MG-129 Our experience of<i>in silico</i>gene panel testing for clinically heterogeneous disorders using exome sequencing
Bibliographic record
Abstract
Background 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. Methods/results At The Hospital for Sick Children (Toronto, Canada), we have developed and clinically validated in silico 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). Conclusions 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 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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".