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      Investigating the role of rare coding variability in Mendelian dementia genes ( APP, PSEN1, PSEN2, GRN, MAPT, and PRNP) in late-onset Alzheimer's disease

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          Abstract

          The overlapping clinical and neuropathologic features between late-onset apparently sporadic Alzheimer's disease (LOAD), familial Alzheimer's disease (FAD), and other neurodegenerative dementias (frontotemporal dementia, corticobasal degeneration, progressive supranuclear palsy, and Creutzfeldt-Jakob disease) raise the question of whether shared genetic risk factors may explain the similar phenotype among these disparate disorders. To investigate this intriguing hypothesis, we analyzed rare coding variability in 6 Mendelian dementia genes ( APP, PSEN1, PSEN2, GRN, MAPT, and PRNP), in 141 LOAD patients and 179 elderly controls, neuropathologically proven, from the UK. In our cohort, 14 LOAD cases (10%) and 11 controls (6%) carry at least 1 rare variant in the genes studied. We report a novel variant in PSEN1 (p.I168T) and a rare variant in PSEN2 (p.A237V), absent in controls and both likely pathogenic. Our findings support previous studies, suggesting that (1) rare coding variability in PSEN1 and PSEN2 may influence the susceptibility for LOAD and (2) GRN, MAPT, and PRNP are not major contributors to LOAD. Thus, genetic screening is pivotal for the clinical differential diagnosis of these neurodegenerative dementias.

          Highlights

          • We have used exome sequencing to investigate rare coding variability in Mendelian dementia genes ( APP, PSEN1, PSEN2, GRN, MAPT, and PRNP) in a cohort composed of 141 late-onset sporadic Alzheimer's disease cases and 179 elderly controls, autopsy proven from the UK.

          • We report a novel mutation in PSEN1 (p.I168T) and a rare variant in PSEN2 (p.A237V), both likely pathogenic.

          • We conclude that PSEN1 and PSEN2 harbor susceptibility factors for sporadic Alzheimer's disease. By contrast, GRN, MAPT, and PRNP do not play a major role for the development of late-onset sporadic Alzheimer's disease.

          • Genetic screening is therefore pivotal for a clinical differential diagnosis of sporadic late-onset Alzheimer's disease and other neurodegenerative dementias (frontotemporal dementia, corticobasal degeneration, progressive supranuclear palsy, and Creutzfeldt-Jakob disease).

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          Most cited references37

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          Is Open Access

          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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              Is Open Access

              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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                Author and article information

                Contributors
                Journal
                Neurobiol Aging
                Neurobiol. Aging
                Neurobiology of Aging
                Elsevier
                0197-4580
                1558-1497
                1 December 2014
                December 2014
                : 35
                : 12
                : 2881.e1-2881.e6
                Affiliations
                [a ]Department of Molecular Neuroscience, UCL Institute of Neurology, University College London, London, UK
                [b ]Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
                [c ]King's College London Institute of Psychiatry, London, UK
                [d ]Translation Cell Sciences-Human Genetics, School of Life Sciences, Queens Medical Centre, University of Nottingham, Nottingham, UK
                Author notes
                []Corresponding author at: Neurogenetics Laboratory, National Institutes of Health, 35 Convent Drive, Bethesda, MD 20892, USA and UCL Institute of Neurology Queen Square London WC1N 3BG, UK. Tel.: +44 (0)20 3456 7890; fax: +44 (0)20 7278 5069. celeste.sassi.10@ 123456ucl.ac.uk
                [1]

                The Alzheimer's Research UK (ARUK) Consortium: Peter Passmore, David Craig, Janet Johnston, Bernadette McGuinness, Stephen Todd, Queen's University Belfast, UK; Reinhard Heun, Royal Derby Hospital, UK; Heike Kölsch, University of Bonn, Germany; Patrick G. Kehoe, University of Bristol, UK; Nigel M. Hooper, University of Leeds, UK; Emma R.L.C. Vardy, University of Newcastle, UK; David M. Mann, Stuart Pickering-Brown, University of Manchester, UK; Kristelle Brown, James Lowe, Kevin Morgan, University of Nottingham, UK; A. David Smith, Gordon Wilcock, Donald Warden, University of Oxford (OPTIMA), UK; Clive Holmes, University of Southampton, UK.

                Article
                S0197-4580(14)00403-5
                10.1016/j.neurobiolaging.2014.06.002
                4236585
                25104557
                90111707-fe66-41ea-a475-b168218d790f
                © 2014 Elsevier Inc. All rights reserved.
                History
                : 20 April 2014
                : 6 June 2014
                : 7 June 2014
                Categories
                Genetic Report Abstract

                Neurosciences
                alzheimer's disease,neurodegenerative dementia,app,psen1,psen2,mapt,grn,prnp,exome sequencing
                Neurosciences
                alzheimer's disease, neurodegenerative dementia, app, psen1, psen2, mapt, grn, prnp, exome sequencing

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