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Effect of host genetics on the gut microbiome in 7,738 participants of the Dutch Microbiome Project

An Author Correction to this article was published on 25 July 2022

This article has been updated

Abstract

Host genetics are known to influence the gut microbiome, yet their role remains poorly understood. To robustly characterize these effects, we performed a genome-wide association study of 207 taxa and 205 pathways representing microbial composition and function in 7,738 participants of the Dutch Microbiome Project. Two robust, study-wide significant (P < 1.89 × 10−10) signals near the LCT and ABO genes were found to be associated with multiple microbial taxa and pathways and were replicated in two independent cohorts. The LCT locus associations seemed modulated by lactose intake, whereas those at ABO could be explained by participant secretor status determined by their FUT2 genotype. Twenty-two other loci showed suggestive evidence (P < 5 × 10−8) of association with microbial taxa and pathways. At a more lenient threshold, the number of loci we identified strongly correlated with trait heritability, suggesting that much larger sample sizes are needed to elucidate the remaining effects of host genetics on the gut microbiome.

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Fig. 1: Genome-wide association scan results.
Fig. 2: Association at the LCT locus and interaction with lactose intake.
Fig. 3: Association with blood types and interaction with FUT2.
Fig. 4: Weighted Spearman correlation between estimated heritability and number of suggestive loci.
Fig. 5: Power analysis taking into account bacterial presence levels.

Data availability

Raw sequencing microbiome data are available at European Genome-Phenome archive (accession number EGAS00001005027). Genotyping data and participant metadata are not publicly available to protect participants’ privacy, and neither can be deposited in public repositories to respect the research agreements in the informed consent. The data can be accessed by all bona-fide researchers with a scientific proposal by contacting the LifeLines Biobank (instructions at https://www.lifelines.nl/researcher/how-to-apply). Researchers will need to fill in an application form that will be reviewed within 2 weeks. If the proposed research complies with LifeLines regulations, such as noncommercial use and warranty of participants’ privacy, then researchers will receive a financial offer and a data and material transfer agreement to sign. In general, data will be released within 2 weeks after signing the offer and data and material transfer agreement. The data will be released in a remote system (the LifeLines workspace) running on a high-performance computer cluster to ensure data quality and security. The full GWAS summary statistical data for all 207 taxa and 205 pathways are instead available for direct download at NHGRI-EBI GWAS Catalog (https://www.ebi.ac.uk/gwas/) under the study accession numbers GCST90027446-GCST90027857 (accession numbers for each specific taxa and pathways can be found in Supplementary Table 13) or at https://dutchmicrobiomeproject.molgeniscloud.org. The processed microbiome data (taxonomy and pathway abundance per individual) can also be downloaded after filling in a request form available at the same website and after signing a data access agreement. This study also used the following databases: UniRef90 v.0.1.1 protein database and the ChocoPhlAn pangenome databases available within the Humann2 pipeline (https://huttenhower.sph.harvard.edu/humann2/), the Genome Taxonomy Database (https://gtdb.ecogenomic.org/) and the IEU GWAS database (https://gwas.mrcieu.ac.uk/). All other data supporting the findings of this study are available within the paper and Supplementary Note.

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Acknowledgements

We acknowledge the services of the LifeLines Cohort Study, the contributing research centers delivering data to LifeLines and all the study participants. The LifeLines initiative was made possible by subsidy from the Dutch Ministry of Health, Welfare and Sport; the Dutch Ministry of Economic Affairs; the University Medical Center Groningen (UMCG); the University of Groningen (UG) and the Provinces of the North of the Netherlands (Drenthe, Friesland and Groningen). This project was carried out under LifeLines project number OV18_0464. We thank Mathieu Plateel and Jody Geelderloos-Arends for their contribution in genotyping the LifeLines samples, Kate McIntyre for help developing the manuscript, Marije van der Geest for setting up the website for sharing summary statistics and Patrick Deelen for discussion of results. We also thank the UMCG Genomics Coordination Center, the UG Center for Information Technology and their sponsors (BBMRI-NL and TarGet) for storage and computational infrastructure and Novogene for providing gut metagenome sequencing of all DMP samples. Finally, we thank the UK Biobank for making their resource available. Analyses of UK Biobank data described in this work were carried out under project number 48548 to C.W. The generation and management of genotype data for the LifeLines Cohort Study was supported by the UMCG Genetics LifeLines Initiative. Genotyping quality control was supported by UMCG (HAP grant CD017.0031/ronde 2017-2/nr 324). Metagenomics sequencing of the cohort was mainly funded by the CardioVasculair Onderzoek Nederland (CVON) (grant CVON 2012-03) to M. Hofken (who died in 2016), J.F. and A.Z., as well as other grants to R.K.W. and C.W. (listed below). This work was further supported by the collaborative TIMID project LSHM18057-SGF financed by the allowance made available by Top Sector Life Sciences & Health to Samenwerkende Gezondheidsfondsen to stimulate public/private partnerships and cofinancing by health foundations that are part of the Samenwerkende Gezondheidsfondsen (R.K.W.); the the Seerave Foundation (R.K.W.); European Research Council (ERC) starting grant 715772 (S.Z.), consolidator grant 101001678 (J.F.) and advanced grant ERC-671274 (C.W.); Netherlands Organization for Scientific Research VIDI grant 016.178.056 (A.Z.), gravitation grant ExposomeNL 024.004.017 (A.Z.), VICI grant VI.C.202.022 (J.F.), gravitation grant The Netherlands Organ-on-Chip Initiative 024.003.001 (C.W.) and Spinoza award NWOSPI 92-266 (C.W.); CVON grant 2018-27 (A.Z. and J.F.); the EurHealth-1Health INTERREG V A 202085 project (H.J.M.H.); Foundation De Cock-Hadders grant 20:20-13 (L.C.); a joint fellowship from the UMCG and China Scholarship Council (CSC201708320268 to L.C.); and Colciencias fellowship ed.783 (E.A.L.-M.).

