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.

The effect of host genetics on the gut microbiome

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 CLEC4FCD207 at 2p13.3 and CLEC4AFAM90A1 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.

This is a preview of subscription content

Access options

Buy article

Get time limited or full article access on ReadCube.

$32.00

All prices are NET prices.

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.

Accession codes

Primary accessions

Sequence Read Archive

References

  1. Tigchelaar, E.F. et al. Gut microbiota composition associated with stool consistency. Gut 65, 540–542 (2015).

    PubMed  Article  Google Scholar 

  2. Imhann, F. et al. Proton pump inhibitors affect the gut microbiome. Gut 65, 740–748 (2015).

    PubMed  Article  CAS  Google Scholar 

  3. Zhernakova, A. et al. Population-based metagenomics analysis reveals markers for gut microbiome composition and diversity. Science 352, 565–569 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  4. David, L.A. et al. Diet rapidly and reproducibly alters the human gut microbiome. Nature 505, 559–563 (2014).

    CAS  PubMed  Article  Google Scholar 

  5. Scott, K.P., Gratz, S.W., Sheridan, P.O., Flint, H.J. & Duncan, S.H. The influence of diet on the gut microbiota. Pharmacol. Res. 69, 52–60 (2013).

    CAS  PubMed  Article  Google Scholar 

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

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  7. Org, E. et al. Genetic and environmental control of host–gut microbiota interactions. Genome Res. 25, 1558–1569 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  8. Leamy, L.J. et al. Host genetics and diet, but not immunoglobulin A expression, converge to shape compositional features of the gut microbiome in an advanced intercross population of mice. Genome Biol. 15, 552 (2014).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

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

    CAS  PubMed  PubMed Central  Article  Google Scholar 

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

    PubMed  PubMed Central  Article  CAS  Google Scholar 

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

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  12. Srinivas, G. et al. Genome-wide mapping of gene–microbiota interactions in susceptibility to autoimmune skin blistering. Nat. Commun. 4, 2462 (2013).

    PubMed  Article  CAS  Google Scholar 

  13. Parks, B.W. et al. Genetic control of obesity and gut microbiota composition in response to high-fat, high-sucrose diet in mice. Cell Metab. 17, 141–152 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  14. McKnite, A.M. et al. Murine gut microbiota is defined by host genetics and modulates variation of metabolic traits. PLoS One 7, e39191 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  15. Jostins, L. et al. Host–microbe interactions have shaped the genetic architecture of inflammatory bowel disease. Nature 491, 119–124 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  16. Knights, D. et al. Complex host genetics influence the microbiome in inflammatory bowel disease. Genome Med. 6, 107 (2014).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  17. Lamas, B. et al. CARD9 impacts colitis by altering gut microbiota metabolism of tryptophan into aryl hydrocarbon receptor ligands. Nat. Med. 22, 598–605 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  18. Tigchelaar, E.F. et al. Cohort profile: LifeLines DEEP, a prospective, general population cohort study in the northern Netherlands: study design and baseline characteristics. BMJ Open 5, e006772 (2015).

    PubMed  PubMed Central  Article  Google Scholar 

  19. Netea, M.G. et al. Understanding human immune function using the resources from the Human Functional Genomics Project. Nat. Med. 22, 831–833 (2016).

    CAS  PubMed  Article  Google Scholar 

  20. Mujagic, Z. et al. Small intestinal permeability is increased in diarrhoea predominant IBS, while alterations in gastroduodenal permeability in all IBS subtypes are largely attributable to confounders. Aliment. Pharmacol. Ther. 40, 288–297 (2014).

    CAS  PubMed  Article  Google Scholar 

  21. Juste, C. et al. Bacterial protein signals are associated with Crohn's disease. Gut 63, 1566–1577 (2014).

    CAS  PubMed  Article  Google Scholar 

  22. Liu, T.-C. et al. O-011 Paneth cell phenotypes define a subtype of pediatric Crohn's disease through alterations in host–microbial interactions. Inflamm. Bowel Dis. 22 (Suppl. 1), S4 (2016).

    Article  Google Scholar 

  23. Torres, J. et al. The features of mucosa-associated microbiota in primary sclerosing cholangitis. Aliment. Pharmacol. Ther. 43, 790–801 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  24. Hromatka, B.S. et al. Genetic variants associated with motion sickness point to roles for inner ear development, neurological processes and glucose homeostasis. Hum. Mol. Genet. 24, 2700–2708 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  25. Locke, A.E. et al. Genetic studies of body mass index yield new insights for obesity biology. Nature 518, 197–206 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  26. Williams, A.L. et al. Sequence variants in SLC16A11 are a common risk factor for type 2 diabetes in Mexico. Nature 506, 97–101 (2014).

