Skip to content

Advertisement

  • Microbiome Announcement
  • Open Access

Meta-analysis of human genome-microbiome association studies: the MiBioGen consortium initiative

  • 1, 2, 3Email author,
  • 4,
  • 5,
  • 6, 7,
  • 7,
  • 4, 8,
  • 9,
  • 5, 10, 11,
  • 12,
  • 13,
  • 14,
  • 15, 16,
  • 17,
  • ,
  • 9,
  • 9,
  • 9,
  • 15, 16,
  • 14,
  • 4,
  • 18,
  • 13,
  • 4,
  • 19, 20, 21,
  • 2, 3Email author,
  • 5Email author and
  • 4Email author
Contributed equally
Microbiome20186:101

https://doi.org/10.1186/s40168-018-0479-3

  • Received: 7 December 2017
  • Accepted: 10 May 2018
  • Published:

Abstract

Background

In recent years, human microbiota, especially gut microbiota, have emerged as an important yet complex trait influencing human metabolism, immunology, and diseases. Many studies are investigating the forces underlying the observed variation, including the human genetic variants that shape human microbiota. Several preliminary genome-wide association studies (GWAS) have been completed, but more are necessary to achieve a fuller picture.

Results

Here, we announce the MiBioGen consortium initiative, which has assembled 18 population-level cohorts and some 19,000 participants. Its aim is to generate new knowledge for the rapidly developing field of microbiota research. Each cohort has surveyed the gut microbiome via 16S rRNA sequencing and genotyped their participants with full-genome SNP arrays. We have standardized the analytical pipelines for both the microbiota phenotypes and genotypes, and all the data have been processed using identical approaches. Our analysis of microbiome composition shows that we can reduce the potential artifacts introduced by technical differences in generating microbiota data. We are now in the process of benchmarking the association tests and performing meta-analyses of genome-wide associations. All pipeline and summary statistics results will be shared using public data repositories.

Conclusion

We present the largest consortium to date devoted to microbiota-GWAS. We have adapted our analytical pipelines to suit multi-cohort analyses and expect to gain insight into host-microbiota cross-talk at the genome-wide level. And, as an open consortium, we invite more cohorts to join us (by contacting one of the corresponding authors) and to follow the analytical pipeline we have developed.

Keywords

  • Gut microbiome
  • Genome-wide association studies (GWAS)
  • Meta-analysis

Background

Our understanding of the microbial communities populating the human body (human microbiota) has progressed tremendously in recent years, catalyzed by the use of next-generation sequencing techniques that overcome the limitations of anaerobic cultivation [1]. Much effort has been devoted to understanding the taxonomic and functional diversity of the microbiota and their encoded collective gene pool, the microbiome, with most research activity focusing on the microbes in our gastrointestinal tract [2, 3]. Much of the research has centered on elucidating links between microbes and various diseases [4], for instance, obesity, inflammatory bowel disease, and diabetes. This has including several studies that went beyond association to demonstrate causal roles of the gut microbiome in disease development.

More knowledge of the microbial ecosystem and the role of different factors in its structure is an essential path leading to more understanding of human biology [5]. Cross-sectional studies carried out in several population-based cohorts have identified the major environmental factors (nutrition, medication, and diet) influencing the composition and functional capacities of the human microbiome [6, 7]. Yet these studies also showed that a large proportion of microbial diversity remained unexplained after considering the environmental influences, thereby raising questions on the role of host genetics.

Given the complex interplay between the microbiome and host physiology, a certain percentage of host genetics, as well as genetic interactions with environmental factors, is expected to shape the composition of the microbial community [8]. Proof-of-principle genome-wide screens (e.g., quantitative trait loci (QTL) studies) have been carried out in model organisms like mouse [9], while the majority of published studies on humans have used a candidate gene approach to cope with sample size limitations. Recently, analyses of twin cohorts have demonstrated a genetic contribution to variation in the relative proportions of specific members of microbiota [10], for example, investigations in 1126 twins identified associations to 28 loci, including genetic variants in LCT [11].

