Quality Control Procedures for Genome Wide Association Studies

By Stephen D. Turner, Loren L. Armstrong, Y. Bradford, Christopher S. Carlson, Dana C. Crawford, Andrew T. Crenshaw, M. de Andrade, K. Doheny, Jonathan L. Haines, G. Hayes and 16 more. Current Protocols in Human Genetics, 2011

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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 complex disease. Regardless of context, the practical utility of this information will ultimately depend upon the quality of the original data. Quality control (QC) procedures for GWAS are computationally intensive, operationally challenging, and constantly evolving. Here we enumerate some of the challenges in QC of GWAS data and describe the approaches that the electronic MEdical Records and Genomics (eMERGE) network is using for quality assurance in GWAS data, thereby minimizing potential bias and error in GWAS results. We discuss common issues associated with QC of GWAS data, 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 propose best practices and discuss areas of ongoing and future research. Curr. Protoc. Hum. Genet. 68:1.19.1‐1.19.18 © 2011 by John Wiley & Sons, Inc.

Subfields: Genetics, Rheumatology

View Paper: https://www.semanticscholar.org/paper/33f738be99e9efec0ef7bb1a356b49369c7c8429

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Citations in the corpus

FastUniq: A Fast De Novo Duplicates Removal Tool for Paired Short Reads (2012, background): “Thus, quality control is generally considered the first step in data analyses and is a mandatory prerequisite to downstream analyses and further studies [6].”
Genome-wide association studies in ancestrally diverse populations: opportunities, methods, pitfalls, and recommendations. (2019, background): “Several guides to quality control of genotype data exist in the literature (Anderson et al., 2010; de Bakker et al., 2008; Laurie et al., 2010; Turner et al., 2011).”
Methods of integrating data to uncover genotype–phenotype interactions (2015, methodology): “For example, several quality control pipelines have been established and implemented for genome-wide SNP data, such as the Electronic Medical Records and Genomics (eMERGE) and Gene Environment Association Studies (GENEVA) networks 23–25 .”
Genome- and Phenome-Wide Analysis of Cardiac Conduction Identifies Markers of Arrhythmia Risk (2013, methodology): “Data were cleaned using the quality control (QC) pipeline developed by the eMERGE Genomics Working Group.21 This process includes evaluation of sample and marker call rate, sex mismatch and anomalies, duplicate and HapMap concordance, batch effects, Hardy-Weinberg equilibrium, sample relatedness, an”
Use of diverse electronic medical record systems to identify genetic risk for type 2 diabetes within a genome-wide association study (2012, methodology): “Genotype cleaning and quality control was performed collaboratively by all five sites using a previously described approach.(25) Genotype data for rs7903146 in TCF7L2 on chromosome 10 from individuals passing quality control, and identified as a T2D case or control was used for this analysis to test”