    CAS  PubMed  Article  Google Scholar 

  27. Fu, J. et al. The gut microbiome contributes to a substantial proportion of the variation in blood lipids. Circ. Res. 117, 817–824 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  28. Martínez, I. et al. Gut microbiome composition is linked to whole grain–induced immunological improvements. ISME J. 7, 269–280 (2013).

    PubMed  Article  CAS  Google Scholar 

  29. Tyler, A.D. et al. Characterization of the gut-associated microbiome in inflammatory pouch complications following ileal pouch-anal anastomosis. PLoS One 8, e66934 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  30. Landy, J. et al. Variable alterations of the microbiota, without metabolic or immunological change, following faecal microbiota transplantation in patients with chronic pouchitis. Sci. Rep. 5, 12955 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  31. Kalabis, J., Rosenberg, I. & Podolsky, D.K. Vangl1 protein acts as a downstream effector of intestinal trefoil factor (ITF)/TFF3 signaling and regulates wound healing of intestinal epithelium. J. Biol. Chem. 281, 6434–6441 (2006).

    CAS  PubMed  Article  Google Scholar 

  32. Bae, J.A. et al. An unconventional KITENIN/ErbB4-mediated downstream signal of EGF upregulates c-Jun and the invasiveness of colorectal cancer cells. Clin. Cancer Res. 20, 4115–4128 (2014).

    CAS  PubMed  Article  Google Scholar 

  33. Lee, S. et al. Expression of KITENIN in human colorectal cancer and its relation to tumor behavior and progression. Pathol. Int. 61, 210–220 (2011).

    CAS  PubMed  Article  Google Scholar 

  34. Klingberg, S. et al. Inverse relation between dietary intake of naturally occurring plant sterols and serum cholesterol in northern Sweden. Am. J. Clin. Nutr. 87, 993–1001 (2008).

    CAS  PubMed  Article  Google Scholar 

  35. Kaplan, R.C. et al. A genome-wide association study identifies novel loci associated with circulating IGF-I and IGFBP-3. Hum. Mol. Genet. 20, 1241–1251 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  36. Liu, Y.J. et al. Genome-wide association scans identified CTNNBL1 as a novel gene for obesity. Hum. Mol. Genet. 17, 1803–1813 (2008).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  37. Strawbridge, R.J. et al. Genome-wide association identifies nine common variants associated with fasting proinsulin levels and provides new insights into the pathophysiology of type 2 diabetes. Diabetes 60, 2624–2634 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  38. Khan, M.T., Browne, W.R., van Dijl, J.M. & Harmsen, H.J.M. How can Faecalibacterium prausnitzii employ riboflavin for extracellular electron transfer? Antioxid. Redox Signal. 17, 1433–1440 (2012).

    CAS  PubMed  Article  Google Scholar 

  39. Morgan, X.C. et al. Dysfunction of the intestinal microbiome in inflammatory bowel disease and treatment. Genome Biol. 13, R79 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  40. Lozupone, C.A., Stombaugh, J.I., Gordon, J.I., Jansson, J.K. & Knight, R. Diversity, stability and resilience of the human gut microbiota. Nature 489, 220–230 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  41. Bønnelykke, K. et al. Meta-analysis of genome-wide association studies identifies ten loci influencing allergic sensitization. Nat. Genet. 45, 902–906 (2013).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  42. Gevers, D. et al. The treatment-naive microbiome in new-onset Crohn's disease. Cell Host Microbe 15, 382–392 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  43. Caesar, R., Tremaroli, V., Kovatcheva-Datchary, P., Cani, P.D. & Bäckhed, F. Crosstalk between gut microbiota and dietary lipids aggravates WAT inflammation through TLR signaling. Cell Metab. 22, 658–668 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  44. Yahiro, K. et al. DAP1, a negative regulator of autophagy, controls SubAB-mediated apoptosis and autophagy. Infect. Immun. 82, 4899–4908 (2014).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  45. Koren, I., Reem, E. & Kimchi, A. DAP1, a novel substrate of mTOR, negatively regulates autophagy. Curr. Biol. 20, 1093–1098 (2010).

