Recent population-based^1,2,3,4 and clinical studies⁵ have identified a range of factors associated with human gut microbiome variation. Murine quantitative trait loci⁶, human twin studies⁷ and microbiome genome-wide association studies^{1,3,8,9,10,11,12} 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.

最近基于人群的^1,2,3,4和临床研究⁵已经确定了与人类肠道微生物组变异相关的一系列因素。小鼠定量性状基因座⁶，人类双胞胎研究⁷和微生物组全基因组关联研究^{1,3,8,9,10,11,12}为微生物组组成的遗传贡献提供了证据。尽管如此，整个人类研究之间的遗传关联仍然存在较差的重叠。使用适当的分类单元特异性模型，以及独立队列的支持，我们显示了人类宿主基因型与肠道微生物组变异之间的关联。我们还建议，由于遗传贡献和宿主环境的可遗传成分之间可能重叠，因此使用遗传关联对应用分析的解释变得复杂。使用粪便16S核糖体RNA基因序列和宿主基因型数据从佛兰德肠道菌群项目（Ñ  = 2223）和两个德国队列（对焦，Ñ  = 950; PopGen，Ñ = 717），我们确定了涉及多种微生物特征的遗传关联。其中两个协会的研究水平阈值为P  = 1.57×10 ^-10。之间的关联的瘤胃球菌和rs150018970邻近RAPGEF1 9号染色体上，和之间Coprococcus内和rs561177583 LINC01787 1号染色体上的探索性分析使用具有很强的证据11以外的全基因组关联的关联进行（P  <2.5×10 ^-8）和先前报道的rs4988235（之间的关联的信号MCM6 / LCT）和双歧杆菌。在这14个单核苷酸多态性中，有证据表明信号与其他全基因组关联研究（包括初潮年龄和心脏代谢特征的研究）重叠。孟德尔随机分析能够估计微生物性状与疾病（包括双歧杆菌和身体组成）之间的关联；但是，在缺乏明确的微生物组驱动作用的情况下，解释时需要谨慎。总的来说，这项工作标志着越来越多的遗传协会目录，将提供有关宿主基因型对肠道微生物组的贡献的见解。尽管如此，关联信号的不确定起源可能会使未来的工作变得更加复杂，这些工作试图剖析功能或将关联用于因果推理分析。