Bonder et al., Turpin et al., and Wang et al. then simultaneously reported GWAS results from three independent cohorts, each revealing glimpses into the genetic landscape underlying the gut microbiota structure [1214]. Together, these GWAS have identified some 100 genome-wide significant loci associated with community structure, taxon abundance, and gut microbiome biodiversity. However, similar to initial GWAS efforts in many other complex traits, there was little overlap seen in the three sets of summary statistics (Fig. 1). SLIT3 was the only gene to pass a standard genome-wide significance threshold of 5 × 10−8 in the TwinsUK and Bonder et al. studies [11, 12], but the two reported single nucleotide polymorphisms (SNPs) within this gene are not proxies of each other, nor do they correlate to the same bacteria or pathway. Despite little overlap in the associated genetic variants, which were limited to the LCT locus, associations to various C-type lectin genes were observed by both Bonder et al. and Wang et al. [11, 12, 14]. These discordances emphasize the need to increase the number of samples in the discovery setting to improve statistical power and to reduce the probability of false-positive associations. Cross-multi-cohort analysis will also overcome limitations imposed by population stratification as well as technical artifacts, including the differences in model choice [15].
Fig. 1
Fig. 1

Overview of genome-wide significant loci discovered in four recent GWAS studies [912]. For simplicity, only the regions harboring a coding gene are shown, and for Wang et al. [14], the list was further refined to genes implicated in previous mouse QTL studies and to additional loci identified by an improved method (shown in gray, Rühlemann et al. Gut microbes, 2017). So far, the only overlap found in the three studies is the SLIT3 locus, although two studies reported two SNPs not in linkage disequilibrium. The LCT locus was not significant in the initial analysis using an additive model, but analyzing functional SNPs in the recessive model identified a significant association for LCT in the Dutch cohort [15]

We have therefore established the MiBioGen consortium to study the influence of human genetics on gut microbiota. This collaborative effort currently comprises 18 cohorts worldwide and new members will join us after completing their data collection. We aim to develop a uniform pipeline to allow maximum harmonization across the microbiome data and to use GWAS meta-analyses to provide a fuller picture of human gene-microbiome associations. Furthermore, since all the cohorts have been well phenotyped, their data will aid future investigations into other research questions.

MiBioGen initiative and cohort descriptions

Most of the 18 studies participating in the consortium are prospective cohort studies in countries in Europe, Asia, and North America (Table 1). Besides genetics and microbiome data, the cohorts have also been deeply phenotyped, covering multiple individual outcomes (e.g., anthropometric, metabolic, disease-related). These cohorts also incorporate a wide age spectrum, including both children and adults. The number of individuals per cohort study ranges from 139 to 2482, with a total of 19,790 individuals (18,965 after quality control (QC)). In terms of both sample size and geographic distribution, the MiBioGen consortium is, to our knowledge, the most comprehensive effort for investigating host-genetics-versus-microbiome-associations on a population scale.
Table 1

Information on the 18 cohorts participating in the MiBioGen consortium to date

Cohort name

Population (ethnicity)

16S domain

Genotyping platforms used

Sample size (after QC)

Description

BSPSPC

Germany (Caucasian)

V1-V2

Illumina 550K, Immunochip, Metabochip, Affymetrix 6.0, Axiom

912

Representative of population

CARDIA

USA (Caucasian and African-American)

V3-V4

Illumina Exome, Affymetrix 6.0

282

Representative of population

NeuroIMAGE + COMPULS

Netherlands (Caucasian)

V1-V2

PsychChip (Broad Institute, Boston, USA)

153

Healthy group + ADHD group

COPSAC

Denmark (Caucasian)

V4

Illumina OmniExpressExome

424

Children (unselected)

FGFP

Belgium (Caucasian)

V4

Illumina OmniExpressExome

2482

Representative of population

FoCus

Germany (Caucasian)

V1-V2

Illumina Immunochip, Exome

1555

Representative of population + obese sub-cohort

GEM

Canada, USA, Israel (Caucasian, Israeli)

V4

Illumina HumanCoreExome, Immunochip

1543

Healthy individuals

Generation R

Netherlands (multi-ethnic)

V3-V4

Illumina 610 k

2111

Representative of population

KSCS

South Korea (Eastern Asian)

V3-V4

Illumina HumanCore BeadChips 12v

833

Representative of population

LLD

Netherlands (Caucasian)

V4

Illumina Immunochip, Cytochip

1089

Representative of population

METSIM

Finland (Caucasian)

