PRPH2, one of the most frequently inherited retinal dystrophy (IRD)-causing genes, implies a high phenotypic variability. This study aims to analyze the PRPH2 mutational spectrum in one of the largest cohorts worldwide, and to describe novel pathogenic variants and genotype–phenotype correlations. A study of 220 patients from 103 families recruited from a database of 5000 families. A molecular diagnosis was performed using classical molecular approaches and next-generation sequencing. Common haplotypes were ascertained by analyzing single-nucleotide polymorphisms. We identified 56 variants, including 11 novel variants. Most of them were missense variants (64%) and were located in the D2-loop protein domain (77%). The most frequently occurring variants were p.Gly167Ser, p.Gly208Asp and p.Pro221_Cys222del. Haplotype analysis revealed a shared region in families carrying p.Leu41Pro or p.Pro221_Cys222del. Patients with retinitis pigmentosa presented an earlier disease onset. We describe the largest cohort of IRD families associated with PRPH2 from a single center. Most variants were located in the D2-loop domain, highlighting its importance in interacting with other proteins. Our work suggests a likely founder effect for the variants p.Leu41Pro and p.Pro221_Cys222del in our Spanish cohort. Phenotypes with a primary rod alteration presented more severe affectation. Finally, the high phenotypic variability in PRPH2 hinders the possibility of drawing genotype–phenotype correlations.

Screening for pathogenic variants in the diagnosis of rare genetic diseases can now be performed on all genes thanks to the application of whole exome and genome sequencing (WES, WGS). Yet the repertoire of gene–disease associations is not complete. Several computer-based algorithms and databases integrate distinct gene–gene functional networks to accelerate the discovery of gene–disease associations. We hypothesize that the ability of every type of information to extract relevant insights is disease-dependent. We compiled 33 functional networks classified into 13 knowledge categories (KCs) and observed large variability in their ability to recover genes associated with 91 genetic diseases, as measured using efficiency and exclusivity. We developed GLOWgenes, a network-based algorithm that applies random walk with restart to evaluate KCs’ ability to recover genes from a given list associated with a phenotype and modulates the prediction of new candidates accordingly. Comparison with other integration strategies and tools shows that our disease-aware approach can boost the discovery of new gene–disease associations, especially for the less obvious ones. KC contribution also varies if obtained using recently discovered genes. Applied to 15 unsolved WES, GLOWgenes proposed three new genes to be involved in the phenotypes of patients with syndromic inherited retinal dystrophies.

Advances in whole-genome sequencing (WGS) have significantly enhanced our ability to detect genomic variants underlying inherited diseases. In this study, we performed long-read WGS on 24 patients with inherited retinal dystrophies (IRDs) to validate the utility of nanopore sequencing in detecting genomic variations. We confirmed the presence of all previously detected variants and demonstrated that this approach allows for the precise refinement of structural variants (SVs). Furthermore, we could perform genotype phasing by sequencing only the probands, confirming that the variants were inherited in trans. Moreover, nanopore sequencing enables the detection of complex variants, such as transposon insertions and structural rearrangements. This comprehensive assessment illustrates the power of long-read sequencing in capturing diverse forms of genomic variation and in improving diagnostic accuracy in IRDs.