EXPLORING CANDIDATE GENES FOR EPILEPSY BY COMPUTATIONAL DISEASE-GENE IDENTIFICATION STRATEGY
Sha Y, Liu Q, Wang Y, Dong C, Song L
*Corresponding Author: Ying Sha, Department of Neurology, First Hospital of Jilin University, Jilin Province, People’s Republic of China; Tel.: +86-1387-878-9232; Fax: +86-1387-778-7078; E-mail: shaying2010@ 126.com
page: 35

RESULTS AND DISCUSSION

Forty-eight genes were selected as potential epilepsy susceptibility genes (Table 1). Interesting genes indicated by all the tools are known to be associated with epilepsy, such as APOE, GABRA1 and GABRG2. GABRA1 and GABRG2 are subunits of the ligand-gated chloride channel receptors of GABA, the major inhibitory neurotransmitter in the mammalian brain. For the APOE gene, many studies have found that its polymorphism is strongly correlated with different kinds of epilepsy. The ApoE-ε allele interacts with longstanding seizures to affect verbal and nonverbal memory performance in patients with medically intractable temporal lobe epilepsy [13]. This allele also increases the risk of postictal confusion [14]. Salzmann et al. [15] confirmed that the role of APOE in the temporal lobe epilepsy. In our study, all these widely referred genes were discovered by most of the bioinformatic tools. Canonical Pathways. Four canonical pathways were selected by the identified candidate genes (Table 2) with significant confidence: GABA receptor signaling, interleukin-6 (IL-6) signaling, G-protein coupled receptor signaling, type 2 diabetes mellitus signaling and airway inflammation in asthma. Here, 35 of the 48 identified genes were used in the pathway analysis of the IPA system. These genes generated four significant networks (Figure 1): Network 1 with a score of 38 and 17 focus genes, which is related to carbohydrate metabolism, small molecule biochemistry and cellular movement; Network 2 with a score of 27 and 13 focus genes, which is related to psychological disorders, genetic disorder and neurological disease according to the description in the IPA system; Network 3 with a score of 22 and 11 focus genes, related to cardiovascular disease, neurological disease and drug metabolism; and Network 4 with a score of eight and five focus genes, related to cell signaling, molecular transport, vitamin and mineral metabolism. Comparison of Online Analytical Tools. There was wide variation between the tools regarding which genes were prioritized, and their rank orders. PosMed and PandS were most similar in their prioritization. A combination of two or all of the tools was superior for ranking positional candidates [16]. Some methods have a significant identity of candidate genes, which may affect the accuracy of gene prioritization. PanS, GenWanderer and PosMed use the same input information and show a high degree of similarity on their outputs. The selection of a candidate gene by several methods using the same input data may be less valuable than the selection of a candidate by several methods using disparate data sources [1]. In this study, we excluded candidate genes that were solely identified by two of the online tools, i.e., GenWanderer and PosMed. We also restricted the selection to only the top 10 genes as candidate list so as to provide more convergent results. We concluded that bioinformatic tools were helpful in the hunt for complex disease genes. Our list of most likely candidate genes and the associated pathways should assist further experimental design and analysis, and therefore, our understanding of the pathogenesis of epilepsy. In addition, we have to say that using different online analytical tools might generate different results, and we selected the tools that were successfully applied to previous studies. We only selected four tools from a number of available resources, all of which easily perform the analyses, fast to obtain results and the corresponding results are reliable. In fact, there are some more tools widely applied in previous studies. However, due to a variety of reasons, we did not adopt them into our study. For example, GeneSeeker [17] is not currently operating and we cannot obtain a complete result; Endeavor is very slow during the prioritization process; Prioritizer [18] needs to download a large size of full software and associated data, while the output text for the G2D tool [19] is not easily edited for comparison.



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