Heritability enrichment of specifically expressed genes identifies disease-relevant tissues and cell types (original) (raw)

References

1. ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2012).
   Article CAS Google Scholar
2. Kundaje, A. et al. Integrative analysis of 111 reference human epigenomes. Nature 518, 317–330 (2015).
   Article CAS PubMed PubMed Central Google Scholar
3. GTEx Consortium. The Genotype–Tissue Expression (GTEx) pilot analysis: multi-tissue gene regulation in humans. Science 348, 648–660 (2015).
   Article PubMed Central CAS Google Scholar
4. Ernst, J. et al. Mapping and analysis of chromatin-state dynamics in nine human cell types. Nature 473, 43–49 (2011).
   Article CAS PubMed PubMed Central Google Scholar
5. Trynka, G. et al. Chromatin marks identify critical cell types for fine mapping complex trait variants. Nat. Genet. 45, 124–130 (2013).
   Article CAS PubMed Google Scholar
6. Farh, K. K.-H. et al. Genetic and epigenetic fine-mapping of causal autoimmune disease variants. Nature 518, 337–343 (2015).
   Article CAS PubMed Google Scholar
7. Finucane, H. K. et al. Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat. Genet. 47, 1228–1235 (2015).
   Article CAS PubMed PubMed Central Google Scholar
8. Li, Y. & Kellis, M. Joint Bayesian inference of risk variants and tissue-specific epigenomic enrichments across multiple complex human diseases. Nucleic Acids Res. 44, e144 (2016).
   Article PubMed PubMed Central CAS Google Scholar
9. Maurano, M. T. et al. Systematic localization of common disease-associated variation in regulatory DNA. Science 337, 1190–1195 (2012).
   Article CAS PubMed PubMed Central Google Scholar
10. Pickrell, J. K. Joint analysis of functional genomic data and genome-wide association studies of 18 human traits. Am. J. Hum. Genet. 94, 559–573 (2014).
    Article CAS PubMed PubMed Central Google Scholar
11. Kichaev, G. et al. Integrating functional data to prioritize causal variants in statistical fine-mapping studies. PLoS Genet. 10, e1004722 (2014).
    Article PubMed PubMed Central CAS Google Scholar
12. Gusev, A. et al. Partitioning heritability of regulatory and cell-type-specific variants across 11 common diseases. Am. J. Hum. Genet. 95, 535–552 (2014).
    Article CAS PubMed PubMed Central Google Scholar
13. Ongen, H. et al. Estimating the causal tissues for complex traits and diseases. Nat. Genet. 49, 1676-1683 (2016).
14. Hu, X. et al. Integrating autoimmune risk loci with gene-expression data identifies specific pathogenic immune cell subsets. Am. J. Hum. Genet. 89, 496–506 (2011).
    Article CAS PubMed PubMed Central Google Scholar
15. Slowikowski, K., Hu, X. & Raychaudhuri, S. SNPsea: an algorithm to identify cell types, tissues and pathways affected by risk loci. Bioinformatics 30, 2496–2497 (2014).
    Article CAS PubMed PubMed Central Google Scholar
16. Gormley, P. et al. Meta-analysis of 375,000 individuals identifies 38 susceptibility loci for migraine. Nat. Genet. 48, 856–866 (2016).
    Article CAS PubMed PubMed Central Google Scholar
17. Pers, T. H. et al. Biological interpretation of genome-wide association studies using predicted gene functions. Nat. Commun. 6, 5890 (2015).
    Article CAS PubMed Google Scholar
18. Fehrmann, R. S. N. et al. Gene expression analysis identifies global gene-dosage sensitivity in cancer. Nat. Genet. 47, 115–125 (2015).
    Article CAS PubMed Google Scholar
19. Cahoy, J. D. et al. A transcriptome database for astrocytes, neurons and oligodendrocytes: a new resource for understanding brain development and function. J. Neurosci. 28, 264–278 (2008).
    Article CAS PubMed PubMed Central Google Scholar
20. Akbarian, S. et al. The PsychENCODE project. Nat. Neurosci. 18, 1707–1712 (2015).
    Article CAS PubMed PubMed Central Google Scholar
21. Heng, T. S. P. et al. The Immunological Genome Project: networks of gene expression in immune cells. Nat. Immunol. 9, 1091–1094 (2008).
    Article CAS PubMed Google Scholar
22. Auton, A. et al. A global reference for human genetic variation. Nature 526, 68–74 (2015).
    Article CAS PubMed Google Scholar
23. Sudlow, C. et al. UK Biobank: an open-access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 12, e1001779 (2015).