V4

Illumina OmniExpressExome

531

Representative of population

MIBS

Netherlands (Caucasian)

V4

Illumina OmniExpressExome

111

Healthy volunteers

PNP

Israel (Israeli)

V3-V4

Illumina Metabochip

1066

Healthy volunteers

Rotterdam Study

Netherlands (Caucasian)

V3-V4

Illumina 550k

1427

Representative of population

SHIP

Germany (Caucasian)

V1-V2

Affymetrix 6.0, Illumina OmniExpressExome, Exomechip

1904

Representative of population

TwinsUK

UK (Caucasian)

V4

HumanHap300, Hap610Q, 1M-Duo, 1.2M-Duo

1793

Twins

NTR

Netherlands (Caucasian)

V4

Affymetrix 6.0

499

Twins

PopCol

Sweden

V1-V2

Illumina MiSeq

250

Representative of population

Total

   

18,965

 

As we have multiple phenotypes in addition to microbiome and host genotypes available, we can assess the putative effect of the gut microbiome on human health. Several of the cohorts were set up to investigate certain phenotypes and/or diseases, for instance, GEM (healthy relatives of patients with Crohn’s disease) [13], or FoCus (a nutritional intervention study) [14]. As a basis for epidemiological studies, various metadata were collected by the different cohorts including anthropometric measures, blood chemistry, dietary pattern, intestinal permeability, and lifestyle. These factors have been shown to influence microbiota composition [6, 7, 14]. All these metadata and phenotypes provide opportunities for assessing the biological significance of gene-microbiome associations, and for gaining insights into gene-environment interactions and the interaction between host genotype–microbiome–diseases.

Methods

To provide a platform for robust and reliable results and also to simplify study participation in MiBioGen, we have standardized all the procedures and protocols that participating cohorts need to follow. The MiBioGen data processing pipeline comprises four steps: (1) microbiome data processing, (2) genotype data processing, (3) genome-wide association analyses, and (4) meta-analyses.

Microbiome data processing

The microbiome data included in our consortium was mainly generated using an Illumina sequencing platform (MiSeq or HiSeq). The most frequently sequenced hyper-variable region of the 16S rRNA gene was V4 (eight cohorts, n = 8472), although five cohorts sequenced the V3-V4 region (n = 5719), and another four sequenced the V1-V2 region (n = 4774). We assessed the compatibility of the datasets obtained from sequencing different regions by comparing technical replicates of ten samples (three replicates each) generated from different hyper-variable regions. This analysis showed that the influence of technical differences in microbiome profiles is less than the inter-individual differences (Additional file 1). Nevertheless, including different hyper-variable regions requires compatible methods of 16S rRNA gene-amplicon data processing, and it is no longer feasible to use “open” (de novo) operational taxonomic units (OTU) picking protocols. Further analysis of technical replicates using closed-reference OTU picking showed that the clustering results also have large technical artifacts (Additional file 1). In contrast, the between-replicate similarity on genera- and higher taxonomic levels showed reasonable concordance (Additional file 1). As a result, we implemented the 16S data processing pipeline, which comprised a naive Bayesian classifier from the Ribosomal Database Project [16], and the most recent, full, SILVA database (release 128): we only analyzed taxonomical results using genus- and higher taxonomic levels.

As well as a standard taxonomy binning procedure, all the additional steps have been standardized across the consortium, including downsampling to 10,000 reads with fixed seed to allow for replicability, procedures of transformations, and corrections for covariates, and the thresholds set for bacterial taxa to be included in the analysis (any taxon should be present in more than 10% of the cohort’s samples). This filtering effectively reduces the total number of tests and also makes cross-validation and meta-analysis feasible among all the participating cohorts. 16S data processing is currently being performed in all the cohorts and shows a high level of congruence: the core-measurable microbiome (CMM) [9], defined as the list of bacterial taxa present in more than 10% of the samples in a cohort, is stable across the participating cohorts and shapes around 80% of each cohort’s microbiome composition.

Genotype data processing

Individual genome-wide genotype data was generated by the different cohort studies using different genotyping platforms and arrays (Table 1). In order to utilize the genome-wide data and remove artifacts resulting from the different platforms, we imputed missing genotypes to extend the resolution on a genome-wide level. We standardized the imputation procedure for each cohort, including the pre-imputation quality control, reference imputation panel, imputation server and software, as well as the post-imputation filtering to include SNPs in the analyses.

