Statistical methods for genome-wide association studies

Maggie Haitian Wang; Heather J Cordell; Kristel Van Steen

doi:10.1016/j.semcancer.2018.04.008

Statistical methods for genome-wide association studies

Semin Cancer Biol. 2019 Apr:55:53-60. doi: 10.1016/j.semcancer.2018.04.008. Epub 2018 May 1.

Authors

Maggie Haitian Wang¹, Heather J Cordell², Kristel Van Steen³

Affiliations

¹ Division of Biostatistics, JC School of Public Health and Primary Care, The Chinese University of Hong Kong, N.T., Hong Kong SAR, China; CUHK Shenzhen Institute, Shenzhen, China. Electronic address: maggiew@cuhk.edu.hk.
² Institute of Genetic Medicine, Newcastle University, International Centre for Life, Central Parkway, Newcastle upon Tyne, NE1 3BZ, UK.
³ WELBIO, GIGA-R Medical Genomics - BIO3, University of Liège, Liège, Belgium; Department of Human Genetics - Systems Medicine, University of Leuven, Leuven, Belgium.

PMID: 29727703
DOI: 10.1016/j.semcancer.2018.04.008

Abstract

Genome-wide association studies (GWAS) detect common genetic variants associated with complex disorders. With their comprehensive coverage of common single nucleotide polymorphisms and comparatively low cost, GWAS are an attractive tool in the clinical and commercial genetic testing. This review introduces the pipeline of statistical methods used in GWAS analysis, from data quality control, association tests, population structure control, interaction effects and results visualization, through to post-GWAS validation methods and related issues.

Keywords: Association tests; GWAS; Quality control; Review; Statistical methods.

Publication types

Research Support, Non-U.S. Gov't
Review

MeSH terms

Genetic Testing / statistics & numerical data*
Genome-Wide Association Study / statistics & numerical data*
Genotype
Humans
Phenotype
Polymorphism, Single Nucleotide / genetics*