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      A pathogenic in-frame deletion-insertion variant in BEST1 phenocopies Stargardt disease

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          Abstract

          Here, we describe affected members of a 2-generation family with a Stargardt disease–like phenotype caused by a 2–base pair deletion insertion, c.1014_1015delGAinsCT;p.(Trp338_Asn339delinsCysTyr), in BEST1. The variant was identified by whole-exome sequencing, and its pathogenicity was verified through chloride channel recording using WT and transfected mutant HEK293 cells. Clinical examination of both patients revealed similar phenotypes at 2 different disease stages that were attributable to differences in their age at presentation. Hyperautofluorescent flecks along the arcades were observed in the proband, while the affected mother exhibited more advanced retinal pigment epithelium (RPE) loss in the central macula. Full-field electroretinogram testing was unremarkable in the daughter; however, moderate attenuation of generalized cone function was detected in the mother. Results from electrooculogram testing in the daughter were consistent with widespread dysfunction of the RPE characteristic of Best disease. Whole-cell patch-clamp recordings revealed a statistically significant decrease in chloride conductance of the mutant compared with WT cells. This report on a mother and daughter with a BEST1 genotype that phenocopies Stargardt disease broadens the clinical spectrum of BEST1-associated retinopathy.

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          Most cited references40

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          ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data

          High-throughput sequencing platforms are generating massive amounts of genetic variation data for diverse genomes, but it remains a challenge to pinpoint a small subset of functionally important variants. To fill these unmet needs, we developed the ANNOVAR tool to annotate single nucleotide variants (SNVs) and insertions/deletions, such as examining their functional consequence on genes, inferring cytogenetic bands, reporting functional importance scores, finding variants in conserved regions, or identifying variants reported in the 1000 Genomes Project and dbSNP. ANNOVAR can utilize annotation databases from the UCSC Genome Browser or any annotation data set conforming to Generic Feature Format version 3 (GFF3). We also illustrate a ‘variants reduction’ protocol on 4.7 million SNVs and indels from a human genome, including two causal mutations for Miller syndrome, a rare recessive disease. Through a stepwise procedure, we excluded variants that are unlikely to be causal, and identified 20 candidate genes including the causal gene. Using a desktop computer, ANNOVAR requires ∼4 min to perform gene-based annotation and ∼15 min to perform variants reduction on 4.7 million variants, making it practical to handle hundreds of human genomes in a day. ANNOVAR is freely available at http://www.openbioinformatics.org/annovar/ .
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            Fast and accurate long-read alignment with Burrows–Wheeler transform

            Motivation: Many programs for aligning short sequencing reads to a reference genome have been developed in the last 2 years. Most of them are very efficient for short reads but inefficient or not applicable for reads >200 bp because the algorithms are heavily and specifically tuned for short queries with low sequencing error rate. However, some sequencing platforms already produce longer reads and others are expected to become available soon. For longer reads, hashing-based software such as BLAT and SSAHA2 remain the only choices. Nonetheless, these methods are substantially slower than short-read aligners in terms of aligned bases per unit time. Results: We designed and implemented a new algorithm, Burrows-Wheeler Aligner's Smith-Waterman Alignment (BWA-SW), to align long sequences up to 1 Mb against a large sequence database (e.g. the human genome) with a few gigabytes of memory. The algorithm is as accurate as SSAHA2, more accurate than BLAT, and is several to tens of times faster than both. Availability: http://bio-bwa.sourceforge.net Contact: rd@sanger.ac.uk
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              SIFT: Predicting amino acid changes that affect protein function.

              P C Ng (2003)
              Single nucleotide polymorphism (SNP) studies and random mutagenesis projects identify amino acid substitutions in protein-coding regions. Each substitution has the potential to affect protein function. SIFT (Sorting Intolerant From Tolerant) is a program that predicts whether an amino acid substitution affects protein function so that users can prioritize substitutions for further study. We have shown that SIFT can distinguish between functionally neutral and deleterious amino acid changes in mutagenesis studies and on human polymorphisms. SIFT is available at http://blocks.fhcrc.org/sift/SIFT.html.
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                Author and article information

                Contributors
                Journal
                JCI Insight
                JCI Insight
                JCI Insight
                JCI Insight
                American Society for Clinical Investigation
                2379-3708
                8 December 2022
                8 December 2022
                8 December 2022
                : 7
                : 23
                : e162687
                Affiliations
                [1 ]Jonas Children’s Vision Care and Bernard and Shirlee Brown Glaucoma Laboratory, Columbia University, New York, New York, USA.
                [2 ]SUNY Downstate Health Sciences University, New York, New York, USA.
                [3 ]Department of Ophthalmology,
                [4 ]Department of Genetics and Development, and
                [5 ]Department of Pathology and Cell Biology, Columbia University, New York, New York, USA.
                [6 ]Institute of Human Nutrition, Columbia Stem Cell Initiative, New York, New York, USA.
                Author notes
                Address correspondence to: Stephen H. Tsang, Edward S. Harkness Eye Institute, New York-Presbyterian Hospital/Columbia University Irving Medical Center, 635 West 165th Street, Box 112, New York, New York 10032, USA. Phone: 212.342.1186; Email: sht2@ 123456columbia.edu .
                Author information
                http://orcid.org/0000-0001-7803-9632
                http://orcid.org/0000-0002-5350-9412
                http://orcid.org/0000-0002-9520-8140
                http://orcid.org/0000-0001-5920-8576
                http://orcid.org/0000-0001-8959-7074
                http://orcid.org/0000-0002-3344-4447
                http://orcid.org/0000-0001-9082-2427
                Article
                162687
                10.1172/jci.insight.162687
                9746905
                36264634
                114b46f5-3d08-4b5c-b55f-1c9c96c8a59e
                © 2022 Kolesnikova et al.

                This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 14 June 2022
                : 18 October 2022
                Funding
                Funded by: NIH Clinical Center, https://doi.org/10.13039/100000098;
                Award ID: 5P30CA013696,U01 EY030580,U54OD020351,R24EY028758,R24EY027285,5P30EY019007,R01EY018213,R01EY024698,R01EY026682,R01EY028203,R01EY029315,R21AG050437,GM127652,EY028758
                Funded by: Irma T. Hirschl Trust, https://doi.org/10.13039/100006984;
                Award ID: HRSCHL CU20-4313
                Funded by: New York State Stem Cell Science, https://doi.org/10.13039/100012636;
                Award ID: SDHDOH01-C32590GG-3450000
                Funded by: Foundation Fighting Blindness, https://doi.org/10.13039/100001116;
                Award ID: TA-NMT-0116-0692-COLU,PPA-1218-0751-COLU
                Categories
                Research Article

                genetics,ophthalmology,genetic diseases,retinopathy
                genetics, ophthalmology, genetic diseases, retinopathy

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