Using previously genotyped controls in genome-wide association studies (GWAS): application to the Stroke Genetics Network (SiGN)
Genome-wide association studies (GWAS) are widely applied to identify susceptibility loci for a variety of diseases using genotyping arrays that interrogate known polymorphisms throughout the genome. A particular strength of GWAS is that it is unbiased with respect to specific genomic elements (e.g...
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doaj-b8f596e031e1470da5739e5e0f4d43d92020-11-24T22:56:14ZengFrontiers Media S.A.Frontiers in Genetics1664-80212014-04-01510.3389/fgene.2014.0009581523Using previously genotyped controls in genome-wide association studies (GWAS): application to the Stroke Genetics Network (SiGN)Braxton D Mitchell0Braxton D Mitchell1Myriam eFornage2Patrick F. McArdle3Yu-Ching eCheng4Sara ePulit5Quenna eWong6Tushar eDave7Stephen R Williams8Stephen R Williams9Roderick eCorriveau10Katrina eGwinn11Kimberly eDoheny12Cathy eLaurie13Stephen S Rich14Paul ede Bakker15Paul ede Bakker16University of Maryland School of MedicineVeterans Administration Medical CenterUniversity of Texas Health Science CenterUniversity of Maryland School of MedicineUniversity of Maryland School of MedicineUniversity Medical Center UtrechtUniversity of WashingtonUniversity of Maryland School of MedicineUniversity of VirginiaUniversity of VirginiaNational Institute of Neurological Disorders and StrokeNational Institute of Neurological Disorders and StrokeJohns Hopkins University School of MedicineUniversity of WashingtonUniversity of VirginiaUniversity Medical Center UtrechtUniversity Medical Center UtrechtGenome-wide association studies (GWAS) are widely applied to identify susceptibility loci for a variety of diseases using genotyping arrays that interrogate known polymorphisms throughout the genome. A particular strength of GWAS is that it is unbiased with respect to specific genomic elements (e.g., coding or regulatory regions of genes), and it has revealed important associations that would have never been suspected based on prior knowledge or assumptions. To date, the discovered SNPs associated with complex human traits tend to have small effect sizes, requiring very large sample sizes to achieve robust statistical power. To address these issues, a number of efficient strategies have emerged for conducting GWAS, including combining study results across multiple studies using meta-analysis, collecting cases through electronic health records, and using samples collected from other studies as controls that have already been genotyped and made publicly available (e.g., through deposition of de-identified data into dbGaP or EGA).<br/><br/>In certain scenarios, it may be attractive to use already genotyped controls and divert resources to standardized collection, phenotyping, and genotyping of cases only. This strategy, however, requires that careful attention be paid to the choice of public controls and to the comparability of genetic data between cases and the public controls to ensure that any allele frequency differences observed between groups is attributable to locus-specific effects rather than to a systematic bias due to poor matching (population stratification) or differential genotype calling (batch effects).<br/><br/>The goal of this paper is to describe some of the potential pitfalls in using previously genotyped control data. We focus on considerations related to the choice of control groups, the use of different genotyping platforms, and approaches to deal with population stratification when cases and controls are genotyped across different platforms.http://journal.frontiersin.org/Journal/10.3389/fgene.2014.00095/fullGenome-Wide Association Studypopulation stratificationpowercase-control studygenetic association study |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Braxton D Mitchell Braxton D Mitchell Myriam eFornage Patrick F. McArdle Yu-Ching eCheng Sara ePulit Quenna eWong Tushar eDave Stephen R Williams Stephen R Williams Roderick eCorriveau Katrina eGwinn Kimberly eDoheny Cathy eLaurie Stephen S Rich Paul ede Bakker Paul ede Bakker |
spellingShingle |
Braxton D Mitchell Braxton D Mitchell Myriam eFornage Patrick F. McArdle Yu-Ching eCheng Sara ePulit Quenna eWong Tushar eDave Stephen R Williams Stephen R Williams Roderick eCorriveau Katrina eGwinn Kimberly eDoheny Cathy eLaurie Stephen S Rich Paul ede Bakker Paul ede Bakker Using previously genotyped controls in genome-wide association studies (GWAS): application to the Stroke Genetics Network (SiGN) Frontiers in Genetics Genome-Wide Association Study population stratification power case-control study genetic association study |
author_facet |
Braxton D Mitchell Braxton D Mitchell Myriam eFornage Patrick F. McArdle Yu-Ching eCheng Sara ePulit Quenna eWong Tushar eDave Stephen R Williams Stephen R Williams Roderick eCorriveau Katrina eGwinn Kimberly eDoheny Cathy eLaurie Stephen S Rich Paul ede Bakker Paul ede Bakker |
author_sort |
Braxton D Mitchell |
title |
Using previously genotyped controls in genome-wide association studies (GWAS): application to the Stroke Genetics Network (SiGN) |
title_short |
Using previously genotyped controls in genome-wide association studies (GWAS): application to the Stroke Genetics Network (SiGN) |
title_full |
Using previously genotyped controls in genome-wide association studies (GWAS): application to the Stroke Genetics Network (SiGN) |
title_fullStr |
Using previously genotyped controls in genome-wide association studies (GWAS): application to the Stroke Genetics Network (SiGN) |
title_full_unstemmed |
Using previously genotyped controls in genome-wide association studies (GWAS): application to the Stroke Genetics Network (SiGN) |
title_sort |
using previously genotyped controls in genome-wide association studies (gwas): application to the stroke genetics network (sign) |
publisher |
Frontiers Media S.A. |
series |
Frontiers in Genetics |
issn |
1664-8021 |
publishDate |
2014-04-01 |
description |
Genome-wide association studies (GWAS) are widely applied to identify susceptibility loci for a variety of diseases using genotyping arrays that interrogate known polymorphisms throughout the genome. A particular strength of GWAS is that it is unbiased with respect to specific genomic elements (e.g., coding or regulatory regions of genes), and it has revealed important associations that would have never been suspected based on prior knowledge or assumptions. To date, the discovered SNPs associated with complex human traits tend to have small effect sizes, requiring very large sample sizes to achieve robust statistical power. To address these issues, a number of efficient strategies have emerged for conducting GWAS, including combining study results across multiple studies using meta-analysis, collecting cases through electronic health records, and using samples collected from other studies as controls that have already been genotyped and made publicly available (e.g., through deposition of de-identified data into dbGaP or EGA).<br/><br/>In certain scenarios, it may be attractive to use already genotyped controls and divert resources to standardized collection, phenotyping, and genotyping of cases only. This strategy, however, requires that careful attention be paid to the choice of public controls and to the comparability of genetic data between cases and the public controls to ensure that any allele frequency differences observed between groups is attributable to locus-specific effects rather than to a systematic bias due to poor matching (population stratification) or differential genotype calling (batch effects).<br/><br/>The goal of this paper is to describe some of the potential pitfalls in using previously genotyped control data. We focus on considerations related to the choice of control groups, the use of different genotyping platforms, and approaches to deal with population stratification when cases and controls are genotyped across different platforms. |
topic |
Genome-Wide Association Study population stratification power case-control study genetic association study |
url |
http://journal.frontiersin.org/Journal/10.3389/fgene.2014.00095/full |
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