# A comprehensive assessment of demographic, environmental, and host genetic associations with gut microbiome diversity in healthy individuals

Petar Scepanovic, Flavia Hodel, Stanislas Mondot, Valentin Partula, Allyson Byrd, Christian Hammer, Cécile Alanio, Jacob Bergstedt, Etienne Patin, Mathilde Touvier, et al.

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## To cite this version:

Petar Scepanovic, Flavia Hodel, Stanislas Mondot, Valentin Partula, Allyson Byrd, et al. A comprehensive assessment of demographic, environmental, and host genetic associations with gut microbiome diversity in healthy individuals. Microbiome, 2019, 7 (1), pp.130. 10.1186/s40168-019-0747-x. pasteur-02307232

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# A comprehensive assessment of demographic, environmental, and host genetic associations with gut microbiome diversity in healthy individuals

Petar Scepanovic¹,<fnref n="2" />, Flavia Hodel¹,<fnref n="2" />, Stanislas Mondot³, Valentin Partula⁴,⁵, Allyson Byrd⁶, Christian Hammer⁶,⁷, Cécile Alanio⁸, Jacob Bergstedt⁹, Etienne Patin¹⁰,¹¹, Mathilde Touvier⁴, Olivier Lantz¹²,¹³, Matthew L. Albert⁶, Darragh Duffy¹⁴, Lluis Quintana-Murci¹⁰,¹¹, Jacques Fellay¹,<fnref n="2" />,¹⁵* and The Milieu Intérieur Consortium

## Abstract

**Background:** The gut microbiome is an important determinant of human health. Its composition has been shown to be influenced by multiple environmental factors and likely by host genetic variation. In the framework of the *Milieu Intérieur* Consortium, a total of 1000 healthy individuals of western European ancestry, with a 1:1 sex ratio and evenly stratified across five decades of life (age 20–69), were recruited. We generated 16S ribosomal RNA profiles from stool samples for 858 participants. We investigated genetic and non-genetic factors that contribute to individual differences in fecal microbiome composition.

**Results:** Among 110 demographic, clinical, and environmental factors, 11 were identified as significantly correlated with α-diversity, β-diversity, or abundance of specific microbial communities in multivariable models. Age and blood alanine aminotransferase levels showed the strongest associations with microbiome diversity. In total, all non-genetic factors explained 16.4% of the variance. We then searched for associations between > 5 million single nucleotide polymorphisms and the same indicators of fecal microbiome diversity, including the significant non-genetic factors as covariates. No genome-wide significant associations were identified after correction for multiple testing. A small fraction of previously reported associations between human genetic variants and specific taxa could be replicated in our cohort, while no replication was observed for any of the diversity metrics.

**Conclusion:** In a well-characterized cohort of healthy individuals, we identified several non-genetic variables associated with fecal microbiome diversity. In contrast, host genetics only had a negligible influence. Demographic and environmental factors are thus the main contributors to fecal microbiome composition in healthy individuals.

**Trial registration:** ClinicalTrials.gov identifier NCT01699893

**Keywords:** Microbiome, Gut, Human, Genomics, 16S rRNA gene sequencing, GWAS, Healthy, Demographics, Environment

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BMC

© The Author(s). 2019 **Open Access** This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

<footnote>* Correspondence: jacques.fellay@epfl.ch
¹School of Life Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland</footnote>

<footnote n="2">²Swiss Institute of Bioinformatics, Lausanne, Switzerland
Full list of author information is available at the end of the article</footnote>
## Background

A wide diversity of microbial species colonizes the human body, providing considerable benefits to the host through a range of different functions [1]. Notably, these microbes generate metabolites that can act as energy sources for cell metabolism, promote the development and the functionality of the immune system, and prevent colonization by pathogenic microorganisms [2].

The human intestine harbors a particularly diverse microbial ecosystem. Multiple 16S ribosomal RNA (rRNA) gene sequencing and metagenomic studies established that each individual gut microbiome harbors a unique combination of microbial life [3, 4]. An estimated 150 to 400 bacterial species reside in each person's gut [5].

Typically, the human gut microbiome is dominated by five bacterial phyla: *Firmicutes*, *Bacteroidetes*, *Proteobacteria*, *Actinobacteria*, and *Verrucomicrobia* [6, 7]. These contain almost all of the bacterial species found in the human gastrointestinal tract, which can also be classified in higher-level taxonomic groups such as genera, families, orders, and classes [8]. The relative proportions of microbial species vary extensively between individuals [9] and have been shown to be age-dependent [10]. The microbiome composition evolves rapidly during the first 3 years of life, followed by a more gradual maturation [11], and then is predicted to remain relatively stable throughout adult life [12].

A variety of environmental and clinical factors including diet, lifestyle, diseases, and medications can induce substantial shifts in the microbiome composition [13, 14]. Multiple studies have shown that diet and medications are the main forces influencing gut microbial diversity [15–22]. Yet, they only explain a small percentage of the microbiome variation observed in the human population. Host genetics has also been proposed as a contributor in determining the relative

abundance of specific gut microbes [23, 24]. Several studies have searched for associations between human genetic variation and gut microbiome diversity [20–22, 25–28], but only a few genetic loci have been replicated across these studies. As a consequence, most of the interindividual variability in gut microbiome composition remains unexplained.

In this study, we leveraged the in-depth phenotypic and genotypic information available for the *Milieu intérieur* (MI) cohort—a population-based study of 1000 healthy individuals of western European ancestry, evenly stratified by sex (1:1) and age. We investigated the role of socio-demographic and environmental factors in inter-individual gut microbiome variation (Fig. 1). In particular, we were able to assess the impact of family status, income, occupational status and educational level, smoking habits, sleeping habits, psychological problems, and nutritional behavior. We also evaluated the influence of basic physiological parameters (such as body mass index), family and personal medical history (including vaccination history), and multiple laboratory results (comprising mostly blood biochemical measurements). Finally, we investigated the potential impact of human genetic variation using a genome-wide association study (GWAS) framework, including as covariates, the non-genetic factors that were found to be correlated with various measures of gut microbiome diversity.

