MG-130 Utilising whole exome sequencing to identify causative variants in genetically heterogeneous disorders
Bibliographic record
Abstract
<h3>Background</h3> The rapid pace of innovation within the era of genomic sequencing has provided unprecedented insights into the genetic basis of human disease. Clinical whole exome sequencing provides a means for clinicians and clinical labs to take advantage of this ongoing expansion of knowledge, to benefit patient care and offer individualised medicine. <h3>Methods/results</h3> We have developed a workflow and customised informatics pipeline suitable for processing large numbers of clinical exomes to achieve a diagnosis for genetically heterogeneous disorders. In order to guide genetic variant interpretation, we have implemented the Phenotips software tool within the hospital to collect detailed phenotype information from physicians, through a web-based interface. We describe our exome variant interpretation algorithm that integrates multiple control and clinical databases, as well as our on-site patient database to achieve a diagnosis. In order to maximise diagnostic yield, our analysis includes variants affecting all genes known to cause genetic disorders, which are prioritised according to phenotypic information provided by the referring physician. In addition, we provide patients the option of reporting secondary findings as described in the ACMG guidelines. <h3>Conclusions</h3> This comprehensive approach provides an effective strategy for identifying causative variants in patients with genetically and phenotypically heterogeneous disorders, who would otherwise remain undiagnosed.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 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".