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      Transcriptome and genome sequencing uncovers functional variation in humans

      1 , 2 , 3 , # , 4 , 5 , 5 , 6 , 7 , 5 , 8 , 9 ,   9 , 4 , 5 , 6 , 10 , 11 , 9 , 7 , 12 , 4 , 7 , 10 , 1 , 9 , 13 , 5 , 6 , 14 , 15 , 14 , 15 , 5 , 6 , 7 , 16 , 1 , 2 , 3 , 11 , 12 , 1 , 2 , 3 , 1 , 2 , 3 , 4 , 4 , 4 , 13 , 9 , 8 , 1 , 8 , 8 , 17 , 9 , 11 , 18 , The Geuvadis Consortium, 13 , 19 , 10 , 13 , 19 , 20 , 1 , 2 , 10 , 12 , 7 , 9 , 11 , 18 , 10 , 5 , 6 , 4 , 5 , 6 , 1 , 2 , 3 , #


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          Genome sequencing projects are discovering millions of genetic variants in humans, and interpretation of their functional effects is essential for understanding the genetic basis of variation in human traits. Here we report sequencing and deep analysis of mRNA and miRNA from lymphoblastoid cell lines of 462 individuals from the 1000 Genomes Project – the first uniformly processed RNA-seq data from multiple human populations with high-quality genome sequences. We discovered extremely widespread genetic variation affecting regulation of the majority of genes, with transcript structure and expression level variation being equally common but genetically largely independent. Our characterization of causal regulatory variation sheds light on cellular mechanisms of regulatory and loss-of-function variation, and allowed us to infer putative causal variants for dozens of disease-associated loci. Altogether, this study provides a deep understanding of the cellular mechanisms of transcriptome variation and of the landscape of functional variants in the human genome.

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          Most cited references 35

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          An Integrated Encyclopedia of DNA Elements in the Human Genome

          Summary The human genome encodes the blueprint of life, but the function of the vast majority of its nearly three billion bases is unknown. The Encyclopedia of DNA Elements (ENCODE) project has systematically mapped regions of transcription, transcription factor association, chromatin structure, and histone modification. These data enabled us to assign biochemical functions for 80% of the genome, in particular outside of the well-studied protein-coding regions. Many discovered candidate regulatory elements are physically associated with one another and with expressed genes, providing new insights into the mechanisms of gene regulation. The newly identified elements also show a statistical correspondence to sequence variants linked to human disease, and can thereby guide interpretation of this variation. Overall the project provides new insights into the organization and regulation of our genes and genome, and an expansive resource of functional annotations for biomedical research.
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            Is Open Access

            An integrated map of genetic variation from 1,092 human genomes

            Summary Through characterising the geographic and functional spectrum of human genetic variation, the 1000 Genomes Project aims to build a resource to help understand the genetic contribution to disease. We describe the genomes of 1,092 individuals from 14 populations, constructed using a combination of low-coverage whole-genome and exome sequencing. By developing methodologies to integrate information across multiple algorithms and diverse data sources we provide a validated haplotype map of 38 million SNPs, 1.4 million indels and over 14 thousand larger deletions. We show that individuals from different populations carry different profiles of rare and common variants and that low-frequency variants show substantial geographic differentiation, which is further increased by the action of purifying selection. We show that evolutionary conservation and coding consequence are key determinants of the strength of purifying selection, that rare-variant load varies substantially across biological pathways and that each individual harbours hundreds of rare non-coding variants at conserved sites, such as transcription-factor-motif disrupting changes. This resource, which captures up to 98% of accessible SNPs at a frequency of 1% in populations of medical genetics focus, enables analysis of common and low-frequency variants in individuals from diverse, including admixed, populations.
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              Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls.