## Results

### Gut microbiome diversity in healthy donors

To characterize the bacterial diversity of the gut flora of the 1000 healthy donors, we performed 16S rRNA gene sequencing on standardized collections of fecal samples. From this cohort, we obtained profiles for 858 individuals and we normalized the data for sequencing depth

<figure><img src="image_5.png" /></figure>
(see the “Methods” section). A total of 8422 operational
taxonomy units (OTUs) were detected, corresponding to
11 phyla, 24 classes, 43 orders, 103 families, 328 genera,
and 698 species. On average, we detected 193 species
per individual (standard error 1.9, standard deviation
55.1), with a minimum of 58 and a maximum of 346
species. Inter-individual variability was already marked
at the phylum level. Figure 2a presents the relative abun-
dances of the 8 phyla observed in more than 10% of
study participants. *Firmicutes* and *Proteobacteria* were
detected in all individuals, and *Bacteroidetes* in all but
one individual. *Firmicutes* was the dominant phylum in
the vast majority of individuals (91.8%).

Starting from the OTU counts, we calculated α and β
microbiome diversity metrics (see the “Methods” section).
As measures of α-diversity, which describes diversity
within each sample, we used observed richness (number
of distinct species present in the given sample), Chao1
richness estimate (estimate of the number of unobserved
species), ACE (abundance-based coverage estimator), and
Simpson's diversity index (probability that two randomly
picked sequences belong to the same species). The histo-
grams of their raw and transformed distributions are
shown in Additional file 1: Figure S1A and S1B. We
present here the results obtained using Simpson's diversity
index as a representative metric of α-diversity. The results
for other indicated metrics are presented in the supple-
mentary material. Figure 2b presents the distribution of
Simpson's diversity indexes depicting the continuous dis-
tribution and high diversity of the gut microbiome in the
majority of study participants. The distributions of the
other α-diversity metrics are shown in Additional file 1:
Figure S1C.

As measures of β-diversity, which describes the dif-
ference in taxonomic composition between samples,
we used compositional Jaccard (unweighted), as well
as Bray-Curtis (weighed) and phylogenetic Unifrac
(weighted) dissimilarity matrices. We present here the
results obtained using Bray-Curtis dissimilarity matrix
as a representative metric of β-diversity. The results
for other indexes are presented in the supplementary
material. Figure 2c presents the multidimensional scal-
ing (MDS) plot of the Bray-Curtis dissimilarity matrix
coloring study participants by a relative abundance of
*Firmicutes*, indicating an absence of marked stratifica-
tion. Similar homogeneous distributions of other dis-
similarity metrics on the MDS plot are available in
Additional file 1: Figure S2.

Associations of non-genetic variables with gut
microbiome parameters

Demographic, lifestyle, and environmental variables were
collected via a detailed questionnaire, while biochemical pa-
rameters were measured in blood samples. Correlations

between dietary consumption parameters and gut micro-
biome have previously been investigated in the MI cohort
[29]. We considered an additional 274 variables and filtered
them based on prevalence, missingness, and collinearity,
resulting in a final number of 110 variables to be included
in association analyses (see the “Methods” section). Figure 1
outlines the six categories of non-genetic variables consid-
ered and shows representative examples. The full list with a
detailed description of the tested variables is provided in
Additional file 2: Table S1.

To investigate the potential impact of relevant demo-
graphic, social, behavioral, nutritional, and medical data
on the fecal microbiome, we searched for associations of
diversity metrics and individual taxa with the 110 non-
genetic variables selected above using Spearman rank
testing (Additional file 2: Table S2). In total, 25 variables
were significant (Additional file 1: Figure S3A), with on
average 15 of them associated with each α-diversity
metric (Additional file 1: Figure S3B) in univariate tests.
Five variables (age, level of ALT, glomerular filtration
rate, having breakfast and eating in fast-food restaurants)
were significant (FDR < 0.05) for all α-diversity metrics
(Additional file 1: Figure S3A and Figure S3C). We then
used ANOVAs to test these in multivariable models, also
including four dietary variables: consumption of raw
fruits, fish, fatty sweet products, and sodas (which were
previously found to be significantly associated with α-diy-
versity in the same study population [29]). Only age and
the levels of alanine aminotransferase (ALT), a liver en-
zyme whose elevated plasma levels indicate liver damage,
remained significant in these analyses (Fig. 3 and Add-
itional file 2: Table S3). Simpson's diversity index was
positively associated with age and negatively associated
with ALT levels, as shown in Additional file 1: Figure
S4A and Figure S4B.

We then investigated the impact of non-genetic vari-
ables on the β-diversity indexes, running PERMANO-
VAs for the 110 variables. PERMANOVA tests a
multivariate model where distance matrix is a re-
sponse variable. The results of these tests are pre-
sented in Additional file 2: Table S4. A total of 35
factors were significantly associated (FDR < 0.05) in
univariate tests (Additional file 1: Figure S5A) with, on
average, 24 being associated with each β-diversity
index (Additional file 1: Figure S5B). Fifteen factors
were significant for all 3 β-diversity metrics (Add-
itional file 1: Figure S5C). Those were then tested in
multivariable models, also including raw fruit con-
sumption (which was previously found to be signifi-
cantly associated with β-diversity in our study
population [29]) and reran PERMANOVAs. A total of
10 factors remained significant in the final models
(Fig. 4 and Additional file 2: Table S5). Of these, age,
sex, and plasma levels of ALT were the strongest
<figure><img src="image_6.png" /><figcaption>B</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>C</figcaption></figure>

<figure><img src="image_8.png" /></figure>

**Fig. 2** Gut microbiome diversity. **a** Box-plots of relative abundances of 8 phyla that were observed in more than 10% of the donors. Outliers are also represented. **b** Violin plot of Simpson's diversity index values observed among MI study participants. **c** Multidimensional scaling plot of Bray-Curtis dissimilarity matrix with study participants colored according to relative abundance of *Firmicutes*

associated factors. Also significant were chickenpox
vaccination, having breakfast, having lunch, diastolic
blood pressure, consumption of raw fruits, decreased
or increased appetite, and medical record of tooth ex-
traction. Sex and age were able to explain the biggest
portion of the observed variance of all the significantly
associated variables, albeit with small individual coeffi-
cients of correlation (R² < 0.01, Fig. 4). We then

calculated the cumulative explained variance of Bray-
Curtis dissimilarity by using all the non-genetic vari-
ables available. This analysis revealed that 16.4% of the
variance can be explained by non-genetic factors
(Additional file 2: Table S6).

