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, Argili Emanuela, Le Carolyn, Sherr Elliott H, Gleeson Joseph, 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, 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.
medRxiv. 2023 Oct 5:2023.10.05.23296595. doi: 10.1101/2023.10.05.23296595.
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 GREGoR consortium. Each family's CNV data was analyzed using the platform and candidate CNVs classified using the 2020 ACMG/ClinGen CNV interpretation standards. We developed additional evidence criteria to address situations not covered by the current standards. The addition of CNV calling to exome analysis identified causal CNVs for 173 families (2.6%). The estimated sizes of CNVs ranged from 293 bp to 80 Mb with estimates that 44% would not have been detected by standard chromosomal microarrays. The causal CNVs consisted of 141 deletions, 15 duplications, 4 suspected complex structural variants (SVs), 3 insertions and 10 complex SVs, the latter two groups being identified by orthogonal validation methods. We interpreted 153 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CNV。在准确分类CNV致病性方面仍然存在挑战。使用GATK - gCNV对来自GREGoR联盟孟德尔基因组学布罗德研究所中心收集的6633个家庭（15759名个体）队列的外显子组进行了CNV检测，这些家庭具有异质表型和不同的既往基因检测情况。使用该平台分析每个家庭的CNV数据，并根据2020年美国医学遗传学与基因组学学会（ACMG）/临床基因组资源（ClinGen）CNV解读标准对候选CNV进行分类。我们制定了额外的证据标准以应对当前标准未涵盖的情况。在外显子组分析中增加CNV检测，为173个家庭（2.6%）确定了致病CNV。CNV的估计大小范围从293 bp到80 Mb，估计有44%的CNV无法通过标准染色体微阵列检测到。致病CNV包括141个缺失、15个重复、4个疑似复杂结构变异（SV）、3个插入和10个复杂SV，后两组通过正交验证方法识别。我们将153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检测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了比阵列更高分辨率的替代方法。我们对分类方法所做的改进推进了评估CNV致病性的系统框架。
