Regulation at <i>Drosophila</i>’s <i>Malic Enzyme</i> highlights the complexity of transvection and its sensitivity to genetic background
Bibliographic record
Abstract
Transvection, a type of trans-regulation of gene expression in which regulatory elements on one chromosome influence elements on a paired homologous chromosome, is itself a complex biological phenotype subject to modification by genetic background effects. However, relatively few studies have explored how transvection is affected by distal genetic variation, perhaps because it is strongly influenced by local regulatory elements and chromosomal architecture. With the emergence of the "hub" model of transvection and a series of studies showing variation in transvection effects, it is becoming clear that genetic background plays an important role in how transvection influences gene transcription. We explored the effects of genetic background on transvection by performing two independent genome wide association studies (GWASs) using the Drosophila genetic reference panel (DGRP) and a suite of Malic enzyme (Men) excision alleles. We found substantial variation in the amount of transvection in the 149 DGRP lines used, with broad-sense heritability of 0.89 and 0.84, depending on the excision allele used. The specific genetic variation identified was dependent on the excision allele used, highlighting the complex genetic interactions influencing transvection. We focussed primarily on genes identified as significant using a relaxed P-value cutoff in both GWASs. The most strongly associated genetic variant mapped to an intergenic single nucleotide polymorphism (SNP), located upstream of Tiggrin (Tig), a gene that codes for an extracellular matrix protein. Variants in other genes, such transcription factors (CG7368 and Sima), RNA binding proteins (CG10418, Rbp6, and Rig), enzymes (AdamTS-A, CG9743, and Pgant8), proteins influencing cell cycle progression (Dally and Eip63E) and signaling proteins (Atg-1, Axo, Egfr, and Path) also associated with transvection in Men. Although not intuitively obvious how many of these genes may influence transvection, some have been previously identified as promoting or antagonizing somatic homolog pairing. These results identify several candidate genes to further explore in the understanding of transvection in Men and in other genes regulated by transvection. Overall, these findings highlight the complexity of the interactions involved in gene regulation, even in phenotypes, such as transvection, that were traditionally considered to be primarily influenced by local genetic variation.
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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".