Next, we searched for associations between demo-
graphic and environmental variables and individual
taxa. We used multivariate association with linear
<figure><img src="image_9.png" /></figure>

models to search for associations between the 110 factors discussed above and 475 taxa that were observed in more than 10% of study participants. The full list of tested taxa is available in Additional file 2: Table S7. The results of all the test performed are available in Additional file 2: Table S8. Table 1 shows the only three significant associations (FDR corrected *p* value < 0.05). We observed associations of age with the *Comamonadaceae* family and the *Schlegelella* genus and of consumption of mineral supplements with the *Clostridium papyrosolvens* species. We further confirmed these results by using additional tests. For age associations, we used Spearman's rank correlations and observed association *p* values of 2.37 × 10⁻⁹ and 8.65 × 10⁻⁷ with *Comamonadaceae* and *Schlegelella*, respectively, while for the association between consumption of mineral supplements and *Clostridium papyrosolvens*, we used Wilcoxon rank test and obtained a *p* value of 5.3 × 10⁻³. Finally, we searched for nominally significant associations (*p* value < 0.05) for the two variables that associated with both *α*- and *β*-diversity metrics: age was nominally associated with 72 taxa, while ALT level was nominally associated with 15 taxa (Additional file 2: Table S8).

Data plots showing positive correlations of the three identified associations are presented in Additional file 1: Figure S6A-C.

### Association of human genetic variants with gut microbiome parameters

We next searched for potential associations between human genetic variants and gut microbiome diversity, using a GWAS framework. We here hypothesize that common human genetic polymorphisms might have an effect on the abundance of specific taxa or on overall microbiome diversity.

We included in the regression models all the statistically significant demographic and environmental variables identified above, for each respective phenotype. The full list of all the covariates used, including the first two principal components of the genotyping matrix, is available in Additional file 2: Table S9.

We performed GWAS using the four *α*-diversity metrics and the three *β*-diversity indexes as phenotypic outcomes. We did not observe any statistically significant association upon correction for the number of polymorphisms and of phenotypes tested ($P_{\alpha\text{-threshold}} < 1.25 \times 10^{-8}$ and $P_{\beta\text{-threshold}} < 1.67 \times 10^{-8}$) (Fig. 5a and Additional file 1: Figure S7; Fig. 5b and Additional file 1: Figure S8). On the other hand, few genomic loci were showing trends of significant associations and we report all the SNPs that had association *p* value lower than $10^{-6}$ with *α*- and *β*-diversity metrics in Additional file 2: Table S10 and Table S11, respectively. The quantile-quantile plots and lambda values, assessing the false
<figure><img src="image_10.png" /></figure>

positive rate and genomic inflation rate for all genome-wide analyses, are shown in Additional file 1: Figure S9 and Figure S10. We then attempted to replicate the previously published associations between specific SNPs and β-diversity, by relaxing the genome-wide significant threshold [19–21]. Upon correction for the 66 SNPs considered ($P_{threshold} < 0.05/66$), none was significantly associated (Additional file 2: Table S12).

We also used a GWAS approach to search for associations between the abundance of individual taxa and human genetic variation. We used a quantitative phenotype (non-zero log-transformed relative abundance) and a binary phenotype (presence vs. absence) for each taxon. After correction for the number of polymorphisms and of phenotypes tested, we did not observe any statistically significant

signal. A total of 170 suggestive associations ($P_{SuggestiveThreshold} < 5 \times 10^{-8}$) were detected with the quantitative phenotype of 53 taxa, and 65 suggestive SNPs were detected with the binary phenotype of 23 taxa. The lists of these SNPs and their association *p* values are available in Additional file 2: Table S13 and Additional file 2: Table S14, respectively.

We also imputed HLA and KIR alleles and tested them for association with all the considered phenotypes, observing no significant associations (Additional file 1: Figure S11 and association summary statistics results available).

We then attempted to replicate associations for the SNPs previously reported to be associated with individual taxa (Additional file 2: Table S15) [19–22, 25, 27].

<table><caption>**Table 1** Significant associations of non-genetic variables with individual taxa</caption><thead><tr><td>Covariate</td><td>Taxa</td><td>Prevalence (%)</td><td>Coefficient</td><td>p value</td><td>Q value</td></tr></thead><tbody><tr><td>Age</td><td>Comamonadaceae</td><td>36.8</td><td>3.99 x 10<sup>-4</sup></td><td>3.09 x 10<sup>-9</sup></td><td>5.89 x 10<sup>-5</sup></td></tr><tr><td>Age</td><td>Schlegelella</td><td>29.6</td><td>3.32 x 10<sup>-4</sup></td><td>5.48 x 10<sup>-6</sup></td><td>3 x 10<sup>-2</sup></td></tr><tr><td>Consumption of mineral supplements</td><td>Clostridium papyrosolvens</td><td>13.8</td><td>2.44 x 10<sup>-2</sup></td><td>8.32 x 10<sup>-7</sup></td><td>4.72 x 10<sup>-3</sup></td></tr></tbody></table>
<figure><img src="image_11.png" /></figure>

Only 13 out of 336 SNPs passed the corrected nominal significance threshold ($P_{\text{threshold}} < 1.49 \times 10^{-4}$, i.e., 0.05/336) for association with a quantitative phenotype. Of these, 9 were concordant at the phylum level with the original report (i.e., the strongest associated taxon in

our study belonged to the same phylum as the previously observed association). For binary phenotypes, 10 SNPs passed the corrected nominal significance threshold, including 2 that were concordant at the phylum level.
Discussion

We investigated the potential influence of demographic,
environmental, clinical, and genetic factors on the fecal
microbiome composition in 858 unrelated healthy indi-
viduals of French descent. The *Milieu Intérieur* cohort is
particularly well suited for such a comprehensive assess-
ment [30]. The study participants have a homogeneous
genetic background, live in the same region, and are
evenly stratified by sex and age, which provides an excel-
lent opportunity to search for unique determinants of
gut microbiome diversity.

