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Implication of the order of blending and tuning when computing the genomic relationship matrix in single‐step GBLUP
Single‐step genomic BLUP (ssGBLUP) relies on the combination of the genomic ([Formula: see text]) and pedigree relationship matrices for all ([Formula: see text]) and genotyped ([Formula: see text]) animals. The procedure ensures [Formula: see text] and [Formula: see text] are compatible so that bot...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10087221/ https://www.ncbi.nlm.nih.gov/pubmed/35946919 http://dx.doi.org/10.1111/jbg.12734 |
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author | McWhorter, Taylor M. Bermann, Matias Garcia, Andre L. S. Legarra, Andrés Aguilar, Ignacio Misztal, Ignacy Lourenco, Daniela |
author_facet | McWhorter, Taylor M. Bermann, Matias Garcia, Andre L. S. Legarra, Andrés Aguilar, Ignacio Misztal, Ignacy Lourenco, Daniela |
author_sort | McWhorter, Taylor M. |
collection | PubMed |
description | Single‐step genomic BLUP (ssGBLUP) relies on the combination of the genomic ([Formula: see text]) and pedigree relationship matrices for all ([Formula: see text]) and genotyped ([Formula: see text]) animals. The procedure ensures [Formula: see text] and [Formula: see text] are compatible so that both matrices refer to the same genetic base (‘tuning’). Then [Formula: see text] is combined with a proportion of [Formula: see text] (‘blending’) to avoid singularity problems and to account for the polygenic component not accounted for by markers. This computational procedure has been implemented in the reverse order (blending before tuning) following the sequential research developments. However, blending before tuning may result in less optimal tuning because the blended matrix already contains a proportion of [Formula: see text]. In this study, the impact of ‘tuning before blending’ was compared with ‘blending before tuning’ on genomic estimated breeding values (GEBV), single nucleotide polymorphism (SNP) effects and indirect predictions (IP) from ssGBLUP using American Angus Association and Holstein Association USA, Inc. data. Two slightly different tuning methods were used; one that adjusts the mean diagonals and off‐diagonals of [Formula: see text] to be similar to those in [Formula: see text] and another one that adjusts based on the average difference between all elements of [Formula: see text] and [Formula: see text]. Over 6 million Angus growth records and 5.9 million Holstein udder depth records were available. Genomic information was available on 51,478 Angus and 105,116 Holstein animals. Average realized relationship estimates among groups of animals were similar across scenarios. Scatterplots show that GEBV, SNP effects and IP did not noticeably change for all animals in the evaluation regardless of the order of computations and when using blending parameter of 0.05. Formulas were derived to determine the blending parameter that maximizes changes in the genomic relationship matrix and GEBV when changing the order of blending and tuning. Algebraically, the change is maximized when the blending parameter is equal to 0.5. Overall, tuning [Formula: see text] before blending, regardless of blending parameter used, had a negligible impact on genomic predictions and SNP effects in this study. |
format | Online Article Text |
id | pubmed-10087221 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-100872212023-04-12 Implication of the order of blending and tuning when computing the genomic relationship matrix in single‐step GBLUP McWhorter, Taylor M. Bermann, Matias Garcia, Andre L. S. Legarra, Andrés Aguilar, Ignacio Misztal, Ignacy Lourenco, Daniela J Anim Breed Genet Original Articles Single‐step genomic BLUP (ssGBLUP) relies on the combination of the genomic ([Formula: see text]) and pedigree relationship matrices for all ([Formula: see text]) and genotyped ([Formula: see text]) animals. The procedure ensures [Formula: see text] and [Formula: see text] are compatible so that both matrices refer to the same genetic base (‘tuning’). Then [Formula: see text] is combined with a proportion of [Formula: see text] (‘blending’) to avoid singularity problems and to account for the polygenic component not accounted for by markers. This computational procedure has been implemented in the reverse order (blending before tuning) following the sequential research developments. However, blending before tuning may result in less optimal tuning because the blended matrix already contains a proportion of [Formula: see text]. In this study, the impact of ‘tuning before blending’ was compared with ‘blending before tuning’ on genomic estimated breeding values (GEBV), single nucleotide polymorphism (SNP) effects and indirect predictions (IP) from ssGBLUP using American Angus Association and Holstein Association USA, Inc. data. Two slightly different tuning methods were used; one that adjusts the mean diagonals and off‐diagonals of [Formula: see text] to be similar to those in [Formula: see text] and another one that adjusts based on the average difference between all elements of [Formula: see text] and [Formula: see text]. Over 6 million Angus growth records and 5.9 million Holstein udder depth records were available. Genomic information was available on 51,478 Angus and 105,116 Holstein animals. Average realized relationship estimates among groups of animals were similar across scenarios. Scatterplots show that GEBV, SNP effects and IP did not noticeably change for all animals in the evaluation regardless of the order of computations and when using blending parameter of 0.05. Formulas were derived to determine the blending parameter that maximizes changes in the genomic relationship matrix and GEBV when changing the order of blending and tuning. Algebraically, the change is maximized when the blending parameter is equal to 0.5. Overall, tuning [Formula: see text] before blending, regardless of blending parameter used, had a negligible impact on genomic predictions and SNP effects in this study. John Wiley and Sons Inc. 2022-08-10 2023-01 /pmc/articles/PMC10087221/ /pubmed/35946919 http://dx.doi.org/10.1111/jbg.12734 Text en © 2022 The Authors. Journal of Animal Breeding and Genetics published by John Wiley & Sons Ltd. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ (https://creativecommons.org/licenses/by-nc-nd/4.0/) License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made. |
spellingShingle | Original Articles McWhorter, Taylor M. Bermann, Matias Garcia, Andre L. S. Legarra, Andrés Aguilar, Ignacio Misztal, Ignacy Lourenco, Daniela Implication of the order of blending and tuning when computing the genomic relationship matrix in single‐step GBLUP |
title | Implication of the order of blending and tuning when computing the genomic relationship matrix in single‐step GBLUP
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title_full | Implication of the order of blending and tuning when computing the genomic relationship matrix in single‐step GBLUP
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title_fullStr | Implication of the order of blending and tuning when computing the genomic relationship matrix in single‐step GBLUP
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title_full_unstemmed | Implication of the order of blending and tuning when computing the genomic relationship matrix in single‐step GBLUP
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title_short | Implication of the order of blending and tuning when computing the genomic relationship matrix in single‐step GBLUP
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title_sort | implication of the order of blending and tuning when computing the genomic relationship matrix in single‐step gblup |
topic | Original Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10087221/ https://www.ncbi.nlm.nih.gov/pubmed/35946919 http://dx.doi.org/10.1111/jbg.12734 |
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