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Identifying and mitigating batch effects in whole genome sequencing data

BACKGROUND: Large sample sets of whole genome sequencing with deep coverage are being generated, however assembling datasets from different sources inevitably introduces batch effects. These batch effects are not well understood and can be due to changes in the sequencing protocol or bioinformatics...

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Autores principales: Tom, Jennifer A., Reeder, Jens, Forrest, William F., Graham, Robert R., Hunkapiller, Julie, Behrens, Timothy W., Bhangale, Tushar R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5525370/
https://www.ncbi.nlm.nih.gov/pubmed/28738841
http://dx.doi.org/10.1186/s12859-017-1756-z
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author Tom, Jennifer A.
Reeder, Jens
Forrest, William F.
Graham, Robert R.
Hunkapiller, Julie
Behrens, Timothy W.
Bhangale, Tushar R.
author_facet Tom, Jennifer A.
Reeder, Jens
Forrest, William F.
Graham, Robert R.
Hunkapiller, Julie
Behrens, Timothy W.
Bhangale, Tushar R.
author_sort Tom, Jennifer A.
collection PubMed
description BACKGROUND: Large sample sets of whole genome sequencing with deep coverage are being generated, however assembling datasets from different sources inevitably introduces batch effects. These batch effects are not well understood and can be due to changes in the sequencing protocol or bioinformatics tools used to process the data. No systematic algorithms or heuristics exist to detect and filter batch effects or remove associations impacted by batch effects in whole genome sequencing data. RESULTS: We describe key quality metrics, provide a freely available software package to compute them, and demonstrate that identification of batch effects is aided by principal components analysis of these metrics. To mitigate batch effects, we developed new site-specific filters that identified and removed variants that falsely associated with the phenotype due to batch effect. These include filtering based on: a haplotype based genotype correction, a differential genotype quality test, and removing sites with missing genotype rate greater than 30% after setting genotypes with quality scores less than 20 to missing. This method removed 96.1% of unconfirmed genome-wide significant SNP associations and 97.6% of unconfirmed genome-wide significant indel associations. We performed analyses to demonstrate that: 1) These filters impacted variants known to be disease associated as 2 out of 16 confirmed associations in an AMD candidate SNP analysis were filtered, representing a reduction in power of 12.5%, 2) In the absence of batch effects, these filters removed only a small proportion of variants across the genome (type I error rate of 3%), and 3) in an independent dataset, the method removed 90.2% of unconfirmed genome-wide SNP associations and 89.8% of unconfirmed genome-wide indel associations. CONCLUSIONS: Researchers currently do not have effective tools to identify and mitigate batch effects in whole genome sequencing data. We developed and validated methods and filters to address this deficiency. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-017-1756-z) contains supplementary material, which is available to authorized users.
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spelling pubmed-55253702017-08-02 Identifying and mitigating batch effects in whole genome sequencing data Tom, Jennifer A. Reeder, Jens Forrest, William F. Graham, Robert R. Hunkapiller, Julie Behrens, Timothy W. Bhangale, Tushar R. BMC Bioinformatics Research Article BACKGROUND: Large sample sets of whole genome sequencing with deep coverage are being generated, however assembling datasets from different sources inevitably introduces batch effects. These batch effects are not well understood and can be due to changes in the sequencing protocol or bioinformatics tools used to process the data. No systematic algorithms or heuristics exist to detect and filter batch effects or remove associations impacted by batch effects in whole genome sequencing data. RESULTS: We describe key quality metrics, provide a freely available software package to compute them, and demonstrate that identification of batch effects is aided by principal components analysis of these metrics. To mitigate batch effects, we developed new site-specific filters that identified and removed variants that falsely associated with the phenotype due to batch effect. These include filtering based on: a haplotype based genotype correction, a differential genotype quality test, and removing sites with missing genotype rate greater than 30% after setting genotypes with quality scores less than 20 to missing. This method removed 96.1% of unconfirmed genome-wide significant SNP associations and 97.6% of unconfirmed genome-wide significant indel associations. We performed analyses to demonstrate that: 1) These filters impacted variants known to be disease associated as 2 out of 16 confirmed associations in an AMD candidate SNP analysis were filtered, representing a reduction in power of 12.5%, 2) In the absence of batch effects, these filters removed only a small proportion of variants across the genome (type I error rate of 3%), and 3) in an independent dataset, the method removed 90.2% of unconfirmed genome-wide SNP associations and 89.8% of unconfirmed genome-wide indel associations. CONCLUSIONS: Researchers currently do not have effective tools to identify and mitigate batch effects in whole genome sequencing data. We developed and validated methods and filters to address this deficiency. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-017-1756-z) contains supplementary material, which is available to authorized users. BioMed Central 2017-07-24 /pmc/articles/PMC5525370/ /pubmed/28738841 http://dx.doi.org/10.1186/s12859-017-1756-z Text en © The Author(s). 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Tom, Jennifer A.
Reeder, Jens
Forrest, William F.
Graham, Robert R.
Hunkapiller, Julie
Behrens, Timothy W.
Bhangale, Tushar R.
Identifying and mitigating batch effects in whole genome sequencing data
title Identifying and mitigating batch effects in whole genome sequencing data
title_full Identifying and mitigating batch effects in whole genome sequencing data
title_fullStr Identifying and mitigating batch effects in whole genome sequencing data
title_full_unstemmed Identifying and mitigating batch effects in whole genome sequencing data
title_short Identifying and mitigating batch effects in whole genome sequencing data
title_sort identifying and mitigating batch effects in whole genome sequencing data
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5525370/
https://www.ncbi.nlm.nih.gov/pubmed/28738841
http://dx.doi.org/10.1186/s12859-017-1756-z
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