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      Analysis of transcript-deleterious variants in Mendelian disorders: implications for RNA-based diagnostics

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          Abstract

          Background

          At least 50% of patients with suspected Mendelian disorders remain undiagnosed after whole-exome sequencing (WES), and the extent to which non-coding variants that are not captured by WES contribute to this fraction is unclear. Whole transcriptome sequencing is a promising supplement to WES, although empirical data on the contribution of RNA analysis to the diagnosis of Mendelian diseases on a large scale are scarce.

          Results

          Here, we describe our experience with transcript-deleterious variants (TDVs) based on a cohort of 5647 families with suspected Mendelian diseases. We first interrogate all families for which the respective Mendelian phenotype could be mapped to a single locus to obtain an unbiased estimate of the contribution of TDVs at 18.9%. We examine the entire cohort and find that TDVs account for 15% of all “solved” cases. We compare the results of RT-PCR to in silico prediction. Definitive results from RT-PCR are obtained from blood-derived RNA for the overwhelming majority of variants (84.1%), and only a small minority (2.6%) fail analysis on all available RNA sources (blood-, skin fibroblast-, and urine renal epithelial cells-derived), which has important implications for the clinical application of RNA-seq. We also show that RNA analysis can establish the diagnosis in 13.5% of 155 patients who had received “negative” clinical WES reports. Finally, our data suggest a role for TDVs in modulating penetrance even in otherwise highly penetrant Mendelian disorders.

          Conclusions

          Our results provide much needed empirical data for the impending implementation of diagnostic RNA-seq in conjunction with genome sequencing.

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          Most cited references50

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          The Sequence Alignment/Map format and SAMtools

          Summary: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments. Availability: http://samtools.sourceforge.net Contact: rd@sanger.ac.uk
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            STAR: ultrafast universal RNA-seq aligner.

            Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.
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              Standards and Guidelines for the Interpretation of Sequence Variants: A Joint Consensus Recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology

              The American College of Medical Genetics and Genomics (ACMG) previously developed guidance for the interpretation of sequence variants. 1 In the past decade, sequencing technology has evolved rapidly with the advent of high-throughput next generation sequencing. By adopting and leveraging next generation sequencing, clinical laboratories are now performing an ever increasing catalogue of genetic testing spanning genotyping, single genes, gene panels, exomes, genomes, transcriptomes and epigenetic assays for genetic disorders. By virtue of increased complexity, this paradigm shift in genetic testing has been accompanied by new challenges in sequence interpretation. In this context, the ACMG convened a workgroup in 2013 comprised of representatives from the ACMG, the Association for Molecular Pathology (AMP) and the College of American Pathologists (CAP) to revisit and revise the standards and guidelines for the interpretation of sequence variants. The group consisted of clinical laboratory directors and clinicians. This report represents expert opinion of the workgroup with input from ACMG, AMP and CAP stakeholders. These recommendations primarily apply to the breadth of genetic tests used in clinical laboratories including genotyping, single genes, panels, exomes and genomes. This report recommends the use of specific standard terminology: ‘pathogenic’, ‘likely pathogenic’, ‘uncertain significance’, ‘likely benign’, and ‘benign’ to describe variants identified in Mendelian disorders. Moreover, this recommendation describes a process for classification of variants into these five categories based on criteria using typical types of variant evidence (e.g. population data, computational data, functional data, segregation data, etc.). Because of the increased complexity of analysis and interpretation of clinical genetic testing described in this report, the ACMG strongly recommends that clinical molecular genetic testing should be performed in a CLIA-approved laboratory with results interpreted by a board-certified clinical molecular geneticist or molecular genetic pathologist or equivalent.
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                Author and article information

                Contributors
                Xin.Gao@kaust.edu.sa
                falkuraya@kfshrc.edu.sa
                Journal
                Genome Biol
                Genome Biol
                Genome Biology
                BioMed Central (London )
                1474-7596
                1474-760X
                17 June 2020
                17 June 2020
                2020
                : 21
                : 145
                Affiliations
                [1 ]GRID grid.415310.2, ISNI 0000 0001 2191 4301, Department of Genetics, , King Faisal Specialist Hospital and Research Center, ; Riyadh, Saudi Arabia
                [2 ]GRID grid.45672.32, ISNI 0000 0001 1926 5090, Computational Bioscience Research Center (CBRC), Computer, Electrical, and Mathematical Sciences and Engineering (CEMSE) Division, , King Abdullah University of Science and Technology (KAUST), ; Thuwal, Saudi Arabia
                [3 ]Département de génétique, AP-HP, Hôpital Bichat, Université de Paris, LVTS INSERM U1148, Paris, France
                [4 ]GRID grid.415310.2, ISNI 0000 0001 2191 4301, Deparmtent of Medical Genetics, , King Faisal Specialist Hospital and Research Center, ; Riyadh, Saudi Arabia
                [5 ]GRID grid.415310.2, ISNI 0000 0001 2191 4301, Department of Pediatrics, , King Faisal Specialist Hospital and Research Center, ; Riyadh, Saudi Arabia
                [6 ]GRID grid.415989.8, ISNI 0000 0000 9759 8141, Department of Pediatrics, , Prince Sultan Military Medical City, ; Riyadh, Saudi Arabia
                [7 ]GRID grid.411335.1, ISNI 0000 0004 1758 7207, Department of Anatomy and Cell Biology, College of Medicine, , Alfaisal University, ; Riyadh, Saudi Arabia
                [8 ]GRID grid.415277.2, ISNI 0000 0004 0593 1832, Division of Pediatric Gastroenterology, Children’s Hospital, , King Fahad Medical City, ; Riyadh, Saudi Arabia
                Author information
                http://orcid.org/0000-0003-4158-341X
                Article
                2053
                10.1186/s13059-020-02053-9
                7298854
                32552793
                d267ed8d-241e-4de7-9166-7599ad932c13
                © The Author(s) 2020

                Open AccessThis 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 10 October 2019
                : 21 May 2020
                Funding
                Funded by: KSCDR
                Award ID: NA
                Award Recipient :
                Categories
                Research
                Custom metadata
                © The Author(s) 2020

                Genetics
                negative wes,rna-based diagnostics,mapping,mendelian,transcriptomics
                Genetics
                negative wes, rna-based diagnostics, mapping, mendelian, transcriptomics

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