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Genome-wide association study in 8,956 German individuals identifies influence of ABO histo-blood groups on gut microbiome

Nature Genetics volume 53, pages 147–155 (2021)Cite this article

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Abstract

The intestinal microbiome is implicated as an important modulating factor in multiple inflammatory1,2, neurologic3 and neoplastic diseases4. Recent genome-wide association studies yielded inconsistent, underpowered and rarely replicated results such that the role of human host genetics as a contributing factor to microbiome assembly and structure remains uncertain5,6,7,8,9,10,11. Nevertheless, twin studies clearly suggest host genetics as a driver of microbiome composition11. In a genome-wide association analysis of 8,956 German individuals, we identified 38 genetic loci to be associated with single bacteria and overall microbiome composition. Further analyses confirm the identified associations of ABO histo-blood groups and FUT2 secretor status with Bacteroides and Faecalibacterium spp. Mendelian randomization analysis suggests causative and protective effects of gut microbes, with clade-specific effects on inflammatory bowel disease. This holistic investigative approach of the host, its genetics and its associated microbial communities as a ‘metaorganism’ broaden our understanding of disease etiology, and emphasize the potential for implementing microbiota in disease treatment and management.

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Fig. 1: Summary of cohort properties.
Fig. 2: Results from the GWAS.
Fig. 3: ABO histo-blood groups show connection to gut microbial features.

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Data availability

Cohort-level summaries of microbial feature abundances are available in Supplementary Table 1. Complete summary statistics of all tested microbial features are available via the NHGRI-EBI GWAS catalog (https://www.ebi.ac.uk/gwas), GCP ID: GCP000068; study accession nos. GCST90011301–GCST90011730. The German mGWAS browser application is available for local query of results from Dockerhub: https://hub.docker.com/r/mruehlemann/german_mgwas_browser_app. Due to the informed consent obtained from the participants, phenotypes, as well as genotyping and not all 16S rRNA gene-sequencing data, can be deposited publicly; however, all data are available upon request from the respective biobanks (see Supplementary Note for details). PopGen and Focus: 16S rRNA-sequencing data are available at the National Center for Biotechnology Information Sequence Read Archive, accession no. PRJNA673102; http://www.uksh.de/p2n/Information+for+Researchers.html. KORA FF4: https://epi.helmholtz-muenchen.de. SHIP and SHIP-TREND: https://www.fvcm.med.uni-greifswald.de/dd_service/data_use_intro.php (German website; English-speaking assistance for the application process can be requested via: transfer@uni-greifswald.de).

Code availability

Microbiome data pre-processing, GWAS analysis and post-processing code are available via github: https://github.com/mruehlemann/german_mgwas_code.

References

  1. Lloyd-Price, J. et al. Multi-omics of the gut microbial ecosystem in inflammatory bowel diseases. Nature 569, 655–662 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Franzosa, E. A. et al. Gut microbiome structure and metabolic activity in inflammatory bowel disease. Nat. Microbiol. 4, 293–305 (2019).

    Article  CAS  PubMed  Google Scholar 

  3. Cryan, J. F., O’Riordan, K. J., Sandhu, K., Peterson, V. & Dinan, T. G. The gut microbiome in neurological disorders. Lancet Neurol. 19, 179–194 (2019).

    Article  PubMed  Google Scholar 

  4. Wirbel, J. et al. Meta-analysis of fecal metagenomes reveals global microbial signatures that are specific for colorectal cancer. Nat. Med. 25, 679–689 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Blekhman, R. et al. Host genetic variation impacts microbiome composition across human body sites. Genome Biol. 16, 191 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  6. Goodrich, J. K. et al. Human genetics shape the gut microbiome. Cell 159, 789–799 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Wang, J. et al. Genome-wide association analysis identifies variation in vitamin D receptor and other host factors influencing the gut microbiota. Nat. Genet. 48, 1396–1406 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Turpin, W. et al. Association of host genome with intestinal microbial composition in a large healthy cohort. Nat. Genet. 48, 1413–1417 (2016).

    Article  CAS  PubMed  Google Scholar 

  9. Bonder, M. J. et al. The effect of host genetics on the gut microbiome. Nat. Genet. 48, 1407–1412 (2016).

    Article  CAS  PubMed  Google Scholar 

  10. Rothschild, D. et al. Environment dominates over host genetics in shaping human gut microbiota. Nature 555, 210–215 (2018).

