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      Sporadic autism exomes reveal a highly interconnected protein network of de novo mutations

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          Abstract

          It is well established that autism spectrum disorders (ASD) have a strong genetic component. However, for at least 70% of cases, the underlying genetic cause is unknown 1 . Under the hypothesis that de novo mutations underlie a substantial fraction of the risk for developing ASD in families with no previous history of ASD or related phenotypes—so-called sporadic or simplex families 2, 3 , we sequenced all coding regions of the genome, i.e. the exome, for parent-child trios exhibiting sporadic ASD, including 189 new trios and 20 previously reported 4 . Additionally, we also sequenced the exomes of 50 unaffected siblings corresponding to these new (n = 31) and previously reported trios (n = 19) 4 , for a total of 677 individual exomes from 209 families. Here we show de novo point mutations are overwhelmingly paternal in origin (4:1 bias) and positively correlated with paternal age, consistent with the modest increased risk for children of older fathers to develop ASD 5 . Moreover, 39% (49/126) of the most severe or disruptive de novo mutations map to a highly interconnected beta-catenin/chromatin remodeling protein network ranked significantly for autism candidate genes. In proband exomes, recurrent protein-altering mutations were observed in two genes, CHD8 and NTNG1. Mutation screening of six candidate genes in 1,703 ASD probands identified additional de novo, protein-altering mutations in GRIN2B, LAMC3, and SCN1A. Combined with copy number variant (CNV) data, these results suggest extreme locus heterogeneity but also provide a target for future discovery, diagnostics, and therapeutics.

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          Most cited references 57

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          Fast and accurate short read alignment with Burrows–Wheeler transform

          Motivation: The enormous amount of short reads generated by the new DNA sequencing technologies call for the development of fast and accurate read alignment programs. A first generation of hash table-based methods has been developed, including MAQ, which is accurate, feature rich and fast enough to align short reads from a single individual. However, MAQ does not support gapped alignment for single-end reads, which makes it unsuitable for alignment of longer reads where indels may occur frequently. The speed of MAQ is also a concern when the alignment is scaled up to the resequencing of hundreds of individuals. Results: We implemented Burrows-Wheeler Alignment tool (BWA), a new read alignment package that is based on backward search with Burrows–Wheeler Transform (BWT), to efficiently align short sequencing reads against a large reference sequence such as the human genome, allowing mismatches and gaps. BWA supports both base space reads, e.g. from Illumina sequencing machines, and color space reads from AB SOLiD machines. Evaluations on both simulated and real data suggest that BWA is ∼10–20× faster than MAQ, while achieving similar accuracy. In addition, BWA outputs alignment in the new standard SAM (Sequence Alignment/Map) format. Variant calling and other downstream analyses after the alignment can be achieved with the open source SAMtools software package. Availability: http://maq.sourceforge.net Contact: rd@sanger.ac.uk
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            Mapping and quantifying mammalian transcriptomes by RNA-Seq.

            We have mapped and quantified mouse transcriptomes by deeply sequencing them and recording how frequently each gene is represented in the sequence sample (RNA-Seq). This provides a digital measure of the presence and prevalence of transcripts from known and previously unknown genes. We report reference measurements composed of 41-52 million mapped 25-base-pair reads for poly(A)-selected RNA from adult mouse brain, liver and skeletal muscle tissues. We used RNA standards to quantify transcript prevalence and to test the linear range of transcript detection, which spanned five orders of magnitude. Although >90% of uniquely mapped reads fell within known exons, the remaining data suggest new and revised gene models, including changed or additional promoters, exons and 3' untranscribed regions, as well as new candidate microRNA precursors. RNA splice events, which are not readily measured by standard gene expression microarray or serial analysis of gene expression methods, were detected directly by mapping splice-crossing sequence reads. We observed 1.45 x 10(5) distinct splices, and alternative splices were prominent, with 3,500 different genes expressing one or more alternate internal splices.
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              A framework for variation discovery and genotyping using next-generation DNA sequencing data

              Recent advances in sequencing technology make it possible to comprehensively catalogue genetic variation in population samples, creating a foundation for understanding human disease, ancestry and evolution. The amounts of raw data produced are prodigious and many computational steps are required to translate this output into high-quality variant calls. We present a unified analytic framework to discover and genotype variation among multiple samples simultaneously that achieves sensitive and specific results across five sequencing technologies and three distinct, canonical experimental designs. Our process includes (1) initial read mapping; (2) local realignment around indels; (3) base quality score recalibration; (4) SNP discovery and genotyping to find all potential variants; and (5) machine learning to separate true segregating variation from machine artifacts common to next-generation sequencing technologies. We discuss the application of these tools, instantiated in the Genome Analysis Toolkit (GATK), to deep whole-genome, whole-exome capture, and multi-sample low-pass (~4×) 1000 Genomes Project datasets.
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                Author and article information

                Journal
                0410462
                6011
                Nature
                Nature
                Nature
                0028-0836
                1476-4687
                27 February 2012
                04 April 2012
                10 November 2012
                : 485
                : 7397
                : 246-250
                Affiliations
                [1 ]Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
                [2 ]Department of Computer Science and Engineering, University of Washington, Seattle, WA, USA
                [3 ]Santa Fe Institute, Santa Fe, NM, USA
                [4 ]Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA, USA
                [5 ]Howard Hughes Medical Institute, Seattle, WA, USA
                Author notes
                [* ]Corresponding authors: Evan E. Eichler, Ph.D., University of Washington School of Medicine, Howard Hughes Medical Institute, Foege S210A, 3720 15th Ave NE, Box 355065, Seattle, WA 98195, Phone: (206) 543-9526, eee@ 123456gs.washington.edu . Jay Shendure, M.D., Ph.D., Department of Genome Sciences, University of Washington, Foege S413C, 3720 15th Ave NE, Box 355065, Seattle, WA 98195, Phone: (206) 685-8543, shendure@ 123456uw.edu

                Author information

                E.E.E is on the scientific advisory board for Pacific Biosciences and SynapDx. J.S. is a member of the scientific advisory boards of Tandem Technologies, Stratos Genomics, Good Start Genetics, Halo Genomics, and Adaptive TCR. B.J.O. is an inventor on patent PCT/US2009/30620: Mutations in Contactin Associated Protein 2 are Associated with Increased Risk for Idiopathic Autism.

                Article
                NIHMS359279
                10.1038/nature10989
                3350576
                22495309

                Users may view, print, copy, download and text and data- mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use: http://www.nature.com/authors/editorial_policies/license.html#terms

                Funding
                Funded by: National Institute of Child Health & Human Development : NICHD
                Award ID: R01 HD065285-02 || HD
                Funded by: Howard Hughes Medical Institute :
                Award ID: || HHMI_
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