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Using pre-selected variants from large-scale whole-genome sequence data for single-step genomic predictions in pigs
BACKGROUND: Whole-genome sequence (WGS) data harbor causative variants that may not be present in standard single nucleotide polymorphism (SNP) chip data. The objective of this study was to investigate the impact of using preselected variants from WGS for single-step genomic predictions in maternal...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10373252/ https://www.ncbi.nlm.nih.gov/pubmed/37495982 http://dx.doi.org/10.1186/s12711-023-00831-0 |
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author | Jang, Sungbong Ros-Freixedes, Roger Hickey, John M. Chen, Ching-Yi Holl, Justin Herring, William O. Misztal, Ignacy Lourenco, Daniela |
author_facet | Jang, Sungbong Ros-Freixedes, Roger Hickey, John M. Chen, Ching-Yi Holl, Justin Herring, William O. Misztal, Ignacy Lourenco, Daniela |
author_sort | Jang, Sungbong |
collection | PubMed |
description | BACKGROUND: Whole-genome sequence (WGS) data harbor causative variants that may not be present in standard single nucleotide polymorphism (SNP) chip data. The objective of this study was to investigate the impact of using preselected variants from WGS for single-step genomic predictions in maternal and terminal pig lines with up to 1.8k sequenced and 104k sequence imputed animals per line. METHODS: Two maternal and four terminal lines were investigated for eight and seven traits, respectively. The number of sequenced animals ranged from 1365 to 1491 for the maternal lines and 381 to 1865 for the terminal lines. Imputation to sequence occurred within each line for 66k to 76k animals for the maternal lines and 29k to 104k animals for the terminal lines. Two preselected SNP sets were generated based on a genome-wide association study (GWAS). Top40k included the SNPs with the lowest p-value in each of the 40k genomic windows, and ChipPlusSign included significant variants integrated into the porcine SNP chip used for routine genotyping. We compared the performance of single-step genomic predictions between using preselected SNP sets assuming equal or different variances and the standard porcine SNP chip. RESULTS: In the maternal lines, ChipPlusSign and Top40k showed an average increase in accuracy of 0.6 and 4.9%, respectively, compared to the regular porcine SNP chip. The greatest increase was obtained with Top40k, particularly for fertility traits, for which the initial accuracy based on the standard SNP chip was low. However, in the terminal lines, Top40k resulted in an average loss of accuracy of 1%. ChipPlusSign provided a positive, although small, gain in accuracy (0.9%). Assigning different variances for the SNPs slightly improved accuracies when using variances obtained from BayesR. However, increases were inconsistent across the lines and traits. CONCLUSIONS: The benefit of using sequence data depends on the line, the size of the genotyped population, and how the WGS variants are preselected. When WGS data are available on hundreds of thousands of animals, using sequence data presents an advantage but this remains limited in pigs. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12711-023-00831-0. |
format | Online Article Text |
id | pubmed-10373252 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-103732522023-07-28 Using pre-selected variants from large-scale whole-genome sequence data for single-step genomic predictions in pigs Jang, Sungbong Ros-Freixedes, Roger Hickey, John M. Chen, Ching-Yi Holl, Justin Herring, William O. Misztal, Ignacy Lourenco, Daniela Genet Sel Evol Research Article BACKGROUND: Whole-genome sequence (WGS) data harbor causative variants that may not be present in standard single nucleotide polymorphism (SNP) chip data. The objective of this study was to investigate the impact of using preselected variants from WGS for single-step genomic predictions in maternal and terminal pig lines with up to 1.8k sequenced and 104k sequence imputed animals per line. METHODS: Two maternal and four terminal lines were investigated for eight and seven traits, respectively. The number of sequenced animals ranged from 1365 to 1491 for the maternal lines and 381 to 1865 for the terminal lines. Imputation to sequence occurred within each line for 66k to 76k animals for the maternal lines and 29k to 104k animals for the terminal lines. Two preselected SNP sets were generated based on a genome-wide association study (GWAS). Top40k included the SNPs with the lowest p-value in each of the 40k genomic windows, and ChipPlusSign included significant variants integrated into the porcine SNP chip used for routine genotyping. We compared the performance of single-step genomic predictions between using preselected SNP sets assuming equal or different variances and the standard porcine SNP chip. RESULTS: In the maternal lines, ChipPlusSign and Top40k showed an average increase in accuracy of 0.6 and 4.9%, respectively, compared to the regular porcine SNP chip. The greatest increase was obtained with Top40k, particularly for fertility traits, for which the initial accuracy based on the standard SNP chip was low. However, in the terminal lines, Top40k resulted in an average loss of accuracy of 1%. ChipPlusSign provided a positive, although small, gain in accuracy (0.9%). Assigning different variances for the SNPs slightly improved accuracies when using variances obtained from BayesR. However, increases were inconsistent across the lines and traits. CONCLUSIONS: The benefit of using sequence data depends on the line, the size of the genotyped population, and how the WGS variants are preselected. When WGS data are available on hundreds of thousands of animals, using sequence data presents an advantage but this remains limited in pigs. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12711-023-00831-0. BioMed Central 2023-07-26 /pmc/articles/PMC10373252/ /pubmed/37495982 http://dx.doi.org/10.1186/s12711-023-00831-0 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Article Jang, Sungbong Ros-Freixedes, Roger Hickey, John M. Chen, Ching-Yi Holl, Justin Herring, William O. Misztal, Ignacy Lourenco, Daniela Using pre-selected variants from large-scale whole-genome sequence data for single-step genomic predictions in pigs |
title | Using pre-selected variants from large-scale whole-genome sequence data for single-step genomic predictions in pigs |
title_full | Using pre-selected variants from large-scale whole-genome sequence data for single-step genomic predictions in pigs |
title_fullStr | Using pre-selected variants from large-scale whole-genome sequence data for single-step genomic predictions in pigs |
title_full_unstemmed | Using pre-selected variants from large-scale whole-genome sequence data for single-step genomic predictions in pigs |
title_short | Using pre-selected variants from large-scale whole-genome sequence data for single-step genomic predictions in pigs |
title_sort | using pre-selected variants from large-scale whole-genome sequence data for single-step genomic predictions in pigs |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10373252/ https://www.ncbi.nlm.nih.gov/pubmed/37495982 http://dx.doi.org/10.1186/s12711-023-00831-0 |
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