    Article  CAS  PubMed  Google Scholar 

  11. Goodrich, J. K. et al. Genetic determinants of the gut microbiome in UK twins. Cell Host Microbe 19, 731–743 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Krawczak, M. et al. PopGen: population-based recruitment of patients and controls for the analysis of complex genotype–phenotype relationships. Community Genet. 9, 55–61 (2006).

    PubMed  Google Scholar 

  13. Völzke, H. [Study of Health in Pomerania (SHIP). Concept, design and selected results]. Bundesgesundheitsblatt—Gesundheitsforschung—Gesundheitsschutz 55, 790–794 (2012).

    Article  PubMed  Google Scholar 

  14. Völzke, H. et al. Cohort profile: the study of health in Pomerania. Int. J. Epidemiol. 40, 294–307 (2011).

    Article  PubMed  Google Scholar 

  15. Holle, R., Happich, M., Löwel, H. & Wichmann, H. E., MONICA/KORA Study Group. KORA—a research platform for population based health research. Gesundheitswesen Bundesverb. 67, S19–S25 (2005).

    Article  Google Scholar 

  16. Reitmeier, S. et al. Arrhythmic gut microbiome signatures for risk profiling of type-2 diabetes. Cell Host Microbe 28, 258–272.e6 (2020).

    Article  CAS  PubMed  Google Scholar 

  17. Johnson, J. S. et al. Evaluation of 16S rRNA gene sequencing for species and strain-level microbiome analysis. Nat. Commun. 10, 5029 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  18. Lozupone, C. & Knight, R. UniFrac: a new phylogenetic method for comparing microbial communities. Appl. Environ. Microbiol. 71, 8228–8235 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Davenport, E. R. et al. Genome-wide association studies of the human gut microbiota. PLoS ONE 10, e0140301 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  20. Wegiel, B. et al. Biliverdin inhibits Toll-like receptor-4 (TLR4) expression through nitric oxide-dependent nuclear translocation of biliverdin reductase. Proc. Natl Acad. Sci. USA 108, 18849–18854 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Schirmer, M. et al. Linking the human gut microbiome to inflammatory cytokine production capacity. Cell 167, 1125–1136.e8 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. McGovern, D. P. B. et al. Fucosyltransferase 2 (FUT2) non-secretor status is associated with Crohn’s disease. Hum. Mol. Genet. 19, 3468–3476 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. 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 

  24. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. 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).

    Article  PubMed  PubMed Central  Google Scholar 

  26. Smith, G. D. & Ebrahim, S. Mendelian Randomization: Genetic Variants as Instruments for Strengthening Causal Inference in Observational Studies (National Academies Press, 2008).

  27. Wade, K. H. & Hall, L. J. Improving causality in microbiome research: can human genetic epidemiology help? Wellcome Open Res. 4, 199 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  28. Hemani, G. et al. The MR-Base platform supports systematic causal inference across the human phenome. eLife 7, e34408 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  29. Arumugam, M. et al. Enterotypes of the human gut microbiome. Nature 473, 174–180 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Wu, G. D. et al. Linking long-term dietary patterns with gut microbial enterotypes. Science 334, 105–108 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Costea, P. I. et al. Enterotypes in the landscape of gut microbial community composition. Nat. Microbiol. 3, 8–16 (2018).

    Article  CAS  PubMed  Google Scholar 

  32. Vieira-Silva, S. et al. Quantitative microbiome profiling disentangles inflammation- and bile duct obstruction-associated microbiota alterations across PSC/IBD diagnoses. Nat. Microbiol. 4, 1826–1831 (2019).

    Article  CAS  PubMed  Google Scholar 

  33. Zhou, Y. & Zhi, F. Lower level of bacteroides in the gut microbiota is associated with inflammatory bowel disease: a meta-analysis. BioMed. Res. Int. 2016, 5828959 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  34. Wang, K. et al. Parabacteroides distasonis alleviates obesity and metabolic dysfunctions via production of succinate and secondary bile acids. Cell Rep. 26, 222–235.e5 (2019).