    Article PubMed PubMed Central Google Scholar
24. Anttila, V. et al. Analysis of shared heritability in common disorders of the brain. Preprint at bioRxiv https://doi.org/10.1101/048991 (2016).
25. Lambert, J.-C. et al. Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer’s disease. Nat. Genet. 45, 1452–1458 (2013).
    Article CAS PubMed PubMed Central Google Scholar
26. Cross-Disorder Group of the Psychiatric Genomics Consortium. Genetic relationship between five psychiatric disorders estimated from genome-wide SNPs. Nat. Genet. 45, 984–994 (2013).
    Article PubMed Central CAS Google Scholar
27. International League Against Epilepsy Consortium on Complex Epilepsies. Genetic determinants of common epilepsies: a meta-analysis of genome-wide association studies. Lancet Neurol. 13, 893–903 (2014).
    Article CAS Google Scholar
28. Woo, D. et al. Meta-analysis of genome-wide association studies identifies 1q22 as a susceptibility locus for intracerebral hemorrhage. Am. J. Hum. Genet. 94, 511–521 (2014).
    Article CAS PubMed PubMed Central Google Scholar
29. Traylor, M. et al. Genetic risk factors for ischemic stroke and its subtypes (the METASTROKE collaboration): a meta-analysis of genome-wide association studies. Lancet Neurol. 11, 951–962 (2012).
    Article PubMed PubMed Central Google Scholar
30. Patsopoulos, N. A. et al. Genome-wide meta-analysis identifies novel multiple sclerosis susceptibility loci. Ann. Neurol. 70, 897–912 (2011).
    Article CAS PubMed PubMed Central Google Scholar
31. Nalls, M. A. et al. Large-scale meta-analysis of genome-wide association data identifies six new risk loci for Parkinson’s disease. Nat. Genet. 46, 989–993 (2014).
    Article CAS PubMed PubMed Central Google Scholar
32. Schizophrenia Working Group of the Psychiatric Genomics Consortium. Biological insights from 108 schizophrenia-associated genetic loci. Nature 511, 421–427 (2014).
    Article CAS PubMed Central Google Scholar
33. Okbay, A. et al. Genome-wide association study identifies 74 loci associated with educational attainment. Nature 533, 539–542 (2016).
    Article CAS PubMed PubMed Central Google Scholar
34. Okbay, A. et al. Genetic variants associated with subjective well-being, depressive symptoms and neuroticism identified through genome-wide analyses. Nat. Genet. 48, 624–633 (2016).
    Article CAS PubMed PubMed Central Google Scholar
35. Teslovich, T. M. et al. Biological, clinical and population relevance of 95 loci for blood lipids. Nature 466, 707–713 (2010).
    Article CAS PubMed PubMed Central Google Scholar
36. Schunkert, H. et al. Large-scale association analysis identifies 13 new susceptibility loci for coronary artery disease. Nat. Genet. 43, 333–338 (2011).
    Article CAS PubMed PubMed Central Google Scholar
37. Manning, A. K. et al. A genome-wide approach accounting for body mass index identifies genetic variants influencing fasting glycemic traits and insulin resistance. Nat. Genet. 44, 659–669 (2012).
    Article CAS PubMed PubMed Central Google Scholar
38. Okada, Y. et al. Genetics of rheumatoid arthritis contributes to biology and drug discovery. Nature 506, 376–381 (2014).
    Article CAS PubMed Google Scholar
39. Jostins, L. et al. Host–microbe interactions have shaped the genetic architecture of inflammatory bowel disease. Nature 491, 119–124 (2012).
    Article CAS PubMed PubMed Central Google Scholar
40. Bradfield, J. P. et al. A genome-wide meta-analysis of six type 1 diabetes cohorts identifies multiple associated loci. PLoS Genet. 7, e1002293 (2011).
    Article CAS PubMed PubMed Central Google Scholar
41. Dubois, P. C. A. et al. Multiple common variants for celiac disease influencing immune gene expression. Nat. Genet. 42, 295–302 (2010).
    Article CAS PubMed PubMed Central Google Scholar
42. Bentham, J. et al. Genetic association analyses implicate aberrant regulation of innate and adaptive immunity genes in the pathogenesis of systemic lupus erythematosus. Nat. Genet. 47, 1457–1464 (2015).
    Article CAS PubMed PubMed Central Google Scholar
43. Cordell, H. J. et al. International genome-wide meta-analysis identifies new primary biliary cirrhosis risk loci and targetable pathogenic pathways. Nat. Commun. 6, 8019 (2015).
