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      Dense genotyping identifies and localizes multiple common and rare variant association signals in celiac disease

      1 , 2 , 2 , 1 , 2 , 1 , 3 , 4 , 5 , 6 , 7 , 8 , 1 , 6 , 1 , 2 , 9 , 10 , 11 , 1 , 1 , 12 , 1 , 2 , 1 , 11 , 13 , 14 , 15 , 16 , 11 , 17 , 6 , 18 , 1 , 19 , 17 , 14 , 20 , 8 , 21 , 22 , 1 , 23 , 11 , 24 , 1 , 21 , 25 , 8 , 26 , 18 , 27 , 28 , 8 , 20 , 29 , 30 , CEGEC (Spanish Consortium on the Genetics of Coeliac Disease), PreventCD Study Group, Wellcome Trust Case Control Consortium, 26 , 31 , 8 , 13 , 32 , 33 , 11 , 34 , 11 , 1 , 2

      Nature genetics

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          We densely genotyped, using 1000 Genomes Project pilot CEU and additional re-sequencing study variants, 183 reported immune-mediated disease non- HLA risk loci in 12,041 celiac disease cases and 12,228 controls. We identified 13 new celiac disease risk loci at genome wide significance, bringing the total number of known loci (including HLA) to 40. Multiple independent association signals are found at over a third of these loci, attributable to a combination of common, low frequency, and rare genetic variants. In comparison with previously available data such as HapMap3, our dense genotyping in a large sample size provided increased resolution of the pattern of linkage disequilibrium, and suggested localization of many signals to finer scale regions. In particular, 29 of 54 fine-mapped signals appeared localized to specific single genes - and in some instances to gene regulatory elements. We define a complex genetic architecture of risk regions, and refine risk signals, providing a next step towards elucidating causal disease mechanisms.

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          Most cited references 40

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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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            Rare Variants Create Synthetic Genome-Wide Associations

            Introduction Efforts to fine map the causal variants responsible for genome-wide association studies (GWAS) signals have been largely predicated on the common disease common variant theory, postulating a common variant as the culprit for observed associations. This has led to extensive resequencing efforts that have been largely unsuccessful [1]–[5]. Here, we explore the possibility that part of the reason for this may be that the disease class causing an observed association may consist of multiple low-frequency variants across large regions of the genome—a phenomenon we call synthetic association. For convenience, these less common variants will be referred to here as “rare,” but we emphasize that we use this term loosely, only to refer to variants less common than those routinely studied in GWAS. The basic idea of how synthetic associations emerge in this model is illustrated in Figure 1, which shows how rare variants, by chance, can occur disproportionately in some parts of a gene genealogy. Any variant “higher up in the genealogy” that partitions those parts of the genealogy containing more disease variants than average will be identified as disease-associated. It is well appreciated that a noncausal variant will show association with a causal variant if the two are in strong linkage disequilibrium (LD). We use the previously introduced term synthetic association [6], however, to describe how such indirect association can occur between a common variant and at least one and possibly many rarer causal variants. Using the term synthetic as opposed to indirect emphasizes that the properties of the association signal are very different when the responsible variant or variants are much less frequent than the marker that carries the signal, as we detail below. 10.1371/journal.pbio.1000294.g001 Figure 1 Example genealogies showing causal variants and the strongest association for a common variant. (A) A genealogy with 10,000 original haplotypes was generated with 3,000 cases and 3,000 controls, genotype relative risk (γ) = 4, and nine causal variants. The branches containing the strongest synthetic association are indicated in blue. The branches containing the rare causal variants are in red. (B) A second genealogy was generated using the same parameters. These genealogies demonstrate two scenarios with genome-wide significant synthetic associations: the first (upper genealogy) had a high risk allele frequency (RAF = 0.49), and the second (lower genealogy) had a low RAF (0.08). To assess the tendency of rare disease-causing variants to create synthetic signals of association that are credited to single polymorphisms that are much more common in the population than the causal variants, we have simulated 10,000 haplotypes based on a coalescent model in a region either with or without recombination (Materials and Methods). We assumed that gene variants that influence disease have an allele frequency between 0.005 and 0.02, which is generally below the range of reliable detection (either by inclusion or indirect representation) using the genome-wide association platforms currently in use. We assumed a baseline probability of disease of φ for individuals with none of the rare genetic risk factors. The presence of at least one rare risk allele at the locus increased the probability of disease from φ to γ. We considered two values of φ (0.01, 0.1) and chose values of the penetrance γ such that the genotypic relative risk (GRR) of the rare causal variants varied incrementally between 2 and 6, where GRR is the ratio γ/φ. These values were chosen to explore the space around a GRR of 4, a threshold above which consistent linkage signals would be expected [7]. We simulated scenarios with one, three, five, seven, and nine rare causal variants. Results Across the conditions we have studied, not only is it possible to achieve genome-wide significance for common variants when one or more rare variants are the only contributors to disease, it is often the likely outcome (Figure 2). Overall, 30% of the simulations were able to detect an association with a common SNP at genome-wide significance (p 5%, Hardy-Weinberg equilibrium p-value >1×10−6, SNP call rate >95%), using the PLINK software [40]. For the sickle cell anemia GWAS, we compared 194 cases and 7,407 controls of inferred African ancestry via multidimensional scaling, with a genomic control inflation factor of 1.01. For hearing loss, we performed a GWAS on 418 cases and 6,892 control subjects, all of whom were of genetically inferred European ancestry via multidimensional scaling, with a genomic control inflation factor of 1.02.
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              GOEAST: a web-based software toolkit for Gene Ontology enrichment analysis

