eQTL epistasis: detecting epistatic effects and inferring hierarchical relationships of genes in biological pathways. Results: We justify the imperfectness of Fisher’s model in the simulation study and its application to the biological data. Then, we propose a novel generic epistasis model that provides a flexible solution for various biological putative epistatic models in practice. The proposed method enables one to efficiently characterize the functional dependence between genes. Moreover, we suggest a statistical strategy for determining a recessive or dominant link among epistatic expression quantitative trait locus to enable the ability to infer the hierarchical relationships. The proposed method is assessed by simulation experiments of various settings and is applied to human brain data regarding schizophrenia. Availability and implementation: The MATLAB source codes are publicly available at: http://biomecis.uta.edu/epistasis.
References in zbMATH (referenced in 1 article )
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- Yuan, Lin; Yuan, Chang-An; Huang, De-Shuang: FAACOSE: a fast adaptive ant colony optimization algorithm for detecting SNP epistasis (2017)