DOI: 10.5061/dryad.pg4f4qrwf
title: Genotype-phenotype associations in CRB1 bi-allelic patients: a novel mutation and a systematic review

description: 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&gt;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&lt;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&lt;0.05). In contrast, homozygous nonsense
 mutations were associated with macular pigments, periphery pigments, and a
 high risk of LCA (P&lt;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&lt;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.
description: <strong>Ethics statement
 </strong> 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).   <strong>Mutational Screening, Pathogenicity
 interpretation and co-segregation
 analysis</strong> 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.   <strong>Literature Search, study selection, and data
 collection</strong> 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.
   <strong>Ocular and Clinical
 data</strong> 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.   <strong>Statistical
 analysis</strong> 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
 χ<sup>2</sup> 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.