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Combined effects of host genetics and diet on human gut microbiota and incident disease in a single population cohort

Nature Genetics volume 54, pages 134–142 (2022)Cite this article

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An Author Correction to this article was published on 29 February 2024

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Abstract

Human genetic variation affects the gut microbiota through a complex combination of environmental and host factors. Here we characterize genetic variations associated with microbial abundances in a single large-scale population-based cohort of 5,959 genotyped individuals with matched gut microbial metagenomes, and dietary and health records (prevalent and follow-up). We identified 567 independent SNP–taxon associations. Variants at the LCT locus associated with Bifidobacterium and other taxa, but they differed according to dairy intake. Furthermore, levels of Faecalicatena lactaris associated with ABO, and suggested preferential utilization of secreted blood antigens as energy source in the gut. Enterococcus faecalis levels associated with variants in the MED13L locus, which has been linked to colorectal cancer. Mendelian randomization analysis indicated a potential causal effect of Morganella on major depressive disorder, consistent with observational incident disease analysis. Overall, we identify and characterize the intricate nature of host–microbiota interactions and their association with disease.

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Fig. 1: Genome-wide association of human genetic and gut microbial variations.
Fig. 2: Interaction of human genotype, dairy diet and gut bacterial variation with the LCT locus.
Fig. 3: Functional profiling of reference genomes from two bacterial taxa associated with the ABO locus.
Fig. 4: Effects of host genetics and dietary fiber intake on gut abundance variation of two bacterial taxa associated with the ABO locus.
Fig. 5: Effect of host genetics and prevalent CRC on gut levels of E. faecalis associated with MED13L variation across participants of the FR02 cohort.
Fig. 6: MR-based causal effects and incident depression analysis link Morganella with MDD.

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Data availability

Complete summary statistics of microbial taxa with genome-wide significant hits are publicly available in the NHGRI-EBI GWAS Catalog (https://www.ebi.ac.uk/gwas/) from accession GCST90032172 to GCST90032644. The metagenomic data from FINRISK 2002 samples are available from the European Genome-Phenome Archive (study ID: EGAS00001005020). The phenotype data contain sensitive information from healthcare registers and are not publicly available to avoid compromising research participant privacy/consent. They are available through the THL biobank upon submission of a research plan and signing a data transfer agreement (https://thl.fi/en/web/thl-biobank/for-researchers/application-process). Additional databases used in this work include GTDB release 89 (https://gtdb.ecogenomic.org/) and CAZy (last accessed 31 July 2019) (http://www.cazy.org/).

Code availability

Scripts used to analyze nonidentifiable data in this study have been made available on Zenodo (https://doi.org/10.5281/zenodo.5641303).

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Acknowledgements

The study protocol of FINRISK 2002 was approved by the Coordinating Ethical Committee of the Helsinki and Uusimaa Hospital District (Ref. 558/E3/2001). All participants signed an informed consent. The study was conducted according to the World Medical Association Declaration of Helsinki on ethical principles. All necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived. We thank all participants of the FINRISK 2002 survey for their contributions to this work. The FINRISK surveys are mainly funded by budgetary funds from the Finnish Institute for Health and Welfare with additional funding from several domestic foundations. Y.Q. was partially supported by The Albert Shimmins Fund (Faculty of Science Postgraduate Writing-Up Award 2020). M.I. was supported by the Munz Chair of Cardiovascular Prediction and Prevention and the NIHR Cambridge Biomedical Research Centre (BRC-1215-20014). V.S. was supported by the Finnish Foundation for Cardiovascular Research. L.L. was supported by the Academy of Finland (decision 295741) and EU/H2020 (FindingPheno; grant 952914). T.N. was supported by the Emil Aaltonen Foundation, the Finnish Medical Foundation, the Paavo Nurmi Foundation and the Academy of Finland (grant no. 321351). A.S.H. was supported by the Academy of Finland, grant no. 321356. R.L. receives funding support from NIEHS (grant no. 5P42ES010337), NCATS (grant no. 5UL1TR001442), NIDDK (grant nos. U01DK061734, R01DK106419, P30DK120515, R01DK121378, R01DK124318) and DOD PRCRP (grant no. W81XWH-18-2-0026). S.C.R. is funded by a BHF Programme Grant (RG/18/13/33946). This study was supported by the Victorian Government’s Operational Infrastructure Support (OIS) program, and by core funding from the British Heart Foundation (grant no. RG/13/13/30194; RG/18/13/33946) and the NIHR Cambridge Biomedical Research Centre (BRC-1215-20014). National Institute for Health Research (Cambridge Biomedical Research Centre at the Cambridge University Hospitals NHS Foundation Trust) (The views expressed are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care). This work was supported by Health Data Research UK, which is funded by the UK Medical Research Council, the Engineering and Physical Sciences Research Council, the Economic and Social Research Council, the Department of Health and Social Care (England), the Chief Scientist Office of the Scottish Government Health and Social Care Directorates, the Health and Social Care Research and Development Division (Welsh Government), the Public Health Agency (Northern Ireland), the British Heart Foundation and Wellcome. We thank Dr Annalisa Buniello (EMBL-EBI, Cambridge, UK) for valuable help with GWAS Catalog submissions.

