Genome-wide associations of human gut microbiome variation and implications for causal inference analyses

Abstract

Recent population-based1,2,3,4 and clinical studies5 have identified a range of factors associated with human gut microbiome variation. Murine quantitative trait loci6, human twin studies7 and microbiome genome-wide association studies1,3,8,9,10,11,12 have provided evidence for genetic contributions to microbiome composition. Despite this, there is still poor overlap in genetic association across human studies. Using appropriate taxon-specific models, along with support from independent cohorts, we show an association between human host genotype and gut microbiome variation. We also suggest that interpretation of applied analyses using genetic associations is complicated by the probable overlap between genetic contributions and heritable components of host environment. Using faecal 16S ribosomal RNA gene sequences and host genotype data from the Flemish Gut Flora Project (n = 2,223) and two German cohorts (FoCus, n = 950; PopGen, n = 717), we identify genetic associations involving multiple microbial traits. Two of these associations achieved a study-level threshold of P = 1.57 × 10−10; an association between Ruminococcus and rs150018970 near RAPGEF1 on chromosome 9, and between Coprococcus and rs561177583 within LINC01787 on chromosome 1. Exploratory analyses were undertaken using 11 other genome-wide associations with strong evidence for association (P < 2.5 × 10−8) and a previously reported signal of association between rs4988235 (MCM6/LCT) and Bifidobacterium. Across these 14 single-nucleotide polymorphisms there was evidence of signal overlap with other genome-wide association studies, including those for age at menarche and cardiometabolic traits. Mendelian randomization analysis was able to estimate associations between microbial traits and disease (including Bifidobacterium and body composition); however, in the absence of clear microbiome-driven effects, caution is needed in interpretation. Overall, this work marks a growing catalogue of genetic associations that will provide insight into the contribution of host genotype to gut microbiome. Despite this, the uncertain origin of association signals will likely complicate future work looking to dissect function or use associations for causal inference analysis.

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Fig. 1: MT-associated loci and heritability.
Fig. 2: Genetic variants associated with microbial traits.

Data availability

All microbiome GWAS summary statistics are available online at the University of Bristol data repository, data.bris, at https://doi.org/10.5523/bris.22bqn399f9i432q56gt3wfhzlc. FGFP rarefaction count and transformed microbial trait data can be found in Supplementary Table 2. FGFP genotype data and host metadata from this study are not open access, but are available in accordance and in consent with ethical permission through managed access subject to a data use agreement with the FGFP and organized via principal investigator J.R. The process of enquiry for data access is outlined as follows: following data request by email to jeroen.raes@kuleuven.be, the FGFP data access committee will evaluate access permission, which will be granted upon signature of a data use agreement between the governing legal entities. This is outlined on the study website http://www.raeslab.org/companion/fgfp-gwas/. Raw 16S data are available at the European Genome/Phenome Archive under accession no. EGAS00001004420. The datasets from Universitätsklinikums Schleswig-Holstein are available by application through their biobank (https://www.uksh.de/p2n/).

Code availability

The full analysis pipeline is available at https://github.com/kul-fgfpgwas/rep-cookbook and includes four parts: (1) microbiome processing, (2) genotype quality control and imputation, (3) genome-wide association analysis and (4) phylogenetic analysis.

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Acknowledgements

FGFP procedures were approved by the Medical Ethics Committee of the University of Brussels–Brussels University Hospital (approval no. 143201215505, 5 December 2012). A declaration concerning the FGFP privacy policy was submitted to the Belgian Commission for the Protection of Privacy. Written informed consent was obtained from all participants. The FGFP was funded with the support of the Flemish government (no. IWT130359), the Research Fund–Flanders (FWO) Odysseus programme (no. G.0924.09), the King Baudouin Foundation (no. 2012-J80000-004), FP7 METACARDIS HEALTH-F4-2012-305312, VIB, the Rega Institute for Medical Research and KU Leuven. We acknowledge the contribution of Flemish general practitioners and pharmacists to data and sample collection. Finally, we thank all FGFP volunteers for their participation in the project. R.B. is funded by FWO through a postdoctoral fellowship (no. 1221620N). S.V.-S. is supported by Marie Curie Actions FP7 People COFUND–Proposal 267139 (acronym OMICS@VIB). N.J.T. is a Wellcome Trust Investigator (no. 202802/Z/16/Z), is supported by the University of Bristol NIHR Biomedical Research Centre (no. BRC-1215-20011) and works within the CRUK Integrative Cancer Epidemiology Programme (no. C18281/A19169). S.R. and N.J.T. work within a unit that receives funding from the MRC and University of Bristol (nos. MC_UU_12013/3 and MC_UU_00011/6). D.A.H. is supported by the Wellcome Investigator Award (no. 202802/Z/16/Z). K.H.W. is supported by the Elizabeth Blackwell Institute for Health Research, University of Bristol and the Wellcome Trust Institutional Strategic Support Fund (no. 204813/Z/16/Z). M.C.R. was funded by the German Research Foundation Collaborative Research Centre 1182 (no. CRC1182; Origin and Function of Metaorganisms).

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Authors

Contributions

J.R. and N.J.T. conceived and designed the study. J.W., R.Y.T., G.F., M.J., S.V.-S. and L.H. performed sampling and metadata collection. J.W., R.Y.T., L.R. and C.V. extracted and sequenced DNA. D.A.H., R.B., K.H.W., J.W. and M.C.R. performed microbiome GWAS and MR analysis. D.A.H., R.B., J.W., K.H.W., N.T. and J.R. wrote the manuscript. All authors revised and commented on the manuscript.

Corresponding authors

Correspondence to Nicholas J. Timpson or Jeroen Raes.