    CAS  PubMed  Article  Google Scholar 

  46. Glocker, E.O. et al. Inflammatory bowel disease and mutations affecting the interleukin-10 receptor. N. Engl. J. Med. 361, 2033–2045 (2009).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  47. Galeone, M., Colucci, R., D'Erme, A.M., Moretti, S. & Lotti, T. Potential infectious etiology of Behçet's disease. Patholog. Res. Int. 2012, 595380 (2012).

    PubMed  Google Scholar 

  48. Huuskonen, J., Olkkonen, V.M., Jauhiainen, M. & Ehnholm, C. The impact of phospholipid transfer protein (PLTP) on HDL metabolism. Atherosclerosis 155, 269–281 (2001).

    CAS  PubMed  Article  Google Scholar 

  49. Getz, G.S. & Reardon, C.A. Apoprotein E as a lipid transport and signaling protein in the blood, liver, and artery wall. J. Lipid Res. 50 (Suppl.), S156–S161 (2009).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  50. Hevener, A.L. et al. Muscle-specific Pparg deletion causes insulin resistance. Nat. Med. 9, 1491–1497 (2003).

    CAS  PubMed  Article  Google Scholar 

  51. Hegele, R.A., Cao, H., Frankowski, C., Mathews, S.T. & Leff, T. PPARG F388L, a transactivation-deficient mutant, in familial partial lipodystrophy. Diabetes 51, 3586–3590 (2002).

    CAS  PubMed  Article  Google Scholar 

  52. Doney, A.S.F. et al. Association of the Pro12Ala and C1431T variants of PPARG and their haplotypes with susceptibility to type 2 diabetes. Diabetologia 47, 555–558 (2004).

    CAS  PubMed  Article  Google Scholar 

  53. Hao, H.-X. et al. ZNRF3 promotes Wnt receptor turnover in an R-spondin–sensitive manner. Nature 485, 195–200 (2012).

    CAS  PubMed  Article  Google Scholar 

  54. Farin, H.F. et al. Visualization of a short-range Wnt gradient in the intestinal stem-cell niche. Nature 530, 340–343 (2016).

    CAS  PubMed  Article  Google Scholar 

  55. Zhou, Y. et al. ZNRF3 acts as a tumour suppressor by the Wnt signalling pathway in human gastric adenocarcinoma. J. Mol. Histol. 44, 555–563 (2013).

    CAS  PubMed  Article  Google Scholar 

  56. Casals, F. et al. Genetic adaptation of the antibacterial human innate immunity network. BMC Evol. Biol. 11, 202 (2011).

    PubMed  PubMed Central  Article  Google Scholar 

  57. Bunge, J., Willis, A. & Walsh, F. Estimating the number of species in microbial diversity studies. Annu. Rev. Stat. Appl. 1, 427–445 (2014).

    Article  Google Scholar 

  58. Caballero, S. & Pamer, E.G. Microbiota-mediated inflammation and antimicrobial defense in the intestine. Annu. Rev. Immunol. 33, 227–256 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  59. Singh, V. et al. Interplay between enterobactin, myeloperoxidase and lipocalin 2 regulates E. coli survival in the inflamed gut. Nat. Commun. 6, 7113 (2015).

    CAS  PubMed  Article  Google Scholar 

  60. Nguyen, H.T.T. et al. Crohn's disease–associated adherent invasive Escherichia coli modulate levels of microRNAs in intestinal epithelial cells to reduce autophagy. Gastroenterology 146, 508–519 (2014).

    CAS  PubMed  Article  Google Scholar 

  61. Sadaghian Sadabad, M. et al. The ATG16L1-T300A allele impairs clearance of pathosymbionts in the inflamed ileal mucosa of Crohn's disease patients. Gut 64, 1546–1552 (2014).

    PubMed  Article  CAS  Google Scholar 

  62. Dambuza, I.M. & Brown, G.D. C-type lectins in immunity: recent developments. Curr. Opin. Immunol. 32, 21–27 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  63. Iliev, I. D. et al. Interactions between commensal fungi and the C-type lectin receptor Dectin-1 influence colitis. Science 336, 1314–1317 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  64. Enattah, N.S. et al. Evidence of still-ongoing convergence evolution of the lactase persistence T-13910 alleles in humans. Am. J. Hum. Genet. 81, 615–625 (2007).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  65. Tishkoff, S.A. et al. Convergent adaptation of human lactase persistence in Africa and Europe. Nat. Genet. 39, 31–40 (2007).

