Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Multiancestry association study identifies new asthma risk loci that colocalize with immune-cell enhancer marks

Abstract

We examined common variation in asthma risk by conducting a meta-analysis of worldwide asthma genome-wide association studies (23,948 asthma cases, 118,538 controls) of individuals from ethnically diverse populations. We identified five new asthma loci, found two new associations at two known asthma loci, established asthma associations at two loci previously implicated in the comorbidity of asthma plus hay fever, and confirmed nine known loci. Investigation of pleiotropy showed large overlaps in genetic variants with autoimmune and inflammatory diseases. The enrichment in enhancer marks at asthma risk loci, especially in immune cells, suggested a major role of these loci in the regulation of immunologically related mechanisms.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Manhattan plots of the results of European-ancestry and multiancestry random-effects meta-analyses of asthma risk.
Fig. 2: GRAIL circle plot of connectivity among genes at asthma risk loci.

Similar content being viewed by others

References

  1. Akinbami, L. J. et al. Trends in asthma prevalence, health care use, and mortality in the United States, 2001–2010. (U.S. Department of Health and Human Services, Washington, DC, 2012; 1–8. (NCHS Data Brief no. 94).

    Google Scholar 

  2. Duffy, D. L., Martin, N. G., Battistutta, D., Hopper, J. L. & Mathews, J. D. Genetics of asthma and hay fever in Australian twins. Am. Rev. Respir. Dis. 142, 1351–1358 (1990).

    CAS  PubMed  Google Scholar 

  3. Welter, D. et al. The NHGRI GWAS Catalog, a curated resource of SNP-trait associations. Nucleic Acids Res. 42, D1001–D1006 (2014).

    CAS  PubMed  Google Scholar 

  4. Igartua, C. et al. Ethnic-specific associations of rare and low-frequency DNA sequence variants with asthma. Nat. Commun. 6, 5965 (2015).

    CAS  PubMed  Google Scholar 

  5. Bouzigon, E. et al. Effect of 17q21 variants and smoking exposure in early-onset asthma. N. Engl. J. Med. 359, 1985–1994 (2008).

    CAS  PubMed  Google Scholar 

  6. Galanter, J. M. et al. Genome-wide association study and admixture mapping identify different asthma-associated loci in Latinos: the Genes-environments & Admixture in Latino Americans study. J. Allergy Clin. Immunol. 134, 295–305 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. Hirota, T. et al. Genome-wide association study identifies three new susceptibility loci for adult asthma in the Japanese population. Nat. Genet. 43, 893–896 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Ferreira, M. A. et al. Genome-wide association analysis identifies 11 risk variants associated with the asthma with hay fever phenotype. J. Allergy Clin. Immunol. 133, 1564–1571 (2014).

    CAS  PubMed  Google Scholar 

  9. Higgins, J. P. & Thompson, S. G. Quantifying heterogeneity in a meta-analysis. Stat. Med. 21, 1539–1558 (2002).

    PubMed  Google Scholar 

  10. Grundberg, E. et al. Mapping cis- and trans-regulatory effects across multiple tissues in twins. Nat. Genet. 44, 1084–1089 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

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

    PubMed Central  Google Scholar 

  13. Liang, L. et al. A cross-platform analysis of 14,177 expression quantitative trait loci derived from lymphoblastoid cell lines. Genome Res. 23, 716–726 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. Hao, K. et al. Lung eQTLs to help reveal the molecular underpinnings of asthma. PLoS Genet. 8, e1003029 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. Noguchi, E. et al. Genome-wide association study identifies HLA-DP as a susceptibility gene for pediatric asthma in Asian populations. PLoS Genet. 7, e1002170 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. Ding, L. et al. Rank-based genome-wide analysis reveals the association of ryanodine receptor-2 gene variants with childhood asthma among human populations. Hum. Genomics 7, 16 (2013).

