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      Optical genome mapping unveils hidden structural variants in neurodevelopmental disorders

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          Abstract

          While short-read sequencing currently dominates genetic research and diagnostics, it frequently falls short of capturing certain structural variants (SVs), which are often implicated in the etiology of neurodevelopmental disorders (NDDs). Optical genome mapping (OGM) is an innovative technique capable of capturing SVs that are undetectable or challenging-to-detect via short-read methods. This study aimed to investigate NDDs using OGM, specifically focusing on cases that remained unsolved after standard exome sequencing. OGM was performed in 47 families using ultra-high molecular weight DNA. Single-molecule maps were assembled de novo, followed by SV and copy number variant calling. We identified 7 variants of interest, of which 5 (10.6%) were classified as likely pathogenic or pathogenic, located in BCL11A, OPHN1, PHF8, SON, and NFIA. We also identified an inversion disrupting NAALADL2, a gene which previously was found to harbor complex rearrangements in two NDD cases. Variants in known NDD genes or candidate variants of interest missed by exome sequencing mainly consisted of larger insertions (> 1kbp), inversions, and deletions/duplications of a low number of exons (1–4 exons). In conclusion, in addition to improving molecular diagnosis in NDDs, this technique may also reveal novel NDD genes which may harbor complex SVs often missed by standard sequencing techniques.

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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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            The mutational constraint spectrum quantified from variation in 141,456 humans

            Genetic variants that inactivate protein-coding genes are a powerful source of information about the phenotypic consequences of gene disruption: genes that are crucial for the function of an organism will be depleted of such variants in natural populations, whereas non-essential genes will tolerate their accumulation. However, predicted loss-of-function variants are enriched for annotation errors, and tend to be found at extremely low frequencies, so their analysis requires careful variant annotation and very large sample sizes 1 . Here we describe the aggregation of 125,748 exomes and 15,708 genomes from human sequencing studies into the Genome Aggregation Database (gnomAD). We identify 443,769 high-confidence predicted loss-of-function variants in this cohort after filtering for artefacts caused by sequencing and annotation errors. Using an improved model of human mutation rates, we classify human protein-coding genes along a spectrum that represents tolerance to inactivation, validate this classification using data from model organisms and engineered human cells, and show that it can be used to improve the power of gene discovery for both common and rare diseases.
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              Technical standards for the interpretation and reporting of constitutional copy number variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics (ACMG) and the Clinical Genome Resource (ClinGen)

              Copy number analysis to detect disease-causing losses and gains across the genome is recommended for the evaluation of individuals with neurodevelopmental disorders and/or multiple congenital anomalies, as well as for fetuses with ultrasound abnormalities. In the decade that this analysis has been in widespread clinical use, tremendous strides have been made in understanding the effects of copy number variants (CNVs) in both affected individuals and the general population. However, continued broad implementation of array- and next-generation sequencing-based technologies will expand the types of CNVs encountered in the clinical setting, as well as our understanding of their impact on human health. To assist clinical laboratories in the classification and reporting of CNVs, irrespective of the technology used to identify them, the American College of Medical Genetics and Genomics has developed the following professional standards in collaboration with the NIH-funded Clinical Genome Resource (ClinGen) project. This update introduces a quantitative, evidence-based scoring framework; encourages the implementation of the 5-tier classification system widely used in sequence variant classification; and recommends “uncoupling” the evidence-based classification of a variant from its potential implications for a particular individual. These professional standards will guide the evaluation of constitutional CNVs and encourage consistency and transparency across clinical laboratories.
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                Author and article information

                Contributors
                is2632@cumc.columbia.edu
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                16 May 2024
                16 May 2024
                2024
                : 14
                : 11239
                Affiliations
                [1 ]GRID grid.239585.0, ISNI 0000 0001 2285 2675, Department of Neurology, Center for Statistical Genetics, Gertrude H. Sergievsky Center, , Columbia University Medical Center, Columbia University, ; 630 W 168Th St, New York, NY 10032 USA
                [2 ]Kårkulla Samkommun, Kirjala, Finland
                [3 ]Päijät-Häme Wellbeing Services, Neurology, Lahti, Finland
                [4 ]GRID grid.7737.4, ISNI 0000 0004 0410 2071, Department of Child Neurology, , University of Helsinki and Helsinki University Hospital, ; Helsinki, Finland
                [5 ]GRID grid.413739.b, ISNI 0000 0004 0628 3152, Kanta-Häme Central Hospital, ; Hämeenlinna, Finland
                [6 ]Institute of Biomedicine, University of Turku, ( https://ror.org/05vghhr25) Turku, Finland
                [7 ]The Wellbeing Services County of Ostrobothnia, Kokkola, Finland
                [8 ]GRID grid.239585.0, ISNI 0000 0001 2285 2675, Taub Institute for Alzheimer’s Disease and the Aging Brain, Columbia University Medical Center, ; New York, NY USA
                [9 ]The Wellbeing Services County of Kainuu, Kajaani, Finland
                [10 ]Northern Finland Laboratory Centre NordLab and Medical Research Centre, Oulu University Hospital and University of Oulu, ( https://ror.org/045ney286) Oulu, Finland
                [11 ]Department of Medical Genetics, University of Helsinki, ( https://ror.org/040af2s02) Helsinki, Finland
                Author information
                http://orcid.org/0000-0001-7310-6082
                Article
                62009
                10.1038/s41598-024-62009-y
                11099145
                38755281
                578610a1-6af0-48b7-924b-d4a0c9161ebf
                © The Author(s) 2024

                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 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/.

                History
                : 4 January 2024
                : 13 May 2024
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000065, National Institute of Neurological Disorders and Stroke;
                Award ID: R21 NS123325
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000071, National Institute of Child Health and Human Development;
                Award ID: R01 HD109342
                Funded by: Columbia University Sergievsky Center
                Award ID: Pilot Award
                Award Recipient :
                Categories
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                Custom metadata
                © Springer Nature Limited 2024

                Uncategorized
                optical genome mapping,neurodevelopmental disorders,structural variants,copy number variants,unsolved cases,genetics

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