence, is one quantifiable form of epigenetic modification. We used methylation information for predicting plant height (PH) in Arabidopsis thaliana nonparametrically, using reproducing kernel Hilbert spaces (RKHS) regression. Also, we used different criteria for selecting smaller sets of probes, to assess how representative probes could be used in prediction instead of using all probes, which may lessen computational burden and lower experimental costs. Methylation information was used for describing epigenetic similarities between individuals through a kernel matrix, and the performance of predicting PH using this similarity matrix was reasonably good. The predictive correlation reached 0.53 and the same value was attained when only preselected probes were used for prediction. We created a kernel that mimics the genomic relationship matrix in genomic best linear unbiased prediction (G-BLUP) and estimated that, in this particular data set, epigenetic variation accounted for 65% of the phenotypic variance. Our results suggest that methylation information can be useful in whole-genome prediction of complex traits and that it may help to enhance understanding of complex traits when epigenetics is under examination.
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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.