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      Uniparental disomy in a population of 32,067 clinical exome trios

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          Abstract

          Purpose

          Data on the clinical prevalence and spectrum of uniparental disomy (UPD) remain limited. Trio exome sequencing (ES) presents a comprehensive method for detection of UPD alongside sequence and copy-number variant analysis.

          Methods

          We analyzed 32,067 ES trios referred for diagnostic testing to create a profile of UPD events and their disease associations. ES single-nucleotide polymorphism (SNP) and copy-number data were used to identify both whole-chromosome and segmental UPD and to categorize whole-chromosome results as isodisomy, heterodisomy, or mixed.

          Results

          Ninety-nine whole-chromosome and 13 segmental UPD events were identified. Of these, 29 were associated with an imprinting disorder, and 16 were associated with a positive test result through homozygous sequence variants. Isodisomy was more commonly observed in large chromosomes along with a higher rate of homozygous pathogenic variants, while heterodisomy was more frequent in chromosomes associated with imprinting or trisomy mosaicism (14, 15, 16, 20, 22).

          Conclusion

          Whole-chromosome UPD was observed in 0.31% of cases, resulting in a diagnostic finding in 0.14%. Only three UPD-positive cases had a diagnostic finding unrelated to the UPD. Thirteen UPD events were identified in cases with prior normal SNP chromosomal microarray results, demonstrating the additional diagnostic value of UPD detection by trio ES.

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          Most cited references23

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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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            Clinical application of whole-exome sequencing across clinical indications.

            We report the diagnostic yield of whole-exome sequencing (WES) in 3,040 consecutive cases at a single clinical laboratory.
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              BCFtools/RoH: a hidden Markov model approach for detecting autozygosity from next-generation sequencing data

              Summary: Runs of homozygosity (RoHs) are genomic stretches of a diploid genome that show identical alleles on both chromosomes. Longer RoHs are unlikely to have arisen by chance but are likely to denote autozygosity, whereby both copies of the genome descend from the same recent ancestor. Early tools to detect RoH used genotype array data, but substantially more information is available from sequencing data. Here, we present and evaluate BCFtools/RoH, an extension to the BCFtools software package, that detects regions of autozygosity in sequencing data, in particular exome data, using a hidden Markov model. By applying it to simulated data and real data from the 1000 Genomes Project we estimate its accuracy and show that it has higher sensitivity and specificity than existing methods under a range of sequencing error rates and levels of autozygosity. Availability and implementation: BCFtools/RoH and its associated binary/source files are freely available from https://github.com/samtools/BCFtools. Contact: vn2@sanger.ac.uk or pd3@sanger.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.
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                Author and article information

                Contributors
                kretterer@genedx.com
                Journal
                Genet Med
                Genet Med
                Genetics in Medicine
                Nature Publishing Group US (New York )
                1098-3600
                1530-0366
                25 January 2021
                25 January 2021
                2021
                : 23
                : 6
                : 1101-1107
                Affiliations
                GRID grid.428467.b, GeneDx, ; Gaithersburg, MD USA
                Author information
                http://orcid.org/0000-0001-5252-2001
                Article
                1092
                10.1038/s41436-020-01092-8
                8187148
                33495530
                5fd2074e-2c68-4f01-a131-49e7897363d4
                © The Author(s) 2021

                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/.

                History
                : 21 August 2020
                : 24 December 2020
                : 28 December 2020
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                © American College of Medical Genetics and Genomics 2021

                Genetics
                Genetics

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