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Author notes

  1. These authors contributed equally: Michael Inouye, Guillaume Méric.

Authors and Affiliations

  1. Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia

    Youwen Qin, Yang Liu, Scott C. Ritchie, Marta Brożyńska, Michael Inouye & Guillaume Méric

  2. School of BioSciences, The University of Melbourne, Melbourne, Victoria, Australia

    Youwen Qin & Michael Inouye

  3. Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland

    Aki S. Havulinna, Pekka Jousilahti, Liisa Valsta, Teemu Niiranen & Veikko Salomaa

  4. Institute for Molecular Medicine Finland, FIMM-HiLIFE, Helsinki, Finland

    Aki S. Havulinna

  5. Department of Clinical Pathology, Melbourne Medical School, The University of Melbourne, Melbourne, Victoria, Australia

    Yang Liu

  6. Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK

    Scott C. Ritchie & Michael Inouye

  7. British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK

    Scott C. Ritchie & Michael Inouye

  8. Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK

    Alex Tokolyi

  9. Department of Ecology and Evolutionary Biology, Cornell University, Ithaca, NY, USA

    Jon G. Sanders

  10. Cornell Institute for Host-Microbe Interaction and Disease, Cornell University, Ithaca, NY, USA

    Jon G. Sanders

  11. Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, CA, USA

    Qiyun Zhu, Anupriya Tripathi, Mohit Jain & Rob Knight

  12. Division of Biological Sciences, University of California San Diego, La Jolla, CA, USA

    Anupriya Tripathi

  13. Center for Microbiome Innovation, University of California San Diego, La Jolla, CA, USA

    Yoshiki Vázquez-Baeza, Mohit Jain & Rob Knight

  14. Department of Computer Science & Engineering, Jacobs School of Engineering, University of California San Diego, La Jolla, CA, USA

    Yoshiki Vázquez-Baeza & Rob Knight

  15. NAFLD Research Center, Department of Medicine, University of California San Diego, La Jolla, CA, USA

    Rohit Loomba

  16. Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA

    Susan Cheng

  17. Department of Medicine, Turku University Hospital and University of Turku, Turku, Finland

    Teemu Niiranen

  18. Department of Computing, University of Turku, Turku, Finland

    Leo Lahti

  19. British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK

    Michael Inouye

  20. Health Data Research UK Cambridge, Wellcome Genome Campus & University of Cambridge, Cambridge, UK

    Michael Inouye

  21. The Alan Turing Institute, London, UK

    Michael Inouye

  22. Department of Infectious Diseases, Central Clinical School, Monash University, Melbourne, Victoria, Australia

    Guillaume Méric

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Contributions

Y.Q., M.I., V.S. and G.M. designed the work. A.S.H., P.J., J.G.S., L.V., M.B., Q.Z., A. Tripathi, Y.V.-B., T.N., L.L., R.K., V.S. and G.M. acquired the data. Y.Q., Y.L., S.C.R., J.G.S., L.L., A. Tokolyi and G.M. analyzed the data. R.L., S.C., M.J., T.N., L.L., R.K., V.S., M.I. and G.M. supervised the work. All authors wrote the manuscript and gave final approval of the version to be published.

