Title
Views on GWAS statistical analysis
Authors
Xiaowen Cao1,3, Li Xing2, Hua He1, Xuekui Zhang3,*
Affiliation
1Department of Mathematics, Hebei University of Technology, Tianjin, China; 2Department of Mathematics and Statistics, University of Saskatchewan, Saskatoon, SK, Canada; 3Department of Mathematics and Statistics, University of Victoria, BC, Canada
Xuekui Zhang - xuekui@uvic.ca & ubcxzhang@gmail.com; Corresponding author
Article Type
Review
Date
Received April 2, 2020; Revised April 15, 2020; Accepted April 17, 2020; Published May 31, 2020
Abstract
Genome-wide association study (GWAS) is a popular approach to investigate relationship between genetic information and diseases. A large number of associations are tested in a single study, and the test results are corrected by multiple testing adjustment methods. It is observed that a substantial proportion of GWAS studies suffer considerable statistical power to assess reliability. Hence, we document available information on GWAS in this short review for glean insights.
Keywords
Genome-Wide Association Studies; Single Nucleotide Polymorphisms; Statistical power, Multiple Testing Adjustment, Linkage Disequilibrium, Supervised Learning, Unsupervised Learning
Citation
Cao et al. Bioinformation 16(5): 393-397 (2020)
Edited by
P Kangueane
ISSN
0973-2063
Publisher
License
This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.
