Correcting for differential genotyping error in genetic association analysis

Abstract

Differential genotype error in case-control association studies occurs when cases and controls are genotyped under different conditions. Existence of differential errors can considerably bias the association test, resulting in inflation of type I error and spurious significance. With the availability of high-throughput genotyping technologies such as the SNPchip, null markers that are unlinked with the disease can be used to correct for the bias caused by differential errors. A similar method, known as the genomic control, had been used to correct for population stratification in association studies. In this paper, we show that the same idea can be used to correct for the bias caused by differential errors, under the assumption that the null markers and the candidate marker are subject to the same or similar genotyping error model. The variance inflation is shown to be minor and the bias in the association test is the major source of type I error inflation in the presence of differential errors. Our method centralizes the test statistic by deducting the bias estimated from null markers through a quadratic regression method, which adjusts for the variability of null marker allele frequencies. Simulation results show that the proposed method performs very well in correcting for the type I error inflations.