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      Analysis of five chronic inflammatory diseases identifies 27 new associations and highlights disease-specific patterns at shared loci

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      1 , 2 , 2 , 3 , 4 , 1 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 1 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 17 , 7 , 8 , 9 , 20 , 7 , 19 , 21 , 22 , 23 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 30 , 31 , 30 , 31 , The International IBD Genetics Consortium (IIBDGC), International Genetics of Ankylosing Spondylitis Consortium (IGAS), International PSC Study Group (IPSCSG), Genetic Analysis of Psoriasis Consortium (GAPC), Psoriasis Association Genetics Extension (PAGE), 1 , 33 , 34 , 35 , 35 ,   27 , 36 , 37 , 38 , 34 , 34 , 1 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 12 , 13 , 14 , 15 , 2 , 46 , 47 , 48 , 1
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          Abstract

          We simultaneously investigated the genetic landscape of ankylosing spondylitis, Crohn's disease, psoriasis, primary sclerosing cholangitis and ulcerative colitis to investigate pleiotropy and the relationship between these clinically related diseases. Using high-density genotype data from more than 86,000 individuals of European-ancestry we identified 244 independent multi-disease signals including 27 novel genome-wide significant susceptibility loci and 3 unreported shared risk loci. Complex pleiotropy was supported when contrasting multi-disease signals with expression data sets from human, rat and mouse, and epigenetic and expressed enhancer profiles. The comorbidities among the five immune diseases were best explained by biological pleiotropy rather than heterogeneity (a subgroup of cases that is genetically identical to another disease, possibly due to diagnostic misclassification, molecular subtypes, or excessive comorbidity). In particular, the strong comorbidity between primary sclerosing cholangitis and inflammatory bowel disease is likely the result of a unique disease, which is genetically distinct from classical inflammatory bowel disease phenotypes.

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          Most cited references56

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          PLINK: a tool set for whole-genome association and population-based linkage analyses.

          Whole-genome association studies (WGAS) bring new computational, as well as analytic, challenges to researchers. Many existing genetic-analysis tools are not designed to handle such large data sets in a convenient manner and do not necessarily exploit the new opportunities that whole-genome data bring. To address these issues, we developed PLINK, an open-source C/C++ WGAS tool set. With PLINK, large data sets comprising hundreds of thousands of markers genotyped for thousands of individuals can be rapidly manipulated and analyzed in their entirety. As well as providing tools to make the basic analytic steps computationally efficient, PLINK also supports some novel approaches to whole-genome data that take advantage of whole-genome coverage. We introduce PLINK and describe the five main domains of function: data management, summary statistics, population stratification, association analysis, and identity-by-descent estimation. In particular, we focus on the estimation and use of identity-by-state and identity-by-descent information in the context of population-based whole-genome studies. This information can be used to detect and correct for population stratification and to identify extended chromosomal segments that are shared identical by descent between very distantly related individuals. Analysis of the patterns of segmental sharing has the potential to map disease loci that contain multiple rare variants in a population-based linkage analysis.
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            Second-generation PLINK: rising to the challenge of larger and richer datasets

            PLINK 1 is a widely used open-source C/C++ toolset for genome-wide association studies (GWAS) and research in population genetics. However, the steady accumulation of data from imputation and whole-genome sequencing studies has exposed a strong need for even faster and more scalable implementations of key functions. In addition, GWAS and population-genetic data now frequently contain probabilistic calls, phase information, and/or multiallelic variants, none of which can be represented by PLINK 1's primary data format. To address these issues, we are developing a second-generation codebase for PLINK. The first major release from this codebase, PLINK 1.9, introduces extensive use of bit-level parallelism, O(sqrt(n))-time/constant-space Hardy-Weinberg equilibrium and Fisher's exact tests, and many other algorithmic improvements. In combination, these changes accelerate most operations by 1-4 orders of magnitude, and allow the program to handle datasets too large to fit in RAM. This will be followed by PLINK 2.0, which will introduce (a) a new data format capable of efficiently representing probabilities, phase, and multiallelic variants, and (b) extensions of many functions to account for the new types of information. The second-generation versions of PLINK will offer dramatic improvements in performance and compatibility. For the first time, users without access to high-end computing resources can perform several essential analyses of the feature-rich and very large genetic datasets coming into use.
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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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                Author and article information

