Hypergraph Supervised Search for Inferring Multiple Epistatic Interactions with Different Orders
Nonlinear interactive effects of Single Nucleotide Polymorphisms (SNPs), namely, epistatic interactions, have been receiving increasing attention in understanding the mechanism underlying susceptibility to complex diseases. Though many works have been done for their detection, most only focus on the detection of pairwise epistatic interactions. In this study, a Hypergraph Supervised Search (HgSS) is developed based on the co-information measure for inferring multiple epistatic interactions with different orders at a substantially reduced time cost. The co-information measure is employed to exhaustively quantify the interaction effects of low order SNP combinations, as well as the main effects of SNPs. Then, highly suspected SNP combinations and SNPs are used to construct a hypergraph. By deeply analyzing the hypergraph, some clues for better understanding the genetic architecture of complex diseases could be revealed. Experiments are performed on both simulation and real data sets. Results show that HgSS is promising in inferring multiple epistatic interactions with different orders.
KeywordsEpistatic interactions Single nucleotide polymorphisms (SNPs) Genome-wide association study Hypergraph Genetic interaction network
This work was supported by the Scientific Research Reward Foundation for Excellent Young and Middle-age Scientists of Shandong Province (BS2014DX004), the Science and Technology Planning Project of Qufu Normal University (xkj201410), the Opening Laboratory Fund of Qufu Normal University (sk201416), the Scientific Research Foundation of Qufu Normal University (BSQD20130119), the Project of Shandong Province Higher Educational Science and Technology Program (J13LN31), the Award Foundation Project of Excellent Young Scientists in Shandong Province (BS2014DX005), the Shenzhen Municipal Science and Technology Innovation Council (JCYJ20140417172417174), the Shandong Provincial Natural Science Foundation (ZR2013FL016), the China Postdoctoral Science Foundation Funded Project (2014M560264).
Conflict of Interests
The authors declare that there is no conflict of interests regarding the publication of this paper.
- 5.Chanda, P., Sucheston, L., Zhang, A., Brazeau, D., Freudenheim, J.L., Ambrosone, C., Ramanathan, M.: AMBIENCE: a novel approach and efficient algorithm for identifying informative genetic and environmental associations with complex phenotypes. Genetics 180(2), 1191–1210 (2008)CrossRefMATHGoogle Scholar
- 10.Moore, J.H., Gilbert, J.C., Tsai, C.-T., Chiang, F.-T., Holden, T., Barney, N., White, B.C.: A flexible computational framework for detecting, characterizing, and interpreting statistical patterns of epistasis in genetic studies of human disease susceptibility. J. Theor. Biol. 241(2), 252–261 (2006)MathSciNetCrossRefGoogle Scholar
- 13.Hu, T., Andrew, A.S., Karagas, M.R., Moore, J.H.: Statistical epistasis networks reduce the computational complexity of searching three-locus genetic models. In: Pacific Symposium on Biocomputing, pp. 397–408. World Scientific, Singapore (2013)Google Scholar
- 14.Bell, A.J.: The co-information lattice. In: The 4th International Symposium on Independent Component Analysis and Blind Signal Separation, pp. 921–926 (2003)Google Scholar
- 16.Miller, D.J., Zhang, Y., Yu, G., Liu, Y., Chen, L., Langefeld, C.D., Herrington, D., Wang, Y.: An algorithm for learning maximum entropy probability models of disease risk that efficiently searches and sparingly encodes multilocus genomic interactions. Bioinformatics 25(19), 2478–2485 (2009)CrossRefGoogle Scholar