    Article  CAS  PubMed  Google Scholar 

  35. Watanabe, K., Taskesen, E., van. Bochoven, A. & Posthuma, D. Functional mapping and annotation of genetic associations with FUMA. Nat. Commun. 8, 1826 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  36. Davenport, E. R. et al. ABO antigen and secretor statuses are not associated with gut microbiota composition in 1,500 twins. BMC Genomics 17, 941 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  37. Turpin, W. et al. FUT2 genotype and secretory status are not associated with fecal microbial composition and inferred function in healthy subjects. Gut Microbes 9, 357–368 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Rausch, P. et al. Colonic mucosa-associated microbiota is influenced by an interaction of Crohn disease and FUT2 (secretor) genotype. Proc. Natl Acad. Sci. USA 108, 19030–19035 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Weiss, F. U. et al. Fucosyltransferase 2 (FUT2) non-secretor status and blood group B are associated with elevated serum lipase activity in asymptomatic subjects, and an increased risk for chronic pancreatitis: a genetic association study. Gut 64, 646–656 (2015).

    Article  CAS  PubMed  Google Scholar 

  40. Godon, J. J., Zumstein, E., Dabert, P., Habouzit, F. & Moletta, R. Molecular microbial diversity of an anaerobic digestor as determined by small-subunit rDNA sequence analysis. Appl. Environ. Microbiol. 63, 2802–2813 (1997).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Frost, F. et al. Impaired exocrine pancreatic function associates with changes in intestinal microbiota composition and diversity. Gastroenterology 156, 1010–1015 (2019).

    Article  CAS  PubMed  Google Scholar 

  42. Paré, G. et al. Novel association of ABO histo-blood group antigen with soluble ICAM-1: results of a genome-wide association study of 6,578 women. PLoS Genet. 4, e1000118 (2008).

  43. R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2014).

  44. Callahan, B. J. et al. DADA2: high-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Cole, J. R. et al. Ribosomal database project: data and tools for high throughput rRNA analysis. Nucleic Acids Res. 42, D633–D642 (2014).

    Article  CAS  PubMed  Google Scholar 

  46. McMurdie, P. J. & Holmes, S. phyloseq: An R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 8, e61217 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Edgar, R. C. Updating the 97% identity threshold for 16S ribosomal RNA OTUs. Bioinformatics 34, 2371–2375 (2018).

    Article  CAS  PubMed  Google Scholar 

  48. Rognes, T., Flouri, T., Nichols, B., Quince, C. & Mahé, F. VSEARCH: a versatile open source tool for metagenomics. Peer J. 4, e2584 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  49. Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596 (2013).

    Article  CAS  PubMed  Google Scholar 

  50. Price, M. N., Dehal, P. S. & Arkin, A. P. FastTree 2—approximately maximum-likelihood trees for large alignments. PLoS ONE 5, e9490 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  51. Oksanen, J. et al. The vegan package. Community Ecol. Package 10, 631–637 (2007).

    Google Scholar 

  52. Zhou, J. & Ning, D. Stochastic community assembly: does it matter in microbial ecology? Microbiol. Mol. Biol. Rev. 81, e00002–e00017 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  53. Rühlemann, M. C. et al. Application of the distance-based F test in an mGWAS investigating β diversity of intestinal microbiota identifies variants in SLC9A8 (NHE8) and 3 other loci. Gut Microbes 9, 68–75 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  54. Willer, C. J., Li, Y. & Abecasis, G. R. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics 26, 2190–2191 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Qin, Y. et al. Combined effects of host genetics and diet on human gut microbiota and incident disease in a single population cohort. Preprint at medRxiv https://doi.org/10.1101/2020.09.12.20193045 (2020).

  56. Li, J. & Ji, L. Adjusting multiple testing in multilocus analyses using the eigenvalues of a correlation matrix. Heredity 95, 221–227 (2005).

    Article  CAS  PubMed  Google Scholar 

  57. Deeks, J. J., Higgins, J. P. & Altman, D. G. in Cochrane Handbook for Systematic Reviews of Interventions (eds. Higgins, J. P. T. & Green, S.) 243–296 (John Wiley & Sons, 2008).

