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Bacterial SNPs in the human gut microbiome associate with host BMI

Nature Medicine volume 29, pages 2785–2792 (2023)Cite this article

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Abstract

Genome-wide association studies (GWASs) have provided numerous associations between human single-nucleotide polymorphisms (SNPs) and health traits. Likewise, metagenome-wide association studies (MWASs) between bacterial SNPs and human traits can suggest mechanistic links, but very few such studies have been done thus far. In this study, we devised an MWAS framework to detect SNPs and associate them with host phenotypes systematically. We recruited and obtained gut metagenomic samples from a cohort of 7,190 healthy individuals and discovered 1,358 statistically significant associations between a bacterial SNP and host body mass index (BMI), from which we distilled 40 independent associations. Most of these associations were unexplained by diet, medications or physical exercise, and 17 replicated in a geographically independent cohort. We uncovered BMI-associated SNPs in 27 bacterial species, and 12 of them showed no association by standard relative abundance analysis. We revealed a BMI association of an SNP in a potentially inflammatory pathway of Bilophila wadsworthia as well as of a group of SNPs in a region coding for energy metabolism functions in a Faecalibacterium prausnitzii genome. Our results demonstrate the importance of considering nucleotide-level diversity in microbiome studies and pave the way toward improved understanding of interpersonal microbiome differences and their potential health implications.

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Fig. 1: Study overview.
Fig. 2: Bacterial SNPs associate with host BMI.
Fig. 3: Number of BMI-associated SNPs per species.
Fig. 4: Comparison of MWAS results and relative abundance analysis.
Fig. 5: Results replicate in a geographically independent cohort.
Fig. 6: BMI-associated SNPs in Rep_3066.

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Data availability

Data in this paper are part of the Human Phenotype Project. The raw metagenomic data and basic phenotypes (age, sex and BMI) used in this study are available at the European Genome-phenome Archive (https://ega-archive.org/) under accession EGAS00001007204. The other data are accessible to researchers from universities and other research institutions at https://humanphenotypeproject.org/home.

Code availability

Analysis source code is available at https://github.com/LironZa/MWAS.

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Acknowledgements

We thank members of the Segal laboratory for useful discussions. E.S. is supported by the Crown Human Genome Center; the Larson Charitable Foundation New Scientist Fund; the Else Kröner Fresenius Foundation; the White Rose International Foundation; the Ben B. and Joyce E. Eisenberg Foundation; the Nissenbaum Family; Marcos Pinheiro de Andrade and Vanessa Buchheim; Lady Michelle Michels; Aliza Moussaieff; and grants funded by the Minerva Foundation, with funding from the Federal German Ministry for Education and Research and by the European Research Council and the Israel Science Foundation. S.S. is supported by the Israeli Council for Higher Education via the Weizmann Data Science Research Center. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

Authors and Affiliations

  1. Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel

    Liron Zahavi, Amit Lavon, Lee Reicher, Saar Shoer, Anastasia Godneva, Sigal Leviatan, Michal Rein, Adina Weinberger & Eran Segal

  2. Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel

    Liron Zahavi, Amit Lavon, Lee Reicher, Saar Shoer, Anastasia Godneva, Sigal Leviatan, Michal Rein, Adina Weinberger & Eran Segal

  3. Lis Maternity and Women’s Hospital, Tel Aviv Sourasky Medical Center, Tel Aviv University (affiliated with Sackler Faculty of Medicine), Tel Aviv, Israel

    Lee Reicher

  4. Eleven Tx, Ramat Gan, Israel

    Omer Weissbrod

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Contributions

L.Z. conceived and designed the study, designed and conducted the analyses, interpreted the results and wrote the manuscript. A.L. developed methods. L.R. interpreted the results and wrote the manuscript. S.S., A.G. and S.L. designed and conducted sample processing. M.R. processed the dietary data. O.W. developed statistical analyses. A.W. designed the project, developed protocols and oversaw sample collection and processing. E.S. conceived, directed and designed the project and analyses, conducted analyses, interpreted the results and wrote the manuscript.

Corresponding author

Correspondence to Eran Segal.

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Competing interests

O.W. is an employee of Eleven Tx. E.S. is a paid consultant for Pheno.AI. The other authors declare no competing interests.

Peer review

Peer review information

Nature Medicine thanks Sergio Baranzini, Thomas Schmidt and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Alison Farrell, in collaboration with the Nature Medicine team.

