Microarray genotyping resource to determine population stratification in genetic association studies of complex disease. (December 2004)

• Record Type: Journal Article
• Authors: Tebbutt, Scott J.; He, Jian-Qing; Burkett, Kelly M.; Ruan, Jian; Opushnyev, Igor V.; Tripp, Ben W.; Zeznik, Jeffrey A.; Abara, Chiaka O.; Nelson, Colleen C.; Walley, Keith R.
• Is Part Of: Biotechniques. Volume 37:Number 6(2004)
• Page Start: 977
• Page End: 985
• Publication Date: 2004-12
• Subjects: Biology, Experimental -- Periodicals; Molecular biology -- Periodicals; Medical technology -- Periodicals; Biology, Experimental; Medical technology; Molecular biology; Clinical Laboratory Techniques -- Periodicals; Research -- Periodicals; Medical Laboratory Science -- Periodicals; Periodicals; Electronic journals; 570
• DOI: 10.2144/04376RR02
• Languages: English
• ISSNs: 0736-6205

Abstract

We have developed a robust microarray genotyping chip that will help advance studies in genetic epidemiology. In population-based genetic association studies of complex disease, there could be hidden genetic substructure in the study populations, resulting in false-positive associations. Such population stratification may confound efforts to identify true associations between genotype/haplotype and phenotype. Methods relying on genotyping additional null single nucleotide polymorphism (SNP) markers have been proposed, such as genomic control (GC) and structured association (SA), to correct association tests for population stratification. If there is an association of a disease with null SNPs, this suggests that there is a population subset with different genetic background plus different disease susceptibility. Genotyping over 100 null SNPs in the large numbers of patient and control DNA samples that are required in genetic association studies can be prohibitively expensive. We have therefore developed and tested a resequencing chip based on arrayed primer extension (APEX) from over 2000 DNA probe features that facilitate multiple interrogations of each SNP, providing a powerful, accurate, and economical means to simultaneously determine the genotypes at 110 null SNP loci in any individual. Based on 1141 known genotypes from other research groups, our GC SNP chip has an accuracy of 98.5%, including non-calls.