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      Prevalence and architecture of de novo mutations in developmental disorders

      research-article
      The Deciphering Developmental Disorders Study
      Nature
      De novo mutation, Developmental Disease, Seizures, Intellectual Disability, PhenIcons, Average Faces, ANKRD11, ARID1B, KMT2A, DDX3X, ADNP, MED13L, DYRK1A, EP300, SCN2A, SETD5, KCNQ2, MECP2, SYNGAP1, ASXL3, SATB2, TCF4, CDK13, CREBBP, DYNC1H1, FOXP1, PPP2R5D, PURA, CTNNB1, KAT6A, SMARCA2, STXBP1, EHMT1, ITPR1, KAT6B, NSD1, SMC1A, TBL1XR1, CASK, CHD2, CHD4, HDAC8, USP9X, WDR45, AHDC1, CSNK2A1, GNAI1, GNAO1, HNRNPU, KANSL1, KIF1A, MEF2C, PACS1, SLC6A1, CNOT3, CTCF, EEF1A2, FOXG1, GATAD2B, GRIN2B, IQSEC2, POGZ, PUF60, SCN8A, TCF20, BCL11A, BRAF, CDKL5, NFIX, PTPN11, AUTS2, CHAMP1, CNKSR2, DNM1, KCNH1, NAA10, PPM1D, ZBTB18, ZMYND11, ASXL1, COL4A3BP, KCNQ3, MSL3, MYT1L, PDHA1, PPP2R1A, SMAD4, TRIO, WAC, CHD8, GABRB3, KDM5B, PTEN, QRICH1, SET, ZC4H2, ALG13, SCN1A, SUV420H1, SLC35A2

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          Summary

          Individuals with severe, undiagnosed developmental disorders (DDs) are enriched for damaging de novo mutations (DNMs) in developmentally important genes. We exome sequenced 4,293 families with individuals with DDs, and meta-analysed these data with another 3,287 individuals with similar disorders. We show that the most significant factors influencing the diagnostic yield of DNMs are the sex of the affected individual, the relatedness of their parents, whether close relatives are affected and parental ages. We identified 94 genes enriched for damaging DNMs, including 14 without previous compelling evidence. We have characterised the phenotypic diversity among these disorders. We estimate that 42% of our cohort carry pathogenic DNMs in coding sequences, and approximately half disrupt gene function, with the remainder resulting in altered-function. We estimate that developmental disorders caused by DNMs have an average birth prevalence of 1 in 213 to 1 in 448, depending on parental age. Given current global demographics, this equates to almost 400,000 children born per year.

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          Most cited references52

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          Is Open Access

          The Sequence Alignment/Map format and SAMtools

          Summary: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments. Availability: http://samtools.sourceforge.net Contact: rd@sanger.ac.uk
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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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              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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                Author and article information

                Journal
                0410462
                6011
                Nature
                Nature
                Nature
                0028-0836
                1476-4687
                20 June 2018
                25 January 2017
                23 February 2017
                25 June 2018
                : 542
                : 7642
                : 433-438
                Author notes
                Correspondence and requests for materials should be addressed to M.E.H ( meh@ 123456sanger.ac.uk ).
                Article
                EMS78087
                10.1038/nature21062
                6016744
                28135719
                cd9e1205-0b06-4b86-8f3a-84303f30385b

                Users may view, print, copy, and download 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

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                de novo mutation,developmental disease,seizures,intellectual disability,phenicons,average faces,ankrd11,arid1b,kmt2a,ddx3x,adnp,med13l,dyrk1a,ep300,scn2a,setd5,kcnq2,mecp2,syngap1,asxl3,satb2,tcf4,cdk13,crebbp,dync1h1,foxp1,ppp2r5d,pura,ctnnb1,kat6a,smarca2,stxbp1,ehmt1,itpr1,kat6b,nsd1,smc1a,tbl1xr1,cask,chd2,chd4,hdac8,usp9x,wdr45,ahdc1,csnk2a1,gnai1,gnao1,hnrnpu,kansl1,kif1a,mef2c,pacs1,slc6a1,cnot3,ctcf,eef1a2,foxg1,gatad2b,grin2b,iqsec2,pogz,puf60,scn8a,tcf20,bcl11a,braf,cdkl5,nfix,ptpn11,auts2,champ1,cnksr2,dnm1,kcnh1,naa10,ppm1d,zbtb18,zmynd11,asxl1,col4a3bp,kcnq3,msl3,myt1l,pdha1,ppp2r1a,smad4,trio,wac,chd8,gabrb3,kdm5b,pten,qrich1,set,zc4h2,alg13,scn1a,suv420h1,slc35a2

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