Lemire Gabrielle, Sanchis-Juan Alba, Russell Kathryn, Baxter Samantha, Chao Katherine R, Singer-Berk Moriel, Groopman Emily, Wong Isaac, England Eleina, Goodrich Julia, Pais Lynn, Austin-Tse Christina, DiTroia Stephanie, O'Heir Emily, Ganesh Vijay S, Wojcik Monica H, Evangelista Emily, Snow Hana, Osei-Owusu Ikeoluwa, Fu Jack, Singh Mugdha, Mostovoy Yulia, Huang Steve, Garimella Kiran, Kirkham Samantha L, Neil Jennifer E, Shao Diane D, Walsh Christopher A, Argilli Emanuela, Le Carolyn, Sherr Elliott H, Gleeson Joseph G, Shril Shirlee, Schneider Ronen, Hildebrandt Friedhelm, Sankaran Vijay G, Madden Jill A, Genetti Casie A, Beggs Alan H, Agrawal Pankaj B, Bujakowska Kinga M, Place Emily, Pierce Eric A, Donkervoort Sandra, Bönnemann Carsten G, Gallacher Lyndon, Stark Zornitza, Tan Tiong Yang, White Susan M, Töpf Ana, Straub Volker, Fleming Mark D, Pollak Martin R, Õunap Katrin, Pajusalu Sander, Donald Kirsten A, Bruwer Zandre, Ravenscroft Gianina, Laing Nigel G, MacArthur Daniel G, Rehm Heidi L, Talkowski Michael E, Brand Harrison, O'Donnell-Luria Anne
Broad Institute Center for Mendelian Genomics, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Division of Genetics and Genomics, Boston Children's Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA; Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.
Broad Institute Center for Mendelian Genomics, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Harvard Medical School, Boston, MA, USA; Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.
Am J Hum Genet. 2024 May 2;111(5):863-876. doi: 10.1016/j.ajhg.2024.03.008. Epub 2024 Apr 1.
Copy number variants (CNVs) are significant contributors to the pathogenicity of rare genetic diseases and, with new innovative methods, can now reliably be identified from exome sequencing. Challenges still remain in accurate classification of CNV pathogenicity. CNV calling using GATK-gCNV was performed on exomes from a cohort of 6,633 families (15,759 individuals) with heterogeneous phenotypes and variable prior genetic testing collected at the Broad Institute Center for Mendelian Genomics of the Genomics Research to Elucidate the Genetics of Rare Diseases consortium and analyzed using the seqr platform. The addition of CNV detection to exome analysis identified causal CNVs for 171 families (2.6%). The estimated sizes of CNVs ranged from 293 bp to 80 Mb. The causal CNVs consisted of 140 deletions, 15 duplications, 3 suspected complex structural variants (SVs), 3 insertions, and 10 complex SVs, the latter two groups being identified by orthogonal confirmation methods. To classify CNV variant pathogenicity, we used the 2020 American College of Medical Genetics and Genomics/ClinGen CNV interpretation standards and developed additional criteria to evaluate allelic and functional data as well as variants on the X chromosome to further advance the framework. We interpreted 151 CNVs as likely pathogenic/pathogenic and 20 CNVs as high-interest variants of uncertain significance. Calling CNVs from existing exome data increases the diagnostic yield for individuals undiagnosed after standard testing approaches, providing a higher-resolution alternative to arrays at a fraction of the cost of genome sequencing. Our improvements to the classification approach advances the systematic framework to assess the pathogenicity of CNVs.
拷贝数变异（CNV）是罕见遗传病致病性的重要因素，借助新的创新方法，现在可以从外显子组测序中可靠地识别出来。在准确分类CNV致病性方面仍存在挑战。我们在来自6633个家庭（15759名个体）的外显子组上使用GATK - gCNV进行CNV检测，这些家庭具有异质表型且先前进行过不同的基因检测，样本收集于基因组研究阐明罕见病遗传学联盟的布罗德研究所孟德尔基因组学中心，并使用seqr平台进行分析。在外显子组分析中增加CNV检测后，为171个家庭（2.6%）确定了致病CNV。CNV的估计大小范围为293 bp至80 Mb。致病CNV包括140个缺失、15个重复、3个疑似复杂结构变异（SV）、3个插入和10个复杂SV，后两组通过正交确认方法识别。为了分类CNV变异的致病性，我们使用了2020年美国医学遗传学与基因组学学会/临床基因组学（ACMG/ClinGen）的CNV解读标准，并制定了额外标准来评估等位基因和功能数据以及X染色体上的变异，以进一步完善该框架。我们将151个CNV解释为可能致病/致病，20个CNV解释为意义不确定的高关注变异。从现有外显子组数据中调用CNV可提高标准检测方法后仍未确诊个体的诊断率，以基因组测序成本的一小部分提供比阵列更高分辨率的替代方法。我们对分类方法的改进推进了评估CNV致病性的系统框架。
