Type 2 Diabetes Mellitus (T2DM) and obesity have become increasingly prevalent in recent years. Recent studies have focused on identifying causal variations or candidate genes for obesity and T2DM via analysis of expression quantitative trait loci (eQTL) within a single tissue. T2DM and obesity are affected by comprehensive sets of genes in multiple tissues. In the current study, gene expression levels in multiple human tissues from GEO datasets were analyzed, and 21 candidate genes displaying high percentages of differential expression were filtered out. Specifically, DENND1B, LYN, MRPL30, POC1B, PRKCB, RP4-655J12.3, HIBADH, and TMBIM4 were identified from the T2DM-control study, and BCAT1, BMP2K, CSRNP2, MYNN, NCKAP5L, SAP30BP, SLC35B4, SP1, BAP1, GRB14, HSP90AB1, ITGA5, and TOMM5 were identified from the obesity-control study. The majority of these genes are known to be involved in T2DM and obesity. Therefore, analysis of gene expression in various tissues using GEO datasets may be an effective and feasible method to determine novel or causal genes associated with T2DM and obesity. 1. Introduction T2DM, a complex endocrine and metabolic disorder, has become more prevalent in recent years, with significant adverse effects on human health. T2DM is characterized by insulin resistance (IR) and deficient -cell function [1]. Interactions between multiple genetic and environmental factors are proposed to contribute to pathogenesis of the disease [1, 2]. Association of obesity with T2DM has been reported, both within and among different populations [3]. Earlier research has shown that obesity and its duration are major risk factors for T2DM, and IR pathological state generally exists in obesity [4, 5]. In recent years, numerous susceptibility loci have been identified through genome-wide association studies (GWAS) and meta-analyses on T2DM and obesity, and nearby candidate genes are proposed to be directly involved in the diseases [6, 7]. However, the underlying mechanisms by which these susceptibility loci affect and cause T2DM or obesity are currently unclear. Known SNPs associated with disease typically account for only a small fraction of overall disease [8, 9]. Gene expression patterns play a key role in determining pathogenesis and candidate genes of T2DM and obesity. A large-scale computable model has been created to analyze the molecular actions and effects of insulin on muscle gene expression [10]. Based on GWAS results, investigators integrated expression quantitative trait loci (eQTL) with coexpression networks to establish novel genes and
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