Entanglement Mapping: A Novel Method to Detect Interacting SNPs in Genome-Wide Studies

Genome-wide association studies (GWAS) have identified many associated SNPs across a variety of different diseases. However, there remains a significant gap in the heritability explained by these GWAS results and the total heritability predicted for the diseases. This is the missing heritability problem in genetics. One of the sources of missing heritability is gene-gene interactions between SNPs. However, traditional methods for detecting genomic associations have low power to detect these interactions. Machine Learning methods allow us to detect these interactions. Here, we present a new method for identifying interacting SNPs, called Entanglement Mapping (EM). EM works in conjunction with a previously developed Random Forest based method called r2VIM, which can model gene-gene interactions with high power but cannot detect which genes are actually interacting. EM iteratively drops a single SNP and records the effect on importance score. We confirm this here by simulating a genetic model based on interacting and non-interacting SNPs. We have found that interacting SNPs exhibit a higher drop in importance score when one of the interacting SNPs is excluded from the analysis compared to when SNPs that are known to not be interacting are excluded. EM was applied to exome chip genotype data in a study of genetic risk factors for refractive error, a complex eye disorder, and yielded evidence of interaction between two pairs of genetic variants in different genes. EM will be a useful tool in identifying potential new targets for drug development that were missed by traditional GWAS.