Case Report: Whole-Exome Sequencing-Based Copy Number Variation Analysis Identified a Novel DRC1 Homozygous Exon Deletion in a Patient With Primary Ciliary Dyskinesia.

Objective: Whole-exome sequencing (WES) based copy number variation (CNV) analysis has been reported to improve the diagnostic rate in rare genetic diseases. In this study, we aim to find the disease-associated variants in a highly suspected primary ciliary dyskinesia (PCD) patient without a genetic diagnosis by routine WES analysis. Methods: We identified the CNVs using the "Exomedepth" package in an undiagnosed PCD patient with a negative result through routine WES analysis. RNA isolation, PCR amplification, and Sanger sequencing were used to confirm the variant. High-speed video microscopy analysis (HSVA) and immunofluorescence analysis were applied to detect the functional and structural deficiency of nasal cilia and sperm flagella. Papanicolaou staining was employed to characterize the morphology of sperm flagella. Results: NC_000002.11(NM_145038.5): g.26635488_26641606del, c.156-1724_244-2550del, r.156_243del, p. (Glu53Asnfs*13), a novel DRC1 homozygous CNV, was identified by WES-based CNV analysis rather than routine variants calling, in a patient from a non-consanguineous family. HSVA results showed no significant change in ciliary beating frequency but with reduced beating amplitude compared with normal control, and his spermatozoa were almost immotile. The diagnosis of multiple morphological abnormalities of the sperm flagella (MMAF) was established through sperm motility and morphology analysis. PCR amplification and Sanger sequencing confirmed the novel variant of DRC1. Immunofluorescence showed that both cilia and sperm flagella were deficient in protein expression related to the dynein regulatory complex. Conclusion: This report identifies a novel DRC1 disease-associated variant by WES-based CNV analysis from a highly suspected PCD patient with MMAF. Our findings not only expand the genetic spectrum of PCD with MMAF but suggest that in combination with CNV analysis might improve the efficiency of genetic tests.