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PopDel identifies medium-size deletions simultaneously in tens of thousands of genomes
Thousands of genomic structural variants (SVs) segregate in the human population and can impact phenotypic traits and diseases. Their identification in whole-genome sequence data of large cohorts is a major computational challenge. Most current approaches identify SVs in single genomes and afterward...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7851401/ https://www.ncbi.nlm.nih.gov/pubmed/33526789 http://dx.doi.org/10.1038/s41467-020-20850-5 |
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author | Niehus, Sebastian Jónsson, Hákon Schönberger, Janina Björnsson, Eythór Beyter, Doruk Eggertsson, Hannes P. Sulem, Patrick Stefánsson, Kári Halldórsson, Bjarni V. Kehr, Birte |
author_facet | Niehus, Sebastian Jónsson, Hákon Schönberger, Janina Björnsson, Eythór Beyter, Doruk Eggertsson, Hannes P. Sulem, Patrick Stefánsson, Kári Halldórsson, Bjarni V. Kehr, Birte |
author_sort | Niehus, Sebastian |
collection | PubMed |
description | Thousands of genomic structural variants (SVs) segregate in the human population and can impact phenotypic traits and diseases. Their identification in whole-genome sequence data of large cohorts is a major computational challenge. Most current approaches identify SVs in single genomes and afterwards merge the identified variants into a joint call set across many genomes. We describe the approach PopDel, which directly identifies deletions of about 500 to at least 10,000 bp in length in data of many genomes jointly, eliminating the need for subsequent variant merging. PopDel scales to tens of thousands of genomes as we demonstrate in evaluations on up to 49,962 genomes. We show that PopDel reliably reports common, rare and de novo deletions. On genomes with available high-confidence reference call sets PopDel shows excellent recall and precision. Genotype inheritance patterns in up to 6794 trios indicate that genotypes predicted by PopDel are more reliable than those of previous SV callers. Furthermore, PopDel’s running time is competitive with the fastest tested previous tools. The demonstrated scalability and accuracy of PopDel enables routine scans for deletions in large-scale sequencing studies. |
format | Online Article Text |
id | pubmed-7851401 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-78514012021-02-08 PopDel identifies medium-size deletions simultaneously in tens of thousands of genomes Niehus, Sebastian Jónsson, Hákon Schönberger, Janina Björnsson, Eythór Beyter, Doruk Eggertsson, Hannes P. Sulem, Patrick Stefánsson, Kári Halldórsson, Bjarni V. Kehr, Birte Nat Commun Article Thousands of genomic structural variants (SVs) segregate in the human population and can impact phenotypic traits and diseases. Their identification in whole-genome sequence data of large cohorts is a major computational challenge. Most current approaches identify SVs in single genomes and afterwards merge the identified variants into a joint call set across many genomes. We describe the approach PopDel, which directly identifies deletions of about 500 to at least 10,000 bp in length in data of many genomes jointly, eliminating the need for subsequent variant merging. PopDel scales to tens of thousands of genomes as we demonstrate in evaluations on up to 49,962 genomes. We show that PopDel reliably reports common, rare and de novo deletions. On genomes with available high-confidence reference call sets PopDel shows excellent recall and precision. Genotype inheritance patterns in up to 6794 trios indicate that genotypes predicted by PopDel are more reliable than those of previous SV callers. Furthermore, PopDel’s running time is competitive with the fastest tested previous tools. The demonstrated scalability and accuracy of PopDel enables routine scans for deletions in large-scale sequencing studies. Nature Publishing Group UK 2021-02-01 /pmc/articles/PMC7851401/ /pubmed/33526789 http://dx.doi.org/10.1038/s41467-020-20850-5 Text en © The Author(s) 2021 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as 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 images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Niehus, Sebastian Jónsson, Hákon Schönberger, Janina Björnsson, Eythór Beyter, Doruk Eggertsson, Hannes P. Sulem, Patrick Stefánsson, Kári Halldórsson, Bjarni V. Kehr, Birte PopDel identifies medium-size deletions simultaneously in tens of thousands of genomes |
title | PopDel identifies medium-size deletions simultaneously in tens of thousands of genomes |
title_full | PopDel identifies medium-size deletions simultaneously in tens of thousands of genomes |
title_fullStr | PopDel identifies medium-size deletions simultaneously in tens of thousands of genomes |
title_full_unstemmed | PopDel identifies medium-size deletions simultaneously in tens of thousands of genomes |
title_short | PopDel identifies medium-size deletions simultaneously in tens of thousands of genomes |
title_sort | popdel identifies medium-size deletions simultaneously in tens of thousands of genomes |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7851401/ https://www.ncbi.nlm.nih.gov/pubmed/33526789 http://dx.doi.org/10.1038/s41467-020-20850-5 |
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