Quality Control Procedures for Genome‐Wide Association Studies
Van Q. Truong, Jakob A. Woerner, Tess Cherlin, Yuki Bradford, Anastasia Lucas, Chelsea C. Okeh, Manu Shivakumar, Daniel H. Hui, Rachit Kumar, Milton Pividori, Susan Jones, Abigail C. Bossa, Stephen Turner, Marylyn D. Ritchie, Shefali S. Verma
Abstract
Genome-wide association studies (GWAS) are being conducted at an unprecedented rate in population-based cohorts and have increased our understanding of the pathophysiology of many complex diseases. Regardless of the context, the practical utility of this information ultimately depends upon the quality of the data used for statistical analyses. Quality control (QC) procedures for GWAS are constantly evolving. Here, we enumerate some of the challenges in QC of genotyped GWAS data and describe the approaches involving genotype imputation of a sample dataset along with post-imputation quality assurance, thereby minimizing potential bias and error in GWAS results. We discuss common issues associated with QC of the GWAS data (genotyped and imputed), including data file formats, software packages for data manipulation and analysis, sex chromosome anomalies, sample identity, sample relatedness, population substructure, batch effects, and marker quality. We provide detailed guidelines along with a sample dataset to suggest current best practices and discuss areas of ongoing and future research. © 2022 Wiley Periodicals LLC.