Quality control performed prior to imputation was carried out by each cohort independently according to our general recommendations. Imputation was performed on a freely available Michigan server (https://imputationserver.sph.umich.edu/index.html) that uses a two-step approach: phasing with the Eagle v2.3 algorithm, followed by imputation with Minimac [17]. For our consortium, the data was imputed to the Haplotype Reference Consortium (HRC 1.1) reference panels [17]. To allow imputed SNPs in the association studies, we included minor allele frequency filtering (5%), posterior imputation quality (0.4, applied per sample), and variant imputation quality (0.5, applied per SNP). After imputation, each study yielded around 39.1 million SNPs, with 4 to 6 million variants passing post-imputation QC.

Genome-wide association analysis

Previous microbiome GWAS have used different statistical methods to test association of genetic variants with gut microbiome taxa [912], and these might contribute to some of the differences in observed associations. We are therefore developing a uniform analytical pipeline to be implemented by all the studies participating in our consortium; it uses flexible statistical approaches to cope with the non-normality and high dispersion inherent to microbiome data [15]. Several layers of microbiome representations are considered as traits in GWAS: general diversity metrics (alpha- and beta-diversity), series of binomial traits of bacterial presence, and quantitative traits of bacterial relative abundance. At the moment, we are using multiple cohorts for benchmarking, to fine-tune our algorithm and to reduce inter-cohort and technical differences.

Meta-analyses

Given the substantial increase in sample size (10-fold), as well as our large number of 18 cohorts, we expect to be able to identify individual bacteria and new genomic loci that affect microbiome composition in general. Based on the effect size (0.147 × SD, using a genome-wide threshold of 5e−8) in some 1800 individuals [14], this consortium can theoretically provide 80% power to detect effects larger than 0.045 × SD. Our full pipeline can be found and followed at https://github.com/alexa-kur/miQTL_cookbook. We will also publish summary statistical results from each cohort, as well as the full meta-study results, both on GitHub and as supplementary files in our future publications.

Conclusions and future directions

The MiBioGen consortium’s large-scale meta-analysis of 18 cohorts drawn from different populations will permit us to explore the genetic architecture of the gut microbiome. In addition to classic association studies, we will adopt more sophisticated approaches to gain a better understanding of the role of the gut microbiome as a mediator between genetic predisposition and human health/disease. For example, we will explore the association of individual risk scores [18] to common diseases, based on published GWAS results and individual microbiome composition.

We will also explore human gene-environment interactions with respect to gut microbiome composition. Such interactions have been observed for the LCT non-functional variant and for dairy intake in relation to the abundance of Bifidobacteria [10, 19]. Comprehensive studies have explored the independent effects of environmental and genetic forces on the gut microbiome [6, 7, 1214], and we will investigate a number of gene-environment interactions of interest, including gene-diet, using the combined genetic data and extensive environmental metadata. Certain gene-environment interactions can also be examined in those cohorts that collected stool samples at multiple time points. We appreciate that it will be difficult to determine causality, but we will probably be able to identify a series of environment-gene-microbiome triangles, for instance, those involving age, gender, medication usage, or body mass index. Our results will lead to hypotheses on the links underlying microbiome-related physiological processes. We would therefore encourage any cohorts with an interest in analyzing host-microbiota associations in their own data to join the MiBioGen consortium and to contribute to more overall insights into the intricacies of host genomes’ role in shaping the gut microbiota.

Finally, the additional phenotypes available in each cohort will provide a unique opportunity for quantifying the contribution of the gut microbiome to different phenotypes. For example, GWAS analyses have already been focused on metabolic traits and diseases in different cohorts, and much more cross-checking can be carried out using the EBI GWAS Catalog. The overlap in significant loci will reveal intrinsic relationships between the microbiome, genetics, and diseases, thereby adding to our knowledge of the molecular basis of these pathologies. Recently developed strategies, such as linkage disequilibrium score regression [20] and polygenic risk scores [18], as well as downstream pathway enrichment analyses, will help translate genetic associations into real biological insights into the host-microbiome interaction. Our consortium will thus not only contribute to fundamental knowledge on the gut microbiome but also lead on to clinical and therapeutic efforts in treating diseases.

