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Nicotinamide N-methyltransferase (NNMT) catalyzes the methylation of nicotinamide. Our previous works indicate that NNMT is involved in the body mass index and energy metabolism, and recently the association between a SNP (rs694539) of NNMT and a variety of cardiovascular diseases was reported. At present, more than 200 NNMT single nucleotide polymorphisms (SNPs) have been identified in the databases of the human genome projects; however, the association between rs694539 variation and hyperlipidemia has not been reported yet, and whether there are any SNPs in NNMT significantly associated with hyperlipidemia is still unclear. In this paper, we selected 19 SNPs in NNMT as the tagSNPs using Haploview software (Haploview 4.2) first and then performed a case-control study to observe the association between these tagSNPs and hyperlipidemia and finally applied physiological approaches to explore the possible mechanisms through which the NNMT polymorphism induces hyperlipidemia. The results show that a SNP (rs1941404) in NNMT is significantly associated with hyperlipidemia, and the influence of rs1941404 variation on the resting energy expenditure may be the possible mechanism for rs1941404 variation to induce hyperlipidemia. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]