First, we used the rich data collected through ques-
tionnaires that gathered detailed medical history as well
as lifestyle and socio-demographic information. We also
considered laboratory results that could indicate under-
lying physiological differences (e.g., levels of hemoglobin,
glucose, hepatic transaminases). We searched for a po-
tential association of these variables with several α- and
β-diversity metrics of the gut microbiome, as well as
with quantitative and binary phenotypes derived from
the detected abundance of individual microbial taxa.

As the MI cohort was designed to better understand
healthy immunity, strict criteria were used during enroll-
ment to exclude individuals with chronic medical condi-
tions. Similarly to other studies in healthy individuals, the
distribution of major phyla was in the same range as ob-
served before (Additional file 2: Table S16). The use of
prescription drugs, on the other hand, was very limited
among MI participants. In fact, the final set of 110 non-
genetic variables contained only one drug-related variable
("on any type of medication"). Even the use of over-the-
counter drugs, such as proton pump inhibitors, was
observed in less than 1% of the individuals (i.e., only in 4
individuals). The potential impact of drugs on the gut
microbiome, suggested by previous studies [11, 16, 18],
was therefore not evaluated in our study.

The influence of dietary variables on the gut micro-
biome has already been evaluated in the MI cohort [29].
Increased α-diversity was found to be associated with
foods generally considered as healthy (fruits, fish), while
a decrease was associated with foods for which limited
consumption is generally recommended (e.g., fried prod-
ucts). Dissimilarity measure by β-diversity level was
driven by consumption of raw fruits, fried products,
ready-cooked meals, and cheese [29]. In the current ana-
lysis, we focused our attention on additional environ-
mental influences, lifestyle variables, and biochemical
measurements. Age showed a strong positive association
with α-diversity in all models, whereas sex and BMI did
not show any consistent association. Interestingly, we
replicated a correlation between higher plasma levels of
alanine aminotransferase and lower microbiome diver-
sity (previously also observed in a Belgian cohort, but
not replicated in a Dutch study population [16]). The

causality of the observed correlation is unclear. Indeed,
much work is still needed to get a better understanding
of the interplay between the gut microbiome and liver
disease [31].

In the analysis of β-diversity indexes, we identified ten
factors that were significant in the multivariable PERMA-
NOVA models. In line with previous reports [6, 14, 26],
we observed sex and age as the strongest influencers on
all β-diversity indexes, with the lowest association *p* values
and highest proportion of variance explained by these fac-
tors. As other co-variates, such as environmental and
host-extrinsic, are also known to impact the overall com-
position [32], we identified factors related to medical his-
tory (in particular chickenpox vaccination and teeth
extraction), blood measurements (ALT levels and diastolic
blood pressure), and lifestyle (such as tendency to have
breakfast or lunch and variable appetite) having mild, yet
significant, correlations with β-diversity in MI cohort. We
also confirmed the independent effects of diet, in particu-
lar the consumption of raw fruits [29]. Interestingly, we
could not confirm any significant association between
BMI and microbiome diversity, in contrast to the recent
population-based observations in the FGFP study [16].
This apparent contradiction could be partly explained by
the MI study design [30]: the careful selection of healthy
individuals resulted in a more limited distribution of BMI
values among study participants (mean ± SD: 24.26 ± 3.26
kg/m²; min 18.59 and max 32). This ascertainment bias
reduced our power to detect potential correlations be-
tween more extreme BMI values and microbiome diver-
sity measurements [33]. Furthermore, an estimation of the
explained variance in β-diversity metrics demonstrated a
small individual effect of each variable (Additional file 2:
Table S4), which together explained 16.4% of the variance.
This is concordant with previous reports, where a similar
proportion of variance (18.7% [16], 16.4% [17], and 20%
[19]) could be explained by demographic and environ-
mental factors. In contrast to what we observed in the MI
cohort, prescription medication explained an important
fraction of the variance in these other studies (up to 10%
[17]), attesting to the uniqueness of our healthy study
sample.

In our exploration of variables potentially associated
with individual taxa, we observed a strong positive cor-
relation between age and the *Schlegelella* genus (as well
as the family it belongs to: *Comamonadaceae*). This
family is very diverse, and its members have been ob-
served both in man-made environments (various clean
or polluted soils and waters) and in animals or human
clinical samples [34]. The epidemiological or clinical
relevance of this newly observed association is unknown.
We also found an association between *Clostridium*
*papyrosolvens*, belonging to the *Clostridia* class and *Fir-
micutes* phylum, and the oral intake of mineral
supplements. *Clostridium papyrosolvens* is an anaerobic bacterium that is involved in the degradation of diverse carbohydrates (such as cellulose, arabinose, and glucose) [35] and could thus play a role in modulating the individual glycemic response.

Our in-depth investigation of demographic, environmental, and clinical variables allowed us to identify factors that are associated with various measures of gut microbiome composition. Including them as covariates in genome-wide association studies increased our power to potentially detect true genetic effects, by increasing the signal-to-noise ratio. However, after correction for multiple testing, we did not observe any statistically significant associations. This was the case for a total of 7 different α- and β-diversity metrics and for 475 individual taxa, tested either as quantitative or as binary phenotypes. We also attempted to replicate the previously reported associations between human polymorphisms and gut microbiome composition at the β-diversity or the taxonomic levels [19–22, 25, 27]. None of the variants associated with β-diversity metrics replicated. For individual taxa, replication at the phylum level was successful for 2 SNPs using binary phenotypes (presence vs. absence of the phylum) and for 9 SNPs using quantitative phenotypes (abundance). Of these, only one signal was replicated at the family level: the association between rs7856187 and *Lachnospiraceae* [27]. Of note, the only SNP that was significant in a recent meta-analysis [20], rs4988235, did not show any association in our study (Additional file 2: Table S12).

