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On the equivalence between marker effect models and breeding value models and direct genomic values with the Algorithm for Proven and Young
BACKGROUND: Single-step genomic predictions obtained from a breeding value model require calculating the inverse of the genomic relationship matrix [Formula: see text] . The Algorithm for Proven and Young (APY) creates a sparse representation of [Formula: see text] with a low computational cost. APY...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9288049/ https://www.ncbi.nlm.nih.gov/pubmed/35842585 http://dx.doi.org/10.1186/s12711-022-00741-7 |
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author | Bermann, Matias Lourenco, Daniela Forneris, Natalia S. Legarra, Andres Misztal, Ignacy |
author_facet | Bermann, Matias Lourenco, Daniela Forneris, Natalia S. Legarra, Andres Misztal, Ignacy |
author_sort | Bermann, Matias |
collection | PubMed |
description | BACKGROUND: Single-step genomic predictions obtained from a breeding value model require calculating the inverse of the genomic relationship matrix [Formula: see text] . The Algorithm for Proven and Young (APY) creates a sparse representation of [Formula: see text] with a low computational cost. APY consists of selecting a group of core animals and expressing the breeding values of the remaining animals as a linear combination of those from the core animals plus an error term. The objectives of this study were to: (1) extend APY to marker effects models; (2) derive equations for marker effect estimates when APY is used for breeding value models, and (3) show the implication of selecting a specific group of core animals in terms of a marker effects model. RESULTS: We derived a family of marker effects models called APY-SNP-BLUP. It differs from the classic marker effects model in that the row space of the genotype matrix is reduced and an error term is fitted for non-core animals. We derived formulas for marker effect estimates that take this error term in account. The prediction error variance (PEV) of the marker effect estimates depends on the PEV for core animals but not directly on the PEV of the non-core animals. We extended the APY-SNP-BLUP to include a residual polygenic effect and accommodate non-genotyped animals. We show that selecting a specific group of core animals is equivalent to select a subspace of the row space of the genotype matrix. As the number of core animals increases, subspaces corresponding to different sets of core animals tend to overlap, showing that random selection of core animals is algebraically justified. CONCLUSIONS: The APY-(ss)GBLUP models can be expressed in terms of marker effect models. When the number of core animals is equal to the rank of the genotype matrix, APY-SNP-BLUP is identical to the classic marker effects model. If the number of core animals is less than the rank of the genotype matrix, genotypes for non-core animals are imputed as a linear combination of the genotypes of the core animals. For estimating SNP effects, only relationships and estimated breeding values for core animals are needed. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12711-022-00741-7. |
format | Online Article Text |
id | pubmed-9288049 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-92880492022-07-17 On the equivalence between marker effect models and breeding value models and direct genomic values with the Algorithm for Proven and Young Bermann, Matias Lourenco, Daniela Forneris, Natalia S. Legarra, Andres Misztal, Ignacy Genet Sel Evol Research Article BACKGROUND: Single-step genomic predictions obtained from a breeding value model require calculating the inverse of the genomic relationship matrix [Formula: see text] . The Algorithm for Proven and Young (APY) creates a sparse representation of [Formula: see text] with a low computational cost. APY consists of selecting a group of core animals and expressing the breeding values of the remaining animals as a linear combination of those from the core animals plus an error term. The objectives of this study were to: (1) extend APY to marker effects models; (2) derive equations for marker effect estimates when APY is used for breeding value models, and (3) show the implication of selecting a specific group of core animals in terms of a marker effects model. RESULTS: We derived a family of marker effects models called APY-SNP-BLUP. It differs from the classic marker effects model in that the row space of the genotype matrix is reduced and an error term is fitted for non-core animals. We derived formulas for marker effect estimates that take this error term in account. The prediction error variance (PEV) of the marker effect estimates depends on the PEV for core animals but not directly on the PEV of the non-core animals. We extended the APY-SNP-BLUP to include a residual polygenic effect and accommodate non-genotyped animals. We show that selecting a specific group of core animals is equivalent to select a subspace of the row space of the genotype matrix. As the number of core animals increases, subspaces corresponding to different sets of core animals tend to overlap, showing that random selection of core animals is algebraically justified. CONCLUSIONS: The APY-(ss)GBLUP models can be expressed in terms of marker effect models. When the number of core animals is equal to the rank of the genotype matrix, APY-SNP-BLUP is identical to the classic marker effects model. If the number of core animals is less than the rank of the genotype matrix, genotypes for non-core animals are imputed as a linear combination of the genotypes of the core animals. For estimating SNP effects, only relationships and estimated breeding values for core animals are needed. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12711-022-00741-7. BioMed Central 2022-07-16 /pmc/articles/PMC9288049/ /pubmed/35842585 http://dx.doi.org/10.1186/s12711-022-00741-7 Text en © The Author(s) 2022 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 Bermann, Matias Lourenco, Daniela Forneris, Natalia S. Legarra, Andres Misztal, Ignacy On the equivalence between marker effect models and breeding value models and direct genomic values with the Algorithm for Proven and Young |
title | On the equivalence between marker effect models and breeding value models and direct genomic values with the Algorithm for Proven and Young |
title_full | On the equivalence between marker effect models and breeding value models and direct genomic values with the Algorithm for Proven and Young |
title_fullStr | On the equivalence between marker effect models and breeding value models and direct genomic values with the Algorithm for Proven and Young |
title_full_unstemmed | On the equivalence between marker effect models and breeding value models and direct genomic values with the Algorithm for Proven and Young |
title_short | On the equivalence between marker effect models and breeding value models and direct genomic values with the Algorithm for Proven and Young |
title_sort | on the equivalence between marker effect models and breeding value models and direct genomic values with the algorithm for proven and young |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9288049/ https://www.ncbi.nlm.nih.gov/pubmed/35842585 http://dx.doi.org/10.1186/s12711-022-00741-7 |
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