Dataset Information

Genome-wide associations of human gut microbiome variation and implications for causal inference analyses.

ABSTRACT: Recent population-based^1-4 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^1,3,8-12 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.

SUBMITTER: Hughes DA

PROVIDER: S-EPMC7610462 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundObservational studies have shown gut microbiomes were associated with cardiovascular diseases (CVDs), but their roles remain controversial, and these associations have not yet been established causally.MethodsTwo-sample Mendelian randomization (MR) was used to investigate whether gut microbiome had a causal effect on the risk of CVDs. To obtain comprehensive results, we performed two sets of MR analyses, one with single nucleotide polymorphisms (SNPs) that smaller than the genome-wide statistical significance threshold (5 × 10-8) as instrumental variables, and the other with SNPs that lower than the locus-wide significance level (1 × 10-5). Summary-level statistics for CVDs, including coronary artery disease (CAD), myocardial infarction, heart failure, atrial fibrillation, stroke and its subtypes were collected. The ME estimation was performed using the inverse-variance weighted and Wald ratio methods. Sensitivity analysis was performed using the weighted median, MR-Egger, leave-one-out analysis, MR pleiotropy residual sum and outlier and MR Steiger.ResultsBased on the locus-wide significance level, genetically predicted genus Oxalobacter was positively associated with the risk of CAD (odds ratio (OR) = 1.06, 95% confidence interval (CI), 1.03 - 1.10, P = 1.67 × 10-4), family Clostridiaceae_1 was negatively correlated with stroke risk (OR = 0.83,95% CI, 0.75-0.93, P = 7.76 × 10-4) and ischemic stroke risk (OR = 0.823,95% CI, 0.74-0.92, P = 4.15 × 10-4). There was no causal relationship between other genetically predicted gut microbiome components and CVDs risk. Based on the genome-wide statistical significance threshold, the results showed that the gut microbiome had no causal relationship with CVDs risk.ConclusionOur findings reveal that there are beneficial or adverse causal effects of gut microbiome components on CVDs risk and provide novel insights into strategies for the prevention and management of CVDs through the gut microbiome.

Project description:Biological systems contain a large number of molecules that have diverse interactions. A fruitful path to understanding these systems is to represent them with interaction networks, and then describe flow processes in the network with a dynamic model. Boolean modeling, the simplest discrete dynamic modeling framework for biological networks, has proven its value in recapitulating experimental results and making predictions. A first step and major roadblock to the widespread use of Boolean networks in biology is the laborious network inference and construction process. Here we present a streamlined network inference method that combines the discovery of a parsimonious network structure and the identification of Boolean functions that determine the dynamics of the system. This inference method is based on a causal logic analysis method that associates a logic type (sufficient or necessary) to node-pair relationships (whether promoting or inhibitory). We use the causal logic framework to assimilate indirect information obtained from perturbation experiments and infer relationships that have not yet been documented experimentally. We apply this inference method to a well-studied process of hormone signaling in plants, the signaling underlying abscisic acid (ABA)-induced stomatal closure. Applying the causal logic inference method significantly reduces the manual work typically required for network and Boolean model construction. The inferred model agrees with the manually curated model. We also test this method by re-inferring a network representing epithelial to mesenchymal transition based on a subset of the information that was initially used to construct the model. We find that the inference method performs well for various likely scenarios of inference input information. We conclude that our method is an effective approach toward inference of biological networks and can become an efficient step in the iterative process between experiments and computations.

Dataset Information

Genome-wide associations of human gut microbiome variation and implications for causal inference analyses.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets