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BioMed Research International
Volume 2015 (2015), Article ID 576349, 8 pages
Research Article

A Meta-Analysis Strategy for Gene Prioritization Using Gene Expression, SNP Genotype, and eQTL Data

Bio-Intelligence & Data Mining Lab, School of Electronics Engineering, Kyungpook National University, 1370 Sankyuk-dong, Buk-gu, Daegu 702-701, Republic of Korea

Received 26 August 2014; Revised 20 October 2014; Accepted 21 October 2014

Academic Editor: Mingyue Zheng

Copyright © 2015 Jingmin Che and Miyoung Shin. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


In order to understand disease pathogenesis, improve medical diagnosis, or discover effective drug targets, it is important to identify significant genes deeply involved in human disease. For this purpose, many earlier approaches attempted to prioritize candidate genes using gene expression profiles or SNP genotype data, but they often suffer from producing many false-positive results. To address this issue, in this paper, we propose a meta-analysis strategy for gene prioritization that employs three different genetic resources—gene expression data, single nucleotide polymorphism (SNP) genotype data, and expression quantitative trait loci (eQTL) data—in an integrative manner. For integration, we utilized an improved technique for the order of preference by similarity to ideal solution (TOPSIS) to combine scores from distinct resources. This method was evaluated on two publicly available datasets regarding prostate cancer and lung cancer to identify disease-related genes. Consequently, our proposed strategy for gene prioritization showed its superiority to conventional methods in discovering significant disease-related genes with several types of genetic resources, while making good use of potential complementarities among available resources.