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      Primary complex motor stereotypies are associated with de novo damaging DNA coding mutations that identify KDM5B as a risk gene

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          Abstract

          Motor stereotypies are common in children with autism spectrum disorder (ASD), intellectual disability, or sensory deprivation, as well as in typically developing children (“primary” stereotypies, pCMS). The precise pathophysiological mechanism for motor stereotypies is unknown, although genetic etiologies have been suggested. In this study, we perform whole-exome DNA sequencing in 129 parent-child trios with pCMS and 853 control trios (118 cases and 750 controls after quality control). We report an increased rate of de novo predicted-damaging DNA coding variants in pCMS versus controls, identifying KDM5B as a high-confidence risk gene and estimating 184 genes conferring risk. Genes harboring de novo damaging variants in pCMS probands show significant overlap with those in Tourette syndrome, ASD, and those in ASD probands with high versus low stereotypy scores. An exploratory analysis of these pCMS gene expression patterns finds clustering within the cortex and striatum during early mid-fetal development. Exploratory gene ontology and network analyses highlight functional convergence in calcium ion transport, demethylation, cell signaling, cell cycle and development. Continued sequencing of pCMS trios will identify additional risk genes and provide greater insights into biological mechanisms of stereotypies across diagnostic boundaries.

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          The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data.

          Next-generation DNA sequencing (NGS) projects, such as the 1000 Genomes Project, are already revolutionizing our understanding of genetic variation among individuals. However, the massive data sets generated by NGS--the 1000 Genome pilot alone includes nearly five terabases--make writing feature-rich, efficient, and robust analysis tools difficult for even computationally sophisticated individuals. Indeed, many professionals are limited in the scope and the ease with which they can answer scientific questions by the complexity of accessing and manipulating the data produced by these machines. Here, we discuss our Genome Analysis Toolkit (GATK), a structured programming framework designed to ease the development of efficient and robust analysis tools for next-generation DNA sequencers using the functional programming philosophy of MapReduce. The GATK provides a small but rich set of data access patterns that encompass the majority of analysis tool needs. Separating specific analysis calculations from common data management infrastructure enables us to optimize the GATK framework for correctness, stability, and CPU and memory efficiency and to enable distributed and shared memory parallelization. We highlight the capabilities of the GATK by describing the implementation and application of robust, scale-tolerant tools like coverage calculators and single nucleotide polymorphism (SNP) calling. We conclude that the GATK programming framework enables developers and analysts to quickly and easily write efficient and robust NGS tools, many of which have already been incorporated into large-scale sequencing projects like the 1000 Genomes Project and The Cancer Genome Atlas.
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            ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data

            High-throughput sequencing platforms are generating massive amounts of genetic variation data for diverse genomes, but it remains a challenge to pinpoint a small subset of functionally important variants. To fill these unmet needs, we developed the ANNOVAR tool to annotate single nucleotide variants (SNVs) and insertions/deletions, such as examining their functional consequence on genes, inferring cytogenetic bands, reporting functional importance scores, finding variants in conserved regions, or identifying variants reported in the 1000 Genomes Project and dbSNP. ANNOVAR can utilize annotation databases from the UCSC Genome Browser or any annotation data set conforming to Generic Feature Format version 3 (GFF3). We also illustrate a ‘variants reduction’ protocol on 4.7 million SNVs and indels from a human genome, including two causal mutations for Miller syndrome, a rare recessive disease. Through a stepwise procedure, we excluded variants that are unlikely to be causal, and identified 20 candidate genes including the causal gene. Using a desktop computer, ANNOVAR requires ∼4 min to perform gene-based annotation and ∼15 min to perform variants reduction on 4.7 million variants, making it practical to handle hundreds of human genomes in a day. ANNOVAR is freely available at http://www.openbioinformatics.org/annovar/ .
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              Analysis of protein-coding genetic variation in 60,706 humans

