Genome-wide enhancer maps link risk variants to disease genes


  • 1.

    Claussnitzer, M. et al. A brief history of human disease genetics. Nature 577, 179–189 (2020).

    ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 2.

    Farh, K. K.-H. et al. Genetic and epigenetic fine mapping of causal autoimmune disease variants. Nature 518, 337–343 (2015).

    ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar
     

  • 3.

    Maurano, M. T. et al. Systematic localization of common disease-associated variation in regulatory DNA. Science 337, 1190–1195 (2012).

    ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 4.

    Fulco, C. P. et al. Activity-by-contact model of enhancer-promoter regulation from thousands of CRISPR perturbations. Nat. Genet. 51, 1664–1669 (2019).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 5.

    Westra, H.-J. & Franke, L. From genome to function by studying eQTLs. Biochim. Biophys. Acta 1842, 1896–1902 (2014).

    CAS 
    PubMed 
    Article 

    Google Scholar
     

  • 6.

    Gasperini, M., Tome, J. M. & Shendure, J. Towards a comprehensive catalogue of validated and target-linked human enhancers. Nat. Rev. Genet. 21, 292–310 (2020).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 7.

    van Arensbergen, J., van Steensel, B. & Bussemaker, H. J. In search of the determinants of enhancer–promoter interaction specificity. Trends Cell Biol. 24, 695–702 (2014).

    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar
     

  • 8.

    The ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2012).

    ADS 
    PubMed Central 
    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • 9.

    Huang, H. et al. Fine-mapping inflammatory bowel disease loci to single-variant resolution. Nature 547, 173–178 (2017).

    ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 10.

    Maller, J. B. et al. Bayesian refinement of association signals for 14 loci in 3 common diseases. Nat. Genet. 44, 1294–1301 (2012).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 11.

    Ulirsch, J. C. et al. Interrogation of human hematopoiesis at single-cell and single-variant resolution. Nat. Genet. 51, 683–693 (2019).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 12.

    Fulco, C. P. et al. Systematic mapping of functional enhancer–promoter connections with CRISPR interference. Science 354, 769–773 (2016).

    ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 13.

    Rescigno, M. & Di Sabatino, A. Dendritic cells in intestinal homeostasis and disease. J. Clin. Invest. 119, 2441–2450 (2009).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 14.

    Graham, D. B. & Xavier, R. J. Pathway paradigms revealed from the genetics of inflammatory bowel disease. Nature 578, 527–539 (2020).

    ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 15.

    Mountjoy, E. et al. Open Targets Genetics: an open approach to systematically prioritize causal variants and genes at all published human GWAS trait-associated loci. Preprint at https://doi.org/10.1101/2020.09.16.299271 (2020).

  • 16.

    Buniello, A. et al. The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019. Nucleic Acids Res. 47, D1005–D1012 (2019).

    CAS 
    PubMed 
    Article 

    Google Scholar
     

  • 17.

    Stacey, D. et al. ProGeM: a framework for the prioritization of candidate causal genes at molecular quantitative trait loci. Nucleic Acids Res. 47, e3 (2019).

    CAS 
    PubMed 
    Article 

    Google Scholar
     

  • 18.

    Chun, S. et al. Limited statistical evidence for shared genetic effects of eQTLs and autoimmune-disease-associated loci in three major immune-cell types. Nat. Genet. 49, 600–605 (2017).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 19.

    Carvalho-Silva, D. et al. Open Targets Platform: new developments and updates two years on. Nucleic Acids Res. 47, D1056–D1065 (2019).

    CAS 
    PubMed 
    Article 

    Google Scholar
     

  • 20.

    Barbeira, A. N. et al. Integrating predicted transcriptome from multiple tissues improves association detection. PLoS Genet. 15, e1007889 (2019).

    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar
     

  • 21.

    Hauberg, M. E. et al. Large-scale identification of common trait and disease variants affecting gene expression. Am. J. Hum. Genet. 100, 885–894 (2017).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 22.

    Javierre, B. M. et al. Lineage-specific genome architecture links enhancers and non-coding disease variants to target gene promoters. Cell 167, 1369–1384 (2016).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 23.

    Pers, T. H. et al. Biological interpretation of genome-wide association studies using predicted gene functions. Nat. Commun. 6, 5890 (2015).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 24.

