Natural history of retinitis pigmentosa based on genotype, vitamin A/E supplementation, and an electroretinogram biomarker.

BACKGROUNDA randomized clinical trial from 1984 to 1992 indicated that vitamin A supplementation had a beneficial effect on the progression of retinitis pigmentosa (RP), while vitamin E had an adverse effect.METHODSSequencing of banked DNA samples from that trial provided the opportunity to determine whether certain genotypes responded preferentially to vitamin supplementation.RESULTSThe genetic solution rate was 587 out of 765 (77%) of sequenced samples. Combining genetic solutions with electroretinogram outcomes showed that there were systematic differences in severity and progression seen among different genetic subtypes of RP, extending findings made for USH2A, RHO, RPGR, PRPF31, and EYS. Baseline electroretinogram 30-Hz flicker implicit time was an independent, strong predictor of progression rate. Using additional data and baseline implicit time as a predictor, the deleterious effect of vitamin E was still present. Surprisingly, the effect of vitamin A progression in the cohort as a whole was not detectable, with or without data from subsequent trials. Subgroup analyses are also discussed.CONCLUSIONOverall, genetic subtype and implicit time have significant predictive power for a patient's rate of progression, which is useful prognostically. While vitamin E supplementation should still be avoided, these data do not support a generalized neuroprotective effect of vitamin A for all types of RP.TRIAL REGISTRATIONClinicalTrials.gov NCT00000114, NCT00000116, and NCT00346333.FUNDINGFoundation Fighting Blindness and the National Eye Institute: R01 EY012910, R01 EY031036, R01 EY026904, and P30 EY014104.

The importance of automation in genetic diagnosis: Lessons from analyzing an inherited retinal degeneration cohort with the Mendelian Analysis Toolkit (MATK).

PURPOSE: In Mendelian disease diagnosis, variant analysis is a repetitive, error-prone, and time consuming process. To address this, we have developed the Mendelian Analysis Toolkit (MATK), a configurable, automated variant ranking program. METHODS: MATK aggregates variant information from multiple annotation sources and uses expert-designed rules with parameterized weights to produce a ranked list of potentially causal solutions. MATK performance was measured by a comparison between MATK-aided and human-domain expert analyses of 1060 families with inherited retinal degeneration (IRD), analyzed using an IRD-specific gene panel (589 individuals) and exome sequencing (471 families). RESULTS: When comparing MATK-assisted analysis with expert curation in both the IRD-specific gene panel and exome sequencing (1060 subjects), 97.3% of potential solutions found by experts were also identified by the MATK-assisted analysis (541 solutions identified with MATK of 556 solutions found by conventional analysis). Furthermore, MATK-assisted analysis identified 114 additional potential solutions from the 504 cases unsolved by conventional analysis. CONCLUSION: MATK expedites the process of identification of likely solving variants in Mendelian traits, and reduces variability stemming from human error and researcher bias. MATK facilitates data reanalysis to keep up with the constantly improving annotation sources and next-generation sequencing processing pipelines. The software is open source and available at https://gitlab.com/matthew_maher/mendelanalysis.

Broadening INPP5E phenotypic spectrum: detection of rare variants in syndromic and non-syndromic IRD.

Pathogenic variants in INPP5E cause Joubert syndrome (JBTS), a ciliopathy with retinal involvement. However, despite sporadic cases in large cohort sequencing studies, a clear association with non-syndromic inherited retinal degenerations (IRDs) has not been made. We validate this association by reporting 16 non-syndromic IRD patients from ten families with bi-allelic mutations in INPP5E. Additional two patients showed early onset IRD with limited JBTS features. Detailed phenotypic description for all probands is presented. We report 14 rare INPP5E variants, 12 of which have not been reported in previous studies. We present tertiary protein modeling and analyze all INPP5E variants for deleteriousness and phenotypic correlation. We observe that the combined impact of INPP5E variants in JBTS and non-syndromic IRD patients does not reveal a clear genotype-phenotype correlation, suggesting the involvement of genetic modifiers. Our study cements the wide phenotypic spectrum of INPP5E disease, adding proof that sequence defects in this gene can lead to early-onset non-syndromic IRD.

