Leveraging spatiotemporal genomic breeding value estimates of dry matter yield and herbage quality in ryegrass via random regression models
Abstract Joint modeling of correlated multienvironment and multiharvest data of perennial crop species may offer advantages in prediction schemes and a better understanding of the underlying dynamics in space and time. The goal of the present study was to investigate the relevance of incorporating t...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-12-01
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| Series: | The Plant Genome |
| Online Access: | https://doi.org/10.1002/tpg2.20255 |
| _version_ | 1798006481375723520 |
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| author | Elesandro Bornhofen Dario Fè Ingo Lenk Morten Greve Thomas Didion Christian Sig Jensen Torben Asp Luc Janss |
| author_facet | Elesandro Bornhofen Dario Fè Ingo Lenk Morten Greve Thomas Didion Christian Sig Jensen Torben Asp Luc Janss |
| author_sort | Elesandro Bornhofen |
| collection | DOAJ |
| description | Abstract Joint modeling of correlated multienvironment and multiharvest data of perennial crop species may offer advantages in prediction schemes and a better understanding of the underlying dynamics in space and time. The goal of the present study was to investigate the relevance of incorporating the longitudinal dimension of within‐season multiple measurements of forage perennial ryegrass (Lolium perenne L.) traits in a reaction‐norm model setup that additionally accounts for genotype × environment (G × E) interactions. Genetic parameters and accuracy of genomic estimated breeding value (gEBV) predictions were investigated by fitting three genomic random regression models (gRRMs) using Legendre polynomial functions to the data. Genomic DNA sequencing of family pools of diploid perennial ryegrass was performed using DNA nanoball‐based technology and yielded 56,645 single‐nucleotide polymorphisms, which were used to calculate the allele frequency‐based genomic relationship matrix. Biomass yield's estimated additive genetic variance and heritability values were higher in later harvests. The additive genetic correlations were moderate to low in early measurements and peaked at intermediates with fairly stable values across the environmental gradient except for the initial harvest data collection. This led to the conclusion that complex (G × E) arises from spatial and temporal dimensions in the early season with lower reranking trends thereafter. In general, modeling the temporal dimension with a second‐order orthogonal polynomial improved the accuracy of gEBV prediction for nutritive quality traits, but no gain in prediction accuracy was detected for dry matter yield (DMY). This study leverages the flexibility and usefulness of gRRM models for perennial ryegrass breeding and can be readily extended to other multiharvest crops. |
| first_indexed | 2024-04-11T12:56:30Z |
| format | Article |
| id | doaj.art-de3efab5c2b54b2cb0e8bedecdd55325 |
| institution | Directory Open Access Journal |
| issn | 1940-3372 |
| language | English |
| last_indexed | 2024-04-11T12:56:30Z |
| publishDate | 2022-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Plant Genome |
| spelling | doaj.art-de3efab5c2b54b2cb0e8bedecdd553252022-12-22T04:23:04ZengWileyThe Plant Genome1940-33722022-12-01154n/an/a10.1002/tpg2.20255Leveraging spatiotemporal genomic breeding value estimates of dry matter yield and herbage quality in ryegrass via random regression modelsElesandro Bornhofen0Dario Fè1Ingo Lenk2Morten Greve3Thomas Didion4Christian Sig Jensen5Torben Asp6Luc Janss7Center for Quantitative Genetics and Genomics Aarhus Univ. Aarhus DenmarkResearch Division DLF Seeds A/S Store Heddinge DenmarkResearch Division DLF Seeds A/S Store Heddinge DenmarkResearch Division DLF Seeds A/S Store Heddinge DenmarkResearch Division DLF Seeds A/S Store Heddinge DenmarkResearch Division DLF Seeds A/S Store Heddinge DenmarkCenter for Quantitative Genetics and Genomics Aarhus Univ. Slagelse DenmarkCenter for Quantitative Genetics and Genomics Aarhus Univ. Aarhus DenmarkAbstract Joint modeling of correlated multienvironment and multiharvest data of perennial crop species may offer advantages in prediction schemes and a better understanding of the underlying dynamics in space and time. The goal of the present study was to investigate the relevance of incorporating the longitudinal dimension of within‐season multiple measurements of forage perennial ryegrass (Lolium perenne L.) traits in a reaction‐norm model setup that additionally accounts for genotype × environment (G × E) interactions. Genetic parameters and accuracy of genomic estimated breeding value (gEBV) predictions were investigated by fitting three genomic random regression models (gRRMs) using Legendre polynomial functions to the data. Genomic DNA sequencing of family pools of diploid perennial ryegrass was performed using DNA nanoball‐based technology and yielded 56,645 single‐nucleotide polymorphisms, which were used to calculate the allele frequency‐based genomic relationship matrix. Biomass yield's estimated additive genetic variance and heritability values were higher in later harvests. The additive genetic correlations were moderate to low in early measurements and peaked at intermediates with fairly stable values across the environmental gradient except for the initial harvest data collection. This led to the conclusion that complex (G × E) arises from spatial and temporal dimensions in the early season with lower reranking trends thereafter. In general, modeling the temporal dimension with a second‐order orthogonal polynomial improved the accuracy of gEBV prediction for nutritive quality traits, but no gain in prediction accuracy was detected for dry matter yield (DMY). This study leverages the flexibility and usefulness of gRRM models for perennial ryegrass breeding and can be readily extended to other multiharvest crops.https://doi.org/10.1002/tpg2.20255 |
| spellingShingle | Elesandro Bornhofen Dario Fè Ingo Lenk Morten Greve Thomas Didion Christian Sig Jensen Torben Asp Luc Janss Leveraging spatiotemporal genomic breeding value estimates of dry matter yield and herbage quality in ryegrass via random regression models The Plant Genome |
| title | Leveraging spatiotemporal genomic breeding value estimates of dry matter yield and herbage quality in ryegrass via random regression models |
| title_full | Leveraging spatiotemporal genomic breeding value estimates of dry matter yield and herbage quality in ryegrass via random regression models |
| title_fullStr | Leveraging spatiotemporal genomic breeding value estimates of dry matter yield and herbage quality in ryegrass via random regression models |
| title_full_unstemmed | Leveraging spatiotemporal genomic breeding value estimates of dry matter yield and herbage quality in ryegrass via random regression models |
| title_short | Leveraging spatiotemporal genomic breeding value estimates of dry matter yield and herbage quality in ryegrass via random regression models |
| title_sort | leveraging spatiotemporal genomic breeding value estimates of dry matter yield and herbage quality in ryegrass via random regression models |
| url | https://doi.org/10.1002/tpg2.20255 |
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