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Extended data

Extended Data Fig. 1 SNPs overview.

(a) Distribution of the 12,686,191 detected SNPs across 348 species. (b) Number of samples covering different SNPs.

Extended Data Fig. 2 Volcano plot.

Volcano plot shows for each SNP the difference between the average BMI in individuals with mostly the alternative allele (major allele frequency ≤ 0.5) and the average BMI in individuals with mostly the major allele (major allele frequency > 0.5; x-axis); and its p-value (y-axis). Red annotations show gene symbols of the protein-coding SNPs left after the clumping stage (if a gene symbol exists). X-axis was truncated to the range of statistically significant associations ±10%.

Extended Data Fig. 3 BMI differences.

For each of the 40 BMI-associated SNPs that remained after the clumping stage, boxplots (center, median; box, interquartile range; whiskers, 5th and 95th percentiles; notches, 95% confidence interval around the median based on 1,000 times bootstrap) compare host BMI distribution of individuals with no bacteria of this species (left box; Methods), hosts of bacteria with the major allele (middle box; major allele frequency ≥ 0.99) and hosts of bacteria with the minor allele (right box; major allele frequency ≤ 0.01). The grey scale indicates the difference between medians. Groups were compared in a two-sided Mann-Whitney test, and p-values were Bonferroni corrected for 120 hypotheses (40 SNPs, 3 comparisons per SNP).

Extended Data Fig. 4 Quantile-quantile (Q-Q) plots.

Expected (uniform distribution between 1/[the total number of tested SNPs] and 1) p-values compared to the SNPs p-values estimated in the MWAS analysis. (a) All tested SNPs. Red dots are the 40 BMI-associated SNPs remaining after the clumping procedure. (b) Each species estimated and plotted separately using a random color. Straight lines connect adjacent SNP dots to increase readability. (c) Species with more than 13 BMI-associated SNPs. Straight lines connect adjacent SNP dots to increase readability.

Extended Data Fig. 5 Number of correlated SNPs in each linkage group.

Histograms show the number of correlated SNPs that were found in the clumping stage in each linkage group. The total number of groups is 40, which is the final number of SNPs that remained post the clumping procedure. (a) Full range of group sizes. (b) Groups with 1 to 100 SNPs.

Extended Data Fig. 6 Power analysis.

Boxplots (center, median; box, interquartile range; whiskers, 1.5 * interquartile range or the most extreme data point) show the calculated power for associating the 40 SNPs with BMI, given the effect size observed in our cohort and various effective sample sizes (N). Alpha was set to 3.9 × 10−9 based on a cutoff of 0.05 and a Bonferroni correction for 12,686,191 hypotheses.

Extended Data Fig. 7 Random replication control.

For 1000 random choices of 40 SNPs from the discovery analysis, showing how many passed the 0.05 Bonferroni adjusted cutoff for association with BMI in the replication cohort. For reference, the red dotted line shows the number of SNPs that passed the cutoff when the 40 SNPs that were associated with BMI in the discovery cohort were tested — 17.

Extended Data Fig. 8 Replication cohort characteristics.

Age, sex, and BMI distribution of the 8,204 study participants.

Supplementary information

Reporting Summary

Supplementary Tables 1–11

Supplementary Table 1. Number of tested, BMI-associated and post-clumping SNPs per species, in species with BMI-associated SNPs. Table 2. BMI-associated SNPs, post-clumping and their genetic context. SNPs that are within genes. Table 3. BMI-associated SNPs, post-clumping and their genetic context. Intergenic SNPs. Table 4. Associations between bacterial relative abundance and host BMI. Table 5. Replication MWAS in the Dutch cohort. Table 6. Associations between bacterial relative abundance and host BMI in the Dutch cohort. Table 7. BMI-associated SNP (Rep_3066, C_257, pos 6315) and the BMI-associated SNPs that are correlated with it and were excluded in the clumping process. Table 8. Diet, exercise and medication covariates divided by category. Table 9. MWAS analysis with diet covariates. Table 10. MWAS analysis with exercise covariates. Table 11. MWAS analysis with medication covariates.

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Zahavi, L., Lavon, A., Reicher, L. et al. Bacterial SNPs in the human gut microbiome associate with host BMI. Nat Med 29, 2785–2792 (2023). https://doi.org/10.1038/s41591-023-02599-8

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