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      Synaptic, transcriptional, and chromatin genes disrupted in autism

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      1 , 2 , 3 , 1 , 2 , 4 , 1 , 2 , 5 , 3 , 1 , 2 , 6 , 2 , 4 , 5 , 7 , 8 , 9 , 5 , 1 , 2 , 10 , 11 , 12 , 1 , 2 , 1 , 2 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 13 , 14 , 8 , 12 , 22 , 19 , 23 , 24 , 25 , 26 , 17 , 18 , 27 , 28 , 29 , 30 , 1 , 2 , 31 , 32 , 6 , 33 , 25 , 34 , 7 , 35 , 36 , 37 , 10 , 38 , 39 , 2 , 40 , 7 , 8 , 1 , 2 , 41 , 42 , 19 , 43 , 6 , 44 , 32 , 45 , 36 , 46 , 7 , 25 , 47 , 25 , 43 , 43 , 17 , 18 , 7 , the DDD Study, Homozygosity Mapping Collaborative for Autism, UK10K Consortium, the Autism Sequencing Consortium, 26 , 19 , 24 , 48 , 49 , 5 , 50 , 51 , 52 , 25 , 2 , 4 , 53 , 43 , 13 , 14 , 17 , 18 ,   7 , 54 , 55 , 8 , 55 , 6 , 3 , 9 , 17 , 36 , 56 , * , 1 , 2 , 4 , 53 , 57 , 58 , *
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          Summary

          The genetic architecture of autism spectrum disorder involves the interplay of common and rare variation and their impact on hundreds of genes. Using exome sequencing, analysis of rare coding variation in 3,871 autism cases and 9,937 ancestry-matched or parental controls implicates 22 autosomal genes at a false discovery rate (FDR) < 0.05, and a set of 107 autosomal genes strongly enriched for those likely to affect risk (FDR < 0.30). These 107 genes, which show unusual evolutionary constraint against mutations, incur de novo loss-of-function mutations in over 5% of autistic subjects. Many of the genes implicated encode proteins for synaptic, transcriptional, and chromatin remodeling pathways. These include voltage-gated ion channels regulating propagation of action potentials, pacemaking, and excitability-transcription coupling, as well as histone-modifying enzymes and chromatin remodelers, prominently histone post-translational modifications involving lysine methylation/demethylation.

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          Most cited references48

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          A method and server for predicting damaging missense mutations

          To the Editor: Applications of rapidly advancing sequencing technologies exacerbate the need to interpret individual sequence variants. Sequencing of phenotyped clinical subjects will soon become a method of choice in studies of the genetic causes of Mendelian and complex diseases. New exon capture techniques will direct sequencing efforts towards the most informative and easily interpretable protein-coding fraction of the genome. Thus, the demand for computational predictions of the impact of protein sequence variants will continue to grow. Here we present a new method and the corresponding software tool, PolyPhen-2 (http://genetics.bwh.harvard.edu/pph2/), which is different from the early tool PolyPhen1 in the set of predictive features, alignment pipeline, and the method of classification (Fig. 1a). PolyPhen-2 uses eight sequence-based and three structure-based predictive features (Supplementary Table 1) which were selected automatically by an iterative greedy algorithm (Supplementary Methods). Majority of these features involve comparison of a property of the wild-type (ancestral, normal) allele and the corresponding property of the mutant (derived, disease-causing) allele, which together define an amino acid replacement. Most informative features characterize how well the two human alleles fit into the pattern of amino acid replacements within the multiple sequence alignment of homologous proteins, how distant the protein harboring the first deviation from the human wild-type allele is from the human protein, and whether the mutant allele originated at a hypermutable site2. The alignment pipeline selects the set of homologous sequences for the analysis using a clustering algorithm and then constructs and refines their multiple alignment (Supplementary Fig. 1). The functional significance of an allele replacement is predicted from its individual features (Supplementary Figs. 2–4) by Naïve Bayes classifier (Supplementary Methods). We used two pairs of datasets to train and test PolyPhen-2. We compiled the first pair, HumDiv, from all 3,155 damaging alleles with known effects on the molecular function causing human Mendelian diseases, present in the UniProt database, together with 6,321 differences between human proteins and their closely related mammalian homologs, assumed to be non-damaging (Supplementary Methods). The second pair, HumVar3, consists of all the 13,032 human disease-causing mutations from UniProt, together with 8,946 human nsSNPs without annotated involvement in disease, which were treated as non-damaging. We found that PolyPhen-2 performance, as presented by its receiver operating characteristic curves, was consistently superior compared to PolyPhen (Fig. 1b) and it also compared favorably with the three other popular prediction tools4–6 (Fig. 1c). For a false positive rate of 20%, PolyPhen-2 achieves the rate of true positive predictions of 92% and 73% on HumDiv and HumVar, respectively (Supplementary Table 2). One reason for a lower accuracy of predictions on HumVar is that nsSNPs assumed to be non-damaging in HumVar contain a sizable fraction of mildly deleterious alleles. In contrast, most of amino acid replacements assumed non-damaging in HumDiv must be close to selective neutrality. Because alleles that are even mildly but unconditionally deleterious cannot be fixed in the evolving lineage, no method based on comparative sequence analysis is ideal for discriminating between drastically and mildly deleterious mutations, which are assigned to the opposite categories in HumVar. Another reason is that HumDiv uses an extra criterion to avoid possible erroneous annotations of damaging mutations. For a mutation, PolyPhen-2 calculates Naïve Bayes posterior probability that this mutation is damaging and reports estimates of false positive (the chance that the mutation is classified as damaging when it is in fact non-damaging) and true positive (the chance that the mutation is classified as damaging when it is indeed damaging) rates. A mutation is also appraised qualitatively, as benign, possibly damaging, or probably damaging (Supplementary Methods). The user can choose between HumDiv- and HumVar-trained PolyPhen-2. Diagnostics of Mendelian diseases requires distinguishing mutations with drastic effects from all the remaining human variation, including abundant mildly deleterious alleles. Thus, HumVar-trained PolyPhen-2 should be used for this task. In contrast, HumDiv-trained PolyPhen-2 should be used for evaluating rare alleles at loci potentially involved in complex phenotypes, dense mapping of regions identified by genome-wide association studies, and analysis of natural selection from sequence data, where even mildly deleterious alleles must be treated as damaging. Supplementary Material 1
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            De novo gene disruptions in children on the autistic spectrum.

