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      Molecular genetic analysis using targeted NGS analysis of 677 individuals with retinal dystrophy

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          Abstract

          Inherited retinal diseases (IRDs) are a common cause of visual impairment. IRD covers a set of genetically highly heterogeneous disorders with more than 150 genes associated with one or more clinical forms of IRD. Molecular genetic diagnosis has become increasingly important especially due to expanding number of gene therapy strategies under development. Next generation sequencing (NGS) of gene panels has proven a valuable diagnostic tool in IRD. We present the molecular findings of 677 individuals, residing in Denmark, with IRD and report 806 variants of which 187 are novel. We found that deletions and duplications spanning one or more exons can explain 3% of the cases, and thus copy number variation (CNV) analysis is important in molecular genetic diagnostics of IRD. Seven percent of the individuals have variants classified as pathogenic or likely-pathogenic in more than one gene. Possible Danish founder variants in EYS and RP1 are reported. A significant number of variants were classified as variants with unknown significance; reporting of these will hopefully contribute to the elucidation of the actual clinical consequence making the classification less troublesome in the future. In conclusion, this study underlines the relevance of performing targeted sequencing of IRD including CNV analysis as well as the importance of interaction with clinical diagnoses.

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          Most cited references 27

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          Improved splice site detection in Genie.

          We present an improved splice site predictor for the genefinding program Genie. Genie is based on a generalized Hidden Markov Model (GHMM) that describes the grammar of a legal parse of a multi-exon gene in a DNA sequence. In Genie, probabilities are estimated for gene features by using dynamic programming to combine information from multiple content and signal sensors, including sensors that integrate matches to homologous sequences from a database. One of the hardest problems in genefinding is to determine the complete gene structure correctly. The splice site sensors are the key signal sensors that address this problem. We replaced the existing splice site sensors in Genie with two novel neural networks based on dinucleotide frequencies. Using these novel sensors, Genie shows significant improvements in the sensitivity and specificity of gene structure identification. Experimental results in tests using a standard set of annotated genes showed that Genie identified 86% of coding nucleotides correctly with a specificity of 85%, versus 80% and 84% in the older system. In further splice site experiments, we also looked at correlations between splice site scores and intron and exon lengths, as well as at the effect of distance to the nearest splice site on false positive rates.
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            RNA splice junctions of different classes of eukaryotes: sequence statistics and functional implications in gene expression.

            A systematic analysis of the RNA splice junction sequences of eukaryotic protein coding genes was carried out using the GENBANK databank. Nucleotide frequencies obtained for the highly conserved regions around the splice sites for different categories of organisms closely agree with each other. A striking similarity among the rare splice junctions which do not contain AG at the 3' splice site or GT at the 5' splice site indicates the existence of special mechanisms to recognize them, and that these unique signals may be involved in crucial gene-regulation events and in differentiation. A method was developed to predict potential exons in a bare sequence, using a scoring and ranking scheme based on nucleotide weight tables. This method was used to find a majority of the exons in selected known genes, and also predicted potential new exons which may be used in alternative splicing situations.
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              Digenic retinitis pigmentosa due to mutations at the unlinked peripherin/RDS and ROM1 loci.

              In spite of recent advances in identifying genes causing monogenic human disease, very little is known about the genes involved in polygenic disease. Three families were identified with mutations in the unlinked photoreceptor-specific genes ROM1 and peripherin/RDS, in which only double heterozygotes develop retinitis pigmentosa (RP). These findings indicate that the allelic and nonallelic heterogeneity known to be a feature of monogenic RP is complicated further by interactions between unlinked mutations causing digenic RP. Recognition of the inheritance pattern exemplified by these three families might facilitate the identification of other examples of digenic inheritance in human disease.
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                Author and article information

                Contributors
                karen.groenskov@regionh.dk
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                4 February 2019
                4 February 2019
                2019
                : 9
                Affiliations
                [1 ]ISNI 0000 0001 0674 042X, GRID grid.5254.6, Kennedy Center, Department of Clinical Genetics, Rigshospitalet, , University of Copenhagen, ; Glostrup, DK2600 Denmark
                [2 ]ISNI 0000 0001 2034 1839, GRID grid.21155.32, BGI-Shenzhen, ; Shenzhen, 518083 China
                [3 ]ISNI 0000 0001 2034 1839, GRID grid.21155.32, China National GeneBank, , BGI-Shenzhen, ; Shenzhen, 518120 China
                [4 ]ISNI 0000 0000 9241 5705, GRID grid.24381.3c, Department of Laboratory Medicine, , Division of Clinical Immunology and Transfusion Medicine, Karolinska University Hospital Huddinge, ; Stockholm, S141 86 Sweden
                [5 ]ISNI 0000 0001 0674 042X, GRID grid.5254.6, Department of Ophthalmology, Rigshospitalet-Glostrup, , University of Copenhagen, ; Glostrup, DK2600 Denmark
                [6 ]ISNI 0000 0001 0674 042X, GRID grid.5254.6, Department of Clinical Medicine, Faculty of Health and Medical Sciences, , University of Copenhagen, ; Copenhagen, Denmark
                Article
                38007
                10.1038/s41598-018-38007-2
                6362094
                30718709
                © The Author(s) 2019

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                Funding
                Funded by: FundRef https://doi.org/10.13039/100008397, Velux Fonden (Velux Foundation);
                Award ID: Velux32700
                Award ID: Velux32700
                Award ID: Velux32700
                Award ID: Velux32700
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