Corresponding authors

Correspondence to Michael Inouye or Guillaume Méric.

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Competing interests

V.S. has consulted for Novo Nordisk and Sanofi and received honoraria from these companies. He also has ongoing research collaboration with Bayer AG, all unrelated to this study. R.L. serves as a consultant or advisory board member for Anylam/Regeneron, Arrowhead Pharmaceuticals, AstraZeneca, Bird Rock Bio, Boehringer Ingelheim, Bristol-Myer Squibb, Celgene, Cirius, CohBar, Conatus, Eli Lilly, Galmed, Gemphire, Gilead, Glympse bio, GNI, GRI Bio, Inipharm, Intercept, Ionis, Janssen Inc., Merck, Metacrine, Inc., NGM Biopharmaceuticals, Novartis, Novo Nordisk, Pfizer, Prometheus, Promethera, Sanofi, Siemens and Viking Therapeutics. In addition, his institution has received grant support from Allergan, Boehringer Ingelheim, Bristol-Myers Squibb, Cirius, Eli Lilly and Company, Galectin Therapeutics, Galmed Pharmaceuticals, GE, Genfit, Gilead, Intercept, Grail, Janssen, Madrigal Pharmaceuticals, Merck, NGM Biopharmaceuticals, NuSirt, Pfizer, pH Pharma, Prometheus and Siemens. He is also cofounder of Liponexus, Inc. The remaining authors declare no competing interests.

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Extended data

Extended Data Fig. 1

Study flowchart.

Extended Data Fig. 2 Heritability of SNPs associated with microbial taxa.

(a) Associated SNP heritability (h2) for all 2,801 taxa included in the genome-wide association analysis, grouped into their 61 corresponding GTDB phyla, and ordered by median heritability per phylum. Red denotes bacterial phyla, and purple denotes archaeal phyla. The right panel indicates the number of genome-wide significant associated taxa for each phylum. (b) Associated SNP heritability is shown for each associated taxon, grouped by its taxonomic rank. Genome-wide significance was defined as a threshold of p < 5 × 10-8 for all p-values obtained after joint analysis using GTCA-COJO in the GWAS (see Methods). For all box plots (A and B), the central line, box and whiskers represent the median, interquartile range (IQR) and 1.5 times the IQR, respectively.

Extended Data Fig. 3 LocusZoom plots for three loci with study-wide significant associations (p < 3.8 × 10-11).

Associations with top taxa are shown. Top SNPs are indicated in purple diamond. Other SNPs are coloured by their linkage disequilibrium (LD) values with the top SNPs. Genes covered by the region are indicated in the bottom and the genotyping coverage is indicated on top of the plot. (A) Associated SNPs at the LCT locus spans over a 2 Mbp genomic region, while they are grouped on a 400 kbp region for both (B) ABO and (C) the MED13L loci.

Extended Data Fig. 4 Correlation between individual baseline age and the relative abundance of bacteria from the Bifidobacterium genus in lactose intolerant individuals.

Only genetically lactose intolerance individuals (rs4988235:CC) are shown, and coloured by dietary dairy habits (blue: self-reported regular consumption of dairy, n = 763; and red: self-reported irregular dairy diet or lactose-free diet, n = 253). Best fitted lines and 95% confidence intervals are indicated. Two-sided Spearman correlation coefficients are given.

Extended Data Fig. 5 Spearman correlation of relative abundances in 4 taxa associated with the LCT locus.

Abundances of the Bifidobacterium, Negativibacillus, UBA3855 and CAG-81 genera are compared. Abundances in the entire FR02 cohort is compared to those in a subset of genetically lactose-intolerant individuals, and to a subset of genetically lactose-intolerant individuals who reported a regular dairy diet. Coloured boxes denote the strength of correlation (ranging from -1 in red to 1 in dark blue), while a white square denotes a non-significant p-value for the two-sided Spearman correlation (p > 0.05).