    Article CAS PubMed Google Scholar
44. Wood, A. R. et al. Defining the role of common variation in the genomic and biological architecture of adult human height. Nat. Genet. 46, 1173–1186 (2014).
    Article CAS PubMed PubMed Central Google Scholar
45. Tfelt-Hansen, P. C. & Koehler, P. J. One hundred years of migraine research: major clinical and scientific observations from 1910 to 2010. Headache 51, 752–778 (2011).
    Article PubMed Google Scholar
46. Hanford, L. C., Nazarov, A., Hall, G. B. & Sassi, R. B. Cortical thickness in bipolar disorder: a systematic review. Bipolar Disord. 18, 4–18 (2016).
    Article PubMed Google Scholar
47. Callicott, J. H. et al. Physiological dysfunction of the dorsolateral prefrontal cortex in schizophrenia revisited. Cereb. Cortex 10, 1078–1092 (2000).
    Article CAS PubMed Google Scholar
48. Medic, N. et al. Increased body mass index is associated with specific regional alterations in brain structure. Int. J. Obes. 40, 1177–1182 (2016).
    Article CAS Google Scholar
49. Maleki, N. et al. Migraine attacks the basal ganglia. Mol. Pain 7, 71 (2011).
    Article PubMed PubMed Central Google Scholar
50. Herculano-Houzel, S. & Lent, R. Isotropic fractionator: a simple, rapid method for the quantification of total cell and neuron numbers in the brain. J. Neurosci. 25, 2518–2521 (2005).
    Article CAS PubMed PubMed Central Google Scholar
51. Sakai, T. et al. Changes in density of calcium-binding-protein-immunoreactive GABAergic neurons in prefrontal cortex in schizophrenia and bipolar disorder. Neuropathology 28, 143–150 (2008).
    Article PubMed Google Scholar
52. Benes, F. M. & Berretta, S. GABAergic interneurons: implications for understanding schizophrenia and bipolar disorder. Neuropsychopharmacology 25, 1–27 (2001).
    Article CAS PubMed Google Scholar
53. Dhirapong, A. et al. B cell depletion therapy exacerbates murine primary biliary cirrhosis. Hepatology 53, 527–535 (2011).
    Article CAS PubMed Google Scholar
54. Zhang, J. et al. Ongoing activation of autoantigen-specific B cells in primary biliary cirrhosis. Hepatology 60, 1708–1716 (2014).
    Article CAS PubMed Google Scholar
55. Raj, T. et al. Polarization of the effects of autoimmune and neurodegenerative risk alleles in leukocytes. Science 344, 519–523 (2014).
    Article CAS PubMed PubMed Central Google Scholar
56. Huang, K. L. et al. A common haplotype lowers PU.1 expression in myeloid cells and delays onset of Alzheimer’s disease. Nat. Neurosci. 20, 1052–1061 (2017).
    Article CAS PubMed PubMed Central Google Scholar
57. Lloyd, C. M. & Hessel, E. M. Functions of T cells in asthma: more than just TH2 cells. Nat. Rev. Immunol. 10, 838–848 (2010).
    Article CAS PubMed Google Scholar
58. Müller-Ladner, U., Pap, T., Gay, R. E., Neidhart, M. & Gay, S. Mechanisms of disease: the molecular and cellular basis of joint destruction in rheumatoid arthritis. Nat. Clin. Pract. Rheumatol. 1, 102–110 (2005).
    Article PubMed CAS Google Scholar
59. Xavier, R. J. & Podolsky, D. K. Unravelling the pathogenesis of inflammatory bowel disease. Nature 448, 427–434 (2007).
    Article CAS PubMed Google Scholar
60. Sospedra, M. & Martin, R. Immunology of multiple sclerosis. Annu. Rev. Immunol. 23, 683–747 (2005).
    Article CAS PubMed Google Scholar
61. Barbosa, I. G., Machado-Vieira, R., Soares, J. C. & Teixeira, A. L. The immunology of bipolar disorder. Neuroimmunomodulation 21, 117–122 (2014).
    Article CAS PubMed Google Scholar
62. Steiner, J. et al. Acute schizophrenia is accompanied by reduced T cell and increased B cell immunity. Eur. Arch. Psychiatry Clin. Neurosci. 260, 509–518 (2010).
    Article PubMed Google Scholar
63. Sekar, A. et al. Schizophrenia risk from complex variation of complement component 4. Nature 530, 177–183 (2016).
    Article CAS PubMed PubMed Central Google Scholar
64. Corces, M. R. et al. Lineage-specific and single-cell chromatin accessibility charts human hematopoiesis and leukemia evolution. Nat. Genet. 48, 1193–1203 (2016).
    Article CAS PubMed PubMed Central Google Scholar
65. Gazal, S. et al. Linkage-disequilibrium-dependent architecture of human complex traits reveals action of negative selection. Nat. Genet. 49, 1421–1427 (2017).
