Diagnosing missed cases of spinal muscular atrophy in genome, exome, and panel sequencing data sets
- PMID: 39670433
- PMCID: PMC11985284
- DOI: 10.1016/j.gim.2024.101336
Diagnosing missed cases of spinal muscular atrophy in genome, exome, and panel sequencing data sets
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.
Keywords: Analysis tool; Muscle disease; SMA; Segmental duplication; Spinal muscular atrophy.
Copyright © 2024 American College of Medical Genetics and Genomics. All rights reserved.
Conflict of interest statement
Conflict of Interest Heidi L. Rehm receives research funding from Microsoft and previously received funding from Illumina to support rare disease gene discovery and diagnosis. Anne O’Donnell-Luria has consulted for Tome Biosciences, Ono Pharma USA Inc, and Addition Therapeutics and was a member of the scientific advisory board for Congenica Inc and the Simons Foundation SPARK for Autism study. Anna Łusakowska received honoraria for speaking at educational events for Biogen, PTC, and Roche, is a subinvestigator in clinical trials by Roche and PTC, and is involved in a project supported by Biogen (POL-SMA-17-11166). Peter B. Kang has received research support from ML Bio and Sarepta Therapeutics and has consulted for Lupin, Neurogene, NS Pharma, and Teneofour.
Update of
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Diagnosing missed cases of spinal muscular atrophy in genome, exome, and panel sequencing datasets.medRxiv [Preprint]. 2024 Jun 29:2024.02.11.24302646. doi: 10.1101/2024.02.11.24302646. medRxiv. 2024. Update in: Genet Med. 2025 Apr;27(4):101336. doi: 10.1016/j.gim.2024.101336. PMID: 38405995 Free PMC article. Updated. Preprint.
References
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- Lefebvre S et al. Identification and characterization of a spinal muscular atrophy-determining gene. Cell 80, 155–165 (1995). - PubMed
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