    Cao, Q. et al. Reconstruction of enhancer–target networks in 935 samples of human primary cells, tissues and cell lines. Nat. Genet. 49, 1428–1436 (2017).

    CAS 
    PubMed 
    Article 

    Google Scholar
     

  • 25.

    Liu, Y., Sarkar, A., Kheradpour, P., Ernst, J. & Kellis, M. Evidence of reduced recombination rate in human regulatory domains. Genome Biol. 18, 193 (2017).

    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar
     

  • 26.

    Granja, J. M. et al. Single-cell multiomic analysis identifies regulatory programs in mixed-phenotype acute leukemia. Nat. Biotechnol. 37, 1458–1465 (2019).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 27.

    Andersson, R. et al. An atlas of active enhancers across human cell types and tissues. Nature 507, 455–461 (2014).

    ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 28.

    Sheffield, N. C. et al. Patterns of regulatory activity across diverse human cell types predict tissue identity, transcription factor binding, and long-range interactions. Genome Res. 23, 777–788 (2013).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 29.

    Thurman, R. E. et al. The accessible chromatin landscape of the human genome. Nature 489, 75–82 (2012).

    ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 30.

    Gao, T. & Qian, J. EnhancerAtlas 2.0: an updated resource with enhancer annotation in 586 tissue/cell types across nine species. Nucleic Acids Res. 48, D58–D64 (2020).

    CAS 
    PubMed 
    Article 

    Google Scholar
     

  • 31.

    Whalen, S., Truty, R. M. & Pollard, K. S. Enhancer-promoter interactions are encoded by complex genomic signatures on looping chromatin. Nat. Genet. 48, 488–496 (2016).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 32.

    GTEx Consortium. Genetic effects on gene expression across human tissues. Nature 550, 204–213 (2017).

    PubMed Central 
    Article 
    PubMed 

    Google Scholar
     

  • 33.

    Engreitz, J. M. et al. Local regulation of gene expression by lncRNA promoters, transcription and splicing. Nature 539, 452–455 (2016).

    ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 34.

    Wainberg, M. et al. Opportunities and challenges for transcriptome-wide association studies. Nat. Genet. 51, 592–599 (2019).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 35.

    Franke, A. et al. Genome-wide meta-analysis increases to 71 the number of confirmed Crohn’s disease susceptibility loci. Nat. Genet. 42, 1118–1125 (2010).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 36.

    Jostins, L. et al. Host–microbe interactions have shaped the genetic architecture of inflammatory bowel disease. Nature 491, 119–124 (2012).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 37.

    Linares, P. M. & Gisbert, J. P. Role of growth factors in the development of lymphangiogenesis driven by inflammatory bowel disease: a review. Inflamm. Bowel Dis. 17, 1814–1821 (2011).

    PubMed 
    Article 

    Google Scholar
     

  • 38.

    Wang, X. & Goldstein, D. B. Enhancer domains predict gene pathogenicity and inform gene discovery in complex disease. Am. J. Hum. Genet. 106, 215–233 (2020).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 39.

    Imielinski, M. et al. Common variants at five new loci associated with early-onset inflammatory bowel disease. Nat. Genet. 41, 1335–1340 (2009).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 40.

    Elrod, J. W. & Molkentin, J. D. Physiologic functions of cyclophilin D and the mitochondrial permeability transition pore. Circ. J. 77, 1111–1122 (2013).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 41.

    Ip, W. K. E., Hoshi, N., Shouval, D. S., Snapper, S. & Medzhitov, R. Anti-inflammatory effect of IL-10 mediated by metabolic reprogramming of macrophages. Science 356, 513–519 (2017).

    ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 42.

    Bick, A. G. et al. Inherited causes of clonal haematopoiesis in 97,691 whole genomes. Nature 586, 763–768 (2020).

    ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 43.

    Buenrostro, J. D., Wu, B., Chang, H. Y. & Greenleaf, W. J. ATAC-seq: a method for assaying chromatin accessibility genome-wide. Curr. Protoc. Mol .Biol. 109, 21.29.1–21.29.9 (2015).

    Article 

    Google Scholar
     

  • 44.

