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, Łusakowska Anna, Manoj Rajanna, Nalini Atchayaram, Karcagi Veronika, Polavarapu Kiran, Lochmüller Hanns, Horvath Rita, Bönnemann Carsten G, Donkervoort Sandra, Haliloğlu Göknur, Herguner Ozlem, Kang Peter B, Ravenscroft Gianina, Laing Nigel, Scott Hamish S, Töpf Ana, Straub Volker, Pajusalu Sander, Õunap Katrin, Tiao Grace, Rehm Heidi L, O'Donnell-Luria Anne
Program in Medical and Population Genetics, Broad Center for Mendelian Genomics, Broad Institute of MIT and Harvard, Cambridge, MA; Center for Genomic Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA.
Program in Medical and Population Genetics, Broad Center for Mendelian Genomics, Broad Institute of MIT and Harvard, Cambridge, MA; UC Santa Cruz Genomics Institute, UCSC, Santa Cruz, CA.
Genet Med. 2025 Apr;27(4):101336. doi: 10.1016/j.gim.2024.101336. Epub 2024 Dec 9.
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.
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.
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.
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.
我们着手开发一种公开可用的工具，该工具能够在与GRCh37、GRCh38或T2T参考基因组比对的外显子组、基因组或基因panel测序数据集中准确诊断脊髓性肌萎缩症（SMA）。
SMA Finder算法通过评估与SMN1和SMN2旁系同源基因c.840位置重叠的 reads来检测SMA最常见的遗传原因。它使用这些reads来确定个体是否最有可能没有功能性的SMN1拷贝。
我们开发了SMA Finder，并在来自布罗德研究所孟德尔基因组学中心的16626个外显子组和3911个基因组、塔尔图大学医院的1157个外显子组和8762个基因panel样本以及英国生物银行的198868个外显子组和198868个基因组上对其进行了评估。SMA Finder的假阳性率低于200000个样本中的1个，其阳性预测值大于96%，真阳性率为29/29。这些SMA诊断中的大多数最初在临床上被误诊为肢带型肌营养不良症。
我们对SMA Finder在外显子组、基因组和基因panel测序样本上的广泛评估发现它具有近100%的准确性，并证明了其减少诊断延迟的能力，特别是在患有较轻SMA亚型的个体中。鉴于这种准确性、此处确定的常见误诊、SMA临床确诊检测的广泛可用性以及治疗选择的存在，我们建议现在是时候将SMN1添加到美国医学遗传学学会基因组和外显子组测序后可报告次要发现的基因列表中了。