## Conclusions

Our study provides an in-depth investigation of potential demographic, environmental, clinical, and genetic influences on the diversity of the fecal microbiome in healthy individuals. We identified variables associated with overall microbiome composition and with a small number of individual taxa, explaining a non-negligible fraction of microbiome diversity in healthy individuals in the absence of drug treatment. The lack of any significant results in the genome-wide association analyses, on the other hand, indicates that common human genetic variants of large effects do not play a major role in shaping the gut microbiome diversity observed in healthy populations. Future studies should include larger sample sizes and a more comprehensive evaluation of human genetic variation, including rare and structural variants not captured by genotyping arrays. Evaluation of the environmental effects should be optimized for example by longitudinal tracking of study participants. It should be noted that our study, as most previously published works of comparative power, tried to link human genetics and the microbiome by exploring microbiome variation through 16S rRNA gene sequencing. This methodology has obvious limitations, since

it only allows the study of taxonomic composition and diversity measures, while ignoring variation of gene repertories and species pangenomes, which represent a broader and more refined picture of microbiome variability [36–38]. Future efforts evaluating host genetics influence on microbiome composition should thus focus on a refined picture of microbiome variability, obtainable through shotgun metagenomics instead of 16S rRNA gene profiling. Lastly, large-scale microbiome and genomic data should be pooled across cohorts, as recently proposed [39], to accelerate discovery in the field of human-microbiome interactions.

## Methods

### The Milieu Intérieur cohort

The 1000 healthy donors of the *Milieu Intérieur* cohort were recruited by BioTrial (Rennes, France). The cohort is stratified by sex (500 men, 500 women) and age (200 individuals from each decade of life, between 20 and 70 years of age). Participants were selected based on stringent inclusion and exclusion criteria, detailed elsewhere [30]. Briefly, they had no evidence of any severe/chronic/recurrent medical conditions. The main exclusion criteria were seropositivity for human immunodeficiency virus or hepatitis C virus, travel to (sub-) tropical countries within the previous 6 months, recent vaccine administration, and alcohol abuse. Subjects were excluded if they were on treatment at the time or were treated in the 3 months preceding enrolment with, nasal, intestinal, or respiratory antibiotics or antiseptics. Volunteers following a specific diet prescribed by a doctor or dietician for medical reasons (calorie-controlled diet or diet favoring weight loss in very overweight patients, diets to decrease cholesterol levels) and volunteers with food intolerance or allergy were also excluded. To avoid the influence of hormonal fluctuations in women during the peri-menopausal phase, only pre- or post-menopausal women were included. To minimize the influence of population substructure on genomic analyses, the study was restricted to individuals of self-reported Metropolitan French origin for three generations (i.e., with parents and grandparents born in continental France). Fasting whole blood samples were collected from the 1000 participants in lithium heparin tubes between September 2012 and August 2013.

### Fecal DNA extraction and amplicon sequencing

Human stool samples were produced at home no more than 24 h before the scheduled medical visit and collected in a double-lined sealable bag with the outer bag containing a GENbag Anaer atmosphere generator (Aerocult, Biomerieux), used to maintain anaerobic conditions, and an anaerobic indicator strip (Anaerotest, Merck Millipore) to record the strict maintenance of the anaerobic
atmosphere. Upon reception at the clinical site, the fresh
stool samples were aliquoted and stored immediately at –
80 °C. DNA was extracted from the stool as previously
published [40, 41]. DNA quantity was measured with
Qubit using a broad range assay. Barcoding polymerase
chain reaction (PCR) was carried out using indexed
primers targeting the V3-V4 region of the 16S rRNA gene
as described in [42]. AccuPrime™ Pfx SuperMix (Invitro-
gen - 12344-040) was used to perform the PCR. PCR mix
was made up of 18 µL of AccuPrime™ Pfx SuperMix,
0.5 µL of both V3-340F and V4-806R primers (0.2 µM),
and 1 µL of DNA (10 ng). PCR was carried out as follows:
95 °C for 2 min, 30 cycles of 95 °C for 20 s, 55 °C for 15 s,
72 °C for 5 min, and a final step at 72 °C for 10 min.
Amplicon concentration was then normalized to 25 ng
per PCR reaction using SequalPrep™ Normalization Plate
Kit, 96-well (Thermo Fisher Scientific). Equal volumes of
normalized PCR reaction were pooled and thoroughly
mixed. The amplicon libraries were sequenced at the
Institut Curie NGS platform on Illumina MiSeq using the
2*300 base pair V3 kit to 5064 to 240,472 sequencing
reads per sample (mean ± SD: 21,363 ± 19,087 reads).

16S rRNA gene sequencing data processing and
identification of microbial taxa

Raw reads were trimmed using sickle [43], then error
corrected using SPAdes [44] and merged using PEAR
[45]. Reads were clustered into operational taxonomy
units (OTUs) at 97% of identity using vsearch pipeline
[46]. Chimeric OTUs were identified using UCHIME
[47] and discarded from downstream analysis. Micro-
biome profiles obtained were normalized for sequencing
depth (sequencing counts were divided to their sample
size and then multiplied by the size of the smaller sam-
ple) [48]. We further checked the presence of the se-
quencing batch effect and principal coordinate analysis
(PCoA) plot obtained at the genus level presented in
Additional file 1: Figure S12 shows a random distribu-
tion of samples obtained from different sequencing
batches.

Taxonomy of representative OTU sequences was de-
termined using RDP classifier [49]. OTU sequences were
aligned using ssu-align [50]. The phylogenetic tree was
inferred from the OTU multiple alignments using Fas-
tree2 [51]. We further checked the specific taxonomic
assignations identified in our study. *Schlegelella* genus
was made of 15 OTUs that had a similarity score ran-
ging from 60 to 80% with a phylogenetically close previ-
ously identified environmental bacteria *Schlegelella
thermodepolymerans*. Furthermore, taxonomic assigna-
tion of *Clostridium papyrosolvens* was obtained with
73% of accuracy.

For 138 individuals, the gut microbiome compos-
ition could not be established because of technical

issues in the extraction and the sequencing steps (i.e.,
due to low DNA extraction yield, absence of PCR
amplicons, low read counts). These were excluded
from further analysis.

