Using previously genotyped controls in genome-wide association studies (GWAS):application to the Stroke Genetics Network (SiGN)

Frontiers in Genetics

Kimberly eDoheny¹  Roderick eCorriveau²  Katrina eGwinn²  Sara ePulit³  Paul ede Bakker³  Patrick F. McArdle⁴  Tushar eDave⁴  Yu-Ching eCheng⁴  Myriam eFornage⁵  Stephen R Williams⁶  Stephen S Rich⁶  Quenna eWong⁷  Cathy eLaurie⁷  Braxton D Mitchell⁸ 

[1] Johns Hopkins University School of Medicine;National Institute of Neurological Disorders and Stroke;University Medical Center Utrecht;University of Maryland School of Medicine;University of Texas Health Science Center;University of Virginia;University of Washington;Veterans Administration Medical Center;

关键词: Genome-Wide Association Study;    population stratification;    power;    case-control study;    genetic association study;   

DOI  :  10.3389/fgene.2014.00095

来源: DOAJ

【 摘 要 】

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).

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).

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.

【 授权许可】

Unknown