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Using an atlas of gene regulation across 44 human tissues to inform complex disease- and trait-associated variation

Abstract

We apply integrative approaches to expression quantitative loci (eQTLs) from 44 tissues from the Genotype-Tissue Expression project and genome-wide association study data. About 60% of known trait-associated loci are in linkage disequilibrium with a cis-eQTL, over half of which were not found in previous large-scale whole blood studies. Applying polygenic analyses to metabolic, cardiovascular, anthropometric, autoimmune, and neurodegenerative traits, we find that eQTLs are significantly enriched for trait associations in relevant pathogenic tissues and explain a substantial proportion of the heritability (40–80%). For most traits, tissue-shared eQTLs underlie a greater proportion of trait associations, although tissue-specific eQTLs have a greater contribution to some traits, such as blood pressure. By integrating information from biological pathways with eQTL target genes and applying a gene-based approach, we validate previously implicated causal genes and pathways, and propose new variant and gene associations for several complex traits, which we replicate in the UK BioBank and BioVU.

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References

  1. 1.

    GTEx Consortium. The Genotype-Tissue Expression (GTEx) project. Nat. Genet. 45, 580–585 (2013).

  2. 2.

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

  3. 3.

    Ongen, H., Buil, A., Brown, A. A., Dermitzakis, E. T. & Delaneau, O. Fast and efficient QTL mapper for thousands of molecular phenotypes. Bioinformatics 32, 1479–1485 (2016).

  4. 4.

    Han, B. & Eskin, E. Random-effects model aimed at discovering associations in meta-analysis of genome-wide association studies. Am. J. Hum. Genet. 88, 586–598 (2011).

  5. 5.

    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).

  6. 6.

    Denny, J. C. et al. Systematic comparison of phenome-wide association study of electronic medical record data and genome-wide association study data. Nat. Biotechnol. 31, 1102–1110 (2013).

  7. 7.

    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).

  8. 8.

    Dupuis, J. et al. New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk. Nat. Genet. 42, 105–116 (2010).

  9. 9.

    Wen, X. Molecular QTL discovery incorporating genomic annotations using Bayesian false discovery rate control. Ann. Appl. Stat. 10, 1619–1638 (2016).

  10. 10.

    Wen, X., Lee, Y., Luca, F. & Pique-Regi, R. Efficient integrative multi-SNP association analysis via deterministic approximation of posteriors. Am. J. Hum. Genet. 98, 1114–1129 (2016).

  11. 11.

    GTEx Consortium. Human genomics. The Genotype-Tissue Expression (GTEx) pilot analysis: Multitissue gene regulation in humans. Science 348, 648–660 (2015).

  12. 12.

    Pandey, G. K. et al. The risk-associated long noncoding RNA NBAT-1 controls neuroblastoma progression by regulating cell proliferation and neuronal differentiation. Cancer Cell 26, 722–737 (2014).

  13. 13.

    Nica, A. C. et al. Candidate causal regulatory effects by integration of expression QTLs with complex trait genetic associations. PLoS Genet. 6, e1000895 (2010).

  14. 14.

    Ongen, H. et al. Estimating the causal tissues for complex traits and diseases. Nat. Genet. 49, 1676–1683 (2017).

  15. 15.

    Hormozdiari, F. et al. Colocalization of GWAS and eQTL signals detects target genes. Am. J. Hum. Genet. 99, 1245–1260 (2016).

  16. 16.

    CARDIoGRAMplusC4D Consortium. Large-scale association analysis identifies new risk loci for coronary artery disease. Nat. Genet. 45, 25–33 (2013).

  17. 17.

    Mohammadi, P., Castel, S. E., Brown, A. A. & Lappalainen, T. Quantifying the regulatory effect size of cis-acting genetic variation using allelic fold change. Genome Res. 27, 1872–1884 (2017).

  18. 18.

    Berge, K. E. et al. Accumulation of dietary cholesterol in sitosterolemia caused by mutations in adjacent ABC transporters. Science 290, 1771–1775 (2000).

  19. 19.

    Kathiresan, S. et al. Common variants at 30 loci contribute to polygenic dyslipidemia. Nat. Genet. 41, 56–65 (2009).

  20. 20.

    Ward, L. D. & Kellis, M. HaploRegv4: Systematic mining of putative causal variants, cell types, regulators and target genes for human complex traits and disease. Nucleic Acids Res. 44, D877–D881 (2016).

  21. 21.

    Battle, A. et al. Characterizing the genetic basis of transcriptome diversity through RNA-sequencing of 922 individuals. Genome Res. 24, 14–24 (2014).

  22. 22.

    Kukurba, K. R. et al. Impact of the X chromosome and sex on regulatory variation. Genome Res. 26, 768–777 (2016).

  23. 23.

    Westra, H. J. et al. Systematic identification of trans eQTLs as putative drivers of known disease associations. Nat. Genet. 45, 1238–1243 (2013).

  24. 24.

