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A computationally efficient method for approximating reliabilities in large-scale single-step genomic prediction
BACKGROUND: In this study, computationally efficient methods to approximate the reliabilities of genomic estimated breeding values (GEBV) in a single-step genomic prediction model including a residual polygenic (RPG) effect are described. In order to calculate the reliabilities of the genotyped anim...
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/PMC9814342/ https://www.ncbi.nlm.nih.gov/pubmed/36604633 http://dx.doi.org/10.1186/s12711-022-00774-y |
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author | Gao, Hongding Kudinov, Andrei A. Taskinen, Matti Pitkänen, Timo J. Lidauer, Martin H. Mäntysaari, Esa A. Strandén, Ismo |
author_facet | Gao, Hongding Kudinov, Andrei A. Taskinen, Matti Pitkänen, Timo J. Lidauer, Martin H. Mäntysaari, Esa A. Strandén, Ismo |
author_sort | Gao, Hongding |
collection | PubMed |
description | BACKGROUND: In this study, computationally efficient methods to approximate the reliabilities of genomic estimated breeding values (GEBV) in a single-step genomic prediction model including a residual polygenic (RPG) effect are described. In order to calculate the reliabilities of the genotyped animals, a single nucleotide polymorphism best linear unbiased prediction (SNPBLUP) or a genomic BLUP (GBLUP), was used, where two alternatives to account for the RPG effect were tested. In the direct approach, the genomic model included the RPG effect, while in the blended method, it did not but an index was used to weight the genomic and pedigree-based BLUP (PBLUP) reliabilities. In order to calculate the single-step GBLUP reliabilities for the breeding values for the non-genotyped animals, a simplified weighted-PBLUP model that included a general mean and additive genetic effects with weights accounting for the non-genomic and genomic information was used. We compared five schemes for the weights. Two datasets, i.e., a small (Data 1) one and a large (Data 2) one were used. RESULTS: For the genotyped animals in Data 1, correlations between approximate reliabilities using the blended method and exact reliabilities ranged from 0.993 to 0.996 across three lactations. The slopes observed by regressing the reliabilities of GEBV from the exact method on those from the blended method were 1.0 for all three lactations. For Data 2, the correlations and slopes ranged, respectively, from 0.980 to 0.986 and from 0.91 to 0.96, and for the non-genotyped animals in Data 1, they ranged, respectively, from 0.987 to 0.994 and from 0.987 to 1, which indicate that the approximations were in line with the exact results. The best approach achieved correlations of 0.992 to 0.994 across lactations. CONCLUSIONS: Our results demonstrate that the approximated reliabilities calculated using our proposed approach are in good agreement with the exact reliabilities. The blended method for the genotyped animals is computationally more feasible than the direct method when RPG effects are included, particularly for large-scale datasets. The approach can serve as an effective strategy to estimate the reliabilities of GEBV in large-scale single-step genomic predictions. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12711-022-00774-y. |
format | Online Article Text |
id | pubmed-9814342 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-98143422023-01-06 A computationally efficient method for approximating reliabilities in large-scale single-step genomic prediction Gao, Hongding Kudinov, Andrei A. Taskinen, Matti Pitkänen, Timo J. Lidauer, Martin H. Mäntysaari, Esa A. Strandén, Ismo Genet Sel Evol Research Article BACKGROUND: In this study, computationally efficient methods to approximate the reliabilities of genomic estimated breeding values (GEBV) in a single-step genomic prediction model including a residual polygenic (RPG) effect are described. In order to calculate the reliabilities of the genotyped animals, a single nucleotide polymorphism best linear unbiased prediction (SNPBLUP) or a genomic BLUP (GBLUP), was used, where two alternatives to account for the RPG effect were tested. In the direct approach, the genomic model included the RPG effect, while in the blended method, it did not but an index was used to weight the genomic and pedigree-based BLUP (PBLUP) reliabilities. In order to calculate the single-step GBLUP reliabilities for the breeding values for the non-genotyped animals, a simplified weighted-PBLUP model that included a general mean and additive genetic effects with weights accounting for the non-genomic and genomic information was used. We compared five schemes for the weights. Two datasets, i.e., a small (Data 1) one and a large (Data 2) one were used. RESULTS: For the genotyped animals in Data 1, correlations between approximate reliabilities using the blended method and exact reliabilities ranged from 0.993 to 0.996 across three lactations. The slopes observed by regressing the reliabilities of GEBV from the exact method on those from the blended method were 1.0 for all three lactations. For Data 2, the correlations and slopes ranged, respectively, from 0.980 to 0.986 and from 0.91 to 0.96, and for the non-genotyped animals in Data 1, they ranged, respectively, from 0.987 to 0.994 and from 0.987 to 1, which indicate that the approximations were in line with the exact results. The best approach achieved correlations of 0.992 to 0.994 across lactations. CONCLUSIONS: Our results demonstrate that the approximated reliabilities calculated using our proposed approach are in good agreement with the exact reliabilities. The blended method for the genotyped animals is computationally more feasible than the direct method when RPG effects are included, particularly for large-scale datasets. The approach can serve as an effective strategy to estimate the reliabilities of GEBV in large-scale single-step genomic predictions. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12711-022-00774-y. BioMed Central 2023-01-05 /pmc/articles/PMC9814342/ /pubmed/36604633 http://dx.doi.org/10.1186/s12711-022-00774-y Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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 Gao, Hongding Kudinov, Andrei A. Taskinen, Matti Pitkänen, Timo J. Lidauer, Martin H. Mäntysaari, Esa A. Strandén, Ismo A computationally efficient method for approximating reliabilities in large-scale single-step genomic prediction |
title | A computationally efficient method for approximating reliabilities in large-scale single-step genomic prediction |
title_full | A computationally efficient method for approximating reliabilities in large-scale single-step genomic prediction |
title_fullStr | A computationally efficient method for approximating reliabilities in large-scale single-step genomic prediction |
title_full_unstemmed | A computationally efficient method for approximating reliabilities in large-scale single-step genomic prediction |
title_short | A computationally efficient method for approximating reliabilities in large-scale single-step genomic prediction |
title_sort | computationally efficient method for approximating reliabilities in large-scale single-step genomic prediction |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9814342/ https://www.ncbi.nlm.nih.gov/pubmed/36604633 http://dx.doi.org/10.1186/s12711-022-00774-y |
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