Editorial: Statistical methods for genome-wide association studies (GWAS) and transcriptome-wide association studies (TWAS) and their applications

Mengting Shao; Zilong Zhang; Huiyan Sun; Jingni He; Juexin Wang; Qingrun Zhang; Chen Cao

doi:10.3389/fgene.2023.1287673

Editorial: Statistical methods for genome-wide association studies (GWAS) and transcriptome-wide association studies (TWAS) and their applications

Front Genet. 2023 Sep 11:14:1287673. doi: 10.3389/fgene.2023.1287673. eCollection 2023.

Authors

Mengting Shao¹, Zilong Zhang², Huiyan Sun³, Jingni He⁴, Juexin Wang⁵, Qingrun Zhang⁶, Chen Cao¹

Affiliations

¹ School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China.
² School of Computer Science and Technology, Hainan University, Haikou, China.
³ School of Artificial Intelligence, Jilin University, Changchun, China.
⁴ Department of Biochemistry and Molecular Biology, University of Calgary, Calgary, AB, Canada.
⁵ Department of Biohealth Informatics, Indiana University Purdue University Indianapolis, Indianapolis, IN, United States.
⁶ Department of Mathematics and Statistics, University of Calgary, Calgary, AB, Canada.

No abstract available

Keywords: bayesian models; genome-wide association studies; linear mixed models; meta-analysis; pathway analysis; statistical methodology; transcriptome-wide association studies.

Publication types

Editorial