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      Quantifying prion disease penetrance using large population control cohorts.

      1 , 2 , 3 , 3 , 4 , 5 , 5 , 5 , 5 , 6 , 6 , 7 , 6 , 6 , 8 , 8 , 9 , 10 , 11 , 12 , 13 , 13 , 14 , 14 , 15 , 15 , 16 , 16 , 16 , 17 , 17 , 18 , 19 , 19 , 18 , 20 , 20 , 21 , 21 , 22 , 3 , 3 , 23 , 24 , 25 , 26 , 27 , 27 , 28 , 26 , 29 , 29 , 30 , 31 , 31 , 30 , 32 , 32 , 30 , 33 , 30 , 3 , 34

      Science translational medicine

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          Abstract

          More than 100,000 genetic variants are reported to cause Mendelian disease in humans, but the penetrance-the probability that a carrier of the purported disease-causing genotype will indeed develop the disease-is generally unknown. We assess the impact of variants in the prion protein gene (PRNP) on the risk of prion disease by analyzing 16,025 prion disease cases, 60,706 population control exomes, and 531,575 individuals genotyped by 23andMe Inc. We show that missense variants in PRNP previously reported to be pathogenic are at least 30 times more common in the population than expected on the basis of genetic prion disease prevalence. Although some of this excess can be attributed to benign variants falsely assigned as pathogenic, other variants have genuine effects on disease susceptibility but confer lifetime risks ranging from <0.1 to ~100%. We also show that truncating variants in PRNP have position-dependent effects, with true loss-of-function alleles found in healthy older individuals, a finding that supports the safety of therapeutic suppression of prion protein expression.

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          Most cited references 165

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          Standards and Guidelines for the Interpretation of Sequence Variants: A Joint Consensus Recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology

          The American College of Medical Genetics and Genomics (ACMG) previously developed guidance for the interpretation of sequence variants. 1 In the past decade, sequencing technology has evolved rapidly with the advent of high-throughput next generation sequencing. By adopting and leveraging next generation sequencing, clinical laboratories are now performing an ever increasing catalogue of genetic testing spanning genotyping, single genes, gene panels, exomes, genomes, transcriptomes and epigenetic assays for genetic disorders. By virtue of increased complexity, this paradigm shift in genetic testing has been accompanied by new challenges in sequence interpretation. In this context, the ACMG convened a workgroup in 2013 comprised of representatives from the ACMG, the Association for Molecular Pathology (AMP) and the College of American Pathologists (CAP) to revisit and revise the standards and guidelines for the interpretation of sequence variants. The group consisted of clinical laboratory directors and clinicians. This report represents expert opinion of the workgroup with input from ACMG, AMP and CAP stakeholders. These recommendations primarily apply to the breadth of genetic tests used in clinical laboratories including genotyping, single genes, panels, exomes and genomes. This report recommends the use of specific standard terminology: ‘pathogenic’, ‘likely pathogenic’, ‘uncertain significance’, ‘likely benign’, and ‘benign’ to describe variants identified in Mendelian disorders. Moreover, this recommendation describes a process for classification of variants into these five categories based on criteria using typical types of variant evidence (e.g. population data, computational data, functional data, segregation data, etc.). Because of the increased complexity of analysis and interpretation of clinical genetic testing described in this report, the ACMG strongly recommends that clinical molecular genetic testing should be performed in a CLIA-approved laboratory with results interpreted by a board-certified clinical molecular geneticist or molecular genetic pathologist or equivalent.
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            Integrative Genomics Viewer

