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      Autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) have a similar burden of rare protein-truncating variants

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          We analyze the exome sequences of approximately 8,000 children with autism spectrum disorder (ASD) and/or attention-deficit/hyperactivity disorder (ADHD) and 5,000 controls, and we find that ASD and ADHD have a similar burden of rare protein-truncating variants in evolutionarily constrained genes, both significantly higher than controls. This motivates a combined analysis across ASD and ADHD, which identifies microtubule-associated protein 1A ( MAP1A) as a novel exome-wide significant gene conferring risk for childhood psychiatric disorders.

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          Most cited references 32

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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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            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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              Is Open Access

              The Ensembl Variant Effect Predictor

              The Ensembl Variant Effect Predictor is a powerful toolset for the analysis, annotation, and prioritization of genomic variants in coding and non-coding regions. It provides access to an extensive collection of genomic annotation, with a variety of interfaces to suit different requirements, and simple options for configuring and extending analysis. It is open source, free to use, and supports full reproducibility of results. The Ensembl Variant Effect Predictor can simplify and accelerate variant interpretation in a wide range of study designs.

                Author and article information

                Nat Neurosci
                Nat. Neurosci.
                Nature neuroscience
                29 October 2019
                25 November 2019
                December 2019
                25 May 2020
                : 22
                : 12
                : 1961-1965
                [1) ]Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, USA
                [2) ]Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
                [3) ]Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
                [4) ]The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Denmark
                [5) ]iSEQ, Centre for Integrative Sequencing, Aarhus University, Aarhus, Denmark
                [6) ]Department of Biomedicine—Human Genetics, Aarhus University, Aarhus, Denmark
                [7) ]Bioinformatics Research Centre, Aarhus University, Aarhus, Denmark
                [8) ]Center for Neonatal Screening, Department for Congenital Disorders, Statens Serum Institut, Copenhagen, Denmark
                [9) ]A full list of authors can be found in the Supplementary Note
                [10) ]Mental Health Services in the Capital Region of Denmark, Mental Health Center Copenhagen, University of Copenhagen, Copenhagen, Denmark
                [11) ]Psychosis Research Unit, Aarhus University Hospital, Risskov, Denmark
                [12) ]Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
                [13) ]Institute of Biological Psychiatry, MHC Sct. Hans, Mental Health Services Copenhagen, Roskilde, Denmark
                [14) ]Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
                [15) ]National Centre for Register-based Research, Aarhus University, Aarhus, Denmark
                [16) ]Centre for Integrated Register-based Research, Aarhus University, Aarhus, Denmark
                [17) ]Department of Medicine, Harvard Medical School, Boston, MA, USA
                [18) ]Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
                Author notes

                Author contributions

                F.K.S. performed the analysis, and R.K.W., T.S., E.M.W., F.L., D.D., J.A.K., J.G., D.S.P., and J.B.M. contributed to the analysis. F.K.S., R.K.W., C.S., J.B.-G., M.B.-H., M.N., O.M., D.M.H., T.M.W., P.B.M., A.D.B., and the iPSYCH-Broad Consortium were involved in sample selection, handling, processing, and quality control. M.N., O.M., E.B.R., D.M.H., T.M.W., P.B.M., B.M.N., A.D.B., and M.J.D. were the project core PI group. M.J.D. directed the project, and B.M.N. and A.D.B. contributed to project direction. F.K.S. and M.J.D. wrote the manuscript.


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