  58. Yarmolinsky, J. et al. Circulating selenium and prostate cancer risk: a Mendelian randomization analysis. J. Natl Cancer Inst. 110, 1035–1038 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We thank T. Hauptmann, I. Urbach and I. Wulf of the IKMB Microbiome Lab for excellent technical assistance. We thank K. Wade for her valuable input on the MR analysis and M. Schulzky for support in figure design. This work was supported by the Deutsche Forschungsgemeinschaft (DFG) Collaborative Research Center 1182 ‘Origin and Function of Metaorganisms’ (grant no. SFB1182, Project A2 to A.F.) and the DFG Cluster of Excellence 2167 ‘Precision Medicine in Chronic Inflammation (PMI)’ (grant no. EXC2167 to A.F.). The SHIP part of the study was supported by the PePPP-project (ESF/14-BM-A55_0045/16 to M.M.L.) and the RESPONSE-project (BMBF grant no. 03ZZ0921E to M.M.L.). The SHIP is part of the Research Network Community Medicine of the University Medicine Greifswald, which is supported by the German Federal State of Mecklenburg-West Pomerania.

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Authors and Affiliations

  1. Institute of Clinical Molecular Biology, Kiel University, Kiel, Germany

    Malte Christoph Rühlemann, Corinna Bang, Lucas Moitinho-Silva, Louise Bruun Thingholm, Frauke Degenhardt, Michael Wittig, Jan Kässens & Andre Franke

  2. Evolutionary Genomics, Max Planck Institute for Evolutionary Biology, Plön, Germany

    Britt Marie Hermes, Shauni Doms & John F. Baines

  3. Institute of Experimental Medicine, Kiel University, Kiel, Germany

    Britt Marie Hermes, Shauni Doms & John F. Baines

  4. Lübeck Institute of Experimental Dermatology, University of Lübeck, Lübeck, Germany

    Britt Marie Hermes

  5. Department of Dermatology, Kiel University, Kiel, Germany

    Lucas Moitinho-Silva

  6. Department of Medicine A, University Medicine Greifswald, Greifswald, Germany

    Fabian Frost, Frank Ulrich Weiss, Stefan Weiss & Markus M. Lerch

  7. Institute of Epidemiology, Helmholtz Zentrum München, Neuherberg, Germany

    Annette Peters & Harald Grallert

  8. German Center for Diabetes Research, Neuherberg, Germany

    Annette Peters

  9. ZIEL—Institute for Food & Health, Technical University of Munich, Freising, Germany

    Klaus Neuhaus & Dirk Haller

  10. Department of Functional Genomics, Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, Germany

    Uwe Völker, Henry Völzke, Georg Homuth & Stefan Weiss

  11. Department of Internal Medicine 1, Kiel University, Kiel, Germany

    Matthias Laudes

  12. Institute of Epidemiology, Kiel University, Kiel, Germany

    Wolfgang Lieb

  13. Chair of Nutrition and Immunology, Technical University of Munich, Freising, Germany

    Dirk Haller

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  1. Malte Christoph Rühlemann
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Contributions

M.C.R. performed microbiome sample preparation, data generation and curation, implemented ABO blood group inference, implemented statistical models, performed the (meta-)analysis, curated and interpreted results, and wrote the manuscript draft. B.M.H. curated and interpreted results and wrote the manuscript draft. C.B. performed microbiome sample preparation, data generation, curated and interpreted results, and advised in the writing of the draft manuscript. S.D. implemented statistical models, performed the (meta-)analysis and wrote the manuscript draft. L.M.-S. and L.B.T. curated and interpreted results. F.F. and F.D. performed data QC and curation. M.W. implemented ABO blood group inference. J.K. implemented statistical models and performed the (meta-)analysis. F.U.W. performed microbiome sample preparation, data generation and curation. A.P., U.V., S.W. H.G., M.L. and W.L. performed genotype and phenotype data generation and collection. K.H. performed microbiome sample preparation, data generation and curation. H.V. performed genotype and phenotype data generation and collection and data QC and curation. G.H. performed genotype and phenotype data collection. D.H. and M.M.L. designed the experiment. J.F.B. and A.F. designed the experiment and advised on the writing of the draft manuscript. All authors reviewed, edited and approved the final manuscript.

Corresponding author

Correspondence to Andre Franke.

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Peer review information Nature Genetics thanks Tao Zhang, Jonathan Braun and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary information

Supplementary information

Supplementary Notes 1–6 and Supplementary Figs. 1–3.

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Supplementary Tables 1–8.

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Rühlemann, M.C., Hermes, B.M., Bang, C. et al. Genome-wide association study in 8,956 German individuals identifies influence of ABO histo-blood groups on gut microbiome. Nat Genet 53, 147–155 (2021). https://doi.org/10.1038/s41588-020-00747-1

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