Notes

Abbreviations

CMM: 

Core-measurable microbiome

EBI: 

European Bioinformatics Institute

GWAS: 

Genome-wide association studies

HRC: 

Haplotype Reference Consortium

QC: 

Quality control

QTL: 

Quantitative trait loci

Declarations

Acknowledgements

We thank Jackie Senior for editing the manuscript. Further acknowledgement of each cohort can be found in the Additional file 2.

Full list of MiBioGen consortium participants

Tarun Ahluwalia 1, Elad Barkan 2,3, Larbi Bedrani 4, Jordana Bell 5, Hans Bisgaard 1, Michael Boehnke 6, Marc Jan Bonder 7,8, Klaus Bønnelykke 1, Dorret I. Boomsma 9, Kenneth Croitoru 10, Gareth E. Davies 11, Eco de Geus 9, Frauke Degenhardt 12, Mauro D’Amato 13, Erik A. Ehli 11, Osvaldo Espin-Garcia 14,15, Casey T. Finnicum 11, Myriam Fornage 16, Andre Franke 12, Lude Franke 7, Fabian Frost 17, Jingyuan Fu 7,18, Femke-A. Heinsen 12, Georg Homuth 19, David Hughes 20,21, Richard IJzerman 22, Matthew A Jackson 5, Leon Eyrich Jessen 1, Daisy Jonkers 23, Tim Kacprowski 19, Han-Na Kim 24, Hyung-Lae Kim 24, Robert Kraaij 25, Alex Kurilshikov 7, Markku Laakso 26, Lenore Launer 27, Markus M. Lerch 17, Kreete Lüll 28, Aldons J. Lusis 29, Massimo Mangino 5, Julia Mayerle 17,30, Hamdi Mbarek 9, Maria Carolina Medina 25,31,32, Katie Meyer 33, Karen L. Mohlke 34, Elin Org 28, Andrew Paterson 35,36,37, Haydeh Payami 38, Djawad Radjabzadeh 25, Jeroen Raes 39,40, Daphna Rothschild 2,3, Malte Rühlemann 12, Serena Sanna 7, Eran Segal 2,3, Shiraz Shah 1, Michelle Smith 4,10, Tim Spector 5, Claire Steves 5, Jakob Stokholm 1, Joanna W. Szopinska 41, Jonathan Thorsen 1, Nicolas Timpson 20,21, Williams Turpin 4,10, André G. Uitterlinden 25,42, Alejandro Arias Vasquez 41, Henry Völzke 44, Urmo Vosa 7, Zachary Wallen 38, Jun Wang 39,40, Frank Ulrich Weiss 17, Omer Weissbrod 2,3, Cisca Wijmenga 7,45, Gonneke Willemsen 9, Wei Xu 35,46, Yeojun Yun 24, Alexandra Zhernakova 7

1 COPSAC, Copenhagen Prospective Studies on Asthma in Childhood, Herlev and Gentofte Hospital, University of Copenhagen, Copenhagen, Denmark

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

3 Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel

4 Division of Gastroenterology, Department of Medicine, University of Toronto, Toronto, Ontario, Canada

5 Department of Twin Research and Genetic Epidemiology, King’s College London, London, UK

6 Department of Biostatistics and Center for Statistical Genetics, University of Michigan, MI, USA

7 University of Groningen, University Medical Center Groningen, Department of Genetics, Groningen, The Netherlands

8 European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK

9 Department of Biological Psychology, Amsterdam Public Health Research Institute, VU Amsterdam, Amsterdam, The Netherlands

10 Zane Cohen Centre for Digestive Diseases, Mount Sinai Hospital, Toronto, Ontario, Canada

11 Avera Institute for Human Genetics, Avera McKennan Hospital & University Health Center, Sioux Falls, SD, USA

12 Institute of Clinical Molecular Biology, Christian Albrechts University of Kiel, Kiel, Germany

13 Unit of Clinical Epidemiology, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden

14 Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Ontario, Canada

15 Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada

16 Health Science Center at Houston, University of Texas, Houston, TX, USA

17 Department of Medicine A, University Medicine Greifswald, Greifswald, Germany

18 University of Groningen, University Medical Center Groningen, Department of Pediatrics, Groningen, The Netherlands

19 Department of Functional Genomics, Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Germany

20 MRC Integrative Epidemiology Unit at University of Bristol, Bristol, UK

21 Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK

22 Department of Internal Medicine, Diabetes Centre, VU University Medical Centre, Amsterdam, The Netherlands

23 Division of Gastroenterology-Hepatology, Department of Internal Medicine, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University Medical Center, Maastricht, The Netherlands

