Amin Asmaa K, El-Dessouky Sara H, Elmaksoud Marwa Abd, Nabil Amira, Aboulghar Mona M, Senousy Sameh M, Elbagoury Nagham M, Abdel-Aleem Asmaa F, Essawi Mona L, El-Awady Heba A, Ashaat Engy A, Issa Mahmoud Y, Alaadin Khoushoua, Matsa Lova S, Issa Noha M, Zaki Maha S, Eid Maha Mohamed, Sharaf-Eldin Wessam E, Abdalla Ebtesam
Human Genetics Department, Medical Research Institute, Alexandria University, Alexandria, Egypt.
Prenatal Diagnosis and Fetal Medicine Department, Human Genetics and Genome Research Institute, National Research Centre, Cairo, Egypt.
Clin Genet. 2025 Apr 29. doi: 10.1111/cge.14764.
Copy number variants (CNVs) contribute significantly to the pathogenicity of rare genetic diseases and tend to have a more severe effect on phenotype compared to single nucleotide variants (SNVs). In the past decades, exome sequencing (ES) has proven valuable input in the characterization of underlying genetic defects. Our aim was to investigate the impact of integrating CNV analysis tools into standard ES analysis on its diagnostic yield. We worked on ES data from an original cohort of 840 patients, in whom the first analysis was able to detect causative SNVs and indels in 383 (45.6%). Using the ExomeDepth algorithm, clinically relevant CNVs were identified in 55 patients out of the 457 unsolved cases, thus enhancing the diagnostic yield to 52.1%. Among the enrolled subjects, neurodevelopmental delay was the most prevalent phenotype. The detected CNVs comprised 43 deletions (74.1%) and 15 duplications (25.9%), ranging in size from 94 bp to 94.3 Mb, and were classified as 56 pathogenic/likely pathogenic and 2 uncertain with high interest. The study presents further evidence that incorporating CNV analysis tools into ES pipelines improves the diagnostic yield and emphasizes the involvement of CNVs in the etiology of genetic disorders.
拷贝数变异（CNV）对罕见遗传病的致病性有显著影响，与单核苷酸变异（SNV）相比，往往对表型有更严重的影响。在过去几十年中，外显子组测序（ES）已被证明在潜在基因缺陷的特征描述中具有重要价值。我们的目的是研究将CNV分析工具整合到标准ES分析中对其诊断率的影响。我们处理了来自840例患者的原始队列的ES数据，其中首次分析能够在383例（45.6%）中检测到致病的SNV和插入缺失。使用ExomeDepth算法，在457例未解决的病例中有55例鉴定出临床相关的CNV，从而将诊断率提高到52.1%。在纳入的受试者中，神经发育迟缓是最常见的表型。检测到的CNV包括43个缺失（74.1%）和15个重复（25.9%），大小从94 bp到94.3 Mb不等，被分类为56个致病/可能致病和2个具有高度关注的不确定类型。该研究进一步证明，将CNV分析工具纳入ES流程可提高诊断率，并强调CNV在遗传疾病病因学中的作用。
