GWAS3D: detecting human regulatory variants by integrative analysis of genome-wide associations, chromosome interactions and histone modifications

被引:81
|
作者
Li, Mulin Jun [1 ,2 ]
Wang, Lily Yan [1 ,2 ]
Xia, Zhengyuan [2 ,3 ]
Sham, Pak Chung [2 ,4 ,5 ,6 ]
Wang, Junwen [1 ,2 ,4 ]
机构
[1] Univ Hong Kong, LKS Fac Med, Dept Biochem, Hong Kong, Hong Kong, Peoples R China
[2] Univ Hong Kong, Shenzhen Inst Res & Innovat, Shenzhen 518057, Guangdong, Peoples R China
[3] Univ Hong Kong, LKS Fac Med, Dept Anaesthesiol, Hong Kong, Hong Kong, Peoples R China
[4] Univ Hong Kong, LKS Fac Med, Ctr Genom Sci, Hong Kong, Hong Kong, Peoples R China
[5] Univ Hong Kong, LKS Fac Med, Dept Psychiat, Hong Kong, Hong Kong, Peoples R China
[6] Univ Hong Kong, State Key Lab Cognit & Brain Sci, Hong Kong, Hong Kong, Peoples R China
基金
美国国家科学基金会;
关键词
LONG-RANGE INTERACTION; GENETIC-VARIATION; COMPLEX TRAITS; CHROMATIN; ANNOTATION; DISCOVERY; DATABASE; LOCI; SUSCEPTIBILITY; IDENTIFICATION;
D O I
10.1093/nar/gkt456
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
Interpreting the genetic variants located in the regulatory regions, such as enhancers and promoters, is an indispensable step to understand molecular mechanism of complex traits. Recent studies show that genetic variants detected by genome-wide association study (GWAS) are significantly enriched in the regulatory regions. Therefore, detecting, annotating and prioritizing of genetic variants affecting gene regulation are critical to our understanding of genotype-phenotype relationships. Here, we developed a web server GWAS3D to systematically analyze the genetic variants that could affect regulatory elements, by integrating annotations from cell type-specific chromatin states, epigenetic modifications, sequence motifs and cross-species conservation. The regulatory elements are inferred from the genome-wide chromosome interaction data, chromatin marks in 16 different cell types and 73 regulatory factors motifs from the Encyclopedia of DNA Element project. Furthermore, we used these function elements, as well as risk haplotype, binding affinity, conservation and P-values reported from the original GWAS to reprioritize the genetic variants. Using studies from low-density lipoprotein cholesterol, we demonstrated that our reprioritizing approach was effective and cell type specific. In conclusion, GWAS3D provides a comprehensive annotation and visualization tool to help users interpreting their results. The web server is freely available at http://jjwanglab.org/gwas3d.
引用
收藏
页码:W150 / W158
页数:9
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