Deterministic and stochastic modelling of impacts from genomic selection and phenomics on genetic gain for perennial ryegrass dry matter yield
Abstract Increasing the efficiency of current forage breeding programs through adoption of new technologies, such as genomic selection (GS) and phenomics (Ph), is challenging without proof of concept demonstrating cost effective genetic gain (∆G). This paper uses decision support software DeltaGen (...
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Nature Portfolio
2021
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oai:doaj.org-article:7505f85100bd48b186e5f6372f6297f02021-12-02T17:12:21ZDeterministic and stochastic modelling of impacts from genomic selection and phenomics on genetic gain for perennial ryegrass dry matter yield10.1038/s41598-021-92537-w2045-2322https://doaj.org/article/7505f85100bd48b186e5f6372f6297f02021-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-92537-whttps://doaj.org/toc/2045-2322Abstract Increasing the efficiency of current forage breeding programs through adoption of new technologies, such as genomic selection (GS) and phenomics (Ph), is challenging without proof of concept demonstrating cost effective genetic gain (∆G). This paper uses decision support software DeltaGen (tactical tool) and QU-GENE (strategic tool), to model and assess relative efficiency of five breeding methods. The effect on ∆G and cost ($) of integrating GS and Ph into an among half-sib (HS) family phenotypic selection breeding strategy was investigated. Deterministic and stochastic modelling were conducted using mock data sets of 200 and 1000 perennial ryegrass HS families using year-by-season-by-location dry matter (DM) yield data and in silico generated data, respectively. Results demonstrated short (deterministic)- and long-term (stochastic) impacts of breeding strategy and integration of key technologies, GS and Ph, on ∆G. These technologies offer substantial improvements in the rate of ∆G, and in some cases improved cost-efficiency. Applying 1% within HS family GS, predicted a 6.35 and 8.10% ∆G per cycle for DM yield from the 200 HS and 1000 HS, respectively. The application of GS in both among and within HS selection provided a significant boost to total annual ∆G, even at low GS accuracy rA of 0.12. Despite some reduction in ∆G, using Ph to assess seasonal DM yield clearly demonstrated its impact by reducing cost per percentage ∆G relative to standard DM cuts. Open-source software tools, DeltaGen and QuLinePlus/QU-GENE, offer ways to model the impact of breeding methodology and technology integration under a range of breeding scenarios.M. Z. Z. JahuferSai Krishna ArojjuMarty J. FavilleKioumars GhamkharDongwen LuoVivi AriefWen-Hsi YangMingzhu SunIan H. DeLacyAndrew G. GriffithsColin EadyWill ClaytonAlan V. StewartRichard M. GeorgeValerio Hoyos-VillegasKaye E. BasfordBrent BarrettNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-18 (2021) |
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Medicine R Science Q |
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Medicine R Science Q M. Z. Z. Jahufer Sai Krishna Arojju Marty J. Faville Kioumars Ghamkhar Dongwen Luo Vivi Arief Wen-Hsi Yang Mingzhu Sun Ian H. DeLacy Andrew G. Griffiths Colin Eady Will Clayton Alan V. Stewart Richard M. George Valerio Hoyos-Villegas Kaye E. Basford Brent Barrett Deterministic and stochastic modelling of impacts from genomic selection and phenomics on genetic gain for perennial ryegrass dry matter yield |
| description |
Abstract Increasing the efficiency of current forage breeding programs through adoption of new technologies, such as genomic selection (GS) and phenomics (Ph), is challenging without proof of concept demonstrating cost effective genetic gain (∆G). This paper uses decision support software DeltaGen (tactical tool) and QU-GENE (strategic tool), to model and assess relative efficiency of five breeding methods. The effect on ∆G and cost ($) of integrating GS and Ph into an among half-sib (HS) family phenotypic selection breeding strategy was investigated. Deterministic and stochastic modelling were conducted using mock data sets of 200 and 1000 perennial ryegrass HS families using year-by-season-by-location dry matter (DM) yield data and in silico generated data, respectively. Results demonstrated short (deterministic)- and long-term (stochastic) impacts of breeding strategy and integration of key technologies, GS and Ph, on ∆G. These technologies offer substantial improvements in the rate of ∆G, and in some cases improved cost-efficiency. Applying 1% within HS family GS, predicted a 6.35 and 8.10% ∆G per cycle for DM yield from the 200 HS and 1000 HS, respectively. The application of GS in both among and within HS selection provided a significant boost to total annual ∆G, even at low GS accuracy rA of 0.12. Despite some reduction in ∆G, using Ph to assess seasonal DM yield clearly demonstrated its impact by reducing cost per percentage ∆G relative to standard DM cuts. Open-source software tools, DeltaGen and QuLinePlus/QU-GENE, offer ways to model the impact of breeding methodology and technology integration under a range of breeding scenarios. |
| format |
article |
| author |
M. Z. Z. Jahufer Sai Krishna Arojju Marty J. Faville Kioumars Ghamkhar Dongwen Luo Vivi Arief Wen-Hsi Yang Mingzhu Sun Ian H. DeLacy Andrew G. Griffiths Colin Eady Will Clayton Alan V. Stewart Richard M. George Valerio Hoyos-Villegas Kaye E. Basford Brent Barrett |
| author_facet |
M. Z. Z. Jahufer Sai Krishna Arojju Marty J. Faville Kioumars Ghamkhar Dongwen Luo Vivi Arief Wen-Hsi Yang Mingzhu Sun Ian H. DeLacy Andrew G. Griffiths Colin Eady Will Clayton Alan V. Stewart Richard M. George Valerio Hoyos-Villegas Kaye E. Basford Brent Barrett |
| author_sort |
M. Z. Z. Jahufer |
| title |
Deterministic and stochastic modelling of impacts from genomic selection and phenomics on genetic gain for perennial ryegrass dry matter yield |
| title_short |
Deterministic and stochastic modelling of impacts from genomic selection and phenomics on genetic gain for perennial ryegrass dry matter yield |
| title_full |
Deterministic and stochastic modelling of impacts from genomic selection and phenomics on genetic gain for perennial ryegrass dry matter yield |
| title_fullStr |
Deterministic and stochastic modelling of impacts from genomic selection and phenomics on genetic gain for perennial ryegrass dry matter yield |
| title_full_unstemmed |
Deterministic and stochastic modelling of impacts from genomic selection and phenomics on genetic gain for perennial ryegrass dry matter yield |
| title_sort |
deterministic and stochastic modelling of impacts from genomic selection and phenomics on genetic gain for perennial ryegrass dry matter yield |
| publisher |
Nature Portfolio |
| publishDate |
2021 |
| url |
https://doaj.org/article/7505f85100bd48b186e5f6372f6297f0 |
| work_keys_str_mv |
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1718381440280821760 |