Copy-number variation contributes 9% of pathogenicity in the inherited retinal degenerations.

PURPOSE: Current sequencing strategies can genetically solve 55-60% of inherited retinal degeneration (IRD) cases, despite recent progress in sequencing. This can partially be attributed to elusive pathogenic variants (PVs) in known IRD genes, including copy-number variations (CNVs), which have been shown as major contributors to unsolved IRD cases. METHODS: Five hundred IRD patients were analyzed with targeted next-generation sequencing (NGS). The NGS data were used to detect CNVs with ExomeDepth and gCNV and the results were compared with CNV detection with a single-nucleotide polymorphism (SNP) array. Likely causal CNV predictions were validated by quantitative polymerase chain reaction (qPCR). RESULTS: Likely disease-causing single-nucleotide variants (SNVs) and small indels were found in 55.6% of subjects. PVs in USH2A (11.6%), RPGR (4%), and EYS (4%) were the most common. Likely causal CNVs were found in an additional 8.8% of patients. Of the three CNV detection methods, gCNV showed the highest accuracy. Approximately 30% of unsolved subjects had a single likely PV in a recessive IRD gene. CONCLUSION: CNV detection using NGS-based algorithms is a reliable method that greatly increases the genetic diagnostic rate of IRDs. Experimentally validating CNVs helps estimate the rate at which IRDs might be solved by a CNV plus a more elusive variant.

Expanding the phenotypic spectrum in RDH12-associated retinal disease.

Retinol dehydrogenase 12, RDH12, plays a pivotal role in the visual cycle to ensure the maintenance of normal vision. Alterations in activity of this protein result in photoreceptor death and decreased vision beginning at an early age and progressing to substantial vision loss later in life. Here we describe 11 patients with retinal degeneration that underwent next-generation sequencing (NGS) with a targeted panel of all currently known inherited retinal degeneration (IRD) genes and whole-exome sequencing to identify the genetic causality of their retinal disease. These patients display a range of phenotypic severity prompting clinical diagnoses of macular dystrophy, cone-rod dystrophy, retinitis pigmentosa, and early-onset severe retinal dystrophy all attributed to biallelic recessive mutations in RDH12 We report 15 causal alleles and expand the repertoire of known RDH12 mutations with four novel variants: c.215A > G (p.Asp72Gly); c.362T > C (p.Ile121Thr); c.440A > C (p.Asn147Thr); and c.697G > A (p.Val233Ille). The broad phenotypic spectrum observed with biallelic RDH12 mutations has been observed in other genetic forms of IRDs, but the diversity is particularly notable here given the prior association of RDH12 primarily with severe early-onset disease. This breadth emphasizes the importance of broad genetic testing for inherited retinal disorders and extends the pool of individuals who may benefit from imminent gene-targeted therapies.

Large scale matching of function to the genetic identity of retinal ganglion cells.

Understanding the role of neurons in encoding and transmitting information is a major goal in neuroscience. This requires insight on the data-rich neuronal spiking patterns combined, ideally, with morphology and genetic identity. Electrophysiologists have long experienced the trade-offs between anatomically-accurate single-cell recording techniques and high-density multi-cellular recording methods with poor anatomical correlations. In this study, we present a novel technique that combines large-scale micro-electrode array recordings with genetic identification and the anatomical location of the retinal ganglion cell soma. This was obtained through optogenetic stimulation and subsequent confocal imaging of genetically targeted retinal ganglion cell sub-populations in the mouse. With the many molecular options available for optogenetic gene expression, we view this method as a versatile tool for matching function to genetic classifications, which can be extended to include morphological information if the density of labelled cells is at the correct level.