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Identifying Candidate Genes for Type 2 Diabetes Mellitus and Obesity through Gene Expression Profiling in Multiple Tissues or Cells
Author(s) -
Junhui Chen,
Yuhuan Meng,
Jinghui Zhou,
Min Zhuo,
Fei Ling,
Yu Zhang,
Hongli Du,
Xiaoning Wang
Publication year - 2013
Publication title -
journal of diabetes research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.034
H-Index - 50
eISSN - 2314-6753
pISSN - 2314-6745
DOI - 10.1155/2013/970435
Subject(s) - gene , candidate gene , biology , gene expression profiling , expression quantitative trait loci , type 2 diabetes mellitus , obesity , genetics , computational biology , gene expression , quantitative trait locus , bioinformatics , diabetes mellitus , genotype , single nucleotide polymorphism , endocrinology
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.

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