摘要
Background Alcohol dependence （AD） is a serious and common public health problem.The identification of genes that contribute to the AD variation will improve our understanding of the genetic mechanism underlying this complex disease.Previous genome-wide association studies （GWAS） and candidate gene genetic association studies identified individual genes as candidates for alcohol phenotypes,but efforts to generate an integrated view of accumulative genetic variants and pathways under alcohol drinking are lacking.Methods We applied enrichment gene set analysis to existing genetic association results to identify pertinent pathways to AD in this study.A total of 1 438 SNPs （P ＜1.0×10-3） associated to alcohol drinking related traits have been collected from 31 studies （10 candidate gene association studies,19 GWAS of SNPs,and 2 GWAS of copy number variants）.Results Among all of the KEGG pathways,the calcium signaling pathway （hsa04020） showed the most significant enrichment of associations （21 genes） to alcohol consumption phenotypes （P=5.4×10-5）.Furthermore,the calcium signaling pathway is the only pathway that turned out to be significant after multiple test adjustments,achieving Bonferroni P value of 0.8×10-3 and FDR value of 0.6×10-2,respectively.Interestingly,the calcium signaling pathway was previously found to be essential to regulate brain function,and genes in this pathway link to a depressive effect of alcohol consumption on the body.Conclusions Our findings,together with previous biological evidence,suggest the importance of gene polymorphisms of calcium signaling pathway to AD susceptibility.Still,further investigations are warranted to uncover the role of this pathway in AD and related traits.
Background Alcohol dependence （AD） is a serious and common public health problem.The identification of genes that contribute to the AD variation will improve our understanding of the genetic mechanism underlying this complex disease.Previous genome-wide association studies （GWAS） and candidate gene genetic association studies identified individual genes as candidates for alcohol phenotypes,but efforts to generate an integrated view of accumulative genetic variants and pathways under alcohol drinking are lacking.Methods We applied enrichment gene set analysis to existing genetic association results to identify pertinent pathways to AD in this study.A total of 1 438 SNPs （P ＜1.0×10-3） associated to alcohol drinking related traits have been collected from 31 studies （10 candidate gene association studies,19 GWAS of SNPs,and 2 GWAS of copy number variants）.Results Among all of the KEGG pathways,the calcium signaling pathway （hsa04020） showed the most significant enrichment of associations （21 genes） to alcohol consumption phenotypes （P=5.4×10-5）.Furthermore,the calcium signaling pathway is the only pathway that turned out to be significant after multiple test adjustments,achieving Bonferroni P value of 0.8×10-3 and FDR value of 0.6×10-2,respectively.Interestingly,the calcium signaling pathway was previously found to be essential to regulate brain function,and genes in this pathway link to a depressive effect of alcohol consumption on the body.Conclusions Our findings,together with previous biological evidence,suggest the importance of gene polymorphisms of calcium signaling pathway to AD susceptibility.Still,further investigations are warranted to uncover the role of this pathway in AD and related traits.
基金
This work was partially supported by grants from the National Natural Science Foundation of China （No.81302228）,the Foundation for Distinguished Young Talents in Higher Education of Guangdong （No.LYM11040）,the Dean Fund from Southern Medical University （No.JC1103）,the Fundamental Research Funds for the Southern Medical University （No.B1012075）,and the Doctoral Program of Higher Specialized Research Fund （No.20124433120006）.
作者简介
Correspondence to： Dr. Guo Yanfang, Institute of Bioinformatics, School of Basic Medical Science, Southern Medical University, Guangzhou, Guangdong 510515, China （Tel： 86-20-62789376. Email： guoyf@smu.edu.cn）
