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

Abstract

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 (mGWAS) 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 association between human host genotype and gut microbiome variation. We also suggest that interpretation of applied analyses using genetic associations is complicated by the likely overlap between genetic contributions and heritable components of host environment. Using fecal derived 16S rRNA gene sequences and host genotype data from the Flemish Gut Flora Project (FGFP, n=2223) and two German cohorts (FoCus, n=950, PopGen n=717), we identify genetic associations involving multiple microbial traits (MTs). Two of these associations achieved a study-level p-value threshold of 1.57x10−10; an association between Ruminococcus and rs150018970 near RAPGEF1 on chromosome 9, and between Coprococcus and rs561177583 within LINC01787 on chromosome 1. Exploratory analysis was undertaken using 11 other genomewide associations with strong evidence for association (p-value < 2.5x10 −08) and a previously reported signal of association between rs4988235 (MCM6/LCT) and Bifidobacterium. Across these 14 SNPs there was evidence of signal overlap with other GWAS including those for age at menarche and cardiometabolic traits. Mendelian randomization (MR) analysis was able to estimate associations between MTs 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 catalog of genetic associations which 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. Main Human host-microbiome mGWAS are still in their infancy and feature a paucity of overlap for even the most compelling signals across studies 13. This is an observation influenced by environmental variables dominating microbial trait variation 1 and the complications of variation in sample collection, storage conditions, DNA extraction method, PCR primers, and amplicon versus shotgun sequencing 14. While recent advances are Postdoctoral Fellowship (1221620N). SVS is supported by Marie Curie Actions FP7 People

The genetic architecture of human infectious diseases and pathogen-induced cellular phenotypes

“…To expand these findings and further dissect the complex pathogen-colonization patterns in humans, we utilized host genome-wide associations with human gut microbiome variation for 155 pathogens (Hughes et al, 2020) identified through 16S rRNA sequencing. For example, among the top SNP associations with Desulfovibrionaceae (p<0.0001), a sulfatereduced bacterium associated with intraabdominal infections and inflammatory bowel disease (Goldstein et al, 2003;Loubinoux et al, 2002), we observed an enrichment for SNPs associated with intestinal infection in BioVU ( Figure 5C).…”

“…PrediXcan genetic associations for 35 ID traits This paper phewascatalog.org PrediXcan (Gamazon et al, 2018;Gamazon et al, 2015) https://github.com/hakyimlab/Predi Xcan MultiXcan (Barbeira et al, 2019) https://github.com/hakyimlab/multix can-paper GTEx (2015;Battle et al, 2017;Consortium, 2013) https://gtexportal.org/home/ Gene Set Enrichment Analysis (GSEA) (Subramanian et al, 2005) http://software.broadinstitute.org/g sea/index.jsp Human gut microbiome GWAS (Hughes et al, 2020) https://data.bris.ac.uk/data/dataset/ f12b2cf665593ac466149cc6da3ca 867…”

“…The rich resource of genetic information linked to clinical microbiology information (serology and culture data) across bacterial, fungal, and viral genera that we leverage provides a platform to interrogate the genetic basis of compartmentspecific infection and colonization. Linking high-resolution taxonomic classification from 16S ribosomal RNA (rRNA) sequencing to host GWAS information has been used to investigate the contribution of host genetic variation to microbiome composition (Hughes et al, 2020). We therefore exploit host GWAS for 155 pathogens in the microbiome (Hughes et al, 2020) to gain further insights into identified genetic risk factors for an ID trait.…”

“…Linking high-resolution taxonomic classification from 16S ribosomal RNA (rRNA) sequencing to host GWAS information has been used to investigate the contribution of host genetic variation to microbiome composition (Hughes et al, 2020). We therefore exploit host GWAS for 155 pathogens in the microbiome (Hughes et al, 2020) to gain further insights into identified genetic risk factors for an ID trait. To determine the phenotypic consequences of ID-associated genes, including adverse outcomes and complications, we perform a phenome-scale scan across hematologic, respiratory, cardiovascular, and neurologic traits.…”

Gastric Microbiome Diversities in Gastric Cancer Patients from Europe and Asia Mimic the Human Population Structure and Are Partly Driven by Microbiome Quantitative Trait Loci

“…The authors estimate that non-genetic and genetic factors account each for 10% of gut microbiota variations. A recent study conducted in a large cohort of Flemish and German individuals lead to the identification of further genetic associations involving multiple microbial traits, including that between a single nucleotide polymorphism (SNP) on the RAPGEF1 gene, which is involved in the regulation of the gastrointestinal tract’s physiology, and Ruminococcus [ 11 ].…”