Purpose: Searched for novel bi-allelic CRB1 mutations, then analyzed the CRB1 literature at the genotypic and phenotypic levels from 439 patients worldwide.

Approach: We screened various variables such as the CRB1 mutation types, domains, exons, and genotypes and their relation with specific ocular phenotypes. An emphasis was given for the bi-allelic missense and nonsense mutations because of their high prevalence compared to other mutation types. Finally, we quantified the effect of various non-modifiable factors over the BCVA OU using multivariate linear regression models and identified genetic interactions.

Results: We first identified a novel bi-allelic missense in the exon 9 of CRB1; c.2936G>A; p.(Gly979Asp) associated with RCD. CRB1 mutation type, exons, domains, and genotype distribution vary significantly according to Fundus characteristics, such as peripheral pigmentation and condition, optic disc, vessels, macular, and pigmentation (P<0.05). Of the 154 articles retrieved from PubMed, 96 studies with 439 bi-allelic CRB1 patients were included. Missense mutations were significantly associated with an absence of macular pigments, pale optic disc, and periphery pigmentation, resulting in a higher risk of RCD (P<0.05). In contrast, homozygous nonsense mutations were associated with macular pigments, periphery pigments, and a high risk of LCA (P<0.05) and increased BCVA OU (best-corrected visual acuity) levels. We found that age, mutation types, and inherited retinal diseases were critical determinants of BCVA OU as they significantly increased it by 33%, 26%, and 38%, respectively (P<0.05). Loss of function alleles additively increased the risk of LCA, with nonsense having a more profound effect than indels. Finally, our analysis showed that p.(Cys948Tyr) and p.(Lys801Ter) and p.(Lys801Ter); p.(Cys896Ter) might interact to modify BCVA OU levels. 

Conclusion: This meta-analysis updated the literature and identified genotype-phenotype associations in bi-allelic CRB1 patients.

Ethics statement

All our procedures were conducted per the principles outlined in the Declaration of Helsinki. The Institutional Review Board of Beirut Arab University approved our study (2017H-0030-HS-R-0208). The participants provided written informed consent; their ophthalmic examinations were done at the Beirut Eye and ENT Specialist Hospital (Beirut, Lebanon).

Mutational Screening, Pathogenicity interpretation and co-segregation analysis

The index patient and his parents provided whole blood samples subjected to genomic DNA extraction using the QIAamp DNA Mini Kit (Hilden, Germany) from Qiagen. Whole exome sequencing (WES) was conducted as described previously. The bioinformatic analysis was also performed, including the variant filtration and the conservation across species. Unidirectional Sanger sequencing was applied to all available family members’ DNA to analyze the co-segregation.

Literature Search, study selection, and data collection

Our systematic review analyzed the data from published articles on CRB1 bi-allelic mutations containing ocular data. Our protocol was based on the preferred reporting items for systematic reviews and meta-analyses (PRISMA). The review was not registered. All the identified CRB1 variants reported on The Human Gene Mutation Database (HGMD) (last accessed on December 2021) were downloaded. Then each variant was searched on Clinvar (NCBI) (last accessed on December 2022) to obtain all their corresponding articles. The most critical inclusion criterion was clinical and genotypic data availability. In contrast, the exclusion criteria were: 1) the absence of ocular data. 2) The use of non-English language (because of the language barrier). 3) The absence of full text. The included articles were distributed amongst four authors (SES, AD, MB, JN) to collect patients' clinical and genotypic data and were further homogenized. The previous filtering steps led to 96 articles published since 1999. The PRISMA flow diagram is summarized in Figure 1.

Ocular and Clinical data

JON (ophthalmologist) analyzed the fundus autofluorescence (FAF) and optical coherence tomography images and further classified them. FAF variables included; (1) Macular condition classified into; Normal, Yellow Macular Degeneration (YMD), Schizis, No reflex, Edema, Deposits, Degeneration, Coloboma like, Bull’s maculopathy, and Atrophy. (2) Macular Pigmentation classified into; pigmentation presence (yes/no), nummular, granular, bone spicule, and beaten metal), optic disc state (normal, pseudo papilledema, pale, hyperemic, granular, gliotic, and drusen), vessels’ state (normal, tortuosity, preserved para arteriole RPE, no perivascular sheathing, narrowed, edema, constricted, attenuated, para arteriolar RPE changes, and perivascular pigmentation). (3) Peripheral Condition; normal, salt and pepper, RPE changes, RPE atrophy, loss of RPE, deposits, degenerative fundus, and atrophy. (4) Peripheral Pigmentation; normal, marbleized, bone spicule, salt and pepper, and unspecified pigmentation. OCT image descriptions were classified into normal, no macular atrophy, cystoid macular edema (CME), no CME, macular thinning, macular thickening, macular atrophy, hyperreflective, and degeneration.

Statistical analysis

All analyses were conducted using SPSS software version 26 (SPSS, Inc., Chicago, Illinois). Clustered bar charts and Boxplots were generated using Origin software (OriginPro, Version 2022, OriginLab Corporation, Northampton, MA, USA). For power reasons, variables with a sample size of less than five were omitted. Categorical variables such as the type of IRD, genotype, and clinical data were expressed as percentages. Continuous variables such as age, BCVA OU, and refraction were expressed as mean ± standard deviation. BCVA OU data were obtained as Snellen fractions and then transformed into LogMAR scale. BCVA OU was also transformed into a binary variable based on its median (1.3 LogMar). A χ² test of independence was used to test the difference in proportion between the categorical variables with the ocular and clinical data. Kruskal-Wallis one-way ANOVA and Mann-Whitney U tests were used to compare the BCVA OU according to several categorical variables. For the multiple linear regression models over BCVA OU levels, we used the following as independent variables; age, gender, mutation type, and IRD (the three most prevalent disorders; RCD, LCA, and retinal dystrophy). The significance level was set at P≤0.05.