Gut microbiome diversity estimates

Based on OTUs, we calculated two types of microbial
diversity indicators: α- and β-diversity indexes. As es-
timates of α-diversity, we used Simpson's diversity
index, observed richness, Chao1 richness estimate,
and ACE (abundance-based coverage estimator). We
applied Yeo-Johnson transformation with R package
VGAM [52] to normalize these phenotypes. The his-
tograms of raw and transformed distributions are
shown in Additional file 1: Figure S1A and Add-
itional file 1: Figure S1B, respectively. As estimates of
β-diversity, we used Bray-Curtis (weighed), compos-
itional Jaccard (unweighted), and Unifrac (weighted)
dissimilarity matrices. All diversity indicators were
generated on non-rarefied data using the R package
vegan [53] that was corrected for sequencing depth
prior to indexes' computation [48].

Demographic, environmental, and clinical variables

A large number of demographical, environmental, and clinical variables are available in the *Milieu Intérieur* cohort [30]. These notably include infection and vaccination history, childhood diseases, health- and diet-related habits, socio-demographical variables, and laboratory measurements. The questionnaire that was filled by the study participants and used to obtain the majority of the non-genetic variables is available at http://www.milieuinterieur.fr/sites/milieuinterieur.fr/files/crf Mi.pdf. After manual curation, we considered 274 variables as potentially interesting for our analyses. Of those, we removed 130 that (i) were only variable in less than 5% of participants or (ii) were missing in more than 10% of participants. We tested for collinearity among the remaining 144 variables using Spearman rank correlation. All pairwise correlations with a Spearman's ρ > 0.6 or ≤ 0.6 and a false discovery rate (FDR) < 5% were considered colinear; one variable from each pair was removed from further analysis, resulting in a final set of 110 variables (described in Additional file 2: Table S1). Of these, 39 had some missing values (< 1% in 25, 1–5% in 10, 5–10% in 4 individuals), which were imputed using random forest method in the R package mice [54]. We evaluated the effects of various clinical measurements within their normal healthy range, such as those of BMI (mean ± SD: 24.26 ± 3.26 kg/m²) and C-reactive protein (CRP; mean ± SD: 1.99 ± 2.58 mg/L). Several symptoms of depression, such as lack of interest in doing things and poor self-image, and potentially relevant personal and family medical history information (such as route of birth delivery, immunization history with several vaccines, and familial occurrence of diabetes or myocardial infarction) were
investigated. Furthermore, smoking status and nutritional tendencies (such as the salt consumption habits) were kept in our analyses.

## Testing of demographic, environmental, and clinical variables

We searched for associations between the 110 demographic, environmental, and clinical variables selected above and the various gut microbiome phenotypes. For α-diversity indexes (Simpson's index, observed richness, Chao1 richness estimate, and ACE), we used non-parametric Spearman correlations. For β-diversity dissimilarities (Jaccard, Bray-Curtis, and Unifrac matrices), we used permutational analysis of variance (PERMANOVA) with 1000 permutations. PERMANOVAs identify variables that are significantly associated with β-diversity and measure the fraction of variance explained by the factors tested. The variables that were significantly associated (Benjamini-Hochberg FDR < 0.05) with the diversity estimates in the univariable models were included in the respective multivariable models: we used multivariable ANOVAs for α-diversity and PERMANOVAs for β-diversity. We used a backward selection, i.e., we eliminated the variables that were not significant in the first multivariable model, and reran the tests iteratively until all included predictors were significant. Spearman correlations, ANOVA, and PERMANOVAs tests were performed in R v3.5.1. Finally, to search for associations with individual taxa, we implemented multivariate association with linear models by using MaAsLin [55] with default parameters. For each taxon, MaAsLin preforms boosting and feature reduction of metadata, thus selecting each time different set of non-genetic variables to test in the final model. All associations between taxa and non-genetic variables that were tested in the final model are presented in Additional file 2: Table S8 with their respective *p* and *q* values.

## Human DNA genotyping

As previously described [56], the blood was collected in 5-mL sodium EDTA tubes and kept at room temperature (18–25°) until processing. After extraction, DNA was genotyped at 719,665 single nucleotide polymorphisms (SNPs) using the HumanOmniExpress-24 BeadChip (Illumina). The SNP call rate was > 97% in all donors. To increase coverage of rare and potentially functional variation, 966 of the 1000 donors were also genotyped at 245,766 exonic variants using the HumanExome-12 BeadChip. The variant call rate was < 97% in 11 donors, which were thus removed from this dataset. We filtered out from both datasets genetic variants based on a set of criteria detailed in [57]. These quality-control filters yielded a total of 661,332 and 87,960 variants for the HumanOmniExpress and HumanExome

BeadChips, respectively. Average concordance rate for the 16,753 SNPs shared between the two genotyping platforms was 99.99%, and individual concordance rates ranged from 99.8 to 100%.

## Genetic relatedness and structure

Relatedness was detected using KING [58]. Six pairs of related participants (parent-child, first-, and second-degree siblings) were identified. Of those, four pairs had both genotyping and microbiome datasets and one individual from each pair, randomly selected, was removed from the genetic analyses, leaving in total 858 individuals with both genotyping and 16S rRNA gene sequencing data. The genetic structure of the study population was estimated using principal component analysis (PCA), implemented in EIGENSTRAT (v6.1.3) [59]. The PCA plot of the study population is shown in Additional file 1: Figure S13.

## Genotype imputation

As described previously [57], we used Positional Burrows-Wheeler Transform for genotype imputation, starting with the 661,332 quality-controlled SNPs genotyped on the HumanOmniExpress array. Phasing was performed using EAGLE2 (v2.0.5) [60]. As a reference panel, we used the haplotypes from the Haplotype Reference Consortium (release 1.1) [61]. After removing SNPs that had an imputation info score < 0.8, we obtained 22, 235,661 variants. We then merged the imputed dataset with 87,960 variants directly genotyped on the HumanExome BeadChips array and removed variants that were monomorphic or diverged significantly from Hardy-Weinberg equilibrium ($P < 10^{-7}$). We obtained a total of 12,058,650 genetic variants to be used in association analyses.

We used SNP2HLA (v1.03) [62] to impute 104 4-digit human leukocyte antigen (HLA) alleles and 738 amino acid residues (at 315 variable amino acid positions of the HLA class I and II proteins) with a minor allele frequency (MAF) of > 1%.

