The human microbiome has been identified as a significant biological aspect of the human body with correlations to many characteristics of the host including age, sex, diet, and complex disease states such as cancer, obesity, and IBD. In addition, there is evidence that the host genome plays an important role in regulating the microbiome. However, the relationship is not clear and automatic methods will be required to uncover the connections between host genomic and metagenomic datasets. We have developed a method using LASSO regression to screen for associations between SNPs in the host genome and microbiome data. We have applied our method to data from the Human Microbiome Project and identified 97 SNPs (46 non-synonymous) in 90 host genes that show potential association to the microbiome (found in files hmp_16S_snps_genes.xlsx and hmp_mgs_kegg_modules_snps_genes.xlsx). Our method is an early step in automating detection of host-microbiome interactions within genomic data.