A knowledge-driven interaction analysis reveals potential neurodegenerative mechanism of multiple sclerosis susceptibility

Gene-gene interactions are proposed as one important component of the genetic architecture of complex diseases, and are just beginning to be evaluated in the context of genome wide association studies (GWAS). In addition to detecting epistasis, a benefit to interaction analysis is that it also increases power to detect weak main effects. We conducted a knowledge-driven interaction analysis of a GWAS of 931 multiple sclerosis trios to discover gene-gene interactions within established biological contexts. We identify heterogeneous signals, including a gene-gene interaction between CHRM3 and MYLK (joint p = 0.0002), an interaction between two phospholipase-β isoforms, PLCβ1 & PLCβ4 (joint p = 0.0098), and a modest interaction between ACTN1 and MYH9 (joint p = 0.0326), all localized to calcium-signaled cytoskeletal regulation. Furthermore, we discover a main effect (joint p = 5.2E-5) previously unidentified by single-locus analysis within another related gene, SCIN, a calcium-binding cytoskeleton regulatory protein. This work illustrates that knowledge-driven interaction analysis of GWAS data is a feasible approach to identify new genetic effects. The results of this study are among the first gene-gene interactions and non-immune susceptibility loci for multiple sclerosis. Further, the implicated genes cluster within inter-related biological mechanisms that suggest a neurodegenerative component to multiple sclerosis.