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

• Main Authors: Jun Wang, Alexander Kurilshikov, Djawad Radjabzadeh, Williams Turpin, Kenneth Croitoru, Marc Jan Bonder, Matthew A. Jackson, Carolina Medina-Gomez, Fabian Frost, Georg Homuth, Malte Rühlemann, David Hughes, Han-na Kim, MiBioGen Consortium Initiative, Tim D. Spector, Jordana T. Bell, Claire J. Steves, Nicolas Timpson, Andre Franke, Cisca Wijmenga, Katie Meyer, Tim Kacprowski, Lude Franke, Andrew D. Paterson, Jeroen Raes, Robert Kraaij, Alexandra Zhernakova
• Format: Article
• Language: English
• Published: BMC 2018-06-01
• Series: Microbiome
• Subjects: Gut microbiome; Genome-wide association studies (GWAS); Meta-analysis
• Online Access: http://link.springer.com/article/10.1186/s40168-018-0479-3

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.