            Exome sequencing of 343 families, each with a single child on the autism spectrum and at least one unaffected sibling, reveal de novo small indels and point substitutions, which come mostly from the paternal line in an age-dependent manner. We do not see significantly greater numbers of de novo missense mutations in affected versus unaffected children, but gene-disrupting mutations (nonsense, splice site, and frame shifts) are twice as frequent, 59 to 28. Based on this differential and the number of recurrent and total targets of gene disruption found in our and similar studies, we estimate between 350 and 400 autism susceptibility genes. Many of the disrupted genes in these studies are associated with the fragile X protein, FMRP, reinforcing links between autism and synaptic plasticity. We find FMRP-associated genes are under greater purifying selection than the remainder of genes and suggest they are especially dosage-sensitive targets of cognitive disorders. Copyright © 2012 Elsevier Inc. All rights reserved.
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              FMRP stalls ribosomal translocation on mRNAs linked to synaptic function and autism.

              FMRP loss of function causes Fragile X syndrome (FXS) and autistic features. FMRP is a polyribosome-associated neuronal RNA-binding protein, suggesting that it plays a key role in regulating neuronal translation, but there has been little consensus regarding either its RNA targets or mechanism of action. Here, we use high-throughput sequencing of RNAs isolated by crosslinking immunoprecipitation (HITS-CLIP) to identify FMRP interactions with mouse brain polyribosomal mRNAs. FMRP interacts with the coding region of transcripts encoding pre- and postsynaptic proteins and transcripts implicated in autism spectrum disorders (ASD). We developed a brain polyribosome-programmed translation system, revealing that FMRP reversibly stalls ribosomes specifically on its target mRNAs. Our results suggest that loss of a translational brake on the synthesis of a subset of synaptic proteins contributes to FXS. In addition, they provide insight into the molecular basis of the cognitive and allied defects in FXS and ASD and suggest multiple targets for clinical intervention. Copyright © 2011 Elsevier Inc. All rights reserved.
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                Author and article information

