Qinxin Pan from my lab won one of three best paper awards at the 3rd Annual Translational Bioinformatics Conference (TBC) held in Seoul, South Korea on Oct. 2-4, 2013. Her paper on epistasis analysis is one of six from the conference that were invited for publication in a special issue of the Journal of the American Medical Informatics Association (JAMIA).
Qinxin Pan, Ting Hu, Li Shen, Andrew J. Saykin, Jason H. Moore and the Alzheimer’s Disease Neuroimaging Initiative. Topological Analysis of Statistical Epistasis Networks Reveals Pathways Associated with Alzheimer’s Disease. JAMIA, in press (2014).
Most pathway analysis approaches rely on main effects of genes and do not take gene-gene interactions into account. Gene-gene interactions, i.e., epistasis, are believed to account for a portion of the presumed missing heritability. Moreover, conventional methods treat each pathway independently whereas in reality they cooperate and work together as an intertwined system. In this study, we construct statistical epistasis networks (SEN) underlying Alzheimer’s disease (AD) and infer risk-associated pathways from their topological structures. We test for pathways that possess central positions in the SENs and characterize the interactions among pathways. We find that pathway glycosphingolipid biosynthesis ganglio series, which has been hypothesized to be involved in AD pathobiology, holds central positions in the SENs and is actively interacting with a high number of other pathways. Other central pathways include alpha linolenic acid metabolism, sphingolipid metabolism, peroxisome, ether lipid metabolism, primary bile acid biosynthesis etc. In addition to pathways possessing central positions in SENs, we identify a few pathways that are frequently interacting with other pathways. The pathways and pathway interactions identified in our study should be further investigated, especially in the context of epistasis.