BackgroundEpistasis is recognized fundamentally important for understanding the mechanism of disease-causing genetic variation. Though many novel methods for detecting epistasis have been proposed, few studies focus on their comparison. Undertaking a comprehensive comparison study is an urgent task and a pathway of the methods to real applications.ResultsThis paper aims at a comparison study of epistasis detection methods through applying related software packages on datasets. For this purpose, we categorize methods according to their search strategies, and select five representative methods (TEAM, BOOST, SNPRuler, AntEpiSeeker and epiMODE) originating from different underlying techniques for comparison. The methods are tested on simulated datasets with different size, various epistasis models, and with/without noise. The types of noise include missing data, genotyping error and phenocopy. Performance is evaluated by detection power (three forms are introduced), robustness, sensitivity and computational complexity.ConclusionsNone of selected methods is perfect in all scenarios and each has its own merits and limitations. In terms of detection power, AntEpiSeeker performs best on detecting epistasis displaying marginal effects (eME) and BOOST performs best on identifying epistasis displaying no marginal effects (eNME). In terms of robustness, AntEpiSeeker is robust to all types of noise on eME models, BOOST is robust to genotyping error and phenocopy on eNME models, and SNPRuler is robust to phenocopy on eME models and missing data on eNME models. In terms of sensitivity, AntEpiSeeker is the winner on eME models and both SNPRuler and BOOST perform well on eNME models. In terms of computational complexity, BOOST is the fastest among the methods. In terms of overall performance, AntEpiSeeker and BOOST are recommended as the efficient and effective methods. This comparison study may provide guidelines for applying the methods and further clues for epistasis detection.

Dogs were domesticated by human and originated from wolves. Their evolutionary relationships have attracted much scientific interest due to their genetic affinity but different habitats. To identify the differences between dogs and wolves associated with domestication, we analysed the blood transcriptomes of wolves and dogs by RNA-Seq. We obtained a total of 30.87 Gb of raw reads from two dogs and three wolves using RNA-Seq technology. Comparisons of the wolf and dog transcriptomes revealed 524 genes differentially expressed genes between them. We found that some genes related to immune function (DCK, ICAM4, GAPDH and BSG) and aerobic capacity (HBA1, HBA2 and HBB) were more highly expressed in the wolf. Six differentially expressed genes related to the innate immune response (CCL23, TRIM10, DUSP10, RAB27A, CLEC5A and GCH1) were found in the wolf by a Gene Ontology enrichment analysis. Immune system development was also enriched only in the wolf group. The ALAS2, HMBS and FECH genes, shown to be enriched by the Kyoto Encyclopedia of Genes and Genomes analysis, were associated with the higher aerobic capacity and hypoxia endurance of the wolf. The results suggest that the wolf might have greater resistance to pathogens, hypoxia endurance and aerobic capacity than dogs do.

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Complementary DNAs encoding two types of acetylcholinesterase (AChE) were isolated from the silkworm, Bombyx mori. The type 1 (Bmace1) and type 2 (Bmace2) ORFs are 2052 and 1917 bp in length, respectively. Both the complete ORFs of the Bmaces and Cterminal truncated forms were recombined into the Bacmid baculovirus vector under the control of the polyhedrin promoter and expressed in Trichoplusia ni (Tn-5B1-4) cells. The resulting products exhibited AChE activity and glycosylation of the expressed proteins. An inhibition assay indicated that the ace2-type enzyme was more sensitive than the ace1-type enzyme to inhibition by eserine and paraoxon.

BackgroundIdentifying epistasis or epistatic interactions, which refer to nonlinear interaction effects of single nucleotide polymorphisms (SNPs), is essential to understand disease susceptibility and to detect genetic architectures underlying complex diseases. Though many works have been done for identifying epistatic interactions, due to their methodological and computational challenges, the algorithmic development is still ongoing.ResultsIn this study, a method epiACO is proposed to identify epistatic interactions, which based on ant colony optimization algorithm. Highlights of epiACO are the introduced fitness function Svalue, path selection strategies, and a memory based strategy. The Svalue leverages the advantages of both mutual information and Bayesian network to effectively and efficiently measure associations between SNP combinations and the phenotype. Two path selection strategies, i.e., probabilistic path selection strategy and stochastic path selection strategy, are provided to adaptively guide ant behaviors of exploration and exploitation. The memory based strategy is designed to retain candidate solutions found in the previous iterations, and compare them to solutions of the current iteration to generate new candidate solutions, yielding a more accurate way for identifying epistasis.ConclusionsExperiments of epiACO and its comparison with other recent methods epiMODE, TEAM, BOOST, SNPRuler, AntEpiSeeker, AntMiner, MACOED, and IACO are performed on both simulation data sets and a real data set of age-related macular degeneration. Results show that epiACO is promising in identifying epistasis and might be an alternative to existing methods.

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