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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...

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Detalles Bibliográficos
Autores principales: Plagnol, Vincent, Cooper, Jason. D, Todd, John A, Clayton, David G
Formato: Texto
Lenguaje:English
Publicado: Public Library of Science 2007
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1868951/
https://www.ncbi.nlm.nih.gov/pubmed/17511519
http://dx.doi.org/10.1371/journal.pgen.0030074
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author Plagnol, Vincent
Cooper, Jason. D
Todd, John A
Clayton, David G
author_facet Plagnol, Vincent
Cooper, Jason. D
Todd, John A
Clayton, David G
author_sort Plagnol, Vincent
collection PubMed
description 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.
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spelling pubmed-18689512007-05-18 A Method to Address Differential Bias in Genotyping in Large-Scale Association Studies Plagnol, Vincent Cooper, Jason. D Todd, John A Clayton, David G PLoS Genet Research Article 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. Public Library of Science 2007-05 2007-05-18 /pmc/articles/PMC1868951/ /pubmed/17511519 http://dx.doi.org/10.1371/journal.pgen.0030074 Text en © 2007 Plagnol et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Plagnol, Vincent
Cooper, Jason. D
Todd, John A
Clayton, David G
A Method to Address Differential Bias in Genotyping in Large-Scale Association Studies
title A Method to Address Differential Bias in Genotyping in Large-Scale Association Studies
title_full A Method to Address Differential Bias in Genotyping in Large-Scale Association Studies
title_fullStr A Method to Address Differential Bias in Genotyping in Large-Scale Association Studies
title_full_unstemmed A Method to Address Differential Bias in Genotyping in Large-Scale Association Studies
title_short A Method to Address Differential Bias in Genotyping in Large-Scale Association Studies
title_sort method to address differential bias in genotyping in large-scale association studies
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1868951/
https://www.ncbi.nlm.nih.gov/pubmed/17511519
http://dx.doi.org/10.1371/journal.pgen.0030074
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