Danecek Petr, Gardner Eugene J, Fitzgerald Tomas W, Gallone Giuseppe, Kaplanis Joanna, Eberhardt Ruth Y, Wright Caroline F, Firth Helen V, Hurles Matthew E
Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, United Kingdom.
Department of Clinical and Biomedical Sciences, University of Exeter Medical School, Royal Devon and Exeter Hospital, Exeter, United Kingdom.
Genet Med Open. 2024 Jan 28;2:101818. doi: 10.1016/j.gimo.2024.101818. eCollection 2024.
Structural variants such as multiexon deletions and duplications are an important cause of disease but are often overlooked in standard exome/genome sequencing analysis. We aimed to evaluate the detection of copy-number variants (CNVs) from exome sequencing (ES) in comparison with genome-wide low-resolution and exon-resolution chromosomal microarrays (CMAs) and to characterize the properties of de novo CNVs in a large clinical cohort.
We performed CNV detection using ES of 9859 parent-offspring trios in the Deciphering Developmental Disorders (DDD) study and compared them with CNVs detected from exon-resolution array comparative genomic hybridization in 5197 probands from the DDD study.
Integrating calls from multiple ES-based CNV algorithms using random forest machine learning generated a higher quality data set than using individual algorithms. Both ES- and array comparative genomic hybridization-based approaches had the same sensitivity of 89% and detected the same number of unique pathogenic CNVs not called by the other approach. Of DDD probands prescreened with low-resolution CMAs, 2.6% had a pathogenic CNV detected by higher-resolution assays. De novo CNVs were strongly enriched in known DD-associated genes and exhibited no bias in parental age or sex.
ES-based CNV calling has higher sensitivity than low-resolution CMAs currently in clinical use and comparable sensitivity to exon-resolution CMA. With sufficient investment in bioinformatic analysis, exome-based CNV detection could replace low-resolution CMA for detecting pathogenic CNVs.
多外显子缺失和重复等结构变异是疾病的重要病因，但在标准外显子组/基因组测序分析中常被忽视。我们旨在评估外显子组测序（ES）检测拷贝数变异（CNV）的情况，并与全基因组低分辨率和外显子分辨率染色体微阵列（CMA）进行比较，同时在一个大型临床队列中表征新生CNV的特性。
我们在“解读发育障碍（DDD）”研究中对9859个亲子三联体进行了ES检测CNV，并将其与DDD研究中5197名先证者通过外显子分辨率阵列比较基因组杂交检测到的CNV进行比较。
使用随机森林机器学习整合来自多种基于ES的CNV算法的结果，生成的数据集质量高于使用单个算法。基于ES和基于阵列比较基因组杂交的方法灵敏度均为89%，且检测到的其他方法未检测出的独特致病CNV数量相同。在通过低分辨率CMA预筛查的DDD先证者中，2.6%的人通过高分辨率检测方法检测到致病CNV。新生CNV在已知的与DD相关的基因中高度富集，且在父母年龄或性别上无偏向性。
基于ES的CNV检测比目前临床使用的低分辨率CMA具有更高的灵敏度，与外显子分辨率CMA的灵敏度相当。通过对生物信息学分析进行足够的投入，基于外显子组的CNV检测可替代低分辨率CMA来检测致病CNV。
