Genomic prediction of seasonal forage yield in perennial ryegrass
Abstract Background Genomic selection has the potential to accelerate genetic gain in perennial ryegrass breeding, provided complex traits such as forage yield can be predicted with sufficient accuracy. Methods In this study, we compared modelling approaches and feature selection strategies to evalu...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2023-09-01
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| Series: | Grassland Research |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/glr2.12058 |
| _version_ | 1797645280093405184 |
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| author | Agnieszka Konkolewska Steffie Phang Patrick Conaghan Dan Milbourne Aonghus Lawlor Stephen Byrne |
| author_facet | Agnieszka Konkolewska Steffie Phang Patrick Conaghan Dan Milbourne Aonghus Lawlor Stephen Byrne |
| author_sort | Agnieszka Konkolewska |
| collection | DOAJ |
| description | Abstract Background Genomic selection has the potential to accelerate genetic gain in perennial ryegrass breeding, provided complex traits such as forage yield can be predicted with sufficient accuracy. Methods In this study, we compared modelling approaches and feature selection strategies to evaluate the accuracy of genomic prediction models for seasonal forage yield production. Results Overall, model selection had limited impact on predictive ability when using the full data set. For a baseline genomic best linear unbiased prediction model, the highest mean predictive accuracy was obtained for spring grazing (0.78), summer grazing (0.62) and second cut silage (0.56). In terms of feature selection strategies, using uncorrelated single‐nucleotide polymorphisms (SNPs) had no impact on predictive ability, allowing for a potential decrease of the data set dimensions. With a genome‐wide association study, we found a significant SNP marker for spring grazing, located in the genic region annotated as coding for an enzyme responsible for fucosylation of xyloglucans—major components of the plant cell wall. We also presented an approach to increase interpretability of genomic prediction models with the use of Gene Ontology enrichment analysis. Conclusions Approaches for feature selection will be relevant in development of low‐cost genotyping platforms in support of routine and cost‐effective implementation of genomic selection. |
| first_indexed | 2024-03-11T14:44:05Z |
| format | Article |
| id | doaj.art-2d7425b8cc04446e96b012190fe505b0 |
| institution | Directory Open Access Journal |
| issn | 2097-051X 2770-1743 |
| language | English |
| last_indexed | 2024-03-11T14:44:05Z |
| publishDate | 2023-09-01 |
| publisher | Wiley |
| record_format | Article |
| series | Grassland Research |
| spelling | doaj.art-2d7425b8cc04446e96b012190fe505b02023-10-30T12:42:22ZengWileyGrassland Research2097-051X2770-17432023-09-012316718110.1002/glr2.12058Genomic prediction of seasonal forage yield in perennial ryegrassAgnieszka Konkolewska0Steffie Phang1Patrick Conaghan2Dan Milbourne3Aonghus Lawlor4Stephen Byrne5Crop Science Department Teagasc Carlow Co. Carlow IrelandInsight SFI Research Centre for Data Analytics University College Dublin Dublin IrelandGrassland Science Research Department, Animal and Grassland Research and Innovation Centre Teagasc Carlow IrelandCrop Science Department Teagasc Carlow Co. Carlow IrelandInsight SFI Research Centre for Data Analytics University College Dublin Dublin IrelandCrop Science Department Teagasc Carlow Co. Carlow IrelandAbstract Background Genomic selection has the potential to accelerate genetic gain in perennial ryegrass breeding, provided complex traits such as forage yield can be predicted with sufficient accuracy. Methods In this study, we compared modelling approaches and feature selection strategies to evaluate the accuracy of genomic prediction models for seasonal forage yield production. Results Overall, model selection had limited impact on predictive ability when using the full data set. For a baseline genomic best linear unbiased prediction model, the highest mean predictive accuracy was obtained for spring grazing (0.78), summer grazing (0.62) and second cut silage (0.56). In terms of feature selection strategies, using uncorrelated single‐nucleotide polymorphisms (SNPs) had no impact on predictive ability, allowing for a potential decrease of the data set dimensions. With a genome‐wide association study, we found a significant SNP marker for spring grazing, located in the genic region annotated as coding for an enzyme responsible for fucosylation of xyloglucans—major components of the plant cell wall. We also presented an approach to increase interpretability of genomic prediction models with the use of Gene Ontology enrichment analysis. Conclusions Approaches for feature selection will be relevant in development of low‐cost genotyping platforms in support of routine and cost‐effective implementation of genomic selection.https://doi.org/10.1002/glr2.12058forage yieldgenomic selectionperennial ryegrass breeding |
| spellingShingle | Agnieszka Konkolewska Steffie Phang Patrick Conaghan Dan Milbourne Aonghus Lawlor Stephen Byrne Genomic prediction of seasonal forage yield in perennial ryegrass Grassland Research forage yield genomic selection perennial ryegrass breeding |
| title | Genomic prediction of seasonal forage yield in perennial ryegrass |
| title_full | Genomic prediction of seasonal forage yield in perennial ryegrass |
| title_fullStr | Genomic prediction of seasonal forage yield in perennial ryegrass |
| title_full_unstemmed | Genomic prediction of seasonal forage yield in perennial ryegrass |
| title_short | Genomic prediction of seasonal forage yield in perennial ryegrass |
| title_sort | genomic prediction of seasonal forage yield in perennial ryegrass |
| topic | forage yield genomic selection perennial ryegrass breeding |
| url | https://doi.org/10.1002/glr2.12058 |
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