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.

Uncovering potential causal genes for undiagnosed congenital anomalies using an in-house pipeline for trio-based whole-genome sequencing