                Journal
                0410462
                6011
                Nature
                Nature
                Nature
                0028-0836
                1476-4687
                1 December 2014
                29 October 2014
                13 November 2014
                13 May 2015
                : 515
                : 7526
                : 209-215
                Affiliations
                [1 ]Seaver Autism Center for Research and Treatment, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
                [2 ]Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
                [3 ]Ray and Stephanie Lane Center for Computational Biology, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA.
                [4 ]Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
                [5 ]Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA.
                [6 ]Department of Statistics, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA.
                [7 ]Program in Genetics and Genome Biology, The Centre for Applied Genomics, The Hospital for Sick Children, Toronto, Ontario M5G 0A4, Canada.
                [8 ]The Wellcome Trust Sanger Institute, Cambridge, United Kingdom.
                [9 ]Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA.
                [10 ]Department of Psychiatry, Graduate School of Medicine, Nagoya University, Nagoya, Japan.
                [11 ]Department of Child and Adolescent Psychiatry, Psychotherapy, and Psychosomatics, University Medical Center Freiburg; Center for Mental Disorders, Freiburg, Germany.
                [12 ]Department of Child Psychiatry & SGDP Centre, King's College London Institute of Psychiatry, Psychology & Neuroscience, London, United Kingdom.
                [13 ]Vanderbilt Brain Institute, Vanderbilt University School of Medicine, Nashville, Tennessee, USA.
                [14 ]Department of Molecular Physiology and Biophysics and Psychiatry, Vanderbilt University School of Medicine, Nashville, Tennessee, USA.
                [15 ]Genomic Medicine Group. University of Santiago de Compostela and Galician Foundation of Genomic Medicine (SERGAS). Santiago de Compostela, Spain.
                [16 ]Center of Excellence in Genomic Medicine Research, King Abdulaziz University, Jeddah, KSA.
                [17 ]Harvard Medical School, Boston, Massachusetts, USA.
                [18 ]Division of Genetics and Genomics, Boston Children's Hospital, Boston, Massachusetts, USA.
                [19 ]Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, Goethe University Frankfurt, Frankfurt, Germany.
                [20 ]Department of Internal Medicine, University of Utah, Salt Lake City, Utah, USA.
                [21 ]Department of Psychiatry, University of Utah, Salt Lake City, Utah, USA.
                [22 ]Duke Institute for Brain Sciences, Duke University, Durham, North Carolina, USA.
                [23 ]Disciplines of Genetics and Medicine, Memorial University of Newfoundland, St. John's, Newfoundland A1B 3V6, Canada.
                [24 ]Department of Psychiatry, School of Medicine, Trinity College Dublin, Dublin, Ireland.
                [25 ]University of Pennsylvania Perelman School of Medicine, Department of Pathology and Laboratory Medicine, Philadelphia, Pennsylvania 19104, USA
                [26 ]Institute for Juvenile Research, Department of Psychiatry, University of Illinois at Chicago, Chicago, Illinois, USA.
                [27 ]Department of Biostatistics, Columbia University, New York, New York, USA.
                [28 ] Hospital Nacional de Niños Dr Saenz Herrera, CCSS, Child Developmental and Behavioral Unit, San José, Costa Rica.
                [29 ]European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom.
                [30 ]Division of Molecular Genome Analysis, German Cancer Research Center (DKFZ), Heidelberg, Germany.
                [31 ]Department of Pediatrics, Icahn School of Medicine at Mount Sinai , New York, New York, USA.
                [32 ]Institute of Child Health, University College London, London WC1N 1EH, United Kingdom.
                [33 ]Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland SF-33100.
                [34 ]Department of Pharmacology and Systems Therapeutics, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
                [35 ]Department of Psychiatry Kaiser Permanente, San Francisco, USA.
                [36 ]The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA.
                [37 ]MRC Centre for Neuropsychiatric Genetics and Genomics, and the Neuroscience and Mental Health Research Institute, Cardiff University.
                [38 ]Child and Adolescent Psychiatry Department, Hospital General Universitario Gregorio Marañón, IiSGM, CIBERSAM, Universidad Complutense, Madrid, Spain.
                [39 ]Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, UK.
                [40 ]Department of Child Psychiatry, University of Tampere and Tampere University Hospital, Tampere, Finland SF-33101.
                [41 ]Department of Preventive Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
                [42 ]Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, USA.
                [43 ]Department of Psychiatry, University of California at San Francisco, San Francisco, California, USA.
                [44 ]Department of Child and Adolescent Psychiatry, Psychosomatics, and Psychotherapy, Translational Brain Medicine in Psychiatry and Neurology, University Hospital RWTH Aachen / JARA Brain Translational Medicine, Aachen, Germany.
                [45 ]Department of Child and Adolescent Mental Health, Great Ormond Street Hospital for Children, National Health Service Foundation Trust, London, United Kingdom.
                [46 ]Department of Psychiatry and Behavioural Neurosciences, Offord Centre for Child Studies, McMaster University, Hamilton, Ontario L8S 4K1, Canada.
                [47 ]Department of Child and Adolescent Psychiatry, Saarland University Hospital, Homburg, Germany.
                [48 ]Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, SE- 171 77 Stockholm, Sweden.
                [49 ]National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland, USA.
                [50 ]Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA.
                [51 ]Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland.
                [52 ]Psychiatric & Neurodevelopmental Genetics Unit, Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA.
                [53 ]Department of Neuroscience, Icahn School of Medicine at Mount Sinai , New York, New York, USA.
                [54 ]McLaughlin Centre, University of Toronto, Toronto, Ontario M5S 1A1, Canada.
                [55 ]Department of Human Genetics, Emory University School of Medicine, Atlanta, Georgia, USA.
                [56 ]Center for Human Genetic Research, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA.
                [57 ]Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
                [58 ]The Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
                Author notes
                [§]

                Full list of members in the Supplementary Notes.

                [* ]Correspondence should be addressed to J.D.B. ( joseph.buxbaum@ 123456mssm.edu ) or M.J.D. ( mjdaly@ 123456broadinstitute.org ).
                Article
                NIHMS622118
                10.1038/nature13772
                4402723
                25363760
                f7844f21-ef5d-4c12-ac60-7406587f11c1
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