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Abstract:
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Evidence suggests that environmental factors and host genetics may interact to impact human microbiome composition. Identifying host genetic variants associated with human microbiome composition helps to characterize microbiome variation, elucidate biological mechanisms of genetic associations and improve genetic risk prediction. Since a microbiota functions as a community, it is best characterized by beta diversity (a pairwise distance matrix). We develop a statistical framework and a computationally efficient software package, microbiomeGWAS, for identifying host genetic variants associated with microbiome beta diversity. We show that score statistics have positive skewness and kurtosis due to the dependent nature of the pairwise data, which makes asymptotic P-value approximations unacceptably liberal. We develop accurate P-value approximations by correcting for skewness and kurtosis. We exemplify our methods by analyzing a set of 147 genotyped subjects with microbiome profiles from normal lung tissues. We provided evidence that six established lung cancer risk SNPs were collectively associated with microbiome composition for unweighted (P=0.0032) UniFrac distance matrices.
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