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

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

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

Afiliação

Shao M; School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China.
Zhang Z; School of Computer Science and Technology, Hainan University, Haikou, China.
Sun H; School of Artificial Intelligence, Jilin University, Changchun, China.
He J; Department of Biochemistry and Molecular Biology, University of Calgary, Calgary, AB, Canada.
Wang J; Department of Biohealth Informatics, Indiana University Purdue University Indianapolis, Indianapolis, IN, United States.
Zhang Q; Department of Mathematics and Statistics, University of Calgary, Calgary, AB, Canada.
Cao C; School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China.

Front Genet ; 14: 1287673, 2023.

Article em En | MEDLINE | ID: mdl-37766879

Palavras-chave

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

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Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Risk_factors_studies Idioma: En Ano de publicação: 2023 Tipo de documento: Article

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PubMed Links

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Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Risk_factors_studies Idioma: En Ano de publicação: 2023 Tipo de documento: Article