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Extended data

Extended Data Fig. 1 Variability in heritability.

Scatter plots of heritability estimates in the FGFP data set derived from rank normal, box-cox and log2 transformed abundance data (a, b); and estimates between the FGFP data and those previously published (c, d). (a) The plot compares rank normal transformed (x-axis) data to box-cox transformed data (y-axis). (b) The plot compares rank normal transformed (x-axis) data to log2 transformed data (y-axis). (c) The plot compares box-cox transformed data from FGFP (x-axis) and Goodrich et al. (y-axis). (d) The plot compares FGFP rank normal transformed data (x-axis) to the quantile-quantile normalized data of Davenport et al. (y-axis). The color of each point indicates a measure of average abundance in FGFP, and the size of each dot illustrates the prevalence for each taxon. The red line is a loess curve fit through the data. Confidence intervals of the fitted curve is shaded in grey. An estimate of the Spearman’s rho correlation coefficient between the data points is provided in the sub-title of each plot, along with its’ two-sided p-value. Sample size and heritability estimates can be found in Supplementary Table 3.

Extended Data Fig. 2 Effect estimates for top hits.

A forest plot of beta (effect) point estimates (blue dots) and standard errors (horizontal bars) for each of the genome-wide significant SNP-microbial trait (MT) associations. Each of the three cohorts used in the meta-analysis are represented in each MT plot. The vertical bar indicates zero on the effect- or x-axis. Sample sizes for each microbial trait and data used to generate this plot can be found in Supplementary Table 5.

Extended Data Fig. 3 Mendelian randomization framework.

(a) the causal effect of a microbial trait on a disease, whereby any invalidation of the exclusion restriction criteria implies a pleiotropic association between the genetic variant and the disease independent of the microbial trait, (b) the genetic variant seemingly associated with the microbial trait is predominantly associated with the disease, which in turn effects the microbial trait (that is, reverse causation) and (c) the genetic variant is associated with an external variable that is independently associated with the microbial trait and outcome.

Extended Data Fig. 4 FGFP DADA2 data summaries.

Rank ordered mean percent abundance (a) and prevalence (b) for each observed phylum, in the FGFP cohort. Prevalence is defined as the proportion of individuals in the cohort containing a rarefaction read assigned to a taxon, in this instance a phylum. Data for this plot can be found in Supplementary Table 3. (c) A scatter plot of the nMDS, β-diversity for the quality-controlled FGFP cohort. Individuals are colored by their defined enterotype, and boxplots illustrating the enterotype structure across the two nMDS (β-diversity) axes are presented. The stress level, of forcing the individual level data into two axes, in the nMDS analysis is presented in the lower right-hand corner of the scatter plot. Dashed lines are equidistant guidelines. Each box plot presents the mean, first and third quantiles, and 95% confidence intervals of the data distribution. The sample sizes for each enterotype class is Bact1 = 793, Rum = 660, Bact2 = 418, Prev = 388, as presented in Supplementary Table 2.

Extended Data Fig. 5 Selection of taxa for mGWAS analysis.

(a) Association between abundance and prevalence in 16S DAD2 data. A scatter plot illustrating the relationship between average abundance and prevalence in the FGFP cohort. Taxon retained for analysis in the mGWAS are colored in blue, while those that did not meet our analysis criteria are colored in red. The size of each taxon dot represents the number of individuals in the FGFP dataset in which that taxon made up at least 5% of the data or had 500 rarefaction reads. Dashed blue lines represent the prevalence floor (horizontal) and average abundance = 40 (vertical). The grey curve is a loess best fitting curve. Data used to generate this plot can be found in Supplementary Table 3. (b) Clustering dendrogram of 139 mGWAS taxa meeting criteria for analysis in mGWAS. The red horizontal line (0.015) represents the level at which we defined clades or clusters of statistically redundant taxa, of which the lowest taxonomic level taxa was retained for analysis. Data used to generate this plot can be found in Supplementary Table 2. (c) Correlation structure among 139 mGWAS taxa. A Spearman’s correlation plot of the 139 mGWAS worthy taxa. The color key scales from −1 to 1 and indicates the estimated Spearman’s rho correlation coefficient. The 139 taxa are clustered into eight squares, denoting the best eight clusters in the data. Eight was chosen for illustration purposes because there are eight phyla in the data. However, the clustered data does not necessarily conform to the eight phyla. Data used to generate this plot can be found in Supplementary Table 2.

Extended Data Fig. 6 Flowcharts for genotyping and mGWAS.

(a) Flowchart on the genotyping and quality control steps taken to prepare the FGFP data for the mGWAS. (b) Flowchart for 16S DADA2 MT data preparation, FGFP mGWAS and targeted meta-analysis.

Extended Data Fig. 7 GCTA-GREML power estimates.

GCTA-GREML (genome wide complex trait analysis – genomic-relatedness-based restricted maximum-likelihood) genetic (co)variation power estimates. The plot on the left illustrates the relationship between the power (y-axis) to accurately quantify narrow sense heritability (x-axis) for varying sampling sizes (from 300 to 2100). The plot on the right illustrates the same relationship but for presence/absence microbial traits, where the number of presence and absence individuals is balanced.

Supplementary information

Supplementary Information

Supplementary Figs. 1–11, Tables 6, 15 and 16 and discussion.

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Supplementary Tables 1–5, 7–14 and 17–22.

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Hughes, D.A., Bacigalupe, R., Wang, J. et al. Genome-wide associations of human gut microbiome variation and implications for causal inference analyses. Nat Microbiol 5, 1079–1087 (2020). https://doi.org/10.1038/s41564-020-0743-8

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