Diagnosing missed cases of spinal muscular atrophy in genome, exome, and panel sequencing data sets
Bibliographic record
Abstract
PURPOSE: We set out to develop a publicly available tool that could accurately diagnose spinal muscular atrophy (SMA) in exome, genome, or panel sequencing data sets aligned to a GRCh37, GRCh38, or T2T reference genome. METHODS: The SMA Finder algorithm detects the most common genetic causes of SMA by evaluating reads that overlap the c.840 position of the SMN1 and SMN2 paralogs. It uses these reads to determine whether an individual most likely has 0 functional copies of SMN1. RESULTS: We developed SMA Finder and evaluated it on 16,626 exomes and 3911 genomes from the Broad Institute Center for Mendelian Genomics, 1157 exomes and 8762 panel samples from Tartu University Hospital, and 198,868 exomes and 198,868 genomes from the UK Biobank. SMA Finder's false-positive rate was below 1 in 200,000 samples, its positive predictive value was greater than 96%, and its true-positive rate was 29 out of 29. Most of these SMA diagnoses had initially been clinically misdiagnosed as limb-girdle muscular dystrophy. CONCLUSION: Our extensive evaluation of SMA Finder on exome, genome, and panel sequencing samples found it to have nearly 100% accuracy and demonstrated its ability to reduce diagnostic delays, particularly in individuals with milder subtypes of SMA. Given this accuracy, the common misdiagnoses identified here, the widespread availability of clinical confirmatory testing for SMA, and the existence of treatment options, we propose that it is time to add SMN1 to the American College of Medical Genetics list of genes with reportable secondary findings after genome and exome sequencing.
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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 | 0.001 | 0.001 |
| 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".