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      Systematic evaluation of a targeted gene capture sequencing panel for molecular diagnosis of retinitis pigmentosa

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          Abstract

          Background

          Inherited eye diseases are major causes of vision loss in both children and adults. Inherited eye diseases are characterized by clinical variability and pronounced genetic heterogeneity. Genetic testing may provide an accurate diagnosis for ophthalmic genetic disorders and allow gene therapy for specific diseases.

          Methods

          A targeted gene capture panel was designed to capture exons of 283 inherited eye disease genes including 58 known causative retinitis pigmentosa (RP) genes. 180 samples were tested with this panel, 68 were previously tested by Sanger sequencing. Systematic evaluation of our method and comprehensive molecular diagnosis were carried on 99 RP patients.

          Results

          96.85% targeted regions were covered by at least 20 folds, the accuracy of variants detection was 99.994%. In 4 of the 68 samples previously tested by Sanger sequencing, mutations of other diseases not consisting with the clinical diagnosis were detected by next-generation sequencing (NGS) not Sanger. Among the 99 RP patients, 64 (64.6%) were detected with pathogenic mutations, while in 3 patients, it was inconsistent between molecular diagnosis and their initial clinical diagnosis. After revisiting, one patient’s clinical diagnosis was reclassified. In addition, 3 patients were found carrying large deletions.

          Conclusions

          We have systematically evaluated our method and compared it with Sanger sequencing, and have identified a large number of novel mutations in a cohort of 99 RP patients. The results showed a sufficient accuracy of our method and suggested the importance of molecular diagnosis in clinical diagnosis.

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          Most cited references28

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          Next-generation genetic testing for retinitis pigmentosa

          Molecular diagnostics for patients with retinitis pigmentosa (RP) has been hampered by extreme genetic and clinical heterogeneity, with 52 causative genes known to date. Here, we developed a comprehensive next-generation sequencing (NGS) approach for the clinical molecular diagnostics of RP. All known inherited retinal disease genes (n = 111) were captured and simultaneously analyzed using NGS in 100 RP patients without a molecular diagnosis. A systematic data analysis pipeline was developed and validated to prioritize and predict the pathogenicity of all genetic variants identified in each patient, which enabled us to reduce the number of potential pathogenic variants from approximately 1,200 to zero to nine per patient. Subsequent segregation analysis and in silico predictions of pathogenicity resulted in a molecular diagnosis in 36 RP patients, comprising 27 recessive, six dominant, and three X-linked cases. Intriguingly, De novo mutations were present in at least three out of 28 isolated cases with causative mutations. This study demonstrates the enormous potential and clinical utility of NGS in molecular diagnosis of genetically heterogeneous diseases such as RP. De novo dominant mutations appear to play a significant role in patients with isolated RP, having major implications for genetic counselling.
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            Ranking non-synonymous single nucleotide polymorphisms based on disease concepts

            As the number of non-synonymous single nucleotide polymorphisms (nsSNPs) identified through whole-exome/whole-genome sequencing programs increases, researchers and clinicians are becoming increasingly reliant upon computational prediction algorithms designed to prioritize potential functional variants for further study. A large proportion of existing prediction algorithms are ‘disease agnostic’ but are nevertheless quite capable of predicting when a mutation is likely to be deleterious. However, most clinical and research applications of these algorithms relate to specific diseases and would therefore benefit from an approach that discriminates between functional variants specifically related to that disease from those which are not. In a whole-exome/whole-genome sequencing context, such an approach could substantially reduce the number of false positive candidate mutations. Here, we test this postulate by incorporating a disease-specific weighting scheme into the Functional Analysis through Hidden Markov Models (FATHMM) algorithm. When compared to traditional prediction algorithms, we observed an overall reduction in the number of false positives identified using a disease-specific approach to functional prediction across 17 distinct disease concepts/categories. Our results illustrate the potential benefits of making disease-specific predictions when prioritizing candidate variants in relation to specific diseases. A web-based implementation of our algorithm is available at http://fathmm.biocompute.org.uk.
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              Mutations of 60 known causative genes in 157 families with retinitis pigmentosa based on exome sequencing.

              Retinitis pigmentosa (RP) is the most common and highly heterogeneous form of hereditary retinal degeneration. This study was to identify mutations in the 60 genes that were known to be associated with RP in 157 unrelated Chinese families with RP. Genomic DNA from probands was initially analyzed by whole exome sequencing. Sanger sequencing was used to confirm potential candidate variants affecting the encoded residues in the 60 genes, including heterozygous variants from genes that are related to autosomal dominant RP, homozygous or compound heterozygous variants from genes that are related to autosomal recessive RP, and hemizygous variants from genes that are related to X-linked RP. Synonymous and intronic variants were also examined to confirm whether they could affect splicing. A total of 244 candidate variants were detected by exome sequencing. Sanger sequencing confirmed 240 variants out of the 244 candidates. Informatics and segregation analyses suggested 110 potential pathogenic mutations in 28 out of the 60 genes involving 79 of the 157 (50%) families, including 31 (39%, 31/79) families with heterozygous mutations in autosomal dominant genes, 37 (47%, 37/79) families with homozygous (9) or compound heterozygous (28) mutations in autosomal recessive genes, and 11 (14%, 11/79) families with hemizygous mutations in X-linked genes. Of the 110 identified variants, 74 (67%) were novel. The genetic defects in approximately half of the 157 studies families were detected by exome sequencing. A comprehensive analysis of the 60 known genes not only expanded the mutation spectrum and frequency of the 60 genes in Chinese patients with RP, but also provided an overview of the molecular etiology of RP in Chinese patients. The analysis of the known genes also supplied the foundation and clues for discovering novel causative RP genes.
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                Author and article information