24 Department of Biochemistry, School of Medicine, Ewha Womans University, Seoul, South Korea

25 Department of Internal Medicine, Erasmus MC, Rotterdam, The Netherlands

26 Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland and Kuopio, University Hospital, Kuopio, Finland

27 Laboratory of Epidemiology and Population Science, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA

28 Institute of Genomics, University of Tartu, Estonia

29 Department of Medicine, Department of Human Genetics, Molecular Biology Institute, Department of Microbiology, Immunology and Molecular Genetics, University of California, CA, USA

30 Department of Medicine 2, University Hospital, Ludwig-Maximilians-University, Munich, Germany

31 The Generation R Study Group, Erasmus MC, 3000 CA Rotterdam, The Netherlands

32 Department of Epidemiology, Erasmus MC, 3000 CA Rotterdam, The Netherlands

33 Department of Nutrition, Nutrition Research Institute, University of North Carolina at Chapel Hill, Kannapolis, NC, USA

34 Department of Genetics, University of North Carolina at Chapel Hill, NC, USA

35 Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada

36 Division of Epidemiology, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada

37 Genetics and Genome Biology, The Hospital for Sick Children Research Institute, The Hospital for Sick Children, Toronto, Ontario, Canada

38 Departments of Neurology and Genetics, University of Alabama at Birmingham, Birmingham, AL, USA

39 Department of Microbiology and Immunology, Rega Institute. KU Leuven – University of Leuven, Leuven, Belgium

40 VIB Center for Microbiology, Leuven, Belgium

41 Department of Psychiatry, Radboudumc, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands

42 Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands

43 Department of Medicine 2, University Hospital, Ludwig-Maximilians-University, Munich, Germany

44 Institute for Community Medicine, Greifswald University Hospital, Greifswald, Germany

45 K.G. Jebsen Coeliac Disease Research Centre, Department of Immunology, University of Oslo, Norway

46 Department of Biostatistics, Princess Margaret Cancer Centre, Toronto, Ontario, Canada

Funding

Funding and other related information for each cohort can be found in Additional file 2.

Availability of data and materials

Data availability is determined by each cohort, according to the agreements with their participants, as well as their local regulations and institute requirements.

Authors’ contributions

JW and AK analyzed the data, and jointly with JR, RK, and AZ, wrote the paper; the other authors have revised the manuscript. All authors have read the final manuscript and approved it for publication.

Ethics approval and consent to participate

Ethical approval and consent to participate were acquired by each cohort, according to their local regulations and institute requirements.

Consent for publication

Consent for publication was acquired by each cohort, according to their local regulations and institute requirements.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Open AccessThis 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.

Authors’ Affiliations

(1)
CAS Key Laboratory for Pathogenic Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences, Beijing, China
(2)
Department of Microbiology and Immunology, Rega Institute. KU Leuven – University of Leuven, Leuven, Belgium
(3)
VIB Center for Microbiology, Leuven, Belgium
(4)
Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
(5)
Department of Internal Medicine, Erasmus Medical Center, Rotterdam, The Netherlands
(6)
Division of Gastroenterology, Department of Medicine, University of Toronto, Toronto, Ontario, Canada
(7)
Zane Cohen Centre for Digestive Diseases, Mount Sinai Hospital, Toronto, Ontario, Canada
(8)
European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK
(9)
Department of Twin Research and Genetic Epidemiology, King’s College London, London, UK
(10)
The Generation R Study Group, Erasmus MC, 3000, CA, Rotterdam, The Netherlands
(11)
Department of Epidemiology, Erasmus MC, 3000, CA, Rotterdam, The Netherlands
(12)
Department of Medicine A, University Medicine Greifswald, Greifswald, Germany
(13)
Department of Functional Genomics, Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, Germany
(14)
Institute of Clinical Molecular Biology, Christian Albrechts University of Kiel, Kiel, Germany
(15)
MRC Integrative Epidemiology Unit at University of Bristol, Bristol, UK
(16)
Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
(17)
Department of Biochemistry, School of Medicine, Ewha Womans University, Seoul, South Korea
(18)
Department of Nutrition, Nutrition Research Institute, University of North Carolina at Chapel Hill, Kannapolis, NC, USA
(19)
Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
(20)
Division of Epidemiology, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
(21)
Genetics and Genome Biology, The Hospital for Sick Children Research Institute, The Hospital for Sick Children, Toronto, Ontario, Canada

