Genome-wide associations of human gut microbiome variation and implications for causal inference analyses
David Hughes, Rodrigo Bacigalupe, Jun Wang, Malte Rühlemann, Raul Tito, Gwen Falony, Marie Joossens, Sara Vieira-Silva, Liesbet Henckaerts, Leen Rymenans, Chloë Verspecht, Susan Ring, Andre Franke, Kaitlin Wade, Nicholas Timpson, Jeroen Raes
Recent population-based(1-4) and clinical studies(5) have identified a range of factors associated with human gut microbiome variation. Murine quantitative trait loci(6), human twin studies(7) and microbiome genome-wide association studies(1,3,8-12) have provided evidence for genetic contributions to microbiome composition. Despite this, there is still poor overlap in genetic association across human studies. Using appropriate taxon-specific models, along with support from independent cohorts, we show an association between human host genotype and gut microbiome variation. We also suggest that interpretation of applied analyses using genetic associations is complicated by the probable overlap between genetic contributions and heritable components of host environment. Using faecal 16S ribosomal RNA gene sequences and host genotype data from the Flemish Gut Flora Project (n = 2,223) and two German cohorts (FoCus, n = 950; PopGen, n = 717), we identify genetic associations involving multiple microbial traits. Two of these associations achieved a study-level threshold of P = 1.57 × 10(-10); an association between Ruminococcus and rs150018970 near RAPGEF1 on chromosome 9, and between Coprococcus and rs561177583 within LINC01787 on chromosome 1. Exploratory analyses were undertaken using 11 other genome-wide associations with strong evidence for association (P < 2.5 × 10(-8)) and a previously reported signal of association between rs4988235 (MCM6/LCT) and Bifidobacterium. Across these 14 single-nucleotide polymorphisms there was evidence of signal overlap with other genome-wide association studies, including those for age at menarche and cardiometabolic traits. Mendelian randomization analysis was able to estimate associations between microbial traits and disease (including Bifidobacterium and body composition); however, in the absence of clear microbiome-driven effects, caution is needed in interpretation. Overall, this work marks a growing catalogue of genetic associations that will provide insight into the contribution of host genotype to gut microbiome. Despite this, the uncertain origin of association signals will likely complicate future work looking to dissect function or use associations for causal inference analysis.