Simultaneously modeling host genetics and microbiome composition re- veals the heritability and the proportion of variance explained due to the microbiome of immune-related traits

Both host genetics and environmental factors determine human immune-re- lated phenotypes and disease, including asthma, allergies, and rheumatic disorders. Although many known environmental exposures contribute to immune disease risk and severity, the extent to which the human microbiome is responsible for variation in these phenotypes remains largely unknown. Additionally, whether gene-by-environment interactions with the microbiome influence these phenotypes remains uncharacterized, as well as which specific genetic variants and microbes play roles in this process. To address these gaps, we examine both the proportion of variation explained by host genetics (PVE-G, or heritability) and gut microbiome composition (PVE-M) in a unified framework for ~30 immune-related phenotypes using the large TwinsUK cohort, for which microbiome, genetic, and phenotypic data are available for 2,500 individuals. First, we examine the relative contributions of PVE-M and PVE-G for each trait using a linear mixed model framework, by incorporating random effects that account for the extent of sharing of both genotypes (genet- ic relatedness matrix) and microbes (beta diversity matrix) among individuals. We find that PVE-M accounts for up to 44.8% of non-genetic variation in traits such as body mass index (BMI). Second, we explore whether explicitly including gut microbiome composition in genome-wide association study (GWAS) models improves power to detect genetic variants associated with immune phenotypes. To do so, we compare linear mixed models both with or without microbiome random effects, applied to 1.3 million genotyped variants for each phenotype. Including the microbiome in the model does not increase power to detect genetic variants associated with certain phenotypes (including height, asthma, and eczema), but does for other phenotypes (BMI, white blood cell counts, and level of vitamin D in blood). For example, in the case of BMI, including the microbiome term pushes the significance of the top associated SNP to surpass the genome-wide Bonferroni threshold (rs1036286, P = 8.6 x 10-8) as compared to the model without the microbiome term (P = 1.8 x 10-8). Using these data, we have conducted one of the first GWA studies to explicitly model the microbiome, demonstrating that the microbiome is capable of explaining a large proportion of variation due to non-genetic effects and can improve power to detect genetic variants associated with phenotypes