    Article CAS PubMed PubMed Central Google Scholar
66. Boyle, E. A., Li, Y. I. & Pritchard, J. K. An expanded view of complex traits: from polygenic to omnigenic. Cell 169, 1177–1186 (2017).
    Article CAS PubMed PubMed Central Google Scholar
67. Shi, H., Kichaev, G. & Pasaniuc, B. Contrasting the genetic architecture of 30 complex traits from summary association data. Am. J. Hum. Genet. 99, 139–153 (2016).
    Article CAS PubMed PubMed Central Google Scholar
68. Bulik-Sullivan, B. K. et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47, 291–295 (2015).
    Article CAS PubMed PubMed Central Google Scholar
69. Wagner, G. P., Kin, K. & Lynch, V. J. Measurement of mRNA abundance using RNA-seq data: RPKM measure is inconsistent among samples. Theory Biosci. 131, 281–285 (2012).
    Article CAS PubMed Google Scholar
70. Loh, P.-R., Kichaev, G., Gazal, S., Schoech, A. P. & Price, A. L. Mixed model association for biobank-scale data sets. Preprint at bioRxiv https://doi.org/10.1101/194944 (2017).
71. Backenroth, D. et al. Tissue-specific functional effect prediction of genetic variation and applications to complex trait genetics. Preprint at bioRxiv https://doi.org/10.1101/069229 (2016).
72. Wilens, T. E., Biederman, J. & Spencer, T. J. Attention deficit or hyperactivity disorder across the lifespan. Annu. Rev. Med. 53, 113–131 (2002).
    Article CAS PubMed Google Scholar
73. Davis, L. K. et al. Partitioning the heritability of Tourette syndrome and obsessive–compulsive disorder reveals differences in genetic architecture. PLoS Genet. 9, e1003864 (2013).
    Article PubMed PubMed Central CAS Google Scholar
74. Law, C. W., Chen, Y., Shi, W. & Smyth, G. K. voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol. 15, R29 (2014).
    Article PubMed PubMed Central CAS Google Scholar
75. Gjoneska, E. et al. Conserved epigenomic signals in mice and humans reveal immune basis of Alzheimer’s disease. Nature 518, 365–369 (2015).
    Article CAS PubMed PubMed Central Google Scholar
76. Gagliano, S. A. et al. Genomics implicates adaptive and innate immunity in Alzheimer’s and Parkinson’s diseases. Ann. Clin. Transl. Neurol 3, 924–933 (2016).
    Article CAS PubMed PubMed Central Google Scholar
77. Rege, S. & Hodgkinson, S. J. Immune dysregulation and autoimmunity in bipolar disorder: synthesis of the evidence and its clinical application. Aust. N. Z. J. Psychiatry 47, 1136–1151 (2013).
    Article PubMed Google Scholar
78. Elamin, I., Edwards, M. J. & Martino, D. Immune dysfunction in Tourette syndrome. Behav. Neurol. 27, 23–32 (2013).
    Article PubMed PubMed Central Google Scholar
79. Jin, W., Millar, J. S., Broedl, U., Glick, J. M. & Rader, D. J. Inhibition of endothelial lipase causes increased HDL cholesterol levels in vivo. J. Clin. Invest. 111, 357–362 (2003).
    Article CAS PubMed PubMed Central Google Scholar
80. Broedl, U. C. et al. Endothelial lipase promotes the catabolism of ApoB-containing lipoproteins. Circ. Res. 94, 1554–1561 (2004).
    Article CAS PubMed Google Scholar
81. Feingold, K. R. & Grunfeld, C. The role of HDL in innate immunity. J. Lipid Res. 52, 1–3 (2011).
    Article CAS PubMed PubMed Central Google Scholar
82. Lo, J. C. et al. Lymphotoxin-β-receptor-dependent control of lipid homeostasis. Science 316, 285–288 (2007).
    Article CAS PubMed Google Scholar
83. Harrison, D. G. The immune system in hypertension. Trans. Am. Clin. Climatol. Assoc. 125, 130–138 (2014).
    PubMed PubMed Central Google Scholar
84. Hotamisligil, G. S. Inflammation and metabolic disorders. Nature 444, 860–867 (2006).
    Article CAS PubMed Google Scholar
85. Zlotnikov-Klionsky, Y. et al. Perforin-positive dendritic cells exhibit an immunoregulatory role in metabolic syndrome and autoimmunity. Immunity 43, 776–787 (2015).
    Article CAS PubMed Google Scholar
86. Mancuso, N. et al. Integrating gene expression with summary-association statistics to identify genes associated with 30 complex traits. Am. J. Hum. Genet. 100, 473–487 (2017).
    Article CAS PubMed PubMed Central Google Scholar
87. Gusev, A. et al. Integrative approaches for large-scale transcriptome-wide association studies. Nat. Genet. 48, 245–252 (2016).
    Article CAS PubMed PubMed Central Google Scholar

