a method to address differential bias in genotyping in large-scale association studies基因分型方法来解决微分偏见在大规模的关联研究.pdfVIP
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a method to address differential bias in genotyping in large-scale association studies基因分型方法来解决微分偏见在大规模的关联研究
A Method to Address Differential Bias
in Genotyping
in Large-Scale Association Studies
*
Vincent Plagnol , Jason. D. Cooper, John A. Todd, David G. Clayton
Juvenile Diabetes Research Foundation/Wellcome Trust Diabetes and Inflammation Laboratory, Department of Medical Genetics, Cambridge Institute for Medical Research,
University of Cambridge, Cambridge, United Kingdom
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.
Citation: Plagnol V, Cooper JD, Todd JA, Clayton DG (2007) A method to address differential bias in genotyping in large-scale association studies. PLoS Genet 3(5): e74. doi:10.
1371/journal.pgen.0030074
Introduction approach is that it can generate a differential bias in
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