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Title

A short review on Genome-Wide Association Studies

 

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

 

Email

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

Biomedical Informatics

 

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.