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Acknowledgements

We are thankful to R. Herbst, E. Hodis, F. Hormozdiari, M. Kanai, T. Pers, S. Riesenfeld, J. Ulirsch and A. Veres for helpful comments. This research was conducted using the UK Biobank Resource (application number: 16549). This research was funded by NIH grants R01 MH107649 (H.K.F., S.G., B.M.N., A.L.P.), R01 MH109978 (A.G., A.L.P.), U01 CA194393 (H.K.F., A.L.P.) and U01 HG009379 (S.R., A.L.P.). H.K.F. was also supported by the Fannie and John Hertz Foundation and by Eric and Wendy Schmidt. Data on neuron types were generated as part of the PsychENCODE Consortium, supported by: U01MH103392 (S. Akbarian, Icahn School of Medicine at Mount Sinai; P. Sklar, Icahn School of Medicine at Mount Sinai), U01MH103365 (F. Vaccarino, Yale University; M. Gerstein, Yale University; S. Weissman, Yale University), U01MH103346 (P. Farnham, University of Southern California; J. A. Knowles, University of Southern California), U01MH103340 (C. Liu, SUNY Upstate Medical University; K. White, University of Chicago), U01MH103339 (N. Sestan, Yale University; M. State, University of California, San Francisco), R21MH109956 (A. Jaffe, Lieber Institute for Brain Development), R21MH105881 (D. Pinto, Icahn School of Medicine at Mount Sinai), R21MH105853 (A. Jaffe, Lieber Institute for Brain Development; D. Weinberger, Lieber Institute for Brain Development), R21MH103877 (S. Dracheva, Icahn School of Medicine at Mount Sinai; S. Akbarian, Icahn School of Medicine at Mount Sinai), R21MH102791 (A. Jaffe, Lieber Institute for Brain Development), R01MH111721 (F. Goes, Johns Hopkins University; T. Hyde, Lieber Institute for Brain Development), R01MH110928 (M. State, University of California, San Francisco; S. Sanders, University of California, San Francisco; J. Willsey, University of California, San Francisco), R01MH110927 (D. Geschwind, University of California, Los Angeles), R01MH110926 (N. Sestan, Yale University), R01MH110921 (P. Sklar, Icahn School of Medicine at Mount Sinai), R01MH110920 (C. Liu, SUNY Upstate Medical University), R01MH110905 (K. White, University of Chicago), R01MH109715 (D. Pinto, Icahn School of Medicine at Mount Sinai), R01MH109677 (P. Roussos, Icahn School of Medicine at Mount Sinai), R01MH105898, (P. Zandi, Johns Hopkins University; T. M. Hyde, Lieber Institute for Brain Development), R01MH094714, (D. Geschwind, University of California, Los Angeles), P50MH106934, (N. Sestan, Yale University), R01MH105472 (G. Crawford, Duke University; P. Sullivan, University of North Carolina).