    Bulik-Sullivan, B. K. et al. LD score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47, 291–295 (2015).

  25. 25.

    Brown, A. A. et al. Predicting causal variants affecting expression by using whole-genome sequencing and RNA-seq from multiple human tissues. Nat. Genet. 49, 1747–1751 (2017).

  26. 26.

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

  27. 27.

    Wellcome Trust Case Control Consortium. Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 447, 661–678 (2007).

  28. 28.

    Eichler, E. E. et al. Missing heritability and strategies for finding the underlying causes of complex disease. Nat. Rev. Genet. 11, 446–450 (2010).

  29. 29.

    Storey, J. D. & Tibshirani, R. Statistical significance for genomewide studies. Proc. Natl Acad. Sci. USA 100, 9440–9445 (2003).

  30. 30.

    Qiao, Q. & Nyamdorj, R. Is the association of type II diabetes with waist circumference or waist-to-hip ratio stronger than that with body mass index? Eur. J. Clin. Nutr. 64, 30–34 (2010).

  31. 31.

    Cheng, C. H. et al. Waist-to-hip ratio is a better anthropometric index than body mass index for predicting the risk of type 2 diabetes in Taiwanese population. Nutr. Res. 30, 585–593 (2010).

  32. 32.

    Emerging Risk Factors Collaboration. Major lipids, apolipoproteins, and risk of vascular disease. JAMA 302, 1993–2000 (2009).

  33. 33.

    International Consortium for Blood Pressure Genome-Wide Association Studies. Genetic variants in novel pathways influence blood pressure and cardiovascular disease risk. Nature 478, 103–109 (2011).

  34. 34.

    Ehret, G. B. et al. The genetics of blood pressure regulation and its target organs from association studies in 342,415 individuals. Nat. Genet. 48, 1171–1184 (2016).

  35. 35.

    Gamazon, E. R. et al. A gene-based association method for mapping traits using reference transcriptome data. Nat. Genet. 47, 1091–1098 (2015).

  36. 36.

    Barbeira, A. N. et al. Exploring the phenotypic consequences of tissue specific gene expression variation inferred from GWAS summary statistics. Nat. Commun. 9, 1825 (2018).

  37. 37.

    Ganesh, S. K. et al. Loci influencing blood pressure identified using a cardiovascular gene-centric array. Hum. Mol. Genet. 22, 1663–1678 (2013).

  38. 38.

    Li, N. et al. Associations between genetic variations in the FURIN gene and hypertension. BMC Med. Genet. 11, 124 (2010).

  39. 39.

    Rippe, C. et al. Hypertension reduces soluble guanylyl cyclase expression in the mouse aorta via the Notch signaling pathway. Sci. Rep. 7, 1334 (2017).

  40. 40.

    Davies, R. W. et al. A genome-wide association study for coronary artery disease identifies a novel susceptibility locus in the major histocompatibility complex. Circ. Cardiovasc. Genet. 5, 217–225 (2012).

  41. 41.

    Lahoute, C., Herbin, O., Mallat, Z. & Tedgui, A. Adaptive immunity in atherosclerosis: Mechanisms and future therapeutic targets. Nat. Rev. Cardiol. 8, 348–358 (2011).

  42. 42.

    Zhu, Z. et al. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat. Genet. 48, 481–487 (2016).

  43. 43.

    Jinek, M. et al. A programmable dual-RNA-guided DNA endonuclease in adaptive bacterial immunity. Science 337, 816–821 (2012).

  44. 44.

    Barrangou, R. & Doudna, J. A. Applications of CRISPR technologies in research and beyond. Nat. Biotechnol. 34, 933–941 (2016).

  45. 45.

    Khalil, A. M. et al. Many human large intergenic noncoding RNAs associate with chromatin-modifying complexes and affect gene expression. Proc. Natl Acad. Sci. USA 106, 11667–11672 (2009).

  46. 46.

    Bai, Y., Dai, X., Harrison, A. P. & Chen, M. RNA regulatory networks in animals and plants: A long noncoding RNA perspective. Brief. Funct. Genomics 14, 91–101 (2015).

  47. 47.

    Finucane, H. K. et al. Heritability enrichment of specifically expressed genes identifies disease-relevant tissues and cell types. Nat. Genet. 50, 621–629 (2018).

  48. 48.

    Pruim, R. J. et al. LocusZoom: Regional visualization of genome-wide association scan results. Bioinformatics 26, 2336–2337 (2010).

  49. 49.

    Kang, E. Y. et al. ForestPMPlot: A flexible tool for visualizing heterogeneity between studies in meta-analysis. G3 (Bethesda) 6, 1793–1798 (2016).

  50. 50.

    Nikpay, M. et al. A comprehensive 1000 Genomes-based genome-wide association meta-analysis of coronary artery disease. Nat. Genet. 47, 1121–1130 (2015).

  51. 51.