    PubMed  PubMed Central  Google Scholar 

  17. Sleiman, P. M. et al. Variants of DENND1B associated with asthma in children. N. Engl. J. Med. 362, 36–44 (2010).

    CAS  PubMed  Google Scholar 

  18. Himes, B. E. et al. Genome-wide association analysis identifies PDE4D as an asthma-susceptibility gene. Am. J. Hum. Genet. 84, 581–593 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. Torgerson, D. G. et al. Meta-analysis of genome-wide association studies of asthma in ethnically diverse North American populations. Nat. Genet. 43, 887–892 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Bønnelykke, K. et al. A genome-wide association study identifies CDHR3 as a susceptibility locus for early childhood asthma with severe exacerbations. Nat. Genet. 46, 51–55 (2014).

    PubMed  Google Scholar 

  21. Ferreira, M. A. et al. Identification of IL6R and chromosome 11q13.5 as risk loci for asthma. Lancet 378, 1006–1014 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. Moffatt, M. F. et al. A large-scale, consortium-based genomewide association study of asthma. N. Engl. J. Med. 363, 1211–1221 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. Zeller, T. et al. Genetics and beyond: the transcriptome of human monocytes and disease susceptibility. PLoS One 5, e10693 (2010).

    PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  25. Raychaudhuri, S. et al. Identifying relationships among genomic disease regions: predicting genes at pathogenic SNP associations and rare deletions. PLoS Genet. 5, e1000534 (2009).

    PubMed  PubMed Central  Google Scholar 

  26. Hong, S. W., Kim, S. & Lee, D. K. The role of Bach2 in nucleic acid-triggered antiviral innate immune responses. Biochem. Biophys. Res. Commun. 365, 426–432 (2008).

    CAS  PubMed  Google Scholar 

  27. Yang, M., He, R. L., Benovic, J. L. & Ye, R. D. Beta-Arrestin1 interacts with the G-protein subunits β1γ2 and promotes β1γ2-dependent Akt signalling for NF-kappaB activation. Biochem. J 417, 287–296 (2009).

    CAS  PubMed  Google Scholar 

  28. Soler Artigas, M. et al. Genome-wide association and large-scale follow up identifies 16 new loci influencing lung function. Nat. Genet. 43, 1082–1090 (2011).

    PubMed  Google Scholar 

  29. Goenka, S. & Kaplan, M. H. Transcriptional regulation by STAT6. Immunol. Res. 50, 87–96 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. Qian, X., Gao, Y., Ye, X. & Lu, M. Association of STAT6 variants with asthma risk: a systematic review and meta-analysis. Hum. Immunol. 75, 847–853 (2014).

    CAS  PubMed  Google Scholar 

  31. Wang, Y., Tong, X. & Ye, X. Ndfip1 negatively regulates RIG-I-dependent immune signaling by enhancing E3 ligase Smurf1-mediated MAVS degradation. J. Immunol. 189, 5304–5313 (2012).

    CAS  PubMed  Google Scholar 

  32. Venuprasad, K., Zeng, M., Baughan, S. L. & Massoumi, R. Multifaceted role of the ubiquitin ligase Itch in immune regulation. Immunol. Cell Biol. 93, 452–460 (2015).

    CAS  PubMed  Google Scholar 

  33. Javierre, B. M. et al. Lineage-specific genome architecture links enhancers and non-coding disease variants to target gene promoters. Cell 167, 1369–1384 e19 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. Barnes, P. J. Pathophysiology of allergic inflammation. Immunol. Rev. 242, 31–50 (2011).

    CAS  PubMed  Google Scholar 

  35. Vicente, C. T. et al. Long-range modulation of PAG1 expression by 8q21 allergy risk variants. Am. J. Hum. Genet. 97, 329–336 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. Davison, L. J. et al. Long-range DNA looping and gene expression analyses identify DEXI as an autoimmune disease candidate gene. Hum. Mol. Genet 21, 322–333 (2012).

    CAS  PubMed  Google Scholar 

  37. Wang, L. et al. CPAG: software for leveraging pleiotropy in GWAS to reveal similarity between human traits links plasma fatty acids and intestinal inflammation. Genome Biol. 16, 190 (2015).

