Exploring the value of genomic predictions to simultaneously improve production potential and resilience of farmed animals
Sustainable livestock production requires that animals have a high production potential but are also highly resilient to environmental challenges. The first step to simultaneously improve these traits through genetic selection is to accurately predict their genetic merit. In this paper, we used simu...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-05-01
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| Series: | Frontiers in Genetics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fgene.2023.1127530/full |
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| author | Masoud Ghaderi Zefreh Andrea B. Doeschl-Wilson Valentina Riggio Oswald Matika Oswald Matika Ricardo Pong-Wong |
| author_facet | Masoud Ghaderi Zefreh Andrea B. Doeschl-Wilson Valentina Riggio Oswald Matika Oswald Matika Ricardo Pong-Wong |
| author_sort | Masoud Ghaderi Zefreh |
| collection | DOAJ |
| description | Sustainable livestock production requires that animals have a high production potential but are also highly resilient to environmental challenges. The first step to simultaneously improve these traits through genetic selection is to accurately predict their genetic merit. In this paper, we used simulations of sheep populations to assess the effect of genomic data, different genetic evaluation models and phenotyping strategies on prediction accuracies and bias for production potential and resilience. In addition, we also assessed the effect of different selection strategies on the improvement of these traits. Results show that estimation of both traits greatly benefits from taking repeated measurements and from using genomic information. However, the prediction accuracy for production potential is compromised, and resilience estimates tends to be upwards biased, when families are clustered in groups even when genomic information is used. The prediction accuracy was also found to be lower for both traits, resilience and production potential, when the environment challenge levels are unknown. Nevertheless, we observe that genetic gain in both traits can be achieved even in the case of unknown environmental challenge, when families are distributed across a large range of environments. Simultaneous genetic improvement in both traits however greatly benefits from the use of genomic evaluation, reaction norm models and phenotyping in a wide range of environments. Using models without the reaction norm in scenarios where there is a trade-off between resilience and production potential, and phenotypes are collected from a narrow range of environments may result in a loss for one trait. The study demonstrates that genomic selection coupled with reaction-norm models offers great opportunities to simultaneously improve productivity and resilience of farmed animals even in the case of a trade-off. |
| first_indexed | 2024-04-09T13:13:09Z |
| format | Article |
| id | doaj.art-264629b9c40f48e09465f8304dfc0576 |
| institution | Directory Open Access Journal |
| issn | 1664-8021 |
| language | English |
| last_indexed | 2024-04-09T13:13:09Z |
| publishDate | 2023-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Genetics |
| spelling | doaj.art-264629b9c40f48e09465f8304dfc05762023-05-12T05:56:41ZengFrontiers Media S.A.Frontiers in Genetics1664-80212023-05-011410.3389/fgene.2023.11275301127530Exploring the value of genomic predictions to simultaneously improve production potential and resilience of farmed animalsMasoud Ghaderi Zefreh0Andrea B. Doeschl-Wilson1Valentina Riggio2Oswald Matika3Oswald Matika4Ricardo Pong-Wong5The Roslin Institute and R(D)SVS, University of Edinburgh, Edinburgh, United KingdomThe Roslin Institute and R(D)SVS, University of Edinburgh, Edinburgh, United KingdomThe Roslin Institute and R(D)SVS, University of Edinburgh, Edinburgh, United KingdomThe Roslin Institute and R(D)SVS, University of Edinburgh, Edinburgh, United KingdomCentre for Tropical Livestock Genetics and Health (CTLGH), The Roslin Institute and R(D)SVS, University of Edinburgh, Edinburgh, United KingdomThe Roslin Institute and R(D)SVS, University of Edinburgh, Edinburgh, United KingdomSustainable livestock production requires that animals have a high production potential but are also highly resilient to environmental challenges. The first step to simultaneously improve these traits through genetic selection is to accurately predict their genetic merit. In this paper, we used simulations of sheep populations to assess the effect of genomic data, different genetic evaluation models and phenotyping strategies on prediction accuracies and bias for production potential and resilience. In addition, we also assessed the effect of different selection strategies on the improvement of these traits. Results show that estimation of both traits greatly benefits from taking repeated measurements and from using genomic information. However, the prediction accuracy for production potential is compromised, and resilience estimates tends to be upwards biased, when families are clustered in groups even when genomic information is used. The prediction accuracy was also found to be lower for both traits, resilience and production potential, when the environment challenge levels are unknown. Nevertheless, we observe that genetic gain in both traits can be achieved even in the case of unknown environmental challenge, when families are distributed across a large range of environments. Simultaneous genetic improvement in both traits however greatly benefits from the use of genomic evaluation, reaction norm models and phenotyping in a wide range of environments. Using models without the reaction norm in scenarios where there is a trade-off between resilience and production potential, and phenotypes are collected from a narrow range of environments may result in a loss for one trait. The study demonstrates that genomic selection coupled with reaction-norm models offers great opportunities to simultaneously improve productivity and resilience of farmed animals even in the case of a trade-off.https://www.frontiersin.org/articles/10.3389/fgene.2023.1127530/fullresiliencerobustnessreaction normgenomic predictiongenomic selectionGxE |
| spellingShingle | Masoud Ghaderi Zefreh Andrea B. Doeschl-Wilson Valentina Riggio Oswald Matika Oswald Matika Ricardo Pong-Wong Exploring the value of genomic predictions to simultaneously improve production potential and resilience of farmed animals Frontiers in Genetics resilience robustness reaction norm genomic prediction genomic selection GxE |
| title | Exploring the value of genomic predictions to simultaneously improve production potential and resilience of farmed animals |
| title_full | Exploring the value of genomic predictions to simultaneously improve production potential and resilience of farmed animals |
| title_fullStr | Exploring the value of genomic predictions to simultaneously improve production potential and resilience of farmed animals |
| title_full_unstemmed | Exploring the value of genomic predictions to simultaneously improve production potential and resilience of farmed animals |
| title_short | Exploring the value of genomic predictions to simultaneously improve production potential and resilience of farmed animals |
| title_sort | exploring the value of genomic predictions to simultaneously improve production potential and resilience of farmed animals |
| topic | resilience robustness reaction norm genomic prediction genomic selection GxE |
| url | https://www.frontiersin.org/articles/10.3389/fgene.2023.1127530/full |
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