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The effect of host genetics on the gut microbiome

Nature Genetics volume 48, pages 1407–1412 (2016)Cite this article

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Abstract

The gut microbiome is affected by multiple factors, including genetics. In this study, we assessed the influence of host genetics on microbial species, pathways and gene ontology categories, on the basis of metagenomic sequencing in 1,514 subjects. In a genome-wide analysis, we identified associations of 9 loci with microbial taxonomies and 33 loci with microbial pathways and gene ontology terms at P < 5 × 10−8. Additionally, in a targeted analysis of regions involved in complex diseases, innate and adaptive immunity, or food preferences, 32 loci were identified at the suggestive level of P < 5 × 10−6. Most of our reported associations are new, including genome-wide significance for the C-type lectin molecules CLEC4F–CD207 at 2p13.3 and CLEC4A–FAM90A1 at 12p13. We also identified association of a functional LCT SNP with the Bifidobacterium genus (P = 3.45 × 10−8) and provide evidence of a gene–diet interaction in the regulation of Bifidobacterium abundance. Our results demonstrate the importance of understanding host–microbe interactions to gain better insight into human health.

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Figure 1: Data analysis workflow and association summary.
Figure 2: Manhattan plot of genome-wide associations with microbes and functional units (MetaCyc pathways and GO2000 terms).
Figure 3: Complex interaction between a functional LCT variant, dairy intake and Bifidobacterium abundance.

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Acknowledgements

We thank the participants and the staff of LifeLines-DEEP, 500FG and MIBS for their collaboration. We thank J. Dekens, M. Platteel, J. Pietersma and A. Maatman for management and technical support, and K. McIntyre and J. Senior for editing the manuscript.

This project was funded by grants from Top Institute Food and Nutrition, Wageningen, to C.W. (TiFN GH001), the Netherlands Organization for Scientific Research to J.F. (NWO-VIDI 864.13.013), L.F. (ZonMW-VIDI 917.14.374) and R.K.W. (ZonMW-VIDI 016.136.308), and CardioVasculair Onderzoek Nederland to M.H.H., M.G.N., A.Z. and J.F. (CVON 2012-03). A.Z. holds a Rosalind Franklin Fellowship (University of Groningen). This research received funding from the European Research Council under the European Union's Seventh Framework Programme: C.W. is supported by FP7/2007-2013/ERC Advanced Grant (agreement 2012-322698) and a Spinoza Prize from the Netherlands Organization for Scientific Research. M.G.N. holds an ERC Consolidator Grant (310372). L.F. has an FP7/2007-2013 grant (agreement 259867) and an ERC Starting Grant (637640, ImmRisk). Y.L. holds a Netherlands Organization for Scientific Research VENI grant (863.13.011).

Author information

Author notes

  1. Maria Carmen Cenit

    Present address: Present address: Department of Pediatrics, Dr. Peset University Hospital, Valencia, Spain.,

  2. Marc Jan Bonder and Alexander Kurilshikov: These authors contributed equally to this work.

  3. Jingyuan Fu and Alexandra Zhernakova: These authors jointly directed this work.

Authors and Affiliations

  1. Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands

    Marc Jan Bonder, Alexander Kurilshikov, Ettje F Tigchelaar, Patrick Deelen, Daria V Zhernakova, Soesma A Jankipersadsing, Maria Carmen Cenit, Morris A Swertz, Yang Li, Vinod Kumar, Lude Franke, Cisca Wijmenga, Jingyuan Fu & Alexandra Zhernakova

  2. Institute of Chemical Biology and Fundamental Medicine SB RAS, Novosibirsk, Russia

    Alexander Kurilshikov

  3. Department of Natural Sciences, Novosibirsk State University, Novosibirsk, Russia

    Alexander Kurilshikov

  4. Top Institute Food and Nutrition, Wageningen, the Netherlands

    Ettje F Tigchelaar, Zlatan Mujagic & Alexandra Zhernakova

  5. Division of Gastroenterology-Hepatology, Department of Internal Medicine, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University Medical Center, Maastricht, the Netherlands

    Zlatan Mujagic, Ad A M Masclee & Daisy Jonkers

  6. Department of Gastroenterology and Hepatology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands

    Floris Imhann, Arnau Vich Vila & Rinse K Weersma

  7. University of Groningen, University Medical Center Groningen, Genomics Coordination Center, Groningen, the Netherlands

