Diagnosing missed cases of spinal muscular atrophy in genome, exome, and panel sequencing data sets
Jazyk angličtina Země Spojené státy americké Médium print-electronic
Typ dokumentu časopisecké články
Grantová podpora
UM1 HG008900
NHGRI NIH HHS - United States
U24 HG011746
NHGRI NIH HHS - United States
T32 HG010464
NHGRI NIH HHS - United States
R01 HG009141
NHGRI NIH HHS - United States
K23 AR083505
NIAMS NIH HHS - United States
U01 HG011755
NHGRI NIH HHS - United States
PubMed
39670433
PubMed Central
PMC11985284
DOI
10.1016/j.gim.2024.101336
PII: S1098-3600(24)00270-3
Knihovny.cz E-zdroje
- Klíčová slova
- Analysis tool, Muscle disease, SMA, Segmental duplication, Spinal muscular atrophy,
- MeSH
- algoritmy MeSH
- exom genetika MeSH
- genom lidský genetika MeSH
- genomika metody MeSH
- lidé MeSH
- protein přežití motorických neuronů 1 genetika MeSH
- protein přežití motorických neuronů 2 genetika MeSH
- sekvenční analýza DNA metody MeSH
- sekvenování exomu MeSH
- spinální svalová atrofie * genetika diagnóza MeSH
- vysoce účinné nukleotidové sekvenování MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- Názvy látek
- protein přežití motorických neuronů 1 MeSH
- protein přežití motorických neuronů 2 MeSH
- SMN1 protein, human MeSH Prohlížeč
- SMN2 protein, human MeSH Prohlížeč
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.
Centre for Cancer Biology An SA Pathology and UniSA Alliance Adelaide SA Australia
Centre of Medical Research The University of Western Australia Perth Western Australia Australia
Children's Hospital of Eastern Ontario Research Institute Ottawa ON Canada
Department of Clinical Neurosciences University of Cambridge Cambridge United Kingdom
Department of Neurology Neuromuscular Center ERN Medical University of Warsaw Warsaw Poland
Istenhegyi Genetic Diagnostic Centre Molecular Genetic Laboratory Budapest Hungary
National Institute of Mental Health and Neuro Sciences Bengaluru India
Neuromuscular Center Ain Shams University Cairo Egypt
Tampere Neuromuscular Center and Folkhälsan Research Center Helsinki Finland
Zobrazit více v PubMed
Sarv S et al. The Birth Prevalence of Spinal Muscular Atrophy: A Population Specific Approach in Estonia. Front. Genet 12, 796862 (2021). PubMed PMC
Verhaart IEC et al. Prevalence, incidence and carrier frequency of 5q-linked spinal muscular atrophy - a literature review. Orphanet J. Rare Dis 12, 124 (2017). PubMed PMC
Chen X et al. Spinal muscular atrophy diagnosis and carrier screening from genome sequencing data. Genet. Med 22, 945–953 (2020). PubMed PMC
Schorling DC, Pechmann A & Kirschner J Advances in Treatment of Spinal Muscular Atrophy - New Phenotypes, New Challenges, New Implications for Care. J Neuromuscul Dis 7, 1–13 (2020). PubMed PMC
Lefebvre S et al. Identification and characterization of a spinal muscular atrophy-determining gene. Cell 80, 155–165 (1995). PubMed
Li H & Durbin R Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009). PubMed PMC
Poplin R et al. Scaling accurate genetic variant discovery to tens of thousands of samples. bioRxiv 201178 (2018) doi:10.1101/201178. DOI
Chen X et al. Comprehensive SMN1 and SMN2 profiling for spinal muscular atrophy analysis using long-read PacBio HiFi sequencing. Am. J. Hum. Genet 110, 240–250 (2023). PubMed PMC
Töpf A et al. Sequential targeted exome sequencing of 1001 patients affected by unexplained limb-girdle weakness. Genet. Med 22, 1478–1488 (2020). PubMed PMC
Steyaert W et al. Systematic analysis of paralogous regions in 41,755 exomes uncovers clinically relevant variation. Nat. Commun 14, 6845 (2023). PubMed PMC
Nurk S et al. The complete sequence of a human genome. Science 376, 44–53 (2022). PubMed PMC
Karczewski KJ et al. The mutational constraint spectrum quantified from variation in 141,456 humans. Nature 581, 434–443 (2020). PubMed PMC
Robinson PN et al. The Human Phenotype Ontology: a tool for annotating and analyzing human hereditary disease. Am. J. Hum. Genet 83, 610–615 (2008). PubMed PMC
Poterba T et al. The Scalable Variant Call Representation: Enabling Genetic Analysis Beyond One Million Genomes. bioRxiv (2024) doi:10.1101/2024.01.09.574205. PubMed DOI PMC
Sudlow C et al. UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med 12, e1001779 (2015). PubMed PMC
Halldorsson BV et al. The sequences of 150,119 genomes in the UK Biobank. Nature 607, 732–740 (2022). PubMed PMC
Van Hout CV et al. Exome sequencing and characterization of 49,960 individuals in the UK Biobank. Nature 586, 749–756 (2020). PubMed PMC
Baxter SM et al. Centers for Mendelian Genomics: A decade of facilitating gene discovery. Genet. Med 24, 784–797 (2022). PubMed PMC
Vorster E, Essop FB, Rodda JL & Krause A Spinal Muscular Atrophy in the Black South African Population: A Matter of Rearrangement? Front. Genet 11, 54 (2020). PubMed PMC
Cobben JM et al. Deletions of the survival motor neuron gene in unaffected siblings of patients with spinal muscular atrophy. Am. J. Hum. Genet 57, (1995). PubMed PMC
Oprea GE et al. Plastin 3 Is a Protective Modifier of Autosomal Recessive Spinal Muscular Atrophy. Science 320, 524 (2008). PubMed PMC
Chong JX et al. A common spinal muscular atrophy deletion mutation is present on a single founder haplotype in the US Hutterites. Eur. J. Hum. Genet 19, 1045–1051 (2011). PubMed PMC
Peng X et al. Overcoming the Pitfalls of Next-Generation Sequencing-Based Molecular Diagnosis of Shwachman-Diamond Syndrome. J. Mol. Diagn 24, 1240–1253 (2022). PubMed
Krenn M, Jengojan S & Grisold W Spinal muscular atrophy presenting with mild limb-girdle weakness in adulthood: Diagnostic pitfalls in the era of disease-modifying therapies. J. Neurol. Sci 440, 120347 (2022). PubMed
D’Amico A, Mercuri E, Tiziano FD & Bertini E Spinal muscular atrophy. Orphanet J. Rare Dis 6, 71 (2011). PubMed PMC
Lin C-W, Kalb SJ & Yeh W-S Delay in Diagnosis of Spinal Muscular Atrophy: A Systematic Literature Review. Pediatr. Neurol 53, 293–300 (2015). PubMed
Schwartz O et al. Clinical Effectiveness of Newborn Screening for Spinal Muscular Atrophy: A Nonrandomized Controlled Trial. JAMA pediatrics vol. 178 540–547 (2024). PubMed PMC
Miller DT et al. Recommendations for reporting of secondary findings in clinical exome and genome sequencing, 2021 update: a policy statement of the American College of Medical Genetics and Genomics (ACMG). Genet. Med 23, 1391–1398 (2021). PubMed
Hail Team. Hail 0.2 https://github.com/hail-is/hail