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      Exploiting the GTEx resources to decipher the mechanisms at GWAS loci

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          Abstract

          The resources generated by the GTEx consortium offer unprecedented opportunities to advance our understanding of the biology of human diseases. Here, we present an in-depth examination of the phenotypic consequences of transcriptome regulation and a blueprint for the functional interpretation of genome-wide association study-discovered loci. Across a broad set of complex traits and diseases, we demonstrate widespread dose-dependent effects of RNA expression and splicing. We develop a data-driven framework to benchmark methods that prioritize causal genes and find no single approach outperforms the combination of multiple approaches. Using colocalization and association approaches that take into account the observed allelic heterogeneity of gene expression, we propose potential target genes for 47% (2519 out of 5385) of the GWAS loci examined.

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          The online version contains supplementary material available at (10.1186/s13059-020-02252-4).

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          Most cited references57

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          A global reference for human genetic variation

          The 1000 Genomes Project set out to provide a comprehensive description of common human genetic variation by applying whole-genome sequencing to a diverse set of individuals from multiple populations. Here we report completion of the project, having reconstructed the genomes of 2,504 individuals from 26 populations using a combination of low-coverage whole-genome sequencing, deep exome sequencing, and dense microarray genotyping. We characterized a broad spectrum of genetic variation, in total over 88 million variants (84.7 million single nucleotide polymorphisms (SNPs), 3.6 million short insertions/deletions (indels), and 60,000 structural variants), all phased onto high-quality haplotypes. This resource includes >99% of SNP variants with a frequency of >1% for a variety of ancestries. We describe the distribution of genetic variation across the global sample, and discuss the implications for common disease studies.
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            The mutational constraint spectrum quantified from variation in 141,456 humans

            Genetic variants that inactivate protein-coding genes are a powerful source of information about the phenotypic consequences of gene disruption: genes that are crucial for the function of an organism will be depleted of such variants in natural populations, whereas non-essential genes will tolerate their accumulation. However, predicted loss-of-function variants are enriched for annotation errors, and tend to be found at extremely low frequencies, so their analysis requires careful variant annotation and very large sample sizes 1 . Here we describe the aggregation of 125,748 exomes and 15,708 genomes from human sequencing studies into the Genome Aggregation Database (gnomAD). We identify 443,769 high-confidence predicted loss-of-function variants in this cohort after filtering for artefacts caused by sequencing and annotation errors. Using an improved model of human mutation rates, we classify human protein-coding genes along a spectrum that represents tolerance to inactivation, validate this classification using data from model organisms and engineered human cells, and show that it can be used to improve the power of gene discovery for both common and rare diseases.
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              LD Score regression distinguishes confounding from polygenicity in genome-wide association studies.

              Both polygenicity (many small genetic effects) and confounding biases, such as cryptic relatedness and population stratification, can yield an inflated distribution of test statistics in genome-wide association studies (GWAS). However, current methods cannot distinguish between inflation from a true polygenic signal and bias. We have developed an approach, LD Score regression, that quantifies the contribution of each by examining the relationship between test statistics and linkage disequilibrium (LD). The LD Score regression intercept can be used to estimate a more powerful and accurate correction factor than genomic control. We find strong evidence that polygenicity accounts for the majority of the inflation in test statistics in many GWAS of large sample size.
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                Author and article information

