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Open-source benchmarking of IBD segment detection methods for biobank-scale cohorts
In the recent biobank era of genetics, the problem of identical-by-descent (IBD) segment detection received renewed interest, as IBD segments in large cohorts offer unprecedented opportunities in the study of population and genealogical history, as well as genetic association of long haplotypes. Whi...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9724555/ https://www.ncbi.nlm.nih.gov/pubmed/36472573 http://dx.doi.org/10.1093/gigascience/giac111 |
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author | Tang, Kecong Naseri, Ardalan Wei, Yuan Zhang, Shaojie Zhi, Degui |
author_facet | Tang, Kecong Naseri, Ardalan Wei, Yuan Zhang, Shaojie Zhi, Degui |
author_sort | Tang, Kecong |
collection | PubMed |
description | In the recent biobank era of genetics, the problem of identical-by-descent (IBD) segment detection received renewed interest, as IBD segments in large cohorts offer unprecedented opportunities in the study of population and genealogical history, as well as genetic association of long haplotypes. While a new generation of efficient methods for IBD segment detection becomes available, direct comparison of these methods is difficult: existing benchmarks were often evaluated in different datasets, with some not openly accessible; methods benchmarked were run under suboptimal parameters; and benchmark performance metrics were not defined consistently. Here, we developed a comprehensive and completely open-source evaluation of the power, accuracy, and resource consumption of these IBD segment detection methods using realistic population genetic simulations with various settings. Our results pave the road for fair evaluation of IBD segment detection methods and provide an practical guide for users. |
format | Online Article Text |
id | pubmed-9724555 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-97245552022-12-07 Open-source benchmarking of IBD segment detection methods for biobank-scale cohorts Tang, Kecong Naseri, Ardalan Wei, Yuan Zhang, Shaojie Zhi, Degui Gigascience Research In the recent biobank era of genetics, the problem of identical-by-descent (IBD) segment detection received renewed interest, as IBD segments in large cohorts offer unprecedented opportunities in the study of population and genealogical history, as well as genetic association of long haplotypes. While a new generation of efficient methods for IBD segment detection becomes available, direct comparison of these methods is difficult: existing benchmarks were often evaluated in different datasets, with some not openly accessible; methods benchmarked were run under suboptimal parameters; and benchmark performance metrics were not defined consistently. Here, we developed a comprehensive and completely open-source evaluation of the power, accuracy, and resource consumption of these IBD segment detection methods using realistic population genetic simulations with various settings. Our results pave the road for fair evaluation of IBD segment detection methods and provide an practical guide for users. Oxford University Press 2022-12-06 /pmc/articles/PMC9724555/ /pubmed/36472573 http://dx.doi.org/10.1093/gigascience/giac111 Text en © The Author(s) 2022. Published by Oxford University Press GigaScience. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Tang, Kecong Naseri, Ardalan Wei, Yuan Zhang, Shaojie Zhi, Degui Open-source benchmarking of IBD segment detection methods for biobank-scale cohorts |
title | Open-source benchmarking of IBD segment detection methods for biobank-scale cohorts |
title_full | Open-source benchmarking of IBD segment detection methods for biobank-scale cohorts |
title_fullStr | Open-source benchmarking of IBD segment detection methods for biobank-scale cohorts |
title_full_unstemmed | Open-source benchmarking of IBD segment detection methods for biobank-scale cohorts |
title_short | Open-source benchmarking of IBD segment detection methods for biobank-scale cohorts |
title_sort | open-source benchmarking of ibd segment detection methods for biobank-scale cohorts |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9724555/ https://www.ncbi.nlm.nih.gov/pubmed/36472573 http://dx.doi.org/10.1093/gigascience/giac111 |
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