A haplotype-resolved chromatin landscape connects cis-regulatory variants to trait variation in Citrus
Bibliographic record
Abstract
BACKGROUND: Genetic and epigenetic perturbation of cis-regulatory sequences can shift patterns of gene expression and result in novel phenotypes. Phased genome assemblies now enable the local dissection of linkages between cis-regulatory sequences, including their epigenetic state, and allele-specific gene expression to further characterize gene regulation and resulting phenotypes in heterozygous genomes. RESULTS: We assembled a locally phased genome for a mandarin hybrid named 'Fairchild' to explore the molecular signatures of allele-specific gene expression. With local genome phasing, genes with allele-specific expression were paired with haplotype-specific chromatin states, including levels of chromatin accessibility, histone modifications, and DNA methylation. We found that 30% of variation in allele-specific expression could be attributed to haplotype associated factors, with allelic levels of chromatin accessibility and three histone modifications in gene bodies having the most influence. Structural variants in promoter regions were also associated with allele-specific expression, including specific enrichments of hAT and MULE-MuDR DNA transposon sequences. Integration of haplotype-resolved genetic and epigenetic landscapes with high-throughput phenotypic analysis of fruit traits in a panel of 154 accessions with mandarin and pummelo ancestry revealed that trait-associated variants were enriched in regions of open chromatin. Mining of trait-associated variants uncovered a Gypsy retrotransposon insertion in a gene that regulates potassium transport and may contribute to the reduction in fruit size that is observed in mandarins. CONCLUSIONS: Using a locally phased assembly of a heterozygous cultivar of citrus, we dissected the interplay between genetic variants and molecular phenotypes to reveal cis-regulatory sequences with potential functional effects on phenotypes relevant for genetic improvement.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".