                Contributors
                haky@uchicago.edu
                Journal
                Genome Biol
                Genome Biol
                Genome Biology
                BioMed Central (London )
                1474-7596
                1474-760X
                26 January 2021
                26 January 2021
                2021
                : 22
                : 49
                Affiliations
                [1 ]Section of Genetic Medicine, Department of Medicine, The University of Chicago, Chicago, IL USA
                [2 ]GRID grid.412807.8, ISNI 0000 0004 1936 9916, Division of Genetic Medicine, , Department of Medicine, Vanderbilt University Medical Center, ; Nashville, TN USA
                [3 ]GRID grid.152326.1, ISNI 0000 0001 2264 7217, Data Science Institute, Vanderbilt University, ; Nashville, TN USA
                [4 ]GRID grid.5335.0, ISNI 0000000121885934, Clare Hall, University of Cambridge, ; Cambridge, UK
                [5 ]GRID grid.5335.0, ISNI 0000000121885934, MRC Epidemiology Unit, University of Cambridge, ; Cambridge, UK
                [6 ]GRID grid.25879.31, ISNI 0000 0004 1936 8972, Department of Genetics, , University of Pennsylvania, Perelman School of Medicine, ; Philadelphia, PA USA
                [7 ]GRID grid.25879.31, ISNI 0000 0004 1936 8972, Department of Systems Pharmacology and Translational Therapeutics, , University of Pennsylvania, Perelman School of Medicine, ; Philadelphia, PA USA
                [8 ]GRID grid.419548.5, ISNI 0000 0000 9497 5095, Statistical Genetics, Max Planck Institute of Psychiatry, ; Munich, Germany
                [9 ]GRID grid.429884.b, ISNI 0000 0004 1791 0895, New York Genome Center, ; New York, NY USA
                [10 ]GRID grid.21729.3f, ISNI 0000000419368729, Department of Systems Biology, , Columbia University, ; New York, NY USA
                [11 ]GRID grid.170205.1, ISNI 0000 0004 1936 7822, Department of Human Genetics, , University of Chicago, ; Chicago, IL USA
                [12 ]GRID grid.66859.34, The Broad Institute of MIT and Harvard, ; Cambridge, MA USA
                [13 ]GRID grid.38142.3c, ISNI 000000041936754X, Department of Epidemiology, , Harvard T.H. Chan School of Public Health, ; Boston, MA USA
                [14 ]GRID grid.168010.e, ISNI 0000000419368956, Department of Biology, , Stanford University, ; Stanford, 94305 CA USA
                [15 ]GRID grid.38142.3c, ISNI 000000041936754X, Ocular Genomics Institute, Massachusetts Eye and Ear, Harvard Medical School, ; Boston, MA USA
                [16 ]GRID grid.152326.1, ISNI 0000 0001 2264 7217, Department of Biomedical Informatics, Department of Medicine, , Vanderbilt University, ; Nashville, TN USA
                [17 ]GRID grid.152326.1, ISNI 0000 0001 2264 7217, Center for Human Genetics Research, Department of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, ; Nashville, TN USA
                [18 ]GRID grid.59734.3c, ISNI 0000 0001 0670 2351, Department of Genetics and Genomic Sciences, , Icahn School of Medicine at Mount Sinai, ; New York, NY USA
                [19 ]GRID grid.59734.3c, ISNI 0000 0001 0670 2351, Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, ; New York, NY USA
                [20 ]GRID grid.59734.3c, ISNI 0000 0001 0670 2351, The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, ; New York, NY USA
                [21 ]Université de Paris - EA 7537 BIOSTM, Paris, France
                [22 ]GRID grid.4991.5, ISNI 0000 0004 1936 8948, University of Oxford, ; Oxford, UK
                [23 ]GRID grid.168010.e, ISNI 0000000419368956, Department of Genetics, , Stanford University, ; Stanford, CA USA
                [24 ]GRID grid.168010.e, ISNI 0000000419368956, Department of Pathology, , Stanford University, ; Stanford, CA USA
                [25 ]GRID grid.214458.e, ISNI 0000000086837370, Department of Biostatistics, , University of Michigan, ; Ann Arbor, MI USA
                Author information
                http://orcid.org/0000-0003-0333-5685
                Article
                2252
                10.1186/s13059-020-02252-4
                7836161
                33499903
                be500eae-3fc9-4eee-ba93-3c95978bdc5f
                © The Author(s) 2021

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 6 June 2020
                : 18 December 2020
                Funding
                Funded by: National Institute of Diabetes and Digestive and Kidney Diseases (US)
                Award ID: P30DK20595
                Funded by: National Institute of Health
                Award ID: R01MH107666
                Categories
                Research
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                © The Author(s) 2021

                Genetics
                Genetics

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