    Patrick Deelen & Morris A Swertz

  8. Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA

    Tommi Vatanen, Melanie Schirmer & Ramnik J Xavier

  9. Department of Computer Science, Aalto University School of Science, Espoo, Finland

    Tommi Vatanen

  10. Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA

    Melanie Schirmer

  11. Department of Internal Medicine, Radboud University Medical Center, Nijmegen, the Netherlands

    Sanne P Smeekens, Martin Jaeger, Marije Oosting, Leo Joosten & Mihai G Netea

  12. Radboud Center of Infectious Diseases, Radboud University Medical Center, Nijmegen, the Netherlands

    Sanne P Smeekens, Martin Jaeger, Marije Oosting, Leo Joosten & Mihai G Netea

  13. Department of Pediatrics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands

    Soesma A Jankipersadsing, Marten H Hofker & Jingyuan Fu

  14. Department of Medical Microbiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands

    Hermie Harmsen

  15. Center for Computational and Integrative Biology, Massachusetts General Hospital, Boston, Massachusetts, USA

    Ramnik J Xavier

  16. Gastrointestinal Unit, Massachusetts General Hospital, Boston, Massachusetts, USA

    Ramnik J Xavier

  17. Center for the Study of Inflammatory Bowel Disease, Massachusetts General Hospital, Boston, Massachusetts, USA

    Ramnik J Xavier

Authors
  1. Marc Jan Bonder
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  2. Alexander Kurilshikov
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  3. Ettje F Tigchelaar
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  4. Zlatan Mujagic
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  5. Floris Imhann
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  6. Arnau Vich Vila
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  7. Patrick Deelen
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  8. Tommi Vatanen
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  9. Melanie Schirmer
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  10. Sanne P Smeekens
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  11. Daria V Zhernakova
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  12. Soesma A Jankipersadsing
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  13. Martin Jaeger
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  14. Marije Oosting
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  15. Maria Carmen Cenit
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  16. Ad A M Masclee
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  17. Morris A Swertz
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  18. Yang Li
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  19. Vinod Kumar
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  20. Leo Joosten
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  21. Hermie Harmsen
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  22. Rinse K Weersma
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  23. Lude Franke
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  24. Marten H Hofker
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  25. Ramnik J Xavier
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  26. Daisy Jonkers
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  27. Mihai G Netea
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  28. Cisca Wijmenga
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  29. Jingyuan Fu
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  30. Alexandra Zhernakova
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Conceptualization: A.Z., J.F., C.W. and M.J.B. Methodology: M.J.B., A.K., L.F., J.F., P.D., T.V. and M.S. Software: M.J.B., A.K., L.F., J.F., P.D., M.A.S. and D.V.Z. Formal analysis: M.J.B., A.K., J.F. and A.Z. Investigation: A.Z., J.F., M.J.B., A.K., F.I., D.V.Z., S.A.J., A.V.V., E.F.T., H.H. and M.C.C. Resources: C.W., A.Z., L.F., J.F., M.A.S., M.G.N., R.J.X., L.J. and A.A.M.M. Data curation: M.J.B., A.K., J.F., P.D., L.F., S.A.J. and Y.L. Writing–original draft: A.Z., J.F., M.J.B., A.K. and C.W. Writing–review and editing: M.J.B., A.K., E.F.T., Z.M., F.I., A.V.V., P.D., T.V., M.S., S.P.S., D.V.Z., S.A.J., M.J., M.O., M.A.S., M.C.C., Y.L., V.K., H.H., R.K.W., L.F., M.H.H., D.J., M.G.N., C.W., J.F. and A.Z. Visualization: A.K., M.J.B., A.Z. and J.F. Supervision: A.Z., J.F., C.W., L.F., R.K.W. and M.H.H. Project administration: A.Z., J.F., C.W., L.F., M.G.N., D.J., A.A.M.M. and S.P.S. Funding acquisition: A.Z., J.F., C.W., L.F., M.G.N., D.J. and A.A.M.M.

Corresponding authors

Correspondence to Cisca Wijmenga, Jingyuan Fu or Alexandra Zhernakova.

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Competing interests

The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Genome-wide significant microbial QTL plots on the microbial level.

The plots show the effect of SNPs on normalized microbial abundance, showing a combination of violin plots and box plots. The box plots show the median and 25% and 75% quantiles.

Supplementary Figure 2 Genome-wide significant microbial QTL plots on microbial function level (MetaCyc-Pathway).

The plots show the effect of SNPs on normalized microbial functional abundance, showing a combination of violin plots and box plots. The box plots show the median and 25% and 75% quantiles.

Supplementary Figure 3 Genome-wide microbial QTL plots on the microbial function level (GO terms).

The plots show the effect of SNPs on normalized functional abundance, showing a combination of violin plots and box plots. The box plots show the median and 25% and 75% quantiles.

Supplementary Figure 4 Correlation of associated GO terms with taxonomies on the species level.

The plots show the Spearman correlations between the tested taxonomies and GO2000 terms.

Supplementary Figure 5 Relationship between Bifidobacterium and milk consumption and between an LCT SNP (rs4988235) and milk consumption.

(a) Correlation of milk consumption with Bifidobacterium abundance in the three tested cohorts. (b) Relationship of a functional lactase variant and milk consumption in the three cohorts.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–5. (PDF 1520 kb)

Supplementary Table 1

Abundance levels of microbes, MetaCyc pathways and GO2000 terms. (XLSX 181 kb)

Supplementary Table 2

Summary of the tested number of associations per analysis branch. (XLSX 9 kb)

Supplementary Table 3

Genome-wide microbial QTL results. (XLSX 46 kb)

Supplementary Table 4

Estimations for the FDRs presented in the paper. (XLSX 12 kb)

Supplementary Table 5

Correlations between microbial abundance and MetaCyc abundance levels. (XLSX 1860 kb)

Supplementary Table 6

Correlations between microbial abundance and GO2000 abundance levels. (XLSX 1502 kb)

Supplementary Table 7

SNP selection list GWAS-associated SNPs. (XLSX 143 kb)

Supplementary Table 8

SNP selection list for innate immunity and food preference, including references. (XLSX 9 kb)

Supplementary Table 9

Microbial QTLs on abundance and functional levels for SNPs previously related to GWAS. (XLSX 17 kb)

Supplementary Table 10

Microbial QTLs on abundance and functional levels for the variants in the HLA. (XLSX 11 kb)

Supplementary Table 11

Microbial QTLs on abundance and functional levels for SNPs related to innate immunity. (XLSX 28 kb)

Supplementary Table 12

Microbial QTLs on abundance and functional levels for SNPs related to food preference. (XLSX 14 kb)

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Bonder, M., Kurilshikov, A., Tigchelaar, E. et al. The effect of host genetics on the gut microbiome. Nat Genet 48, 1407–1412 (2016). https://doi.org/10.1038/ng.3663

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