MG-129 Our experience of<i>in silico</i>gene panel testing for clinically heterogeneous disorders using exome sequencing
Bibliographic record
Abstract
<h3>Background</h3> The implementation of next generation sequencing technology (NGS) in a molecular diagnostics laboratory has resulted in a major transformation in service delivery. The ability to generate accurate sequence data from thousands of genes in a single experiment has facilitated the development of comprehensive gene panel tests for genetically heterogeneous disorders. Previous methodology of gene panel testing included Sanger sequencing, which was time-consuming, labour-intensive and costly and therefore restricted the number of genes that were included in a panel. <h3>Methods/results</h3> At The Hospital for Sick Children (Toronto, Canada), we have developed and clinically validated <i>in silico</i> gene panels using whole exome sequencing (WES) for several heterogeneous disorders including hereditary spastic paraplegia (HSP), connective tissue and bone disorders (CT), hereditary hearing loss (HL), Noonan syndrome (NS) and autoimmune disorders (AI). Our laboratory’s current strategy of using WES has several advantages including: (1) standardised workflows that facilitate the development of additional gene panels; (2) expansion of current gene panel as new disease associations are discovered; and (3) building of an internal database of allele frequencies to aid in interpretation of variants. Through our validation studies we have shown high reproducibility and accuracy with >99% sensitivity for detection of single nucleotide variants (SNVs) and >95% for detection of small insertion/deletions (indels). <h3>Conclusions</h3> NGS technologies have enabled our laboratory to expand our testing menu to include genes that comprise part of the differential diagnoses for several disorders which will lead to improved detection rates, increased genetic diagnoses and ultimately better clinical care.
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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 | 0.002 |
| 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".