Kim Jeong-Min, Cho Hye-Won, Shin Dong Mun, Kim Oc-Hee, Kim Jihyun, Lee Hyeji, Lee Gang-Hee, An Joon-Yong, Yang Misun, Jo Heui Seung, Jang Ja-Hyun, Chang Yun Sil, Park Hyun-Young, Park Mi-Hyun
Division of Genome Science, Department of Precision Medicine, Korea National Institute of Health, Korea Disease Control and Prevention Agency, Cheongju, Chungbuk, 28159, Republic of Korea.
Department of Integrated Biomedical and Life Science, Korea University, Seoul, 02841, Republic of Korea.
Hum Genomics. 2025 Jan 6;19(1):1. doi: 10.1186/s40246-024-00709-2.
Congenital anomalies (CAs) encompass a wide spectrum of structural and functional abnormalities during fetal development, commonly presenting at birth. Identifying the cause of CA is essential for accurate diagnosis and treatment. Using a target-gene approach, genetic variants could be found in certain CA patients. However, some patients were genetically undiagnosed; therefore, it is imperative to identify the causative variants from whole genome sequence (WGS) data of these patients.
An in-house pipeline utilizing DRAGEN-GATK-Hail was established for trio-based WGS data analysis (n = 18 undiagnosed CA patients and their parents) and thirty-five candidate variants, including SNV/Indel, CNV, and SV were identified. Among them, 10 variants of seven coding genes were selected as possible causal variants by variant pathogenicity, genotype-phenotype analysis, and a multidisciplinary team. Finally, functional validation of six genes including RYR3, NRXN1, FREM2, CSMD1, RARS1, and NOTCH1, revealed various phenotypes in zebrafish models that aligned with those observed in each patient. In addition to the above findings, eleven diagnostic variants initially discovered in a targeted-gene analysis from a previous study were also identified as diagnostic variants and the in-house pipeline demonstrated a significant advantage in accurately and efficiently identifying de novo variants (DNVs), compound heterozygous (CH), and homozygous variants.
Taken together, the in-house pipeline established in this study provides a highly valuable diagnostic tool for the identification of potential candidate variants in patients with CA. Further research into the molecular mechanisms related to the development of CAs could shed light on the functional aspects of these genetic variations and contribute to the development of therapeutic drugs.
先天性异常（CAs）涵盖胎儿发育过程中广泛的结构和功能异常，通常在出生时出现。确定先天性异常的病因对于准确诊断和治疗至关重要。采用靶向基因方法，可在某些先天性异常患者中发现基因变异。然而，一些患者在基因层面仍无法确诊；因此，从这些患者的全基因组序列（WGS）数据中识别致病变异势在必行。
建立了一个利用DRAGEN-GATK-Hail的内部流程，用于基于三联体的WGS数据分析（n = 18例未确诊的先天性异常患者及其父母），并识别出35个候选变异，包括单核苷酸变异/插入缺失（SNV/Indel）、拷贝数变异（CNV）和结构变异（SV）。其中，通过变异致病性、基因型-表型分析以及多学科团队，从7个编码基因的10个变异中筛选出可能的致病变异。最后，对包括RYR3、NRXN1、FREM2、CSMD1、RARS1和NOTCH1在内的6个基因进行功能验证，结果显示斑马鱼模型中的各种表型与每个患者中观察到的表型一致。除上述发现外，先前研究中靶向基因分析最初发现的11个诊断性变异也被确定为诊断性变异，且该内部流程在准确高效识别新生变异（DNV）、复合杂合变异（CH）和纯合变异方面显示出显著优势。
综上所述，本研究建立的内部流程为识别先天性异常患者的潜在候选变异提供了极具价值的诊断工具。对与先天性异常发生发展相关分子机制的进一步研究，可能有助于阐明这些基因变异的功能方面，并推动治疗药物的研发。
