Application of Targeted Next-Generation Sequencing in Patients with Autosomal Recessive Inherited Retinal Dystrophies: Improvement of Genetic and Clinical Diagnosis

Abstract

Background: Inherited retinal dystrophies (IRDs) are a group of retinal diseases characterized by irreversible degeneration of photoreceptors. Many IRDs present significant genetic and clinical heterogeneities, which have challenged the genetic and clinical diagnosis for IRDs. This study aims to determine the genetic lesions with phenotypic correlations in patients with diverse autosomal recessive IRDs using next-generation sequencing (NGS). Methods: A cohort of 20 Chinese families affected with autosomal recessive IRDs were recruited with their detailed family history and clinical information collected. To call disease causative mutations in the patients, targeted sequence capture of IRDs-relevant genes using two in-house designed microarrays followed by NGS was applied. Bioinformatics annotation, intrafamilial cosegregation analyses, in silico prediction, and functional analyses were subsequently conducted for the variants identified by NGS. Genotype-phenotype correlations were also analyzed in the families to assist clinical diagnosis. Results: With this approach, homozygous and biallelic variants were identified in 11 out of the 20 families (55%) as very likely disease causing mutations including a total of 17 alleles, of which 11 are novel. The 17 alleles identified here include 5 missense mutations, 5 nonsense mutations, 3 frameshift mutations, and 4 splice site mutations. Noteworthy, we found biallelic RP1 mutations in a patient with cone-rod dystrophy (CRD), which was not previously correlated with RP1 mutations. Moreover, identification of biallelic mutations in FLVCR1 in a patient with complex systemic phenotypes has finalized the clinical diagnosis as posterior column ataxia with RP (PCARP). Conclusions: Targeted NGS approach could effectively detect mutations in patients with diverse autosomal recessive IRDs, significantly improve clinical diagnosis and gain better insights of genotype-phenotype correlations.