MG-130 Utilising whole exome sequencing to identify causative variants in genetically heterogeneous disorders
Bibliographic record
Abstract
Background 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. Methods/results 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. Conclusions This comprehensive approach provides an effective strategy for identifying causative variants in patients with genetically and phenotypically heterogeneous disorders, who would otherwise remain undiagnosed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".