Whole genome sequencing facilitates intragenic variant interpretation following modifier screening in C. elegans
Bibliographic record
Abstract
BACKGROUND: Intragenic modifiers (in-phase, second-site variants) are known to have dramatic effects on clinical outcomes, affecting disease attributes such as severity or age of onset. However, despite their clinical importance, the focus of many genetic screens in model systems is on the discovery of extragenic variants, with many labs still relying upon more traditional methods to identify modifiers. However, traditional methods such as PCR and Sanger sequencing can be time-intensive and do not permit a thorough understanding of the intragenic modifier effects in the context of non-isogenic genomic backgrounds. RESULTS: Here, we apply high throughput approaches to identify and understand intragenic modifiers using Caenorhabditis elegans. Specifically, we applied whole genome sequencing (WGS) to a mutagen-induced forward genetic screen to identify intragenic suppressors of a temperature-sensitive zyg-1(it25) allele in C. elegans. ZYG-1 is a polo kinase that is important for centriole function and cell divisions, and mutations that truncate its human orthologue, PLK4, have been associated with microcephaly. Combining WGS and CRISPR/Cas9, we rapidly identify intragenic modifiers, show that these variants are distributed non-randomly throughout zyg-1 and that genomic context plays an important role on phenotypic outcomes. CONCLUSIONS: Ultimately, our work shows that WGS facilitates high-throughput identification of intragenic modifiers in clinically relevant genes by reducing hands-on research time and overall costs and by allowing thorough understanding of the intragenic phenotypic effects in the context of different genetic backgrounds.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".