A Method to Address Differential Bias in Genotyping in Large-Scale Association Studies

In a previous paper we have shown that, when DNA samples for cases and controls are prepared in different laboratories prior to high-throughput genotyping, scoring inaccuracies can lead to differential misclassification and, consequently, to increased false-positive rates. Different DNA sourcing is often unavoidable in large-scale disease association studies of multiple case and control sets. Here, we describe methodological improvements to minimise such biases. These fall into two categories: improvements to the basic clustering methods for identifying genotypes from fluorescence intensities, and use of “fuzzy” calls in association tests in order to make appropriate allowance for call uncertainty. We find that the main improvement is a modification of the calling algorithm that links the clustering of cases and controls while allowing for different DNA sourcing. We also find that, in the presence of different DNA sourcing, biases associated with missing data can increase the false-positive rate. Therefore, we propose the use of “fuzzy” calls to deal with uncertain genotypes that would otherwise be labeled as missing.: Genome-wide disease association studies are becoming more common and involve genotyping cases and controls at a large number of SNP markers spread throughout the genome. We have shown previously that such studies can have an inflated false-positive rate, the result of genotype calling inaccuracies when DNA samples for cases and controls were prepared in different laboratories, prior to genotyping. Different DNA sourcing is often unavoidable in the large-scale association studies of multiple case and control sets. Here we describe methodological improvements to minimise such biases. These fall into two categories: improvements to the basic clustering methods for calling genotypes from fluorescence intensities, and use of “fuzzy” calls in association tests in order to make appropriate allowance for call uncertainty.