              Summary Large-scale reference data sets of human genetic variation are critical for the medical and functional interpretation of DNA sequence changes. We describe the aggregation and analysis of high-quality exome (protein-coding region) sequence data for 60,706 individuals of diverse ethnicities generated as part of the Exome Aggregation Consortium (ExAC). This catalogue of human genetic diversity contains an average of one variant every eight bases of the exome, and provides direct evidence for the presence of widespread mutational recurrence. We have used this catalogue to calculate objective metrics of pathogenicity for sequence variants, and to identify genes subject to strong selection against various classes of mutation; identifying 3,230 genes with near-complete depletion of truncating variants with 72% having no currently established human disease phenotype. Finally, we demonstrate that these data can be used for the efficient filtering of candidate disease-causing variants, and for the discovery of human “knockout” variants in protein-coding genes.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: Project administrationRole: ResourcesRole: SoftwareRole: SupervisionRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: Data curationRole: Formal analysisRole: Project administrationRole: Writing – review & editing
                Role: Data curationRole: InvestigationRole: MethodologyRole: Project administrationRole: Writing – review & editing
                Role: Data curationRole: MethodologyRole: Project administrationRole: ValidationRole: Writing – review & editing
                Role: Data curationRole: InvestigationRole: MethodologyRole: Project administrationRole: Validation
                Role: Data curationRole: InvestigationRole: MethodologyRole: Project administrationRole: ValidationRole: Writing – review & editing
                Role: Data curationRole: InvestigationRole: MethodologyRole: ValidationRole: Writing – review & editing
                Role: InvestigationRole: MethodologyRole: Writing – review & editing
                Role: InvestigationRole: MethodologyRole: Writing – review & editing
                Role: Data curationRole: InvestigationRole: MethodologyRole: Project administrationRole: ValidationRole: Writing – review & editing
                Role: ConceptualizationRole: InvestigationRole: MethodologyRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: ValidationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: Project administrationRole: ResourcesRole: SupervisionRole: ValidationRole: Writing – original draftRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS One
                plos
                PLOS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                3 October 2023
                2023
                : 18
                : 10
                : e0291978
                Affiliations
                [1 ] Yale Child Study Center, Yale University School of Medicine, New Haven, CT, United States America
                [2 ] Department of Psychiatry, Yale University School of Medicine, New Haven, CT, United States America
                [3 ] Department of Psychiatry, Vanderbilt University School of Nursing, Nashville, TN, United States America
                [4 ] Departments of Neurology and Pediatrics, Johns Hopkins University School of Medicine, Baltimore, MD, United States America
                [5 ] Department of Pediatrics, Yale University School of Medicine, New Haven, CT, United States America
                Fondazione Policlinico Universitario Gemelli IRCCS, ITALY
                Author notes

                Competing Interests: I have read the journal’s policy, and the authors of this manuscript have the following competing interests: Dr. Fernandez receives research/grant support from the National Institutes of Mental Health. Dr. Olfson receives research support from the National Institutes of Mental Health, the Alan B. Slifka Foundation through the Riva Ariella Ritvo endowment, and the International Obsessive-Compulsive Disorder Foundation. Dr. Singer serves as a consultant for Abide Therapeutics, Inc; Cello Health BioConsulting; ClearView Healthcare Partners; Teva Pharmaceutical Industries Ltd; and Trinity Partners, LLC. Dr. Singer receives publishing royalties from Elsevier and research/grant support from the Tourette Association of America. Other authors declare no potential conflicts. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

                Author information
                https://orcid.org/0000-0003-0830-022X
                Article
                PONE-D-23-11138
                10.1371/journal.pone.0291978
                10547198
                37788244
                b0a93e85-4067-40c0-a0b3-a9cf704965f1
                © 2023 Fernandez et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 13 April 2023
                : 10 September 2023
                Page count
                Figures: 2, Tables: 2, Pages: 18
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/100014370, Simons Foundation Autism Research Initiative;
                Award ID: 239013
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100012135, Allison Family Foundation;
                Award Recipient :
                Funded by: Nesbitt-McMaster Foundation
                Award Recipient :
                Funded by: Klump Family
                Award Recipient :
                Funded by: Graves Family
                Award Recipient :
                This work was supported by grants from the Simons Foundation ( sfari.org, SFARI award #239013, TVF), the Allison Family Foundation (TVF), Nesbitt-McMaster Foundation (HSS), Klump Family (HSS), and Graves Family (HSS). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. There was no additional external funding received for this study.
                Categories
                Research Article
                Medicine and Health Sciences
                Epidemiology
                Medical Risk Factors
                Biology and Life Sciences
                Psychology
                Developmental Psychology
                Pervasive Developmental Disorders
                Autism Spectrum Disorder
                Social Sciences
                Psychology
                Developmental Psychology
                Pervasive Developmental Disorders
                Autism Spectrum Disorder
                Biology and Life Sciences
                Genetics
                Mutation
                Biology and Life Sciences
                Computational Biology
                Genome Analysis
                Gene Ontologies
                Biology and Life Sciences
                Genetics
                Genomics
                Genome Analysis
                Gene Ontologies
                Research and Analysis Methods
                Mathematical and Statistical Techniques
                Statistical Methods
                Multivariate Analysis
                Principal Component Analysis
                Physical Sciences
                Mathematics
                Statistics
                Statistical Methods
                Multivariate Analysis
                Principal Component Analysis
                Medicine and Health Sciences
                Mental Health and Psychiatry
                Neuropsychiatric Disorders
                Tourette Syndrome
                Medicine and Health Sciences
                Neurology
                Brain Damage
                Engineering and Technology
                Industrial Engineering
                Quality Control
                Custom metadata
                The data underlying the results presented in this study have been submitted to the Dryad Digital Repository and assigned a unique DOI (doi: 10.5061/dryad.rfj6q57d5). The data submission is currently in “private for peer review” status, so this DOI will not be live until the manuscript is accepted for publication. However, a private URL to this data is provided by Dryad for use during peer review: https://datadryad.org/stash/share/HU8cwTlay7QNbwTWYhuuBiZcSeV75dtgmkzsB4B08N0 Clicking this link immediately launches a download of the data files in the repository.

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