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Author notes

1. A list of members and affiliations appears in the Supplementary Note.

Authors and Affiliations

1. Broad Institute of MIT and Harvard, Cambridge, MA, USA
   Hilary K. Finucane, Verneri Anttila, Kamil Slowikowski, Andrea Byrnes, Caleb Lareau, Noam Shoresh, Giulio Genovese, Jason D. Buenrostro, Bradley E. Bernstein, Soumya Raychaudhuri, Steven McCarroll, Benjamin M. Neale & Alkes L. Price
2. Department of Mathematics, Massachusetts Institute of Technology, Cambridge, MA, USA
   Hilary K. Finucane
3. Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA
   Hilary K. Finucane, Alexander Gusev, Steven Gazal, Po-Ru Loh, Samuela Pollack & Alkes L. Price
4. Department of Computer Science, Harvard University, Cambridge, MA, USA
   Yakir A. Reshef
5. Analytic and Translational Genetics Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
   Verneri Anttila, Andrea Byrnes & Benjamin M. Neale
6. Bioinformatics and Integrative Genomics, Harvard University, Cambridge, MA, USA
   Kamil Slowikowski
7. Division of Genetics, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
   Kamil Slowikowski & Soumya Raychaudhuri
8. Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA
   Caleb Lareau
9. Department of Genetics, Harvard Medical School, Boston, MA, USA
   Arpiar Saunders, Evan Macosko & Steven McCarroll
10. Medical Research Council (MRC) Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge Biomedical Campus, Cambridge, UK
    John R. B. Perry
11. Harvard Society of Fellows, Harvard University, Cambridge, MA, USA
    Jason D. Buenrostro
12. Department of Pathology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
    Bradley E. Bernstein
13. Division of Rheumatology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
    Soumya Raychaudhuri
14. Partners Center for Personalized Genetic Medicine, Boston, MA, USA
    Soumya Raychaudhuri
15. Faculty of Medical and Human Sciences, University of Manchester, Manchester, UK
    Soumya Raychaudhuri

Authors

1. Hilary K. Finucane
2. Yakir A. Reshef
3. Verneri Anttila
4. Kamil Slowikowski
5. Alexander Gusev
6. Andrea Byrnes
7. Steven Gazal
8. Po-Ru Loh
9. Caleb Lareau
10. Noam Shoresh
11. Giulio Genovese
12. Arpiar Saunders
13. Evan Macosko
14. Samuela Pollack
15. John R. B. Perry
16. Jason D. Buenrostro
17. Bradley E. Bernstein
18. Soumya Raychaudhuri
19. Steven McCarroll
20. Benjamin M. Neale
21. Alkes L. Price

Consortia

The Brainstorm Consortium

Contributions

H.K.F. and A.L.P. designed the study; H.K.F., Y.A.R., K.S. and S.P. analyzed data; H.K.F. and A.L.P. wrote the manuscript with assistance from Y.A.R., V.A., K.S., A.G., A.B., S.G., P.-R.L., C.L., N.S., G.G., A.S., E.M., S.P., J.R.B.P., J.D.B., B.E.B., S.R., S.M. and B.M.N.

Corresponding authors

Correspondence toHilary K. Finucane or Alkes L. Price.

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The authors declare no competing interests.

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Finucane, H.K., Reshef, Y.A., Anttila, V. et al. Heritability enrichment of specifically expressed genes identifies disease-relevant tissues and cell types.Nat Genet 50, 621–629 (2018). https://doi.org/10.1038/s41588-018-0081-4

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• Received: 19 September 2016
• Accepted: 29 January 2018
• Published: 09 April 2018
• Version of record: 09 April 2018
• Issue date: April 2018
• DOI: https://doi.org/10.1038/s41588-018-0081-4