                Contributors
                Role: Investigation
                Role: Validation
                Role: Project administration
                Role: Formal analysis
                Role: Conceptualization
                Role: Data curation
                Role: Methodology
                Role: Supervision
                Role: Software
                Role: Writing – original draft
                Role: Writing – review & editing
                Role: Supervision
                Role: Investigation
                Role: Writing – original draft
                Role: Funding acquisition
                Role: Investigation
                Role: Data curation
                Role: Validation
                Role: Validation
                Role: Methodology
                Role: Conceptualization
                Role: ResourcesRole: Writing – review & editing
                Role: Conceptualization
                Role: Formal analysis
                Role: Conceptualization
                Role: Conceptualization
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                11 April 2018
                2018
                : 13
                : 4
                : e0185237
                Affiliations
                [1 ] BGI-Shenzhen, Shenzhen, China
                [2 ] School of Bioscience and Bioengineering, South China University of Technology, Guangzhou, China
                [3 ] Casey Eye Institute Molecular Diagnostic Laboratory, Portland, Oregon, United States of America
                [4 ] Shenzhen Eye Hospital, Jinan University, Shenzhen, China
                [5 ] BGI-Tianjin, BGI-Shenzhen, Tianjin, China
                [6 ] Key Laboratory of Optoelectronic Devices and Systems of Guangdong Province, Shenzhen University, Shenzhen, China
                [7 ] Shenzhen Key Laboratory of Genomics, Shenzhen, China
                [8 ] The Guangdong Enterprise Key Laboratory of Human Disease Genomics, Shenzhen, China
                [9 ] Nanshan Maternity & Child Healthcare Hospital of Shenzhen, Shenzhen, China
                [10 ] Maternity and Child Health Hospital of Anhui Province, The Maternal and Child Health Clinical College, Anhui Medical University, Hefei, China
                [11 ] School of Basic Medical Sciences, Zhejiang University, Hangzhou, China
                [12 ] Functional Genomics Center, Department of Pathology & Laboratory Medicine, University of Rochester Medical Center, West Henrietta, New York, United States of America
                University of Florida, UNITED STATES
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Article
                PONE-D-16-26836
                10.1371/journal.pone.0185237
                5894961
                29641573
                2fdc6ea6-76e4-4ad1-af0d-a377163bcc01

                This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.

                History
                : 5 July 2016
                : 8 September 2017
                Page count
                Figures: 5, Tables: 5, Pages: 18
                Funding
                Funded by: Shenzhen Municipal Government of China
                Award ID: NO.CXZZ20130517144604091, NO.GJHZ20130417140916986
                Award Recipient :
                Funded by: Shenzhen Key Laboratory of Genomics
                Award ID: CXB200903110066A
                Award Recipient :
                Funded by: Shenzhen Key Laboratory of Genomics
                Award ID: CXB200903110066A
                Award Recipient :
                This research was supported by the Shenzhen Municipal Government of China (NO.CXZZ20130517144604091, NO.GJHZ20130417140916986), the Shenzhen Key Laboratory of Genomics (NO.CXB200903110066A) and the Guangdong Enterprise Key Laboratory of Human Disease Genomics (NO.2011A060906007).
                Categories
                Research Article
                Biology and Life Sciences
                Genetics
                Gene Identification and Analysis
                Mutation Detection
                Biology and Life Sciences
                Genetics
                Mutation
                Nonsense Mutation
                Biology and Life Sciences
                Genetics
                Mutation
                Frameshift Mutation
                Biology and life sciences
                Molecular biology
                Molecular biology techniques
                Sequencing techniques
                DNA sequencing
                Dideoxy DNA sequencing
                Research and analysis methods
                Molecular biology techniques
                Sequencing techniques
                DNA sequencing
                Dideoxy DNA sequencing
                Medicine and Health Sciences
                Ophthalmology
                Eye Diseases
                Biology and Life Sciences
                Genetics
                Mutation
                Insertion Mutation
                Biology and life sciences
                Molecular biology
                Molecular biology techniques
                Sequencing techniques
                DNA sequencing
                Gene Sequencing
                Research and analysis methods
                Molecular biology techniques
                Sequencing techniques
                DNA sequencing
                Gene Sequencing
                Biology and Life Sciences
                Genetics
                Human Genetics
                Custom metadata
                Data are available at the Genome Variation Map (BIG Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences). The accession number is GVM000021 which could be checked in the website http://bigd.big.ac.cn/gvm/getProjectDetail?project=GVM000021.

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