              Gene Ontology (GO) analysis has become a commonly used approach for functional studies of large-scale genomic or transcriptomic data. Although there have been a lot of software with GO-related analysis functions, new tools are still needed to meet the requirements for data generated by newly developed technologies or for advanced analysis purpose. Here, we present a Gene Ontology Enrichment Analysis Software Toolkit (GOEAST), an easy-to-use web-based toolkit that identifies statistically overrepresented GO terms within given gene sets. Compared with available GO analysis tools, GOEAST has the following improved features: (i) GOEAST displays enriched GO terms in graphical format according to their relationships in the hierarchical tree of each GO category (biological process, molecular function and cellular component), therefore, provides better understanding of the correlations among enriched GO terms; (ii) GOEAST supports analysis for data from various sources (probe or probe set IDs of Affymetrix, Illumina, Agilent or customized microarrays, as well as different gene identifiers) and multiple species (about 60 prokaryote and eukaryote species); (iii) One unique feature of GOEAST is to allow cross comparison of the GO enrichment status of multiple experiments to identify functional correlations among them. GOEAST also provides rigorous statistical tests to enhance the reliability of analysis results. GOEAST is freely accessible at http://omicslab.genetics.ac.cn/GOEAST/

                Author and article information

                Nat Genet
                Nat. Genet.
                Nature genetics
                31 October 2011
                06 November 2011
                01 June 2012
                : 43
                : 12
                : 1193-1201
                [1 ]Genetics Department, University Medical Center and University of Groningen, PO Box 30.001, 9700 RB Groningen, The Netherlands
                [2 ]Blizard Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London E1 2AT, United Kingdom
                [3 ]Department of Gastroenterology, VU Medical Center, 1007 MB Amsterdam, The Netherlands
                [4 ]Fondazione IRCCS Ospedale Maggiore Policlinico, Mangiagalli e Regina Elena, Milan, Italy.
                [5 ]Department of Medical Sciences, University of Milan, Milan, Italy.
                [6 ]Genome Centre, Barts and the London School of Medicine and Dentistry, John Vane Science Centre, Charterhouse Square, London, EC1M 6BQ, United Kingdom
                [7 ]Universitat Rovira I Virgili, Department of Paediatric Gastroenterology, Hospital Univesitari de Sant Joan de Reus, , 43201 Reus, Spain
                [8 ]Immunology Dept, Hospital Clínico S. Carlos, Instituto de Investigación Sanitaria San Carlos IdISSC, Madrid, Spain
                [9 ]Division of Gastroenterology, Hepatology and Nutrition, Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
                [10 ]Department of Human Genetics, University of Pittsburgh Graduate School of Public Health, Pittsburgh, Pennsylvania, USA
                [11 ]Wellcome Trust Sanger Institute, Hinxton, Cambridge, CB10 1SA, United Kingdom
                [12 ]Department of Gastroenterology, University Medical Center and Groningen University, 9700 RB Groningen, The Netherlands
                [13 ]Immunogenetics Research Laboratory, Hospital de Cruces, Barakaldo 48903 Bizkaia, Spain