Extended Data Fig. 6 Co-abundance and carbohydrate-active enzymes (CAZyme) distribution patterns in 11 Bifidobacterium species harboured by > 25% of individuals in the FR02 cohort.

(a) Associations between the LCT-MCM6 locus and 11 Bifidobacterium species; (left) top association results between variation of 11 Bifidobacterium species and the LCT locus, with study-wide significant associations (with p-values from the joint analysis using GTCA-COJO below the p < 3.8 × 10-11 threshold) highlighted in bold; (middle) Two-sided Spearman coefficients calculated on CLR-transformed abundances; (right) relative abundances across the FR02 cohort, ranging from 0 (light green) to 1 (dark blue). (b) CAZyme distribution patterns in 327 previously published reference genomes from 11 Bifidobacterium GTDB species which were included in the GTDB release 89 index used to classify metagenomes in this study. The heatmap indicates abundance of corresponding CAZyme families in species, corresponding to the total count of detected families for each species divided by the number of reference genomes examined for the same species. Values <1 (white to light blue) indicate that less than one copy per genome of the corresponding CAZyme family was detected for each species, values >1 (light blue to dark blue) indicate that more than one copy per genome was detected. Preferred substrate groups are based on literature search and descriptions on CAZypedia.org. For all box plots (A), the central line, box and whiskers represent the median, interquartile range (IQR) and 1.5 times the IQR, respectively. Violin plots represent the distribution density of the data points.

Extended Data Fig. 7 Effect of ABO genotypes, blood type and secretor status on microbial diversity and gut levels of ABO-associated taxa.

(a) (left) Alpha diversity represented by Shannon indices; (right) beta diversity, represented by Bray-Curtis distances. Alpha and beta diversity were calculated from individual taxonomic profiles at the genus level. Individuals were segregated according to their predicted blood type and secretor status, both predicted from genotype data. (b) Abundances are compared across stratified groups of individuals from the FR02 cohort according to (left panel): ABO:rs545971 genotype and predicted secretor status (blue: secretor status conferred by FUT2 rs601338:GG/GA genotype; red: non-secretor status conferred by FUT2 rs601338:AA genotype) and (right panel) according to predicted A, AB, B and O blood types, and predicted secretor status. All statistical comparisons denote the p-values of Wilcoxon rank test on the distributions. (c) Effect of AB antigen secretion on gut microbial relative abundance, using the 2,801 taxa considered for GWAS in our study. Taxa with FDR adjusted p value <0.05 are highlighted in red. Red line indicates the expected distribution of p values under the null hypothesis. P values were calculated using Wilcoxon rank test. For all box plots (A and B), the central line, box and whiskers represent the median, interquartile range (IQR) and 1.5 times the IQR, respectively. Violin plots represent the distribution density of the data points.

Extended Data Fig. 8 Sequencing depth does not influence alpha diversity.

Alpha-diversity (Shannon index) were computed and plotted against the log10 (left) or the raw (right) number of sequencing reads for each 5,959 individual gut metagenome in this study. No correlation was observed between sequencing depth and Shannon diversity index (two-sided Spearman’s ⍴=-0.001598, p = 0.90). Grey shaded area corresponds to the 95% confidence interval.

Extended Data Fig. 9 Effect of geographical region of sampling, microbiome sequencing batch or antibiotic prescription on overall microbiome diversity.

Beta-diversity (Bray Curtis dissimilarity indices) was calculated using the R package vegan, and the 4 top PCoA axes (explaining a combined 25.9% of the total microbiome variation) were plotted against each other, with each individual point labelled according to geographical sampling (panel A), gut metagenomic sequencing batch (panel B), or whether antibiotics were prescribed up to 1 month (n = 250/5959) before baseline sampling.

Extended Data Fig. 10 Distribution of F. lactaris relative abundance in groups of individuals with different predicted blood types.

A beeswarm plot is used to visualise the distribution of relative abundances.

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Qin, Y., Havulinna, A.S., Liu, Y. et al. Combined effects of host genetics and diet on human gut microbiota and incident disease in a single population cohort. Nat Genet 54, 134–142 (2022). https://doi.org/10.1038/s41588-021-00991-z

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