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Allele balance bias identifies systematic genotyping errors and false disease associations
In recent years, next‐generation sequencing (NGS) has become a cornerstone of clinical genetics and diagnostics. Many clinical applications require high precision, especially if rare events such as somatic mutations in cancer or genetic variants causing rare diseases need to be identified. Although...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6587442/ https://www.ncbi.nlm.nih.gov/pubmed/30353964 http://dx.doi.org/10.1002/humu.23674 |
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author | Muyas, Francesc Bosio, Mattia Puig, Anna Susak, Hana Domènech, Laura Escaramis, Georgia Zapata, Luis Demidov, German Estivill, Xavier Rabionet, Raquel Ossowski, Stephan |
author_facet | Muyas, Francesc Bosio, Mattia Puig, Anna Susak, Hana Domènech, Laura Escaramis, Georgia Zapata, Luis Demidov, German Estivill, Xavier Rabionet, Raquel Ossowski, Stephan |
author_sort | Muyas, Francesc |
collection | PubMed |
description | In recent years, next‐generation sequencing (NGS) has become a cornerstone of clinical genetics and diagnostics. Many clinical applications require high precision, especially if rare events such as somatic mutations in cancer or genetic variants causing rare diseases need to be identified. Although random sequencing errors can be modeled statistically and deep sequencing minimizes their impact, systematic errors remain a problem even at high depth of coverage. Understanding their source is crucial to increase precision of clinical NGS applications. In this work, we studied the relation between recurrent biases in allele balance (AB), systematic errors, and false positive variant calls across a large cohort of human samples analyzed by whole exome sequencing (WES). We have modeled the AB distribution for biallelic genotypes in 987 WES samples in order to identify positions recurrently deviating significantly from the expectation, a phenomenon we termed allele balance bias (ABB). Furthermore, we have developed a genotype callability score based on ABB for all positions of the human exome, which detects false positive variant calls that passed state‐of‐the‐art filters. Finally, we demonstrate the use of ABB for detection of false associations proposed by rare variant association studies. Availability: https://github.com/Francesc-Muyas/ABB. |
format | Online Article Text |
id | pubmed-6587442 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-65874422019-07-02 Allele balance bias identifies systematic genotyping errors and false disease associations Muyas, Francesc Bosio, Mattia Puig, Anna Susak, Hana Domènech, Laura Escaramis, Georgia Zapata, Luis Demidov, German Estivill, Xavier Rabionet, Raquel Ossowski, Stephan Hum Mutat Methods In recent years, next‐generation sequencing (NGS) has become a cornerstone of clinical genetics and diagnostics. Many clinical applications require high precision, especially if rare events such as somatic mutations in cancer or genetic variants causing rare diseases need to be identified. Although random sequencing errors can be modeled statistically and deep sequencing minimizes their impact, systematic errors remain a problem even at high depth of coverage. Understanding their source is crucial to increase precision of clinical NGS applications. In this work, we studied the relation between recurrent biases in allele balance (AB), systematic errors, and false positive variant calls across a large cohort of human samples analyzed by whole exome sequencing (WES). We have modeled the AB distribution for biallelic genotypes in 987 WES samples in order to identify positions recurrently deviating significantly from the expectation, a phenomenon we termed allele balance bias (ABB). Furthermore, we have developed a genotype callability score based on ABB for all positions of the human exome, which detects false positive variant calls that passed state‐of‐the‐art filters. Finally, we demonstrate the use of ABB for detection of false associations proposed by rare variant association studies. Availability: https://github.com/Francesc-Muyas/ABB. John Wiley and Sons Inc. 2018-11-23 2019-01 /pmc/articles/PMC6587442/ /pubmed/30353964 http://dx.doi.org/10.1002/humu.23674 Text en © 2018 The Authors. Human Mutation published by Wiley Periodicals, Inc. This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Methods Muyas, Francesc Bosio, Mattia Puig, Anna Susak, Hana Domènech, Laura Escaramis, Georgia Zapata, Luis Demidov, German Estivill, Xavier Rabionet, Raquel Ossowski, Stephan Allele balance bias identifies systematic genotyping errors and false disease associations |
title | Allele balance bias identifies systematic genotyping errors and false disease associations |
title_full | Allele balance bias identifies systematic genotyping errors and false disease associations |
title_fullStr | Allele balance bias identifies systematic genotyping errors and false disease associations |
title_full_unstemmed | Allele balance bias identifies systematic genotyping errors and false disease associations |
title_short | Allele balance bias identifies systematic genotyping errors and false disease associations |
title_sort | allele balance bias identifies systematic genotyping errors and false disease associations |
topic | Methods |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6587442/ https://www.ncbi.nlm.nih.gov/pubmed/30353964 http://dx.doi.org/10.1002/humu.23674 |
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