            To the Editor Rapid improvements in sequencing and array-based platforms are resulting in a flood of diverse genome-wide data, including data from exome and whole genome sequencing, epigenetic surveys, expression profiling of coding and non-coding RNAs, SNP and copy number profiling, and functional assays. Analysis of these large, diverse datasets holds the promise of a more comprehensive understanding of the genome and its relation to human disease. Experienced and knowledgeable human review is an essential component of this process, complementing computational approaches. This calls for efficient and intuitive visualization tools able to scale to very large datasets and to flexibly integrate multiple data types, including clinical data. However, the sheer volume and scope of data poses a significant challenge to the development of such tools. To address this challenge we developed the Integrative Genomics Viewer (IGV), a lightweight visualization tool that enables intuitive real-time exploration of diverse, large-scale genomic datasets on standard desktop computers. It supports flexible integration of a wide range of genomic data types including aligned sequence reads, mutations, copy number, RNAi screens, gene expression, methylation, and genomic annotations (Figure S1). The IGV makes use of efficient, multi-resolution file formats to enable real-time exploration of arbitrarily large datasets over all resolution scales, while consuming minimal resources on the client computer (see Supplementary Text). Navigation through a dataset is similar to Google Maps, allowing the user to zoom and pan seamlessly across the genome at any level of detail from whole-genome to base pair (Figure S2). Datasets can be loaded from local or remote sources, including cloud-based resources, enabling investigators to view their own genomic datasets alongside publicly available data from, for example, The Cancer Genome Atlas (TCGA) 1 , 1000 Genomes (www.1000genomes.org/), and ENCODE 2 (www.genome.gov/10005107) projects. In addition, IGV allows collaborators to load and share data locally or remotely over the Web. IGV supports concurrent visualization of diverse data types across hundreds, and up to thousands of samples, and correlation of these integrated datasets with clinical and phenotypic variables. A researcher can define arbitrary sample annotations and associate them with data tracks using a simple tab-delimited file format (see Supplementary Text). These might include, for example, sample identifier (used to link different types of data for the same patient or tissue sample), phenotype, outcome, cluster membership, or any other clinical or experimental label. Annotations are displayed as a heatmap but more importantly are used for grouping, sorting, filtering, and overlaying diverse data types to yield a comprehensive picture of the integrated dataset. This is illustrated in Figure 1, a view of copy number, expression, mutation, and clinical data from 202 glioblastoma samples from the TCGA project in a 3 kb region around the EGFR locus 1, 3 . The investigator first grouped samples by tumor subtype, then by data type (copy number and expression), and finally sorted them by median copy number over the EGFR locus. A shared sample identifier links the copy number and expression tracks, maintaining their relative sort order within the subtypes. Mutation data is overlaid on corresponding copy number and expression tracks, based on shared participant identifier annotations. Several trends in the data stand out, such as a strong correlation between copy number and expression and an overrepresentation of EGFR amplified samples in the Classical subtype. IGV’s scalable architecture makes it well suited for genome-wide exploration of next-generation sequencing (NGS) datasets, including both basic aligned read data as well as derived results, such as read coverage. NGS datasets can approach terabytes in size, so careful management of data is necessary to conserve compute resources and to prevent information overload. IGV varies the displayed level of detail according to resolution scale. At very wide views, such as the whole genome, IGV represents NGS data by a simple coverage plot. Coverage data is often useful for assessing overall quality and diagnosing technical issues in sequencing runs (Figure S3), as well as analysis of ChIP-Seq 4 and RNA-Seq 5 experiments (Figures S4 and S5). As the user zooms below the ~50 kb range, individual aligned reads become visible (Figure 2) and putative SNPs are highlighted as allele counts in the coverage plot. Alignment details for each read are available in popup windows (Figures S6 and S7). Zooming further, individual base mismatches become visible, highlighted by color and intensity according to base call and quality. At this level, the investigator may sort reads by base, quality, strand, sample and other attributes to assess the evidence of a variant. This type of visual inspection can be an efficient and powerful tool for variant call validation, eliminating many false positives and aiding in confirmation of true findings (Figures S6 and S7). Many sequencing protocols produce reads from both ends (“paired ends”) of genomic fragments of known size distribution. IGV uses this information to color-code paired ends if their insert sizes are larger than expected, fall on different chromosomes, or have unexpected pair orientations. Such pairs, when consistent across multiple reads, can be indicative of a genomic rearrangement. When coloring aberrant paired ends, each chromosome is assigned a unique color, so that intra- (same color) and inter- (different color) chromosomal events are readily distinguished (Figures 2 and S8). We note that misalignments, particularly in repeat regions, can also yield unexpected insert sizes, and can be diagnosed with the IGV (Figure S9). There are a number of stand-alone, desktop genome browsers available today 6 including Artemis 7 , EagleView 8 , MapView 9 , Tablet 10 , Savant 11 , Apollo 12 , and the Integrated Genome Browser 13 . Many of them have features that overlap with IGV, particularly for NGS sequence alignment and genome annotation viewing. The Integrated Genome Browser also supports viewing array-based data. See Supplementary Table 1 and Supplementary Text for more detail. IGV focuses on the emerging integrative nature of genomic studies, placing equal emphasis on array-based platforms, such as expression and copy-number arrays, next-generation sequencing, as well as clinical and other sample metadata. Indeed, an important and unique feature of IGV is the ability to view all these different data types together and to use the sample metadata to dynamically group, sort, and filter datasets (Figure 1 above). Another important characteristic of IGV is fast data loading and real-time pan and zoom – at all scales of genome resolution and all dataset sizes, including datasets comprising hundreds of samples. Finally, we have placed great emphasis on the ease of installation and use of IGV, with the goal of making both the viewing and sharing of their data accessible to non-informatics end users. IGV is open source software and freely available at http://www.broadinstitute.org/igv/, including full documentation on use of the software. Supplementary Material 1
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              Rapid and accurate haplotype phasing and missing-data inference for whole-genome association studies by use of localized haplotype clustering.