              There is increasing evidence that genome-wide association (GWA) studies represent a powerful approach to the identification of genes involved in common human diseases. We describe a joint GWA study (using the Affymetrix GeneChip 500K Mapping Array Set) undertaken in the British population, which has examined approximately 2,000 individuals for each of 7 major diseases and a shared set of approximately 3,000 controls. Case-control comparisons identified 24 independent association signals at P < 5 x 10(-7): 1 in bipolar disorder, 1 in coronary artery disease, 9 in Crohn's disease, 3 in rheumatoid arthritis, 7 in type 1 diabetes and 3 in type 2 diabetes. On the basis of prior findings and replication studies thus-far completed, almost all of these signals reflect genuine susceptibility effects. We observed association at many previously identified loci, and found compelling evidence that some loci confer risk for more than one of the diseases studied. Across all diseases, we identified a large number of further signals (including 58 loci with single-point P values between 10(-5) and 5 x 10(-7)) likely to yield additional susceptibility loci. The importance of appropriately large samples was confirmed by the modest effect sizes observed at most loci identified. This study thus represents a thorough validation of the GWA approach. It has also demonstrated that careful use of a shared control group represents a safe and effective approach to GWA analyses of multiple disease phenotypes; has generated a genome-wide genotype database for future studies of common diseases in the British population; and shown that, provided individuals with non-European ancestry are excluded, the extent of population stratification in the British population is generally modest. Our findings offer new avenues for exploring the pathophysiology of these important disorders. We anticipate that our data, results and software, which will be widely available to other investigators, will provide a powerful resource for human genetics research.

                Author and article information

                19 September 2013
                15 September 2013
                26 September 2013
                26 March 2014
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                [1 ]Department of Genetic Medicine and Development, University of Geneva Medical School, 1211 Geneva, Switzerland
                [2 ]Institute for Genetics and Genomics in Geneva (iGE3), University of Geneva, 1211 Geneva, Switzerland
                [3 ]Swiss Institute of Bioinformatics, 1211 Geneva, Switzerland
                [4 ]Centro Nacional d'Anàlisi Genòmica, 08028 Barcelona, Catalonia, Spain
                [5 ]Center for Genomic Regulation (CRG), 08003 Barcelona, Catalonia, Spain
                [6 ]Pompeu Fabra University (UPF), 08003 Barcelona, Catalonia, Spain
                [7 ]Department for Human and Clinical Genetics, Leiden University Medical Center, 2300 RC Leiden, the Netherlands
                [8 ]Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, United Kingdom
                [9 ]European Bioinformatics Institute, EMBL-EBI, Hinxton, United Kingdom
                [10 ]Institute of Clinical Molecular Biology, Christian-Albrechts-University Kiel, D-24105 Kiel, Germany
                [11 ]Institute of Human Genetics, Helmholtz Zentrum München, 85764 Neuherberg, Germany
                [12 ]Department of Medical Sciences, Molecular Medicine and Science for Life Laboratory, Uppsala University, 751 85 Uppsala, Sweden
                [13 ]Max Planck Institute for Molecular Genetics, 14195 Berlin, Germany
                [14 ]Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
                [15 ]Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge MA 02142, USA
                [16 ]Leiden Genome Technology Center, 2300 RC Leiden, the Netherlands
                [17 ]Oxford Centre for Diabetes Endocrinology and Metabolism, University of Oxford, Oxford OX3 7BN, United Kingdom
                [18 ]Institute of Human Genetics, Technische Universität München, 81675 Munich, Germany
                [19 ]Dahlem Centre for Genome Research and Medical Systems Biology, 14195 Berlin, Germany
                [20 ]Fundacion Publica Galega de Medicina Xenomica SERGAS, Genomic Medicine Group CIBERER, Universidade de Santiago de Compostela, Santiago de Compostela, Spain
                Author notes
                [# ]Correspondence and requests for materials should be addressed to tuuli.e.lappalainen@ or emmanouil.dermitzakis@

                These authors contributed equally to this work


                Present address: Present address: Bioinformatics Laboratory, National Laboratory of Cientific Computing (LNCC), Petropolis 25651-075, Rio de Janeiro, Brazil


                Present address: Departments of Pathology and Genetics, Stanford University, Stanford 94305-5324, CA, USA


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                Funded by: National Institute of Mental Health : NIMH
                Award ID: R01 MH090941 || MH



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