    PubMed  PubMed Central  Google Scholar 

  38. Rottem, M. & Shoenfeld, Y. Asthma as a paradigm for autoimmune disease. Int. Arch. Allergy Immunol. 132, 210–214 (2003).

    CAS  PubMed  Google Scholar 

  39. Li, X. et al. Genome-wide association studies of asthma indicate opposite immunopathogenesis direction from autoimmune diseases. J. Allergy Clin. Immunol. 130, 861–868.e7 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. Hayes, J. E. et al. Tissue-specific enrichment of lymphoma risk loci in regulatory elements. PLoS One 10, e0139360 (2015).

    PubMed  PubMed Central  Google Scholar 

  41. Liang, L. et al. An epigenome-wide association study of total serum immunoglobulin E concentration. Nature 520, 670–674 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. Wenzel, S. E. Asthma phenotypes: the evolution from clinical to molecular approaches. Nat. Med. 18, 716–725 (2012).

    CAS  PubMed  Google Scholar 

  43. DerSimonian, R. & Laird, N. Meta-analysis in clinical trials. Control. Clin. Trials 7, 177–188 (1986).

    CAS  PubMed  Google Scholar 

  44. Yang, J. et al. Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits. Nat. Genet. 44, 369–375 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. Li, Y., Willer, C., Sanna, S. & Abecasis, G. Genotype imputation. Annu. Rev. Genomics Hum. Genet. 10, 387–406 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. Raychaudhuri, S. VIZ-GRAIL: visualizing functional connections across disease loci. Bioinformatics 27, 1589–1590 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  48. So, H. C., Gui, A. H., Cherny, S. S. & Sham, P. C. Evaluating the heritability explained by known susceptibility variants: a survey of ten complex diseases. Genet. Epidemiol. 35, 310–317 (2011).

    PubMed  Google Scholar 

Download references

Acknowledgements

We thank all participants who provided data for each study and also thank our valued colleagues who contributed to data collection and phenotypic characterization of clinical samples, genotyping, and analysis of individual datasets. Detailed acknowledgments and funding for individual studies can be found in the Supplementary Note.

Author information

Authors and Affiliations

Authors

Consortia

Contributions

TAGC study management: F.D., K.C.B., W.O.C.C., M.F.M., C.O., and D.L.N.

F.D. and D.L.N. designed the study and wrote the manuscript. F.D., D.L.N., and P.M.-J. designed and conducted the statistical analysis. K.C.B., W.O.C.C., M.F.M., and C.O. designed the study and wrote the manuscript. M.B., A.V., S. Letort, and H.M. carried out the quality control of the data and performed statistical analysis.

AAGC (Australia): study principal investigators (PIs), M.A.F., M.C.M., C.F.R., and P.J.T.; data collection or analysis, M.A.F., M.C.M., C.F.R., G.J., and P.J.T.

ALLERGEN Canadian Asthma Primary Prevention Study (CAPPS) and Study of Asthma, Genes and the Environment (SAGE): study PIs, A.B.B., M.C.-Y., D.D., and A.L.K.; data collection or analysis, D.D. and J.E.P.; study phenotyping, A.B.B. and M.C.-Y.

Saguenay‐Lac‐Saint‐Jean (SLSJ) Study: study PIs, C.L. and T.J.H.; study design and management, C.L.

Analysis in Population-based Cohorts of Asthma Traits (APCAT) Consortium: study PIs, J.N.H., M.-R.J., and V. Salomaa. Framingham Heart Study (FHS): study PI, G.T.O.; data collection or analysis, S.V. and Z.G. The European Prospective Investigation of Cancer (EPIC)-Norfolk: study PI, N.J.W.; data collection or analysis, J.H.Z. and R.S. Northern Finland Birth Cohort of 1966 (NFBC1966): study PI, M.-R.J.; data collection or analysis, A.C.A. and A.R. FINRISK: study PI, V. Salomaa; data collection or analysis, M. Kuokkanen and T. Laitinen. Health 2000 (H2000) Survey: study PIs, M.H. and P.J.; data collection or analysis, M. Kuokkanen and T.H. Helsinki Birth Cohort Study (HBCS): study PI, J.G.E.; data collection or analysis, E.W. and A. Palotie. Young Finns Study (YFS): study PI, O.T.R.; data collection or analysis, T. Lehtimäki and M. Kähönen.

