TY - JOUR
T1 - postGWAS
T2 - A web server for deciphering the causality post the genome-wide association studies
AU - Wang, Tao
AU - Yan, Zhihao
AU - Zhang, Yiming
AU - Lou, Zhuofei
AU - Zheng, Xiaozhu
AU - Mai, Duo Duo
AU - Wang, Yongtian
AU - Shang, Xuequn
AU - Xiao, Bing
AU - Peng, Jiajie
AU - Chen, Jing
N1 - Publisher Copyright:
© 2024 Elsevier Ltd
PY - 2024/3
Y1 - 2024/3
N2 - While genome-wide association studies (GWAS) have unequivocally identified vast disease susceptibility variants, a majority of them are situated in non-coding regions and are in high linkage disequilibrium (LD). To pave the way of translating GWAS signals to clinical drug targets, it is essential to identify the underlying causal variants and further causal genes. To this end, a myriad of post-GWAS methods have been devised, each grounded in distinct principles including fine-mapping, co-localization, and transcriptome-wide association study (TWAS) techniques. Yet, no platform currently exists that seamlessly integrates these diverse post-GWAS methodologies. In this work, we present a user-friendly web server for post-GWAS analysis, that seamlessly integrates 9 distinct methods with 12 models, categorized by fine-mapping, colocalization, and TWAS. The server mainly helps users decipher the causality hindered by complex GWAS signals, including casual variants and casual genes, without the burden of computational skills and complex environment configuration, and provides a convenient platform for post-GWAS analysis, result visualization, facilitating the understanding and interpretation of the genome-wide association studies. The postGWAS server is available at http://g2g.biographml.com/.
AB - While genome-wide association studies (GWAS) have unequivocally identified vast disease susceptibility variants, a majority of them are situated in non-coding regions and are in high linkage disequilibrium (LD). To pave the way of translating GWAS signals to clinical drug targets, it is essential to identify the underlying causal variants and further causal genes. To this end, a myriad of post-GWAS methods have been devised, each grounded in distinct principles including fine-mapping, co-localization, and transcriptome-wide association study (TWAS) techniques. Yet, no platform currently exists that seamlessly integrates these diverse post-GWAS methodologies. In this work, we present a user-friendly web server for post-GWAS analysis, that seamlessly integrates 9 distinct methods with 12 models, categorized by fine-mapping, colocalization, and TWAS. The server mainly helps users decipher the causality hindered by complex GWAS signals, including casual variants and casual genes, without the burden of computational skills and complex environment configuration, and provides a convenient platform for post-GWAS analysis, result visualization, facilitating the understanding and interpretation of the genome-wide association studies. The postGWAS server is available at http://g2g.biographml.com/.
KW - Causal gene
KW - Causal variant
KW - Complex trait
KW - Post-GWAS
KW - Web server
UR - http://www.scopus.com/inward/record.url?scp=85185277336&partnerID=8YFLogxK
U2 - 10.1016/j.compbiomed.2024.108108
DO - 10.1016/j.compbiomed.2024.108108
M3 - 文章
C2 - 38359659
AN - SCOPUS:85185277336
SN - 0010-4825
VL - 171
JO - Computers in Biology and Medicine
JF - Computers in Biology and Medicine
M1 - 108108
ER -