              Whole-genome association studies present many new statistical and computational challenges due to the large quantity of data obtained. One of these challenges is haplotype inference; methods for haplotype inference designed for small data sets from candidate-gene studies do not scale well to the large number of individuals genotyped in whole-genome association studies. We present a new method and software for inference of haplotype phase and missing data that can accurately phase data from whole-genome association studies, and we present the first comparison of haplotype-inference methods for real and simulated data sets with thousands of genotyped individuals. We find that our method outperforms existing methods in terms of both speed and accuracy for large data sets with thousands of individuals and densely spaced genetic markers, and we use our method to phase a real data set of 3,002 individuals genotyped for 490,032 markers in 3.1 days of computing time, with 99% of masked alleles imputed correctly. Our method is implemented in the Beagle software package, which is freely available.
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                Author and article information

                Journal
                Sci Transl Med
                Science translational medicine
                1946-6242
                1946-6234
                Jan 20 2016
                : 8
                : 322
                Affiliations
                [1 ] Program in Medical and Population Genetics, Broad Institute of Massachusetts Institute of Technology (MIT) and Harvard, Cambridge, MA 02142, USA. Analytical and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA. Program in Biological and Biomedical Sciences, Harvard Medical School, Boston, MA 02115, USA. Prion Alliance, Cambridge, MA 02139, USA. eminikel@broadinstitute.org macarthur@atgu.mgh.harvard.edu.
                [2 ] Program in Medical and Population Genetics, Broad Institute of Massachusetts Institute of Technology (MIT) and Harvard, Cambridge, MA 02142, USA. Program in Biological and Biomedical Sciences, Harvard Medical School, Boston, MA 02115, USA. Prion Alliance, Cambridge, MA 02139, USA.
                [3 ] Program in Medical and Population Genetics, Broad Institute of Massachusetts Institute of Technology (MIT) and Harvard, Cambridge, MA 02142, USA. Analytical and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA.
                [4 ] Program in Medical and Population Genetics, Broad Institute of Massachusetts Institute of Technology (MIT) and Harvard, Cambridge, MA 02142, USA. Analytical and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA. Program in Biological and Biomedical Sciences, Harvard Medical School, Boston, MA 02115, USA.
                [5 ] Research, 23andMe Inc., Mountain View, CA 94041, USA.
                [6 ] National Prion Disease Pathology Surveillance Center, Cleveland, OH 44106, USA.
                [7 ] University Hospitals Case Medical Center, Cleveland, OH 44106, USA.
                [8 ] Department of Neurology and Neurobiology of Aging, Kanazawa University Graduate School of Medical Sciences, Kanazawa 920-8640, Japan.
                [9 ] Department of Neurology and Neurological Science, Graduate School, Tokyo Medical and Dental University, Tokyo 113-8519, Japan.
                [10 ] National Center Hospital, National Center of Neurology and Psychiatry, Tokyo 187-8551, Japan.
                [11 ] Department of Public Health, Jichi Medical University, Shimotsuke 329-0498, Japan.
                [12 ] Department of Neurological Science, Tohoku University Graduate School of Medicine, Sendai 980-8575, Japan.