We used KIR*IMP [63] to impute killer-cell immunoglobulin-like receptor (KIR) alleles, after haplotype inference on chromosome 19 with SHAPEIT2 (v2.r790) [64]. A total of 19 KIR types were imputed: 17 loci plus two extended haplotype classifications (A vs. B and KIR haplotype). A MAF threshold of 1% was applied, leaving 16 KIR alleles for association analysis.

## Genetic association analyses

For single-variant association analyses, we only considered SNPs with a MAF higher than 5% (N = 5,293,637). Unless otherwise stated, we used PLINK (v1.9) [65] for association testing. In all tests, we included the first two first principal components of the genotyping matrix as
covariates to correct for residual population stratification. The demographic, environmental, and clinical variables that were identified as significantly associated were also included as covariates in the respective analyses. A full list of covariates for each phenotype is available in Additional file 2: Table S8.

We used linear regression (within PLINK) and microbiome GWAS [66] to test for SNP associations with α-diversity indexes and β-diversity dissimilarities, respectively. Linear regression was also used to search for associations with relative abundance of specific taxa. Only taxa present in at least 10% of individuals were tested (N = 475), i.e., 8/11 (remaining/total) phyla, 16/24 classes, 20/43 orders, 50/103 families, 135/328 genera, and 246/698 species. The list of all tested taxa is presented in Additional file 2: Table S7. We used logistic regression to test binary phenotypes (presence/absence of specific taxa). Here, we excluded taxa that were present in > 90% of individuals, resulting in a total of 374 phenotypes (4 phyla, 8 classes, 15 orders, 38 families, 104 genera, and 205 species). For all GWAS, we used a significance threshold corrected for the number of tests performed. For α-diversity (N = 4): $P_{\alpha-threshold} < 1.25 \times 10^{-8}$, for β-diversity (N = 3): $P_{\beta-threshold} < 1.67 \times 10^{-8}$, for taxa abundance (N = 475): $P_{\text{taxa-linear}} < 1.05 \times 10^{-10}$ and for presence or absence of taxa (N = 374): $P_{\text{taxa-logistic}} < 1.33 \times 10^{-10}$.

**Supplementary information**

Supplementary information accompanies this paper at https://doi.org/10.1186/s40168-019-0747-x.

**Additional file 1: Figure S1.** Raw and transformed distributions and violin plots of α-diversity phenotypes. **Figure S2.** Multidimensional scaling plots of Jaccard and Unifrac distance matrices. **Figure S3.** Number and overlap of non-genetic variables associated with α-diversity phenotypes. **Figure S4.** Correlations of age and ALT levels with Simpson's diversity index. **Figure S5.** Number and overlap of non-genetic variables associated with β-diversity matrices. **Figure S6.** Data plots showing identified correlations of non-genetic variables with three individual taxa. **Figure S7.** Manhattan plots for α-diversity metrics: richness, Chao1 and ACE. **Figure S8.** Manhattan plots for β-diversity matrices: Jaccard and Unifrac. **Figure S9.** QQ plots and lambda values of GWAS of α-diversity phenotypes. **Figure S10.** QQ plots and lambda values of GWAS of β-diversity indexes. **Figure S11.** Manhattan plot of HLA and KIR association results with all phenotypes. **Figure S12.** PCoA plot of samples obtained from different sequencing batches. **Figure S13.** PCA plot of the genetic matrix data of MI donors. (DOCX 2702 kb)

**Additional File 2: Table S1.** Description of all the covariates used in the study. **Table S2.** Spearman correlations of all the covariates with four α-diversity metrics. **Table S3.** Results of multivariable ANOVAs with α-diversity metrics. **Table S4.** PERMANOVA results for all of the covariates with three β-diversity indexes. **Table S5.** Results of multivariable PERMANOVAs with β-diversity indexes. **Table S6.** Explained cumulative variance of Bray-Curtis dissimilarity metric by all non-genetic covariates (dietary and 110 tested in this study). **Table S7.** List of taxa tested for association with genetic variants. **Table S8.** MaAsLin test results between 110 non-genetic variables and 475 taxa. **Table S9.** List of identified covariates that were used for each phenotype in addition to the first two principal components of the genotyping matrix. **Table S10.** List of SNPs with association p-value < $10^{-6}$ with α-diversity metrics. **Table S11.** List of SNPs with association p-value < $10^{-6}$ with β-diversity indexes. **Table S12.** Replication of the SNPs previously reported to be associated with β-diversity.

**Table S13.** Nominal associations of SNPs with relative abundances of taxa. **Table S14.** Nominal associations of SNPs with dichotomized taxa in the MI cohort. **Table S15.** Replication of the SNPs previously reported to be associated with individual taxa. **Table S16.** Mean relative abundance of major phyla in MI and other studies. (XLSX 1442 kb)

**Abbreviations**

ACE: Abundance-based coverage estimator; ALT: Alanine transaminase; ANOVA: Analysis of variance; CRP: C-reactive protein; FDR: False discovery rate; GWAS: Genome-wide association study; HCV: Hepatitis C virus; HIV: Human immunodeficiency virus; HLA: Human leukocyte antigen; KIR: Killer-cell immunoglobulin-like receptors; LD: Linkage disequilibrium; MAF: Minor allele frequency; MDS: Multidimensional scaling; MI: Milieu Intérieur; OTU: Operational taxonomy unit; PCA: Principal component analysis; PCoA: Principal coordinates analysis; PCR: Polymerase chain reaction; PERMANOVA: Permutational analysis of variance; QQ: Quantile-quantile; rRNA: Ribosomal ribonucleic acid; SNP: Single nucleotide polymorphism