                Journal
                9216904
                2419
                Nat Genet
                Nat. Genet.
                Nature genetics
                1061-4036
                1546-1718
                15 March 2016
                14 March 2016
                May 2016
                14 September 2016
                : 48
                : 5
                : 510-518
                Affiliations
                [1 ] Institute of Clinical Molecular Biology, Christian-Albrechts-University of Kiel, Kiel, Germany.
                [2 ] Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1HH, UK.
                [3 ] Nuffield Department of Clinical Neurosciences, Division of Clinical Neurology, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, UK.
                [4 ] Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK.
                [5 ] Department of Convergence Medicine, University of Ulsan College of Medicine & Asan Institute for Life Sciences, Asan Medical Center, Seoul 138-736, Republic of Korea.
                [6 ] Asan Institute for Life Sciences, University of Ulsan College of Medicine, Asan Medical Center, Seoul 138-736, Republic of Korea.
                [7 ] Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
                [8 ] Divisions of Genetics and Rheumatology, Brigham and Women's Hospital, Boston, MA 02446, USA.
                [9 ] Department of Medicine, Harvard Medical School, Boston, MA 02446, USA.
                [10 ] Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada.
                [11 ] Department of Psychiatry, University of Toronto, Toronto, ON, Canada.
                [12 ] Norwegian PSC Research Center, Department of Transplantation Medicine, Division of Cancer Medicine, Surgery and Transplantation, Oslo University Hospital Rikshospitalet, Oslo, Norway.
                [13 ] K.G. Jebsen Inflammation Research Centre, Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
                [14 ] Research Institute of Internal Medicine, Division of Cancer Medicine, Surgery and Transplantation, Oslo University Hospital, Rikshospitalet, Oslo, Norway.
                [15 ] Section of gastroenterology, Department of Transplantation Medicine, Oslo University Hospital, Oslo, Norway.
                [16 ] Department of Neurosciences, University of California, San Diego, La Jolla, CA, USA.
                [17 ] Estonian Genome Center, University of Tartu, Tartu, Estonia.
                [18 ] Division of Endocrinology, Boston Children's Hospital, Cambridge, 02141 Massachusetts, USA.
                [19 ] Center for Basic and Translational Obesity Research, Boston Children's Hospital, Cambridge, 02141 Massachusetts, USA.
                [20 ] University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
                [21 ] Novo Nordisk Foundation Centre for Basic Metabolic Research, University of Copenhagen, Nørre Allé 20, 2200 København N, Denmark.
                [22 ] Department of Epidemiology Research, Statens Serum Institut, Copenhagen, Denmark.
                [23 ] Department of Gastroenterology and Hepatology, University of Groningen and University Medical Center Groningen, Groningen, The Netherlands.
                [24 ] Department of Bioscience and Nutrition, Karolinska Institutet, Stockholm, Sweden.
                [25 ] BioCruces Health Research Institute and Ikerbasque, Basque Foundation for Science, Bilbao, Spain.
                [26 ] Department of Gastroenterology, Faculty of Medicine and Health, Örebro University, Örebro SE- 70182, Sweden.
                [27 ] Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, DK-2200 Copenhagen, Denmark.
                [28 ] Institute of Epidemiology, University Hospital Schleswig-Holstein, 24105 Kiel, Germany.
                [29 ] PopGen Biobank, University Hospital Schleswig-Holstein, 24105 Kiel, Germany.
                [30 ] Institute of Human Genetics, University of Bonn, Bonn, Germany.
                [31 ] Department of Genomics, Life & Brain Center, University of Bonn, Bonn, Germany.
                [33 ] Department of General Internal Medicine, UKSH Campus Kiel, Kiel 24105, Germany.
                [34 ] Department of Dermatology, University Hospital, Schleswig-Holstein, Christian-Albrechts-University, Kiel, Germany.
                [35 ] Center for Basic Research in Digestive Diseases, Division of Gastroenterology and Hepatology, Mayo Clinic, College of Medicine, Rochester, Minnesota, USA.
                [36 ] Department of Radiology, University of California, San Diego, La Jolla, California, USA.
                [37 ] Department of Neurosciences, University of California, San Diego, La Jolla, California, USA.
                [38 ] Division of Genetics and Molecular Medicine, King's College London, London, UK.
                [39 ] Department of Dermatology, University of Michigan, Ann Arbor, Michigan, USA.
                [40 ] Ann Arbor Veterans Affairs Hospital, Ann Arbor, Michigan, USA.
                [41 ] St. John's Institute of Dermatology, Division of Genetics and Molecular Medicine, King's College London, London, UK.
                [42 ] NORMENT - K.G. Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
                [43 ] Division of Mental Health and Addiction, Oslo University Hospital, Ulleval, Oslo, Norway.
                [44 ] F. Widjaja Foundation Inflammatory Bowel and Immunobiology Research, Institute, Los Angeles, California 90048, USA.
                [45 ] Medical Genetics Institute, Cedars-Sinai, Medical Center, Los Angeles, California 90048, USA.
                [46 ] Inflammatory Bowel Disease, Research Group, Addenbrooke's Hospital,University of Cambridge, Cambridge CB2 0QQ, UK.
                [47 ] University of Queensland Diamantina Institute, Translational Research Institute, Brisbane, Queensland, Australia.
                [48 ] Institute of Health & Biomedical Innovation (IHBI), Faculty of Health, Queensland University of Technology (QUT), Translational Research Institute, Brisbane, Queensland, Australia.
                Author notes
                [49]

                Present address: Institute of Clinical Molecular Biology, Christian-Albrechts-University of Kiel, Kiel, Germany.

                [50]

                These authors jointly supervised this work.

                [32]

                A full list of members and affiliations appears in the Supplementary Note .

                Correspondence should be addressed to D.E. ( d.ellinghaus@ 123456ikmb.uni-kiel.de )
                Article
                NIHMS762030
                10.1038/ng.3528
                4848113
                26974007
                8d9a5a46-44b5-4424-8308-e10ab7ca27cf

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