Integrating Functional Analysis in the Next-Generation Sequencing Diagnostic Pipeline of RASopathies
Bibliographic record
Abstract
RASopathies are a group of heterogeneous conditions caused by germline mutations in RAS/MAPK signalling pathway genes. With next-generation sequencing (NGS), sequencing capacity is no longer a limitation to molecular diagnosis. Instead, the rising number of variants of unknown significance (VUSs) poses challenges to clinical interpretation and genetic counselling. We investigated the potential of an integrated pipeline combining NGS and the functional assessment of variants for the diagnosis of RASopathies. We included 63 Chinese patients with RASopathies that had previously tested negative for PTPN11 and HRAS mutations. In these patients, we performed a genetic analysis of genes associated with RASopathies using a multigene NGS panel and Sanger sequencing. For the VUSs, we evaluated evidence from genetic, bioinformatic and functional data. Twenty disease-causing mutations were identified in the 63 patients, providing a primary diagnostic yield of 31.7%. Four VUSs were identified in five patients. The functional assessment supported the pathogenicity of the RAF1 and RIT1 VUSs, while the significance of two VUSs in A2ML1 remained unclear. In summary, functional analysis improved the diagnostic yield from 31.7% to 36.5%. Although technically demanding and time-consuming, a functional genetic diagnostic analysis can ease the clinical translation of these findings to aid bedside interpretation.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".