Among the Brazilian population, the frequency rates of inherited retinal dystrophies and their causative genes are underreported. To increase the knowledge about these dystrophies in our population, we retrospectively studied the medical records of 1,246 Brazilian patients with hereditary retinopathies during 20 years of specialized outpatient clinic care. Of these patients, 559 had undergone at least one genetic test. In this cohort, the most prevalent dystrophies were non-syndromic retinitis pigmentosa (35%), Stargardt disease (21%), Leber congenital amaurosis (9%), and syndromic inherited retinal dystrophies (12%). Most patients had never undergone genetic testing (55%), and among the individuals with molecular test results, 28.4% had negative or inconclusive results compared to 71.6% with a conclusive molecular diagnosis. ABCA4 was the most frequent disease-causing gene, accounting for 20% of the positive cases. Pathogenic variants also occurred frequently in the CEP290, USH2A, CRB1, RPGR, and CHM genes. The relative frequency rates of different inherited retinal dystrophies in Brazil are similar to those found globally. Although mutations in more than 250 genes lead to hereditary retinopathies, only 66 genes were responsible for 70% of the cases, which indicated that smaller and cheaper gene panels can be just as effective and provide more affordable solutions for implementation by the Brazilian public health system.

Abstract-Angiotensin

Background With the advent of whole exome (ES) and genome sequencing (GS) as tools for disease gene discovery, rare variant filtering, prioritization and data sharing have become essential components of the search for disease genes and variants potentially contributing to disease phenotypes. The computational storage, data manipulation, and bioinformatic interpretation of thousands to millions of variants identified in ES and GS, respectively, is a challenging task. To aid in that endeavor, we constructed PhenoDB, GeneMatcher and VariantMatcher. Results PhenoDB is an accessible, freely available, web-based platform that allows users to store, share, analyze and interpret their patients’ phenotypes and variants from ES/GS data. GeneMatcher is accessible to all stakeholders as a web-based tool developed to connect individuals (researchers, clinicians, health care providers and patients) around the globe with interest in the same gene(s), variant(s) or phenotype(s). Finally, VariantMatcher was developed to enable public sharing of variant-level data and phenotypic information from individuals sequenced as part of multiple disease gene discovery projects. Here we provide updates on PhenoDB and GeneMatcher applications and implementation and introduce VariantMatcher. Conclusion Each of these tools has facilitated worldwide data sharing and data analysis and improved our ability to connect genes to phenotypic traits. Further development of these platforms will expand variant analysis, interpretation, novel disease-gene discovery and facilitate functional annotation of the human genome for clinical genomics implementation and the precision medicine initiative.