Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, Box 815, 751 08 Uppsala, Sweden.
Department of Medical Biochemistry and Microbiology, National Bioinformatics Infrastructure Sweden, Science for Life Laboratory, Uppsala University, Box 582, 751 23 Uppsala, Sweden.
Genetics. 2023 Dec 6;225(4). doi: 10.1093/genetics/iyad179.
Structural variations, including copy number variations (CNVs), affect around 20 million bases in the human genome and are common causes of rare conditions. CNVs are rarely investigated in complex disease research because most CNVs are not targeted on the genotyping arrays or the reference panels for genetic imputation. In this study, we characterize CNVs in a Swedish cohort (N = 1,021) using short-read whole-genome sequencing (WGS) and use long-read WGS for validation in a subcohort (N = 15), and explore their effect on 438 plasma proteins. We detected 184,182 polymorphic CNVs and identified 15 CNVs to be associated with 16 proteins (P < 8.22×10-10). Of these, 5 CNVs could be perfectly validated using long-read sequencing, including a CNV which was associated with measurements of the osteoclast-associated immunoglobulin-like receptor (OSCAR) and located upstream of OSCAR, a gene important for bone health. Two other CNVs were identified to be clusters of many short repetitive elements and another represented a complex rearrangement including an inversion. Our findings provide insights into the structure of common CNVs and their effects on the plasma proteome, and highlights the importance of investigating common CNVs, also in relation to complex diseases.
结构变异,包括拷贝数变异(CNVs),影响人类基因组中的约 2000 万个碱基,是罕见疾病的常见原因。CNVs 在复杂疾病研究中很少被研究,因为大多数 CNVs 不在基因分型阵列或遗传推断的参考面板上靶向。在这项研究中,我们使用短读长全基因组测序(WGS)对瑞典队列(N=1021)中的 CNVs 进行了特征描述,并在亚队列(N=15)中使用长读长 WGS 进行了验证,并探索了它们对 438 种血浆蛋白的影响。我们检测到 184182 个多态性 CNVs,并确定了 15 个与 16 种蛋白质相关的 CNVs(P<8.22×10-10)。其中,5 个 CNVs 可以使用长读长测序完美验证,包括与破骨细胞相关免疫球蛋白样受体(OSCAR)测量值相关的 CNV,该 CNV位于 OSCAR 上游,OSCAR 是骨骼健康的重要基因。另外两个 CNVs 被鉴定为许多短重复元件的簇,另一个代表包括倒位在内的复杂重排。我们的研究结果提供了对常见 CNVs 结构及其对血浆蛋白质组影响的深入了解,并强调了研究常见 CNVs 的重要性,包括与复杂疾病的关系。