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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.

Original publication

DOI

10.1371/journal.pgen.0030074

Type

Journal article

Journal

PLoS Genet

Publication Date

18/05/2007

Volume

3

Keywords

Algorithms, Bias, Case-Control Studies, Computer Simulation, Databases, Genetic, Epidemiologic Methods, Female, Gene Frequency, Genetic Predisposition to Disease, Genotype, Humans, Male, Polymorphism, Single Nucleotide, United Kingdom