178 Increasing diagnostic yield from short-read WGS data
Bibliographic record
Abstract
Background/ Objectives GeneSTEPS is a multi-centre study examining the diagnostic yield and clinical utility of rapid trio whole genome sequencing (WGS) in epilepsy patients <12 months old. Current analysis strategies show a 48% diagnostic yield. However, bioinformatic filtering strategies face several limitations, such as variants in non-coding regions, calling of mosaic variants and detection and filtering of structural and mitochondrial variants. Here, we describe initial results of deeper interpretation strategies which have increased diagnostic yield.Methods Four strategies have been applied 1) gene agnostic analysis was used to look for potential novel genes, 2) all known pathogenic variants in the ClinVar database were called, 3) de novo non-coding variants within epilepsy genes were investigated, 4) non-coding variants were investigated in cases with a single hit in an autosomal recessive gene.Results Two novel genes have been identified as potential candidate genes; work is being undertaken to investigate these further. A novel DNM1 de novo deep intronic variant near other variants with proven effect on splicing was detected in one patient and a previously published likely pathogenic variant in the UFM1 promoter region was identified that had not been called by the pipeline due to being non-coding.Conclusion Deeper interpretation approaches improve diagnostic yield from short-read WGS. Other approaches we are applying include improving the detection and filtering of structural and mitochondrial variants, detection of mosaic variants and repeat expansions and further analysis of non-coding regions.Grants GeneSTEPS grant & NIHR Biomedical Research Centre at Great Ormond Street Hospital, London, UK.Acknowledgements for Funding or Support The work described here was funded by the NIHR Biomedical Research Centre at Great Ormond Street Hospital. The authors would like to acknowledge the International Precision Child Health Partnership (IPCHiP).
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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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".