High positive predictive value of CNVs detected by clinical exome sequencing in suspected genetic diseases

Author:

Zeng Yimo,Ding Hongke,Wang Xingwang,Huang Yanlin,Liu Ling,Du Li,Lu Jian,Wu Jing,Zeng Yukun,Mai Mingqin,Zhu Juan,Yu Lihua,He Wei,Guo Fangfang,Peng Haishan,Yao Cuize,Qi Yiming,Liu Yuan,Li Fake,Yang Jiexia,Hu Rong,Liang Jie,Wang Jicheng,Wang Wei,Zhang YanORCID,Yin Aihua

Abstract

Abstract Background Genetic disorders often manifest as abnormal fetal or childhood development. Copy number variations (CNVs) represent a significant genetic mechanism underlying such disorders. Despite their importance, the effectiveness of clinical exome sequencing (CES) in detecting CNVs, particularly small ones, remains incompletely understood. We aimed to evaluate the detection of both large and small CNVs using CES in a substantial clinical cohort, including parent–offspring trios and proband only analysis. Methods We conducted a retrospective analysis of CES data from 2428 families, collected from 2018 to 2021. Detected CNV were categorized as large or small, and various validation techniques including chromosome microarray (CMA), Multiplex ligation-dependent probe amplification assay (MLPA), and/or PCR-based methods, were employed for cross-validation. Results Our CNV discovery pipeline identified 171 CNV events in 154 cases, resulting in an overall detection rate of 6.3%. Validation was performed on 113 CNVs from 103 cases to assess CES reliability. The overall concordance rate between CES and other validation methods was 88.49% (100/113). Specifically, CES demonstrated complete consistency in detecting large CNV. However, for small CNVs, consistency rates were 81.08% (30/37) for deletions and 73.91% (17/23) for duplications. Conclusion CES demonstrated high sensitivity and reliability in CNV detection. It emerges as an economical and dependable option for the clinical CNV detection in cases of developmental abnormalities, especially fetal structural abnormalities.

Funder

Basic and Applied Basic Research Foundation of Guangdong Province

National Natural Science Foundation of China-Guangdong Joint Fund

National Natural Science Foundation of China

Guangzhou Municipal Science and Technology Program key projects

Publisher

Springer Science and Business Media LLC

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