**Acknowledgements**

We would like to thank to all the donors for their contribution to the study. The Milieu Intérieur Consortium is composed of the following team leaders: Laurent Abel (Hôpital Necker, Paris, France), Andres Alcover (Institut Pasteur, Paris, France), Hugues Aschard (Institut Pasteur, Paris, France), Kalla Astrom (Lund University, Lund, Sweden), Philippe Bousso (Institut Pasteur, Paris, France), Pierre Bruhns (Institut Pasteur, Paris, France), Ana Cumano (Institut Pasteur, Paris, France), Caroline Demangel (Institut Pasteur, Paris, France), Ludovic Deriano (Institut Pasteur, Paris, France), James Di Santo (Institut Pasteur, Paris, France), Françoise Dromer (Institut Pasteur, Paris, France), Darragh Duffy (Institut Pasteur, Paris, France), Gérard Eberl (Institut Pasteur, Paris, France), Jost Enninga (Institut Pasteur, Paris, France), Jacques Fellay (EPFL, Lausanne, Switzerland), Odile Gelpi (Institut Pasteur, Paris, France), Ivo Gomperts-Boneca (Institut Pasteur, Paris, France), Milena Hasan (Institut Pasteur, Paris, France), Serge Hercberg (Université Paris 13, Paris, France), Olivier Lantz (Institut Curie, Paris, France), Claude Leclerc (Institut Pasteur, Paris, France), Hugo Mouquet (Institut Pasteur, Paris, France), Sandra Pellegrini (Institut Pasteur, Paris, France), Stanislas Pol (Hôpital Côchin, Paris, France), Antonio Rausell (INSERM UMR 1163 – Institut Imagine, Paris, France), Lars Rogge (Institut Pasteur, Paris, France), Anavaj Sakuntabhai (Institut Pasteur, Paris, France), Olivier Schwartz (Institut Pasteur, Paris, France), Benno Schwikowski (Institut Pasteur, Paris, France), Spencer Shorte (Institut Pasteur, Paris, France), Vassili Soumelis (Institut Curie, Paris, France), Frédéric Tangy (Institut Pasteur, Paris, France), Eric Tartour (Hôpital Européen George Pompidou, Paris, France), Antoine Toubert (Hôpital Saint-Louis, Paris, France), Mathilde Touvier (Université Paris 13, Paris, France), Marie-Noëlle Ungeheuer (Institut Pasteur, Paris, France), Matthew L. Albert (Roche Genentech, South San Francisco, CA, USA), and Lluis Quintana-Murci (Institut Pasteur, Paris, France). Matthew L. Albert and Lluis Quintana-Murci are the coordinators of the consortium. Additional information can be found at http://www.milieuinterieur.fr/en.

**Authors' contributions**

JF, LQ-M, DD, MLA, and OL contributed to the conception of the study. PS, FH, AB, and JF contributed to the design of the study. PS, SM, VP, CH, CA, JB, EP, and MT contributed to the acquisition of the data. PS, FH, and JF contributed to the analysis of the data. PS and JF contributed to the drafting of the manuscript. PS, FH, SM, VP, AB, CH, CA, JB, EP, MT, OL, MLA, DD, LQ-M, and JF contributed to revising of the manuscript. All authors read and approved the final manuscript.

**Funding**

This work benefited from the support of the French government's Invest in the Future Program, managed by the Agence Nationale de la Recherche (ANR, reference 10-LABX-69-01). It was also supported by a grant from the Swiss National Science Foundation (31003A_175603 to JF).

**Availability of data and materials**

Data supporting the conclusions of this article are available in the European Genome-Phenome Archive, under the accession code EGAS00001002460 (https://www.ebi.ac.uk/ega/studies/EGAS00001002460) for the human genotype data and EGAS00001003419 (https://www.ebi.ac.uk/ega/studies/EGAS00001003419) for the microbial sequences. Associated demographic,
lifestyle, environmental, and biochemical metadata can be obtained by con-
tacting the coordinators of the consortium. Full summary association results
are available for download from Zenodo (https://doi.org/10.5281/zenodo.
2643319). The scripts used for processing of microbiome data, running
GWAS's, and association testing with non-genetic variables are available in
GitHub (https://github.com/pscepanovic/MI_GutMicrobiome).

Ethics approval and consent to participate

The clinical study was approved by the Comité de Protection des Personnes - Ouest 6 on June 13, 2012, and by the French Agence Nationale de Sécurité du Médicament on June 22, 2012, and has been performed in accordance with the Declaration of Helsinki. The study is sponsored by the Institut Pasteur (Pasteur ID-RCB Number 2012-A00238-35) and was conducted as a single-center study without any investigational product. The protocol is registered under ClinicalTrials.gov (study number NCT01699893). Informed consent was obtained from the participants after the nature and possible consequences of the studies were explained.

Consent for publication

Not applicable.

Competing interests

C.H., A.B., and M.L.A. are employees of Genentech Inc., a member of The Roche Group. The other authors declare that they have no competing interests.

Author details

¹School of Life Sciences, École Polytechnique Fédérale de Lausanne,
Lausanne, Switzerland. ²Swiss Institute of Bioinformatics, Lausanne,
Switzerland. ³MICALIS Institute (INRA/AgroParisTech), Jouy-en-Josas, France.

⁴Sorbonne-Paris-Cité Research Center for Epidemiology and Statistics CRESS, Nutritional Epidemiology Research Team EREN (INSERM U1153/INRA U1125/CNAM/Université Paris-XIII Nord), Bobigny, France. ⁵University of Paris-VII Denis Diderot, Sorbonne-Paris-Cité University, Paris, France. ⁶Department of Cancer Immunology, Genentech Inc., San Francisco, CA 94080, USA.

⁷Department of Human Genetics, Genentech Inc., San Francisco, CA 94080, USA. ⁸Institute for Immunology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. ⁹Department of Automatic Control, LTH, Lund University, Lund, Sweden. ¹⁰Unit of Human Evolutionary Genetics, Department of Genomes and Genetics, Institut Pasteur, Paris, France.

¹¹Centre National de la Recherche Scientifique, UMR2000, Paris, France.

¹²Institut Curie, PSL Research University, Inserm U932, 75005 Paris, France.

¹³Center of Clinical Investigations, CICBT1428 IGR/Curie, 75005 Paris, France.

¹⁴Immunobiology of Dendritic Cells laboratory (INSERM U1223/Institut Pasteur), Paris, France. ¹⁵Precision Medicine Unit, Lausanne University Hospital, Lausanne, Switzerland.

Received: 20 March 2019 Accepted: 4 September 2019

Published online: 13 September 2019

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