Genomic prediction of forage nutritive value in perennial ryegrass
Abstract Background Despite its importance to animal production potential, genetic gain for forage nutritive value has been limited in perennial ryegrass (Lolium perenne L.) breeding. The objective of this study was to phenotype a training population and develop prediction models to assess the poten...
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| Format: | Article |
| Language: | English |
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Wiley
2024-12-01
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| Series: | Grassland Research |
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| Online Access: | https://doi.org/10.1002/glr2.12104 |
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| author | Agnieszka Konkolewska Michael Dineen Rachel Keirse Patrick Conaghan Dan Milbourne Susanne Barth Aonghus Lawlor Stephen Byrne |
| author_facet | Agnieszka Konkolewska Michael Dineen Rachel Keirse Patrick Conaghan Dan Milbourne Susanne Barth Aonghus Lawlor Stephen Byrne |
| author_sort | Agnieszka Konkolewska |
| collection | DOAJ |
| description | Abstract Background Despite its importance to animal production potential, genetic gain for forage nutritive value has been limited in perennial ryegrass (Lolium perenne L.) breeding. The objective of this study was to phenotype a training population and develop prediction models to assess the potential of predicting organic matter digestibility (OMD) and neutral detergent fiber (NDF) with genotyping‐by‐sequencing data. Methods Near infra‐red reflectance spectroscopy calibrations for OMD and NDF were developed and used to phenotype a spaced plant training population of n = 1606, with matching genotype‐by‐sequencing data, for developing genomic selection models. F 2 families derived from the training population were also evaluated for OMD and NDF in sward plots and used to empirically validate prediction models. Results Sufficient genotypic variation exists in breeding populations to improve forage nutritive value, and spectral bands contributing to calibrations were identified. OMD and NDF can be predicted from genomic data with moderate accuracy (predictive ability in the range of 0.51–0.59 and 0.33–0.57, respectively) and models developed on individual plants outperform those developed from family means. Encouragingly, genomic prediction models developed on parental plants can predict OMD in subsequent generations grown as competitive swards. Conclusions These findings suggest that genetic improvement in forage nutritive value can be accelerated through the application of genomic prediction models. |
| format | Article |
| id | doaj-art-a2de009ecf714ac98394be9735143780 |
| institution | Kabale University |
| issn | 2097-051X 2770-1743 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | Grassland Research |
| spelling | doaj-art-a2de009ecf714ac98394be97351437802025-01-17T12:43:41ZengWileyGrassland Research2097-051X2770-17432024-12-013433134610.1002/glr2.12104Genomic prediction of forage nutritive value in perennial ryegrassAgnieszka Konkolewska0Michael Dineen1Rachel Keirse2Patrick Conaghan3Dan Milbourne4Susanne Barth5Aonghus Lawlor6Stephen Byrne7Teagasc, Crop Science Department Carlow IrelandTeagasc, Grassland Science Research Department, Animal and Grassland Research and Innovation Centre, Moorepark Fermoy IrelandVistaMilk SFI Research Centre, Moorepark Fermoy Co. Cork IrelandVistaMilk SFI Research Centre, Moorepark Fermoy Co. Cork IrelandTeagasc, Crop Science Department Carlow IrelandTeagasc, Crop Science Department Carlow IrelandInsight SFI Research Centre for Data Analytics, School of Computer Science University College Dublin Dublin IrelandTeagasc, Crop Science Department Carlow IrelandAbstract Background Despite its importance to animal production potential, genetic gain for forage nutritive value has been limited in perennial ryegrass (Lolium perenne L.) breeding. The objective of this study was to phenotype a training population and develop prediction models to assess the potential of predicting organic matter digestibility (OMD) and neutral detergent fiber (NDF) with genotyping‐by‐sequencing data. Methods Near infra‐red reflectance spectroscopy calibrations for OMD and NDF were developed and used to phenotype a spaced plant training population of n = 1606, with matching genotype‐by‐sequencing data, for developing genomic selection models. F 2 families derived from the training population were also evaluated for OMD and NDF in sward plots and used to empirically validate prediction models. Results Sufficient genotypic variation exists in breeding populations to improve forage nutritive value, and spectral bands contributing to calibrations were identified. OMD and NDF can be predicted from genomic data with moderate accuracy (predictive ability in the range of 0.51–0.59 and 0.33–0.57, respectively) and models developed on individual plants outperform those developed from family means. Encouragingly, genomic prediction models developed on parental plants can predict OMD in subsequent generations grown as competitive swards. Conclusions These findings suggest that genetic improvement in forage nutritive value can be accelerated through the application of genomic prediction models.https://doi.org/10.1002/glr2.12104grass nutritive valueplant breedingprecision agriculture |
| spellingShingle | Agnieszka Konkolewska Michael Dineen Rachel Keirse Patrick Conaghan Dan Milbourne Susanne Barth Aonghus Lawlor Stephen Byrne Genomic prediction of forage nutritive value in perennial ryegrass Grassland Research grass nutritive value plant breeding precision agriculture |
| title | Genomic prediction of forage nutritive value in perennial ryegrass |
| title_full | Genomic prediction of forage nutritive value in perennial ryegrass |
| title_fullStr | Genomic prediction of forage nutritive value in perennial ryegrass |
| title_full_unstemmed | Genomic prediction of forage nutritive value in perennial ryegrass |
| title_short | Genomic prediction of forage nutritive value in perennial ryegrass |
| title_sort | genomic prediction of forage nutritive value in perennial ryegrass |
| topic | grass nutritive value plant breeding precision agriculture |
| url | https://doi.org/10.1002/glr2.12104 |
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