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E.A.L.-M., A.K. and S.H. performed genetic analyses. A.v.d.G. performed MR and power analyses. A.K., S.H., L.C., A.V.V. and R.G. processed microbiome data. S.A.-S., T.S., V.C., M.A.Y.K., L.A.B. and M.F.B.G. processed samples meta-data. P.B.T.N. and M.A.S. provided resources on the HP computing cluster and assisted with data management. H.J.M.H., C.W., J.F., R.K.W. and A.Z. provided funding resources and designed the DMP. A.Z. and S.S. supervised the study. E.A.L.-M., A.K., A.v.d.G., S.H., S.A.S., A.Z. and S.S. drafted the manuscript. All authors were involved in data interpretation and provided critical input to the manuscript draft.

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Correspondence to Alexandra Zhernakova or Serena Sanna.

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Extended data

Extended Data Fig. 1 Cladogram plot tree of taxonomic relations between bacteria of the class Actinobacteria and their associations with host genetics.

Each node shows a taxonomic level (from outside to inside: phylogenetic group, phylum, class, order, family, genus and species). Note that branch lengths do not represent phylogenetic distance. Inner labels represent genetic locus. External labels represent the clade. Nodes with dotted lines indicate that the GWAS was not performed for that taxa. Node color corresponds to different levels of significance as described in the legend. a, Depicts associations detected at the MCM6/LCT locus with each taxa, using the most significant p-value observed between rs4988235 and rs182549. b, Depicts associations at the ABO locus with each taxa, using the most significant p-value observed between rs8176645 and rs550057.

Extended Data Fig. 2 Association at the LCT locus and interaction with lactose intake in other members of family Bifidobacteriaceae.

Relative abundances of taxa, natural log–transformed and adjusted by age and sex, compared between LP (rs182549 C/T or T/T) and LI (rs182549 C/C) participants and among individuals with low or high daily lactose intake levels. The y axis represents the relative abundance of the microbial feature, natural log–transformed and adjusted by age and sex. Density distribution is displayed with violin plots, while boxplots represent summary statistics: the center line represents the median, the box hinges represent the lower and upper quartiles (percentiles 25 and 75) of the distribution, the upper whisker extends to the maximum value no further than 1.5*IQR (where IQR is the interquartile range) from the upper hinge, the lower whisker extends to the minimum value no further than 1.5*IQR from the lower hinge, and data beyond the end of the whiskers are outliers plotted as individual points. a and c, Relative abundances for the taxa between LP and LI participants. b and d, Comparisons of abundance between lactose intake levels, low (<first quartile) and high (≥ first quartile), stratified by lactose persistence status. The distributions are shown for s. Bifidobacterium adolescentis (top) and s. Bifidobacterium longum (bottom). P-values were obtained with a two-sided Wilcoxon rank test. n: number of participants.

Extended Data Fig. 3 Graphical representation of MR results with a Benjamini–Hochberg FDR q value < 0.1.

a, Effect size in standard deviation units of 3 variants associated with Alistipes abundance changes that were used as instrumental variables (effects estimated on 7,728 independent samples) (x-axis) versus effect size in standard deviation units of the same variants for salt intake (estimated effects estimated on 462,630 independent samples) (y-axis). Error bars represent standard errors (SE) of each effect size (beta + SE and beta-SE). The orange and blue lines represent lines whose slope is the causal estimate from MR methods IVW and Egger, respectively. b, A plot similar to a, but the x axis is the effect size in standard deviation units for instrumental variants selected for Collinsella (effects estimated on 7,210 independent samples) abundance and on the y-axis for Triglyceride levels (effects estimated on 343,992 independent individuals).

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Lopera-Maya, E.A., Kurilshikov, A., van der Graaf, A. et al. Effect of host genetics on the gut microbiome in 7,738 participants of the Dutch Microbiome Project. Nat Genet 54, 143–151 (2022). https://doi.org/10.1038/s41588-021-00992-y

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