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

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: Genome-wide association studies (GWAS); Gut microbiome; Meta-analysis.

Fig. 1

Overview of genome-wide significant loci discovered in four recent GWAS studies [–12]. 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]