Views on GWAS statistical analysis

Xiaowen Cao; Li Xing; Hua He; Xuekui Zhang

doi:10.6026/97320630016393

Views on GWAS statistical analysis

Bioinformation. 2020 May 31;16(5):393-397. doi: 10.6026/97320630016393. eCollection 2020.

Authors

Xiaowen Cao^{1

2}, Li Xing³, Hua He¹, Xuekui Zhang²

Affiliations

¹ Department of Mathematics, Hebei University of Technology, Tianjin, China.
² Department of Mathematics and Statistics, University of Victoria, BC, Canada.
³ Department of Mathematics and Statistics, University of Saskatchewan, Saskatoon, SK, Canada.

Abstract

Genome-wide association study (GWAS) is a popular approach to investigate relationships between genetic information and diseases. A number of associations are tested in a study and the results are often corrected using multiple adjustment methods. It is observed that GWAS studies suffer adequate statistical power for reliability. Hence, we document known models for reliability assessment using improved statistical power in GWAS analysis.

Keywords: Genome-Wide Association Studies; Linkage Disequilibrium; Multiple Testing Adjustment; Single Nucleotide Polymorphisms; Statistical power; Supervised Learning; Unsupervised Learning.