                [13 ] Australian National Creutzfeldt-Jakob Disease Registry, The University of Melbourne, Parkville, Victoria 3010, Australia.
                [14 ] National Creutzfeldt-Jakob Disease Research & Surveillance Unit, Western General Hospital, Edinburgh EH4 2XU, UK.
                [15 ] National Reference Center for the Surveillance of Human Transmissible Spongiform Encephalopathies, Georg-August-University, Goettingen 37073, Germany.
                [16 ] Center for Neuropathology and Prion Research (ZNP), Ludwig-Maximilians-University, Munich 81377, Germany.
                [17 ] Centro de Investigación Biomédica en Red de Enfermedades Neurodegenerativas, Instituto de Salud Carlos III, Madrid 28031, Spain.
                [18 ] INSERM U 1127, CNRS UMR 7225, Sorbonne Universités, Pierre and Marie Curie University Paris 06 UMR S 1127, Institut du Cerveau et de la Moelle Epinière, 75013 Paris, France. Assistance Publique-Hôpitaux de Paris (AP-HP), Cellule Nationale de Référence des Maladies de Creutzfeldt-Jakob, Groupe Hospitalier Pitié-Salpêtrière, F-75013 Paris, France.
                [19 ] AP-HP, Service de Biochimie et Biologie Moléculaire, Hôpital Lariboisière, 75010 Paris, France.
                [20 ] Istituto di Ricovero e Cura a Carattere Scientifico, Institute of Neurological Sciences, Bologna 40123, Italy. Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna 40126, Italy.
                [21 ] Department of Cell Biology and Neurosciences, Istituto Superiore di Sanità, Rome 00161, Italy.
                [22 ] Program in Medical and Population Genetics, Broad Institute of Massachusetts Institute of Technology (MIT) and Harvard, Cambridge, MA 02142, USA. Analytical and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA. Division of Genetics and Genomics, Boston Children's Hospital, Boston, MA 02115, USA.
                [23 ] Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA.
                [24 ] Department of Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio 70210, Finland.
                [25 ] Department of Genetics, University of North Carolina School of Medicine, Chapel Hill, NC 27599, USA.
                [26 ] Karolinska Institutet, Stockholm SE-171 77, Sweden.
                [27 ] Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
                [28 ] Department of Genetics, University of North Carolina School of Medicine, Chapel Hill, NC 27599, USA. Karolinska Institutet, Stockholm SE-171 77, Sweden.
                [29 ] Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
                [30 ] Department of Epidemiology, Erasmus Medical Center (MC), Rotterdam 3000 CA, Netherlands.
                [31 ] Dutch Surveillance Centre for Prion Diseases, Department of Pathology, University Medical Center, Utrecht 3584 CX, Netherlands.
                [32 ] Department of Internal Medicine, Erasmus MC, Rotterdam 3000 CA, Netherlands.
                [33 ] Department of Epidemiology, Erasmus Medical Center (MC), Rotterdam 3000 CA, Netherlands. Department of Internal Medicine, Erasmus MC, Rotterdam 3000 CA, Netherlands.
                [34 ] Program in Medical and Population Genetics, Broad Institute of Massachusetts Institute of Technology (MIT) and Harvard, Cambridge, MA 02142, USA. Analytical and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA. eminikel@broadinstitute.org macarthur@atgu.mgh.harvard.edu.
                Article
                8/322/322ra9 NIHMS760972
                10.1126/scitranslmed.aad5169
                4774245
                26791950
                Copyright © 2016, American Association for the Advancement of Science.

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