    Zhu, J. et al. Genome-wide chromatin state transitions associated with developmental and environmental cues. Cell 152, 642–654 (2013).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 45.

    Kundaje, A. et al. Integrative analysis of 111 reference human epigenomes. Nature 518, 317–330 (2015).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 46.

    Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 47.

    Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 48.

    Amemiya, H. M., Kundaje, A. & Boyle, A. P. The ENCODE blacklist: identification of problematic regions of the genome. Sci. Rep. 9, 9354 (2019).

    ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar
     

  • 49.

    Vierstra, J. et al. Global reference mapping of human transcription factor footprints. Nature 583, 729–736 (2020).

    ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 50.

    Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 51.

    Liu, J. Z. et al. Association analyses identify 38 susceptibility loci for inflammatory bowel disease and highlight shared genetic risk across populations. Nat. Genet. 47, 979–986 (2015).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 52.

    de Lange, K. M. et al. Genome-wide association study implicates immune activation of multiple integrin genes in inflammatory bowel disease. Nat. Genet. 49, 256–261 (2017).

    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar
     

  • 53.

    Loh, P. R., Kichaev, G., Gazal, S., Schoech, A. P. & Price, A. L. Mixed-model association for biobank-scale datasets. Nat. Genet. 50, 906–908 (2018).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 54.

    Zhou, W. et al. Efficiently controlling for case–control imbalance and sample relatedness in large-scale genetic association studies. Nat. Genet. 50, 1335–1341 (2018).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 55.

    Benner, C. et al. Prospects of fine-mapping trait-associated genomic regions by using summary statistics from genome-wide association studies. Am. J. Hum. Genet. 101, 539–551 (2017).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 56.

    Wang, G., Sarkar, A., Carbonetto, P. & Stephens, M. A simple new approach to variable selection in regression, with application to genetic fine mapping. J. R. Stat. Soc. B 82, 1273–1300 (2020).

    MathSciNet 
    Article 

    Google Scholar
     

  • 57.

    McLaren, W. et al. The Ensembl variant effect predictor. Genome Biol. 17, 122 (2016).

    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar
     

  • 58.

    Fujita, P. A. et al. The UCSC genome browser database: update 2011. Nucleic Acids Res. 39, D876–D882 (2011).

    CAS 
    PubMed 
    Article 

    Google Scholar
     

  • 59.

    Carrillo-de-Santa-Pau, E. et al. Automatic identification of informative regions with epigenomic changes associated to hematopoiesis. Nucleic Acids Res. 45, 9244–9259 (2017).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 60.

    Astle, W. J. et al. The allelic landscape of human blood cell trait variation and links to common complex disease. Cell 167, 1415–1429 (2016).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 61.

    Finucane, H. K. et al. Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat. Genet. 47, 1228–1235 (2015).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 62.

    Chen, L. et al. Genetic drivers of epigenetic and transcriptional variation in human immune cells. Cell 167, 1398–1414 (2016).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 63.

    Liberzon, A. et al. Molecular signatures database (MSigDB) 3.0. Bioinformatics 27, 1739–1740 (2011).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 64.

    Kerimov, N. et al. eQTL Catalogue: a compendium of uniformly processed human gene expression and splicing QTLs. Preprint at https://doi.org/10.1101/2020.01.29.924266 (2021).

  • 65.

    de Leeuw, C. A., Mooij, J. M., Heskes, T. & Posthuma, D. MAGMA: generalized gene-set analysis of GWAS data. PLOS Comput. Biol. 11, e1004219 (2015).

    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar
     

  • 66.

    Kerpedjiev, P. et al. HiGlass: web-based visual exploration and analysis of genome interaction maps. Genome Biol. 19, 125 (2018).

    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar
     

  • 67.

    Rao, S. S. et al. A 3D map of the human genome at kilobase resolution reveals principles of chromatin looping. Cell 159, 1665–1680 (2014).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 68.

    Novakovic, B. et al. β-Glucan reverses the epigenetic state of LPS-induced immunological tolerance. Cell 167, 1354–1368 (2016).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 69.

    Donnard, E. et al. Comparative analysis of immune cells reveals a conserved regulatory lexicon. Cell Syst. 6, 381–394 (2018).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     



  • Source link

    Leave a Reply

    Your email address will not be published. Required fields are marked *