The microbes that inhabit the human body are now viewed as an integral component of our biology, and microbiome variability can result in differences in predisposition to disease. The growing interest in this new field of human-microbiome interactions is underscored by the inclusion of the Human Microbiome Project (HMP) in the NIH RoadMap priorities. Our proposed research will complement and extend the goals of the HMP by establishing heritable components of the human microbiome and elucidating the relationship between host genotype and gut microbiome variability. The proposed research will attempt to quantify the magnitude of heritable, inter-individual differences in microbiome composition. To achieve this objective, we will pursue three specific aims in an analysis of genome-wide SNP genotype data already available from the TwinsUK project to identify genetic determinants of microbiome composition. For Specific Aim 1, we will characterize the microbiomes of 4,000 twin pairs through application of high-throughput Illumina sequencing of fecal samples to generate approximately 300,000 16S rRNA gene sequences for each individual. These rRNA sequence data will catalog and quantify inter-individual variability in microbiota and produce a phylogenetic representation of the microbiome species composition from which several diversity metrics will be distilled. A subset of twins will be sampled at 5 time points to assess temporal stability of microbiome composition. For Specific Aim 2, we will establish the heritability of the gut microbiota by identifying measures of diversity and specific taxon composition that are under host genetic control. The genome-wide SNP data will be used to estimate the proportion of the genome that is shared between each dizygotic (DZ) twin pair (which varies between about 35% and 65%). We will use the covariance of this Identity-by-Descent (IBD) sharing with the microbiome metrics to estimate genetic variance components and provide tests of the hypothesis that each microbiome attribute is heritable. For Specific Aim 3, we will apply IBD and association mapping to identify regions of the human genome responsible for the observed variation in gut microbiomes. At each point along the genome the local IBD sharing for each DZ twin-pair will be inferred, and quantitative genetic models will be fitted by maximum likelihood to estimate additive and dominance variance components for regions containing candidate genes (e.g., innate immunity genes/pathways) and for each local region of the genome. Likelihood ratio tests will determine whether each region has a significant QTL. In addition, we will cluster microbiomes by similarity and identify regions of the human genome with high levels of IBD sharing for twin pairs with similar microbiomes. The results of this research will be used to establish links between regions of the human genome and composition of the microbiome. The results of this study have the potential to reveal fundamental human host- microbe interactions that may be applicable to the prevention and treatment of chronic inflammatory diseases.