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

  • mibiogen
  • CAS - Institute of Microbiology
  • KU Leuven
  • Flanders Institute for Biotechnology
  • University of Groningen
  • Erasmus University Rotterdam
  • University of Toronto
  • European Molecular Biology Laboratory
  • King's College London
  • University of Greifswald
  • Kiel University
  • University of Bristol
  • Ewha Womans University
  • University of North Carolina at Chapel Hill
  • University of Copenhagen
  • Weizmann Institute of Science
  • University of Michigan
  • University of Amsterdam
  • Avera Health
  • Karolinska Institutet
  • Sinai Health System
  • University of Texas Health Science Center at Houston
  • Maastricht University
  • University of Eastern Finland
  • National Institutes of Health
  • Estonian Biocentre
  • University of California at Los Angeles
  • Ludwig Maximilian University of Munich
  • University of Alabama at Birmingham
  • Radboud University Nijmegen
  • University of Oslo
  • Department of Biostatistics

Research output: Contribution to journalArticleAcademicpeer-review

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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.

Original languageEnglish
Article number101
JournalMicrobiome
Volume6
Issue number1
DOIs
Publication statusPublished - 8 Jun 2018

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Keywords

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

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