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Evaluating the quality of the 1000 genomes project data
BACKGROUND: Data from the 1000 Genomes project is quite often used as a reference for human genomic analysis. However, its accuracy needs to be assessed to understand the quality of predictions made using this reference. We present here an assessment of the genotyping, phasing, and imputation accura...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6696682/ https://www.ncbi.nlm.nih.gov/pubmed/31416423 http://dx.doi.org/10.1186/s12864-019-5957-x |
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author | Belsare, Saurabh Levy-Sakin, Michal Mostovoy, Yulia Durinck, Steffen Chaudhuri, Subhra Xiao, Ming Peterson, Andrew S. Kwok, Pui-Yan Seshagiri, Somasekar Wall, Jeffrey D. |
author_facet | Belsare, Saurabh Levy-Sakin, Michal Mostovoy, Yulia Durinck, Steffen Chaudhuri, Subhra Xiao, Ming Peterson, Andrew S. Kwok, Pui-Yan Seshagiri, Somasekar Wall, Jeffrey D. |
author_sort | Belsare, Saurabh |
collection | PubMed |
description | BACKGROUND: Data from the 1000 Genomes project is quite often used as a reference for human genomic analysis. However, its accuracy needs to be assessed to understand the quality of predictions made using this reference. We present here an assessment of the genotyping, phasing, and imputation accuracy data in the 1000 Genomes project. We compare the phased haplotype calls from the 1000 Genomes project to experimentally phased haplotypes for 28 of the same individuals sequenced using the 10X Genomics platform. RESULTS: We observe that phasing and imputation for rare variants are unreliable, which likely reflects the limited sample size of the 1000 Genomes project data. Further, it appears that using a population specific reference panel does not improve the accuracy of imputation over using the entire 1000 Genomes data set as a reference panel. We also note that the error rates and trends depend on the choice of definition of error, and hence any error reporting needs to take these definitions into account. CONCLUSIONS: The quality of the 1000 Genomes data needs to be considered while using this database for further studies. This work presents an analysis that can be used for these assessments. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12864-019-5957-x) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-6696682 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-66966822019-08-19 Evaluating the quality of the 1000 genomes project data Belsare, Saurabh Levy-Sakin, Michal Mostovoy, Yulia Durinck, Steffen Chaudhuri, Subhra Xiao, Ming Peterson, Andrew S. Kwok, Pui-Yan Seshagiri, Somasekar Wall, Jeffrey D. BMC Genomics Research Article BACKGROUND: Data from the 1000 Genomes project is quite often used as a reference for human genomic analysis. However, its accuracy needs to be assessed to understand the quality of predictions made using this reference. We present here an assessment of the genotyping, phasing, and imputation accuracy data in the 1000 Genomes project. We compare the phased haplotype calls from the 1000 Genomes project to experimentally phased haplotypes for 28 of the same individuals sequenced using the 10X Genomics platform. RESULTS: We observe that phasing and imputation for rare variants are unreliable, which likely reflects the limited sample size of the 1000 Genomes project data. Further, it appears that using a population specific reference panel does not improve the accuracy of imputation over using the entire 1000 Genomes data set as a reference panel. We also note that the error rates and trends depend on the choice of definition of error, and hence any error reporting needs to take these definitions into account. CONCLUSIONS: The quality of the 1000 Genomes data needs to be considered while using this database for further studies. This work presents an analysis that can be used for these assessments. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12864-019-5957-x) contains supplementary material, which is available to authorized users. BioMed Central 2019-08-16 /pmc/articles/PMC6696682/ /pubmed/31416423 http://dx.doi.org/10.1186/s12864-019-5957-x Text en © The Author(s). 2019 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 Belsare, Saurabh Levy-Sakin, Michal Mostovoy, Yulia Durinck, Steffen Chaudhuri, Subhra Xiao, Ming Peterson, Andrew S. Kwok, Pui-Yan Seshagiri, Somasekar Wall, Jeffrey D. Evaluating the quality of the 1000 genomes project data |
title | Evaluating the quality of the 1000 genomes project data |
title_full | Evaluating the quality of the 1000 genomes project data |
title_fullStr | Evaluating the quality of the 1000 genomes project data |
title_full_unstemmed | Evaluating the quality of the 1000 genomes project data |
title_short | Evaluating the quality of the 1000 genomes project data |
title_sort | evaluating the quality of the 1000 genomes project data |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6696682/ https://www.ncbi.nlm.nih.gov/pubmed/31416423 http://dx.doi.org/10.1186/s12864-019-5957-x |
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