A Genomewide Integrative Analysis of GWAS and eQTLs Data Identifies Multiple Genes and Gene Sets Associated with Obesity
Joint Authors
Fan, Qian Rui
Guo, Xiong
Liu, Li
Zhang, Feng
Liang, Xiao
Du, Yanan
Li, Ping
Wen, Yan
Hao, Jingcan
Wang, Wenyu
He, Awen
Source
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-5, 5 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-05-08
Country of Publication
Egypt
No. of Pages
5
Main Subjects
Abstract EN
To identify novel susceptibility genes and gene sets for obesity, we conducted a genomewide expression association analysis of obesity via integrating genomewide association study (GWAS) and expression quantitative trait loci (eQTLs) data.
GWAS summary data of body mass index (BMI) and waist-to-hip ratio (WHR) was driven from a published study, totally involving 339,224 individuals.
The eQTLs dataset (containing 927,753 eQTLs) was obtained from eQTLs meta-analysis of 5,311 subjects.
Integrative analysis of GWAS and eQTLs data was conducted by SMR software.
The SMR single gene analysis results were further subjected to gene set enrichment analysis (GSEA) for identifying obesity associated gene sets.
A total of 13,311 annotated gene sets were analyzed in this study.
SMR single gene analysis identified 20 BMI associated genes (TUFM, SPI1, APOB48R, etc.).
Also 3 WHR associated genes were detected (CPEB4, WARS2, and L3MBTL3).
The significant association between Chr16p11 and BMI was observed by GSEA (FDR adjusted p value = 0.040).
The TGCTGCT, MIR-15A, MIR-16, MIR-15B, MIR-195, MIR-424, and MIR-497 (FDR adjusted p value = 0.049) gene set appeared to be linked with WHR.
Our results provide novel clues for the genetic mechanism studies of obesity.
This study also illustrated the good performance of SMR for susceptibility gene mapping.
American Psychological Association (APA)
Liu, Li& Fan, Qian Rui& Zhang, Feng& Guo, Xiong& Liang, Xiao& Du, Yanan…[et al.]. 2018. A Genomewide Integrative Analysis of GWAS and eQTLs Data Identifies Multiple Genes and Gene Sets Associated with Obesity. BioMed Research International،Vol. 2018, no. 2018, pp.1-5.
https://search.emarefa.net/detail/BIM-1126160
Modern Language Association (MLA)
Liu, Li…[et al.]. A Genomewide Integrative Analysis of GWAS and eQTLs Data Identifies Multiple Genes and Gene Sets Associated with Obesity. BioMed Research International No. 2018 (2018), pp.1-5.
https://search.emarefa.net/detail/BIM-1126160
American Medical Association (AMA)
Liu, Li& Fan, Qian Rui& Zhang, Feng& Guo, Xiong& Liang, Xiao& Du, Yanan…[et al.]. A Genomewide Integrative Analysis of GWAS and eQTLs Data Identifies Multiple Genes and Gene Sets Associated with Obesity. BioMed Research International. 2018. Vol. 2018, no. 2018, pp.1-5.
https://search.emarefa.net/detail/BIM-1126160
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1126160