Balkan Journal of Medical Genetics

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

MATERIALS AND METHODS

To date, genome-wide linkage scans across multiple populations have been performed to identify underlying quantitative trait loci. We defined as a significant susceptibility locus with a logarithm of odds ratio score of 3.0 or more, as a suggestive locus with a score between 2.2 and 3.0, and as a nominal locus with a score between 1 and 2.2 [3]. We considered only the following suggestive and significant loci: 7q32, 16p13, 5p15, 18q12, 8p11-12, 6p22-p11, 6p24, 15q14, 2q22-23, 5q34, 3q26, 10q25-q26, 13q31, 19q13, 5q12-q14, and 10q21-q22 [5-8]. Identified candidate genes were imported into the Ingenuity Pathways Analysis (IPA) 5.0 to generate putative signaling networks based on pathway interactions extracted from the literature [1]. A network was generated for the input genes using direct and indirect relationships. The networks were ranked by scores that measured the probability that the genes were included in the network by chance alone. Networks with scores >3.0 have a 99.9% confidence of not being generated by chance. Overlapping networks were merged to produce the largest possible network. Correctness of relationships was checked manually on the basis of categorized literature provided by the system. Canonical molecular and cellular pathways associated with the identified genes were elucidated with a statistical significance value (p value of <0.05). To identify epilepsy-related candidate genes, we used the online tools: SNPs3D [9], PROSPECTR and SUSPECTS (PandS) [10], PosMed [11], and GeneWanderer [12]. We used the following known susceptible genes on GeneWanderer and PandS as a training data set: CHRNA4, CHRNA2, CHRNB2, KCNQ2, KCNQ3, SCN1A, SCN2A, SCN1B, GABRA1, GABRG2, CLCN2, LGI1, EFHC1, CHRNB3, and CACNB4 [5-7]. For performing all the online tools, one necessary setting is to provide the start and end locations in the base pair of the corresponding loci. SNPs3D pinpoints all possible genes regardless of specific loci. The other tools produce rankings, and we used only the top 10 genes from each of the tools. We considered a gene to be an interesting candidate if it was indicated by two or more of the tools.

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