African Ancestry Studies from the Candidate Gene Association Resource (CARe) Consortium: study PIs, J.N.H. and S.S.R.; data collection or analysis, C.D.P., D.B.K., L.J.S., R.K., K.M.B., and W.B.W.

Multi‐Ethnic Study of Atherosclerosis (MESA): study PIs, R.G.B. and S.S.R.; data collection or analysis, K.M.D. and A.M.

Atherosclerosis Risk in Communities Study (ARIC): study PI, S.J.L.; data collection or analysis, S.J.L. and L.R.L.

Cardiovascular Health Study (CHS): study PIs, S.A.G. and S.R.H.; data collection or analysis, G.L., S.A.G., and S.R.H.

deCode genetics: study PIs, K.S., I.J., D.F.G., U.T., and G.T.; data collection or analysis, I.J., D.F.G., and G.T.; study phenotyping, U.S.B.

Early Genetics and Lifecourse Epidemiology (EAGLE) Consortium: PI, H.B. Cophenhagen Prospective Study on Asthma in Childhood (COPSAC): study PIs, H.B. and K.B.; data analysis, E. Kreiner and J.W.; study phenotyping, K.B. Danish National Birth Cohort (DNBC): study PI, M.M.; data collection or analysis, B.F. and F. Geller. GENERATION R: study PI, J.C.d.J.; data collection or analysis, R.J.P.v.d.V., L.D., and V.W.V.J. GINIplus/LISAplus: study PI, J. Heinrich; genotyping, data collection or analysis, M. Standl and C.M.T.T.; study phenotyping, J. Heinrich. Manchester Asthma and Allergy Study (MAAS): study PIs, A.S. and A.C.; data collection or analysis, J.A.C. Western Australian Pregnancy Cohort Study (RAINE): study PI, P.H.; data collection or analysis, W.A. and C.E.P.

British 1958 Birth Cohort (B58C) Study: PI and statistical analysis, D.P.S.

EVE Consortium: study PIs, C.O., D.L.N., K.C.B., E. Bleecker, E. Burchard, J. Gauderman, F. Gilliland, S.J.L., F.J.M., D.M., I.R., S.T.W., L.K.W., and B.A.R.; data collection or analysis, D.L.N., J. Gauderman, S.J.L., D.M., D.G.T., B.A.R., B.E.H., P.E.G., M.T.S., C.E., B.E.D.-R.-N., J.J.Y., A.M.L., R.A. Myers, R.A. Mathias, and T.H.B.

Japanese Adult Asthma Research Consortium (JAARC): study PI, T.T.; data collection or analysis, T.T., A.T., and M. Kubo.

Japan Pediatric Asthma Consortium (JPAC): study PI, E.N.; data collection or analysis, H.H. and K.M.

