Quantitative trait variation is revealed in a novel hypomethylated population of woodland strawberry (Fragaria vesca)
Bibliographic record
Abstract
BACKGROUND: Phenotypic variation is determined by a combination of genotype, environment and their interactions. The realization that allelic diversity can be both genetic and epigenetic allows the environmental component to be further separated. Partitioning phenotypic variation observed among inbred lines with an altered epigenome can allow the epigenetic component controlling quantitative traits to be estimated. To assess the contribution of epialleles on phenotypic variation and determine the fidelity with which epialleles are inherited, we have developed a novel hypomethylated population of strawberry (2n = 2x = 14) using 5-azacytidine from which individuals with altered phenotypes can be identified, selected and characterized. RESULTS: The hypomethylated population was generated using an inbred strawberry population in the F. vesca ssp. vesca accession Hawaii 4. Analysis of whole genome sequence data from control and hypomethylated lines indicate that 5-azacytidine exposure does not increase SNP above background levels. The populations contained only Hawaii 4 alleles, removing introgression of alternate F. vesca alleles as a potential source of variation. Although genome sequencing and genetic marker data are unable to rule out 5-azacytidine induced chromosomal rearrangements as a potential source of the trait variation observed, none were detected in our survey. Quantitative trait variation focusing on flowering time and rosette diameter was scored in control and treated populations where expanded levels of variation were observed among the hypomethylated lines. Methylation sensitive molecular markers indicated that 5-azacytidine induced alterations in DNA methylation patterns and inheritance of methylation patterns were confirmed by bisulfite sequencing of targeted regions. It is possible that methylation polymorphisms might underlie or have induced genetic changes underlying the observable differences in quantitative phenotypes. CONCLUSIONS: This population developed in a uniform genetic background provides a resource for the discovery of new variation controlling quantitative traits. Genome sequence analysis indicates that 5-azacytidine did not induce point mutations and the induced variation is largely restricted to DNA methylation. Using this resource, we have identified new variation and demonstrated the inheritance of both variant trait and methylation patterns. Although direct associations remain to be determined, these data suggest epigenetic variation might be subject to selection.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".