References

  1. Qin J, et al. A metagenome-wide association study of gut microbiota in type 2 diabetes. Nat. 2012;490:55–60.View ArticleGoogle Scholar
  2. Yatsunenko YT, et al. Human gut microbiome viewed across age and geography. Nat. 2012;486:222–7.View ArticleGoogle Scholar
  3. Turnbaugh PJ, et al. The human microbiome project. Nature. 2007;449:804–10.View ArticlePubMedPubMed CentralGoogle Scholar
  4. Sommer F, et al. The resilience of the intestinal microbiota influences health and disease. Nature reviews Microbiol. 2017;15:630–8.View ArticleGoogle Scholar
  5. The Human Microbiome Project Consortium. A framework for human microbiome research. Nature. 2012;486:215–21.View ArticleGoogle Scholar
  6. Zhernakova A, et al. Population-based metagenomics analysis reveals markers for gut microbiome composition and diversity. Sci. 2016;352:565–9.View ArticleGoogle Scholar
  7. Falony G, et al. Population-level analysis of gut microbiome variation. Science. 2016;352:560–4.View ArticlePubMedGoogle Scholar
  8. Org E, et al. Genetic and environmental control of host-gut microbiota interactions. Genome Res. 2015;25:1558–69.View ArticlePubMedPubMed CentralGoogle Scholar
  9. Benson AK, et al. Individuality in gut microbiota composition is a complex polygenic trait shaped by multiple environmental and host genetic factors. Proc Nat Acad Sci USA. 2010;107:18933–8.View ArticlePubMedPubMed CentralGoogle Scholar
  10. Goodrich JK, et al. Human genetics shape the gut microbiome. Cell. 2014;159:789–99.View ArticlePubMedPubMed CentralGoogle Scholar
  11. Goodrich JK, et al. Genetic determinants of the gut microbiome in UK twins. Cell Host Microbe. 2016;19:731–43.View ArticlePubMedPubMed CentralGoogle Scholar
  12. Bonder MJ, et al. The effect of host genetics on the gut microbiome. Nat Genet. 2016;48:1407–12.View ArticlePubMedGoogle Scholar
  13. Turpin W, et al. Association of host genome with intestinal microbial composition in a large healthy cohort. Nat Genet. 2016;48:1413–7.View ArticlePubMedGoogle Scholar
  14. Wang J, et al. Genome-wide association analysis identifies variation in vitamin D receptor and other host factors influencing the gut microbiota. Nat Genet. 2016;48:1396–406.View ArticlePubMedPubMed CentralGoogle Scholar
  15. Kurilshikov A, et al. Host genetics and gut microbiome: challenges and perspectives. Trends Immunol. 2017;511:421–7.Google Scholar
  16. Wang Q, et al. Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl Environ Microbiol. 2007;73:5261–7.View ArticlePubMedPubMed CentralGoogle Scholar
  17. Das S, et al. Next-generation genotype imputation service and methods. Nat Genet. 2016;48:1284–7.View ArticlePubMedPubMed CentralGoogle Scholar
  18. Dudbridge F. Power and predictive accuracy of polygenic risk scores. PLoS Genet. 2013;9(3):e1003348.View ArticlePubMedPubMed CentralGoogle Scholar
  19. Goodrich JK, et al. Cross-species comparisons of host genetic associations with the microbiome. Sci. 2016;352:29–32.View ArticleGoogle Scholar
  20. Bulik-Sullivan BK, et al. LD score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. G E N. 2015;47:291–5.Google Scholar

Copyright

Advertisement