The human microbiome has been linked to various host phenotypes and has been implicated in many complex human diseases. Recent genome-wide association studies (GWASs) have used microbiome variation as a complex trait and have uncovered human genetic variants that are associated with the microbiome. Here we summarize results from these studies and illustrate potential regulatory mechanisms by which host genetic variation can interact with microbiome composition. We argue that, similar to human GWASs, it is important to use functional genomics techniques to gain a mechanistic understanding of causal host–microbiome interactions and their role in human disease. We highlight experimental, functional, and computational genomics methodologies for the study of the genomic basis of host–microbiome interactions and describe how these approaches can be utilized to explain how human genetic variation can modulate the effects of the microbiome on the host. Human genetic variation is associated with variation in microbiome composition across populations and body sites. These microbiome-linked variants are enriched in disease-related genes. Identification of expression quantitative trait loci (eQTLs) for microbiome traits may provide mechanistic insights into how the microbiome can interact with host genetic variation. Novel functional genomics experimental approaches can identify microbiome-controlled eQTLs and describe the combined role of human genetic variation and microbiome composition in controlling complex disease.
Bibliographical noteFunding Information:
R.B. is supported in part by funds from the University of Minnesota College of Biological Sciences , the Randy Shaver Cancer Research and Community Fund, Institutional Research Grant #124166-IRG-58-001-55-IRG53 from the American Cancer Society, and a Research Fellowship from the Alfred P. Sloan Foundation. F.L. is supported in part by funds from the National Institutes of Health #R01GM109215 .
© 2017 Elsevier Ltd
- functional genomics
- host–microbiome interactions
- human genomics