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AmelHap: Leveraging drone whole-genome sequence data to create a honey bee HapMap
Honey bee, Apis mellifera, drones are typically haploid, developing from an unfertilized egg, inheriting only their queen’s alleles and none from the many drones she mated with. Thus the ordered combination or ‘phase’ of alleles is known, making drones a valuable haplotype resource. We collated whol...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10086014/ https://www.ncbi.nlm.nih.gov/pubmed/37037860 http://dx.doi.org/10.1038/s41597-023-02097-z |
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author | Parejo, M. Talenti, A. Richardson, M. Vignal, A. Barnett, M. Wragg, D. |
author_facet | Parejo, M. Talenti, A. Richardson, M. Vignal, A. Barnett, M. Wragg, D. |
author_sort | Parejo, M. |
collection | PubMed |
description | Honey bee, Apis mellifera, drones are typically haploid, developing from an unfertilized egg, inheriting only their queen’s alleles and none from the many drones she mated with. Thus the ordered combination or ‘phase’ of alleles is known, making drones a valuable haplotype resource. We collated whole-genome sequence data for 1,407 drones, including 45 newly sequenced Scottish drones, collectively representing 19 countries, 8 subspecies and various hybrids. Following alignment to Amel_HAv3.1, variant calling and quality filtering, we retained 17.4 M high quality variants across 1,328 samples with a genotyping rate of 98.7%. We demonstrate the utility of this haplotype resource, AmelHap, for genotype imputation, returning >95% concordance when up to 61% of data is missing in haploids and up to 12% of data is missing in diploids. AmelHap will serve as a useful resource for the community for imputation from low-depth sequencing or SNP chip data, accurate phasing of diploids for association studies, and as a comprehensive reference panel for population genetic and evolutionary analyses. |
format | Online Article Text |
id | pubmed-10086014 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-100860142023-04-12 AmelHap: Leveraging drone whole-genome sequence data to create a honey bee HapMap Parejo, M. Talenti, A. Richardson, M. Vignal, A. Barnett, M. Wragg, D. Sci Data Data Descriptor Honey bee, Apis mellifera, drones are typically haploid, developing from an unfertilized egg, inheriting only their queen’s alleles and none from the many drones she mated with. Thus the ordered combination or ‘phase’ of alleles is known, making drones a valuable haplotype resource. We collated whole-genome sequence data for 1,407 drones, including 45 newly sequenced Scottish drones, collectively representing 19 countries, 8 subspecies and various hybrids. Following alignment to Amel_HAv3.1, variant calling and quality filtering, we retained 17.4 M high quality variants across 1,328 samples with a genotyping rate of 98.7%. We demonstrate the utility of this haplotype resource, AmelHap, for genotype imputation, returning >95% concordance when up to 61% of data is missing in haploids and up to 12% of data is missing in diploids. AmelHap will serve as a useful resource for the community for imputation from low-depth sequencing or SNP chip data, accurate phasing of diploids for association studies, and as a comprehensive reference panel for population genetic and evolutionary analyses. Nature Publishing Group UK 2023-04-10 /pmc/articles/PMC10086014/ /pubmed/37037860 http://dx.doi.org/10.1038/s41597-023-02097-z Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/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/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Data Descriptor Parejo, M. Talenti, A. Richardson, M. Vignal, A. Barnett, M. Wragg, D. AmelHap: Leveraging drone whole-genome sequence data to create a honey bee HapMap |
title | AmelHap: Leveraging drone whole-genome sequence data to create a honey bee HapMap |
title_full | AmelHap: Leveraging drone whole-genome sequence data to create a honey bee HapMap |
title_fullStr | AmelHap: Leveraging drone whole-genome sequence data to create a honey bee HapMap |
title_full_unstemmed | AmelHap: Leveraging drone whole-genome sequence data to create a honey bee HapMap |
title_short | AmelHap: Leveraging drone whole-genome sequence data to create a honey bee HapMap |
title_sort | amelhap: leveraging drone whole-genome sequence data to create a honey bee hapmap |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10086014/ https://www.ncbi.nlm.nih.gov/pubmed/37037860 http://dx.doi.org/10.1038/s41597-023-02097-z |
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