Informace o publikaci

Diagnosing missed cases of spinal muscular atrophy in genome, exome, and panel sequencing data sets

Autoři	WEISBURD Ben SHARMA Rakshya PATA Villem REIMAND Tiia GANESH Vijay S AUSTIN-TSE Christina OSEI-OWUSU Ikeoluwa O'HEIR Emily O'LEARY Melanie PAIS Lynn STAFKI Seth A DAUGHERTY Audrey L FOLLAND Chiara PERIC Stojan FAHMY Nagia UDD Bjarne HORÁKOVÁ Magda LUSAKOWSKA Anna MANOJ Rajanna NALINI Atchayaram KARCAGI Veronika POLAVARAPU Kiran LOCHMULLER Hanns HORVATH Rita BONNEMANN Carsten G DONKERVOORT Sandra HALILOGLU Goknur HERGUNER Ozlem KANG Peter B RAVENSCROFT Gianina LAING Nigel SCOTT Hamish S TOPF Ana STRAUB Volker PAJUSALU Sander OUNAP Katrin TIAO Grace REHM Heidi L O'DONNELL-LURIA Anne
Rok publikování	2025
Druh	Článek v odborném periodiku
Časopis / Zdroj	Genetics In Medicine
Fakulta / Pracoviště MU	Lékařská fakulta
Citace
www	https://www.sciencedirect.com/science/article/abs/pii/S1098360024002703?via%3Dihub
Doi	https://doi.org/10.1016/j.gim.2024.101336
Klíčová slova	Analysis tool; Diagnosis; Exome; Muscle disease; Segmental duplication; SMA; Spinal muscular atrophy
Popis	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.