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      Prioritizing disease and trait causal variants at the TNFAIP3 locus using functional and genomic features

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          Abstract

          Genome-wide association studies have associated thousands of genetic variants with complex traits and diseases, but pinpointing the causal variant(s) among those in tight linkage disequilibrium with each associated variant remains a major challenge. Here, we use seven experimental assays to characterize all common variants at the multiple disease-associated TNFAIP3 locus in five disease-relevant immune cell lines, based on a set of features related to regulatory potential. Trait/disease-associated variants are enriched among SNPs prioritized based on either: (1) residing within CRISPRi-sensitive regulatory regions, or (2) localizing in a chromatin accessible region while displaying allele-specific reporter activity. Of the 15 trait/disease-associated haplotypes at TNFAIP3, 9 have at least one variant meeting one or both of these criteria, 5 of which are further supported by genetic fine-mapping. Our work provides a comprehensive strategy to characterize genetic variation at important disease-associated loci, and aids in the effort to identify trait causal genetic variants.

          Abstract

          While genome-wide association studies have yielded thousands of trait-associated loci, identifying causal variants remains challenging. Here, the authors perform seven genomics assays in various cell types to prioritize genetic variants in the TNFAIP3 locus, and report high-priority variants within disease-associated haplotypes.

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          DNaseI sensitivity QTLs are a major determinant of human expression variation

          The mapping of expression quantitative trait loci (eQTLs) has emerged as an important tool for linking genetic variation to changes in gene regulation 1-5 . However, it remains difficult to identify the causal variants underlying eQTLs and little is known about the regulatory mechanisms by which they act. To address this gap, we used DNaseI sequencing to measure chromatin accessibility in 70 Yoruba lymphoblastoid cell lines (LCLs), for which genome-wide genotypes and estimates of gene expression levels are also available 6-8 . We obtained a total of 2.7 billion uniquely mapped DNase-seq reads, which allowed us to produce genome-wide maps of chromatin accessibility for each individual. We identified 9,595 locations at which DNase-seq read depth correlates significantly with genotype at a nearby SNP or indel (FDR=10%). We call such variants “DNaseI sensitivity Quantitative Trait Loci” (dsQTLs). We found that dsQTLs are strongly enriched within inferred transcription factor binding sites and are frequently associated with allele-specific changes in transcription factor binding. A substantial fraction (16%) of dsQTLs are also associated with variation in the expression levels of nearby genes, (namely, these loci are also classified as eQTLs). Conversely, we estimate that as many as 55% of eQTL SNPs are also dsQTLs. Our observations indicate that dsQTLs are highly abundant in the human genome, and are likely to be important contributors to phenotypic variation.
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            Fine-mapping inflammatory bowel disease loci to single variant resolution

            Summary The inflammatory bowel diseases (IBD) are chronic gastrointestinal inflammatory disorders that affect millions worldwide. Genome-wide association studies have identified 200 IBD-associated loci, but few have been conclusively resolved to specific functional variants. Here we report fine-mapping of 94 IBD loci using high-density genotyping in 67,852 individuals. We pinpointed 18 associations to a single causal variant with >95% certainty, and an additional 27 associations to a single variant with >50% certainty. These 45 variants are significantly enriched for protein-coding changes (n=13), direct disruption of transcription factor binding sites (n=3) and tissue specific epigenetic marks (n=10), with the latter category showing enrichment in specific immune cells among associations stronger in CD and in gut mucosa among associations stronger in UC. The results of this study suggest that high-resolution fine-mapping in large samples can convert many GWAS discoveries into statistically convincing causal variants, providing a powerful substrate for experimental elucidation of disease mechanisms.
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              Human fetal hemoglobin expression is regulated by the developmental stage-specific repressor BCL11A.