    CAS  PubMed  Article  Google Scholar 

  66. Troelsen, J.T. Adult-type hypolactasia and regulation of lactase expression. Biochim. Biophys. Acta 1723, 19–32 (2005).

    CAS  PubMed  Article  Google Scholar 

  67. Streppel, M.T. et al. Relative validity of the food frequency questionnaire used to assess dietary intake in the Leiden Longevity Study. Nutr. J. 12, 75 (2013).

    PubMed  PubMed Central  Article  Google Scholar 

  68. Siebelink, E., Geelen, A. & de Vries, J.H.M. Self-reported energy intake by FFQ compared with actual energy intake to maintain body weight in 516 adults. Br. J. Nutr. 106, 274–281 (2011).

    CAS  PubMed  Article  Google Scholar 

  69. Genome of the Netherlands Consortium. Whole-genome sequence variation, population structure and demographic history of the Dutch population. Nat. Genet. 46, 818–825 (2014).

  70. Shah, T.S. et al. optiCall: a robust genotype-calling algorithm for rare, low-frequency and common variants. Bioinformatics 28, 1598–1603 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  71. Auton, A. et al. A global reference for human genetic variation. Nature 526, 68–74 (2015).

    Article  CAS  PubMed  Google Scholar 

  72. Deelen, P. et al. Genotype harmonizer: automatic strand alignment and format conversion for genotype data integration. BMC Res. Notes 7, 901 (2014).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  73. Delaneau, O., Zagury, J.-F. & Marchini, J. Improved whole-chromosome phasing for disease and population genetic studies. Nat. Methods 10, 5–6 (2013).

    CAS  PubMed  Article  Google Scholar 

  74. Howie, B., Marchini, J. & Stephens, M. Genotype imputation with thousands of genomes. G3 (Bethesda) 1, 457–470 (2011).

    Article  Google Scholar 

  75. Deelen, P. et al. Improved imputation quality of low-frequency and rare variants in European samples using the 'Genome of The Netherlands'. Eur. J. Hum. Genet. 22, 1321–1326 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  76. Jia, X. et al. Imputing amino acid polymorphisms in human leukocyte antigens. PLoS One 8, e64683 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  77. Bolger, A.M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  78. Truong, D.T. et al. MetaPhlAn2 for enhanced metagenomic taxonomic profiling. Nat. Methods 12, 902–903 (2015).

    CAS  PubMed  Article  Google Scholar 

  79. Dimmer, E.C. et al. The UniProt-GO Annotation database in 2011. Nucleic Acids Res. 40, D565–D570 (2012).

    CAS  PubMed  Article  Google Scholar 

  80. Gene Ontology Consortium. Gene Ontology Consortium: going forward. Nucleic Acids Res. 43, D1049–D1056 (2015).

  81. Vatanen, T. et al. Variation in microbiome LPS immunogenicity contributes to autoimmunity in humans. Cell 165, 842–853 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  82. 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  Article  Google Scholar 

  83. Bonder, M.J., Luijk, R., Zhernakova, D.V. & Moed, M. Disease variants alter transcription factor levels and methylation of their binding sites. Preprint at bioRxiv 033084 (2015).

  84. Robinson, M.J., Sancho, D., Slack, E.C., LeibundGut-Landmann, S. & Reis e Sousa, C. Myeloid C-type lectins in innate immunity. Nat. Immunol. 7, 1258–1265 (2006).

    CAS  PubMed  Article  Google Scholar 

  85. Reikine, S., Nguyen, J.B. & Modis, Y. Pattern recognition and signaling mechanisms of RIG-I and MDA5. Front. Immunol. 5, 342 (2014).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  86. Whitlock, M.C. Combining probability from independent tests: the weighted Z-method is superior to Fisher's approach. J. Evol. Biol. 18, 1368–1373 (2005).

    CAS  Article  PubMed  Google Scholar 

  87. Ardlie, K.G. et al. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science 348, 648–660 (2015).

    Article  CAS  Google Scholar 

  88. Zhernakova, D.V. et al. Hypothesis-free identification of modulators of genetic risk factors. Preprint at bioRxiv 033217 (2015).

Download references

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

Authors and Affiliations

Authors

Contributions

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.

Ethics declarations

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)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/ng.3663

Further reading

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