                [14 ]European Laboratory for Food Induced Disease, University of Naples Federico II, Naples, Italy.
                [15 ]Department of Medicine, Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands
                [16 ]Nijmegen Institute for Infection, Inflammation and Immunity (N4i), Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands
                [17 ]Department of Molecular Medicine, Sapienza University of Rome, Rome, Italy
                [18 ]Dayanand Medical College and Hospital, Ludhiana, Punjab, India
                [19 ]University of Maribor, Faculty of Medicine, Center for Human Molecular Genetics and Pharmacogenomics, Slomskov trg 15, 2000 Maribor, Slovenia
                [20 ]Juvenile Diabetes Research Foundation/Wellcome Trust Diabetes and Inflammation Laboratory, Department of Medical Genetics, Cambridge Institute for Medical Research, University of Cambridge, Cambridge CB2 0XY, United Kingdom
                [21 ]Center for Public Health Genomics, University of Virginia, Charlottesville, VA 22908-0717
                [22 ]UCL Genomics, Institute of Child Health, University College London, 30 Guilford Street, London WC1N 1EH, United Kingdom
                [23 ]Pediatrics Gastroenterology Department, Hospital La Paz, Madrid, Spain
                [24 ]La Fe University Hospital, Pediatric Gastroenterology, Bulevar Sur s/n 46026 Valencia, Spain
                [25 ]Department of Gastroenterology, Hepatology and Immunology, Children’s Memorial Health Institute, Warsaw, Poland
                [26 ]Department of Genetics, University of Delhi, South Campus, New Delhi, India.
                [27 ]The Medical University of Warsaw, Department of Pediatrics, Dzialdowska 1, 01-184 Warsaw, Poland
                [28 ]University of Naples, Fedrico II, Department of Pediatrics, Via S.Pansini 5, 80131 Naples, Italy
                [29 ]Department of Paediatric Gastroenterology, University Medical Centre Utrecht, Utrecht, The Netherlands
                [30 ]Department of Rheumatology, Leiden University Medical Center, Leiden, The Netherlands
                [31 ]Department of Pathology, Children’s Memorial Health Institute, Warsaw, Poland
                [32 ]Department of Paediatrics, Leiden University Medical Centre, Leiden, The Netherlands
                [33 ]Department of Experimental Medicine, Faculty of Medicine University of Milano-Bicocca,Monza, Italy
                [34 ]UCL Genetics Institute, University College London, Gower Street, London WC1E 6BT
                Author notes

                These authors contributed equally to this work


                These authors jointly directed this project.

                AUTHOR CONTRIBUTIONS DAvH and C. Wijmenga led the study. Major contributions were (i) DAvH, KAH, GT and C. Wijmenga wrote the paper; (ii) KAH, GT, VM, NB, JR, MP, MM, RHD and KF performed DNA sample preparation and genotyping assays; (iii) DAvH, VP, KAH, GT performed statistical analysis. Other authors contributed mainly to sample collection and phenotyping. PD led the formation of the Immunochip Consortium, with SNP selection by JB and C. Wallace. All authors reviewed the final manuscript.


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                Funded by: Wellcome Trust :
                Award ID: 084743 || WT
                Funded by: Medical Research Council :
                Award ID: G1001158(95979) || MRC_



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