    Carithers, L. J. et al. A novel approach to high-quality postmortem tissue procurement: The GTEx Project. Biopreserv. Biobank 13, 311–319 (2015).

  52. 52.

    Chang, C. C. et al. Second-generation PLINK: Rising to the challenge of larger and richer datasets. Gigascience 4, 7 (2015).

  53. 53.

    Sudmant, P. H. et al. An integrated map of structural variation in 2,504 human genomes. Nature 526, 75–81 (2015).

  54. 54.

    Morris, A. P. et al. Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes. Nat. Genet. 44, 981–990 (2012).

  55. 55.

    Segre, A. V. et al. Pathways targeted by antidiabetes drugs are enriched for multiple genes associated with type 2 diabetes risk. Diabetes 64, 1470–1483 (2015).

  56. 56.

    Loh, P. R. et al. Efficient Bayesian mixed-model analysis increases association power in large cohorts. Nat. Genet. 47, 284–290 (2015).

  57. 57.

    Yang, R. Y. et al. A systematic survey of human tissue-specific gene expression and splicing reveals new opportunities for therapeutic target identification and evaluation. bioRxiv https://doi.org/10.1101/311563 (2018).

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Acknowledgements

We thank the DIAGRAM, MAGIC, GIANT, GLGC, CARDIoGRAM, ICBP, IGAP, and IIBDGC consortia for making their GWAS meta-analysis summary statistics publicly available. This work was conducted using the UK Biobank Resource (application number 25331). E.R.G. acknowledges support from R01-MH101820, R01-MH090937, R01-MH113362, and R01-CA157823 and benefited immensely from a Fellowship at Clare Hall, University of Cambridge. A.V.S., F.A., M.K., G.G., and K.G.A. acknowledge support from the NIH contract HHSN268201000029C to The Broad Institute, Inc. M.v.d.B. acknowledges support by a Novo Nordisk postdoctoral fellowship run in partnership with the University of Oxford. F.H. and E.E. are supported by NIH grants R01-MH101782 and R01-ES022282. X.W. acknowledges support from NIH grants R01-HG007022 and R01-AR042742. M.I.McC. is a Wellcome Senior Investigator supported by Wellcome (098381, 090532, 106130, 203141) and the NIH (U01-DK105535, R01-MH101814). E.T.D. acknowledges support from the Swiss National Science Foundation, the European Research Council, the NIH-NIMH, and the Louis Jeantet Foundation. N.J.C. is supported by R01-MH113362, R01-MH101820, and R01-MH090937. The datasets used for part of the replication analysis were obtained from Vanderbilt University Medical Center’s BioVU, which is supported by numerous sources: institutional funding, private agencies, and federal grants. These include the NIH funded Shared Instrumentation Grant S10-RR025141, and CTSA grants UL1-TR002243, UL1-TR000445, and UL1-RR024975. Genomic data are also supported by investigator-led projects that include U01-HG004798, R01-NS032830, RC2-GM092618, P50-GM115305, U01-HG006378, U19-HL065962, and R01-HD074711, and additional funding sources listed at https://victr.vanderbilt.edu/pub/biovu/.

Author information

E.R.G. and A.V.S. jointly designed the study and led the analysis. E.R.G., A.V.S., M.v.d.B., and K.G.A. wrote the manuscript. E.R.G., A.V.S., M.v.d.B., X.W., H.S.X., F.H., H.O., A.K., E.M.D., F.A., and J.Q. performed the statistical analysis. E.R.G., A.V.S., M.v.d.B., X.W., H.S.X., F.H., E.M.D., D.L.N., E.E., M.K., G.G., M.I.McC, E.T.D., N.J.C., and K.G.A. interpreted the results of the analysis. All authors contributed to the critical review of the manuscript.

Competing interests

M.I.McC. serves on advisory panels for Pfizer and NovoNordisk. He has received honoraria from Pfizer, NovoNordisk, Sanofi-Aventis, and Eli-Lilly, and research funding from Pfizer, Eli-Lilly, Merck, Takeda, Sanofi Aventis, Astra Zeneca, NovoNordisk, Servier, Janssen, Boehringer Ingelheim, and Roche. M.v.d.B is an employee of Novo Nordisk. H.S.X. and J.Q. are employees of Pfizer.

Correspondence to Eric R. Gamazon or Ayellet V. Segrè.

Supplementary information

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    Supplementary Note and Supplementary Figures 1–21

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    Supplementary Tables 1–23

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DOI

https://doi.org/10.1038/s41588-018-0154-4

Further reading

Fig. 1: Incorporating eQTLs from 44 tissues into GWAS of complex traits.
Fig. 2: eQTL annotation of variants from GWAS catalog.
Fig. 3: Proposing causal genes in inaccessible tissues.
Fig. 4: Heritability estimates explained by eQTLs in 44 tissues.
Fig. 5: Estimated true positive rate of trait associations amongst eQTLs in 44 tissues.
Fig. 6: Discovery and replication of novel associations and genes.