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Improving Genomic Prediction for Seed Quality Traits in Oat (Avena sativa L.) Using Trait-Specific Relationship Matrices

The observable phenotype is the manifestation of information that is passed along different organization levels (transcriptional, translational, and metabolic) of a biological system. The widespread use of various omic technologies (RNA-sequencing, metabolomics, etc.) has provided plant genetics and...

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Autores principales: Campbell, Malachy T., Hu, Haixiao, Yeats, Trevor H., Brzozowski, Lauren J., Caffe-Treml, Melanie, Gutiérrez, Lucía, Smith, Kevin P., Sorrells, Mark E., Gore, Michael A., Jannink, Jean-Luc
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8044359/
https://www.ncbi.nlm.nih.gov/pubmed/33868378
http://dx.doi.org/10.3389/fgene.2021.643733
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author Campbell, Malachy T.
Hu, Haixiao
Yeats, Trevor H.
Brzozowski, Lauren J.
Caffe-Treml, Melanie
Gutiérrez, Lucía
Smith, Kevin P.
Sorrells, Mark E.
Gore, Michael A.
Jannink, Jean-Luc
author_facet Campbell, Malachy T.
Hu, Haixiao
Yeats, Trevor H.
Brzozowski, Lauren J.
Caffe-Treml, Melanie
Gutiérrez, Lucía
Smith, Kevin P.
Sorrells, Mark E.
Gore, Michael A.
Jannink, Jean-Luc
author_sort Campbell, Malachy T.
collection PubMed
description The observable phenotype is the manifestation of information that is passed along different organization levels (transcriptional, translational, and metabolic) of a biological system. The widespread use of various omic technologies (RNA-sequencing, metabolomics, etc.) has provided plant genetics and breeders with a wealth of information on pertinent intermediate molecular processes that may help explain variation in conventional traits such as yield, seed quality, and fitness, among others. A major challenge is effectively using these data to help predict the genetic merit of new, unobserved individuals for conventional agronomic traits. Trait-specific genomic relationship matrices (TGRMs) model the relationships between individuals using genome-wide markers (SNPs) and place greater emphasis on markers that most relevant to the trait compared to conventional genomic relationship matrices. Given that these approaches define relationships based on putative causal loci, it is expected that these approaches should improve predictions for related traits. In this study we evaluated the use of TGRMs to accommodate information on intermediate molecular phenotypes (referred to as endophenotypes) and to predict an agronomic trait, total lipid content, in oat seed. Nine fatty acids were quantified in a panel of 336 oat lines. Marker effects were estimated for each endophenotype, and were used to construct TGRMs. A multikernel TRGM model (MK-TRGM-BLUP) was used to predict total seed lipid content in an independent panel of 210 oat lines. The MK-TRGM-BLUP approach significantly improved predictions for total lipid content when compared to a conventional genomic BLUP (gBLUP) approach. Given that the MK-TGRM-BLUP approach leverages information on the nine fatty acids to predict genetic values for total lipid content in unobserved individuals, we compared the MK-TGRM-BLUP approach to a multi-trait gBLUP (MT-gBLUP) approach that jointly fits phenotypes for fatty acids and total lipid content. The MK-TGRM-BLUP approach significantly outperformed MT-gBLUP. Collectively, these results highlight the utility of using TGRM to accommodate information on endophenotypes and improve genomic prediction for a conventional agronomic trait.
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spelling pubmed-80443592021-04-15 Improving Genomic Prediction for Seed Quality Traits in Oat (Avena sativa L.) Using Trait-Specific Relationship Matrices Campbell, Malachy T. Hu, Haixiao Yeats, Trevor H. Brzozowski, Lauren J. Caffe-Treml, Melanie Gutiérrez, Lucía Smith, Kevin P. Sorrells, Mark E. Gore, Michael A. Jannink, Jean-Luc Front Genet Genetics The observable phenotype is the manifestation of information that is passed along different organization levels (transcriptional, translational, and metabolic) of a biological system. The widespread use of various omic technologies (RNA-sequencing, metabolomics, etc.) has provided plant genetics and breeders with a wealth of information on pertinent intermediate molecular processes that may help explain variation in conventional traits such as yield, seed quality, and fitness, among others. A major challenge is effectively using these data to help predict the genetic merit of new, unobserved individuals for conventional agronomic traits. Trait-specific genomic relationship matrices (TGRMs) model the relationships between individuals using genome-wide markers (SNPs) and place greater emphasis on markers that most relevant to the trait compared to conventional genomic relationship matrices. Given that these approaches define relationships based on putative causal loci, it is expected that these approaches should improve predictions for related traits. In this study we evaluated the use of TGRMs to accommodate information on intermediate molecular phenotypes (referred to as endophenotypes) and to predict an agronomic trait, total lipid content, in oat seed. Nine fatty acids were quantified in a panel of 336 oat lines. Marker effects were estimated for each endophenotype, and were used to construct TGRMs. A multikernel TRGM model (MK-TRGM-BLUP) was used to predict total seed lipid content in an independent panel of 210 oat lines. The MK-TRGM-BLUP approach significantly improved predictions for total lipid content when compared to a conventional genomic BLUP (gBLUP) approach. Given that the MK-TGRM-BLUP approach leverages information on the nine fatty acids to predict genetic values for total lipid content in unobserved individuals, we compared the MK-TGRM-BLUP approach to a multi-trait gBLUP (MT-gBLUP) approach that jointly fits phenotypes for fatty acids and total lipid content. The MK-TGRM-BLUP approach significantly outperformed MT-gBLUP. Collectively, these results highlight the utility of using TGRM to accommodate information on endophenotypes and improve genomic prediction for a conventional agronomic trait. Frontiers Media S.A. 2021-03-31 /pmc/articles/PMC8044359/ /pubmed/33868378 http://dx.doi.org/10.3389/fgene.2021.643733 Text en Copyright © 2021 Campbell, Hu, Yeats, Brzozowski, Caffe-Treml, Gutiérrez, Smith, Sorrells, Gore and Jannink. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Genetics
Campbell, Malachy T.
Hu, Haixiao
Yeats, Trevor H.
Brzozowski, Lauren J.
Caffe-Treml, Melanie
Gutiérrez, Lucía
Smith, Kevin P.
Sorrells, Mark E.
Gore, Michael A.
Jannink, Jean-Luc
Improving Genomic Prediction for Seed Quality Traits in Oat (Avena sativa L.) Using Trait-Specific Relationship Matrices
title Improving Genomic Prediction for Seed Quality Traits in Oat (Avena sativa L.) Using Trait-Specific Relationship Matrices
title_full Improving Genomic Prediction for Seed Quality Traits in Oat (Avena sativa L.) Using Trait-Specific Relationship Matrices
title_fullStr Improving Genomic Prediction for Seed Quality Traits in Oat (Avena sativa L.) Using Trait-Specific Relationship Matrices
title_full_unstemmed Improving Genomic Prediction for Seed Quality Traits in Oat (Avena sativa L.) Using Trait-Specific Relationship Matrices
title_short Improving Genomic Prediction for Seed Quality Traits in Oat (Avena sativa L.) Using Trait-Specific Relationship Matrices
title_sort improving genomic prediction for seed quality traits in oat (avena sativa l.) using trait-specific relationship matrices
topic Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8044359/
https://www.ncbi.nlm.nih.gov/pubmed/33868378
http://dx.doi.org/10.3389/fgene.2021.643733
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