              Differences in the amount of fetal hemoglobin (HbF) that persists into adulthood affect the severity of sickle cell disease and the beta-thalassemia syndromes. Genetic association studies have identified sequence variants in the gene BCL11A that influence HbF levels. Here, we examine BCL11A as a potential regulator of HbF expression. The high-HbF BCL11A genotype is associated with reduced BCL11A expression. Moreover, abundant expression of full-length forms of BCL11A is developmentally restricted to adult erythroid cells. Down-regulation of BCL11A expression in primary adult erythroid cells leads to robust HbF expression. Consistent with a direct role of BCL11A in globin gene regulation, we find that BCL11A occupies several discrete sites in the beta-globin gene cluster. BCL11A emerges as a therapeutic target for reactivation of HbF in beta-hemoglobin disorders.
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                Author and article information

                Contributors
                aregev@broadinstitute.org
                nhacohen@mgh.harvard.edu
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                6 March 2020
                6 March 2020
                2020
                : 11
                : 1237
                Affiliations
                [1 ]GRID grid.66859.34, Broad Institute of MIT and Harvard, ; Cambridge, MA 02142 USA
                [2 ]GRID grid.66859.34, Klarman Cell Observatory, , Broad Institute of MIT and Harvard, ; Cambridge, MA 02142 USA
                [3 ]ISNI 000000041936754X, GRID grid.38142.3c, Department of Systems Biology, , Harvard Medical School, ; Boston, MA 02115 USA
                [4 ]ISNI 000000041936754X, GRID grid.38142.3c, Program in Biological and Biomedical Sciences, , Harvard Medical School, ; Boston, MA 02115 USA
                [5 ]ISNI 0000 0004 0386 9924, GRID grid.32224.35, Analytic and Translational Genetics Unit, , Massachusetts General Hospital, ; Boston, MA 02114 USA
                [6 ]ISNI 000000041936754X, GRID grid.38142.3c, Program in Bioinformatics and Integrative Genomics, , Harvard Medical School, ; Boston, MA 02115 USA
                [7 ]ISNI 000000041936754X, GRID grid.38142.3c, Department of Organismic and Evolutionary Biology, , Harvard University, ; Cambridge, MA 02138 USA
                [8 ]ISNI 000000041936754X, GRID grid.38142.3c, Harvard Society of Fellows, , Harvard University, ; Cambridge, MA 02138 USA
                [9 ]ISNI 0000 0001 2341 2786, GRID grid.116068.8, Department of Biology, , Massachusetts Institute of Technology, ; Cambridge, MA 02142 USA
                [10 ]ISNI 0000 0001 2167 1581, GRID grid.413575.1, Howard Hughes Medical Institute, ; Cambridge, MA 02142 USA
                [11 ]ISNI 0000 0004 0386 9924, GRID grid.32224.35, Center for Cancer Research, , Massachusetts General Hospital, ; Boston, MA 02114 USA
                Author information
                http://orcid.org/0000-0002-6592-1279
                http://orcid.org/0000-0003-4179-4807
                http://orcid.org/0000-0001-5165-4408
                http://orcid.org/0000-0002-7947-0827
                http://orcid.org/0000-0002-4607-8001
                http://orcid.org/0000-0002-8314-7088
                http://orcid.org/0000-0002-5754-1719
                http://orcid.org/0000-0003-3864-9828
                http://orcid.org/0000-0003-2662-4631
                http://orcid.org/0000-0003-3293-3158
                http://orcid.org/0000-0002-2349-2656
                Article
                15022
                10.1038/s41467-020-15022-4
                7060350
                32144282
                5fcbc2a5-8202-42c5-b708-31a2ef5cd171
                © The Author(s) 2020

                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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 3 February 2020
                : 17 February 2020
                Funding
                Funded by: FundRef https://doi.org/10.13039/100000051, U.S. Department of Health & Human Services | NIH | National Human Genome Research Institute (NHGRI);
                Award ID: P50 HG006193
                Award Recipient :
                Categories
                Article
                Custom metadata
                © The Author(s) 2020

                Uncategorized
                epigenomics,functional genomics,immunogenetics,crispr-cas9 genome editing
                Uncategorized
                epigenomics, functional genomics, immunogenetics, crispr-cas9 genome editing

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