GABRIEL Consortium: study PIs, W.O.C.C. and E.V.M.; genotyping, M.L.; data analysis, E.B., F.D., M.F., and D.P.S. Epidemiological study on the Genetics and Environment of Asthma (EGEA): study PIs, V. Siroux and F.D.; genotyping, data collection or analysis, M.L. and E. Bouzigon. Avon Longitudinal Study of Parents and Children (ALSPAC): study PI, J. Henderson; genotyping, data collection or analysis, W.L.M. and R.G.; study phenotyping, J. Henderson. European Community Respiratory Health Survey (ECRHS): study PI, D.J.; data collection or analysis, C.J. and J. Heinrich. Children, Allergy, Milieu, Stockholm, Epidemiology (BAMSE) study: study PIs, E.M., M.W., and G.P. Busselton Health Study: study PIs, A.W.M., A.J., and J.B.; genotyping, data collection or analysis, A.W.M., A.J., J. Hui, and J.B. GABRIEL Advanced Surveys: study PI, E.V.M.; data collection or analysis, M. Kabesch and J. Genuneit. Kursk State Medical University (KSMU) Study: study PI, A. Polonikov; data collection or analysis, M. Solodilova and V.I.; Medical Research Council-funded Collection of Nuclear Families with Asthma (MRCA-UKC): study PIs, W.O.C.C. and M.M.; data collection or analysis, L. Liang. Multicentre Asthma Genetics in Childhood Study (MAGICS): study PI, M. Kabesch; data collection or analysis, A.V.B. and S.M. German Multicentre Allergy Study (MAS): study PI, Y.-A.L.; data collection or analysis, S. Lau and I.M. Prevention and Incidence of Asthma and Mite Allergy (PIAMA) cohort : study PIs, G.H.K. and D.S.P.; data collection or analysis, G.H.K., D.S.P., and U.G. Swiss Cohort Study on Air Pollution and Lung and Heart Diseases in Adults (SAPALDIA): study PI, N.P.-H.; data collection or analysis, M.I. and A.K. Tomsk Study: study PIs, L.M.O. and V.P.P.; data collection or analysis, M.B.F. and P.A.S. UFA Study: study PI, E. Khusnutdinova; data collection or analysis, A.S.K. and Y.F. Industrial Cohorts Research Group (INDUSTRIAL): study PIs, D.H. and T.S.; data collection or analysis, I.M.W. and V. Schlünssen. Severe Asthma Cohorts (SEVERE): study PIs, A.B., K.F.C., and C.E.B.

Netherlands Twin Register (NTR) Study: study PI, D.I.B.; genotyping, data collection or analysis, J.J.H., H.M., and G.W.

Rotterdam Study: study PIs, A.H., B.H.S., and G.G.B.; genotyping, data collection or analysis, G.G.B., B.H.S., D.W.L., L. Lahousse, and A.G.U.

Dutch Asthma Genetics Consortium (DAGC): study PIs, G.H.K. and D.S.P.; genotyping, data collection or analysis, G.H.K., D.S.P., J.A., M.A.E.N., and J.M.V.

All authors provided critical review of the manuscript.

Corresponding authors

Correspondence to Florence Demenais or Dan L. Nicolae.

Ethics declarations

Competing interests

The authors affiliated with deCODE (D.F.G., I.J., K.S., U.T., and G.T.) are employees of deCODE genetics/Amgen. All other coauthors have no conflicts of interest to declare.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–7, Supplementary Tables 3, 5–7, 9, 10, 12–17, 20 and 21, and Supplementary Note.

Life Sciences Reporting Summary

Supplementary Table 1

Description of TAGC studies included in the meta-analysis.

Supplementary Table 2

Information on genotyping methods, imputation, and statistical analysis by study. Details and references for each study are in the Supplementary Note.

Supplementary Table 4

Genome-wide significant SNPs (Prandom ≤ 5 × 10−8) in the European-ancestry meta-analysis.

Supplementary Table 8

Genome-wide significant SNPs (Prandom ≤ 5 × 10−8) in the multi-ancestry meta-analysis.

Supplementary Table 11

Association of 17q12-21 SNPs with asthma in multi-ancestry and pediatric meta-analyses.

Supplementary Table 18

Overlap between TAGC asthma-association signals (Prandom <10−3) and GWAS signals with diseases/traits in the GWAS catalog.

Supplementary Table 19

Enrichment of asthma risk loci in promoter and enhancer marks by cell type. The results presented in this table are for 16 out of the 18 asthma loci shown in Table 1. The 6p21.33 and 6p21.32 loci spanning the HLA complex were excluded because of high variability and LD in the region. Enhancer and promoter marks were defined using the ChromHMM 15-state model applied to 127 ROADMAP/ENCODE reference epigenomes (PMID 25693563).

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Demenais, F., Margaritte-Jeannin, P., Barnes, K.C. et al. Multiancestry association study identifies new asthma risk loci that colocalize with immune-cell enhancer marks. Nat Genet 50, 42–53 (2018). https://doi.org/10.1038/s41588-017-0014-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41588-017-0014-7

This article is cited by

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing