title: A meta-analysis strategy for gene prioritization using gene expression, SNP genotype, and eQTL data.
abstract: 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.
authors: Bio-Intelligence & Data Mining Lab, School of Electronics Engineering, Kyungpook National University, 1370 Sankyuk-dong, Buk-gu, Daegu 702-701, Republic of Korea.
authors: Jingmin
authors: 0000-0002-2286-6943
authors: J
authors: Che
authors: Bio-Intelligence & Data Mining Lab, School of Electronics Engineering, Kyungpook National University, 1370 Sankyuk-dong, Buk-gu, Daegu 702-701, Republic of Korea.
authors: Miyoung
authors: 0000-0002-4850-6014
authors: M
authors: Shin
mesh_terms: D005786:Gene Expression Regulation; D030342:Genetic Diseases, Inborn / Q000175:diagnosis / Q000235:genetics*; D006801:Humans; D008957:Models, Genetic; D020641:Polymorphism, Single Nucleotide; D040641:Quantitative Trait Loci