From QTLs to Adaptation Landscapes: Using Genotype-To-Phenotype Models to Characterize G×E Over Time
Genotype by environment interaction (G×E) for the target trait, e.g. yield, is an emerging property of agricultural systems and results from the interplay between a hierarchy of secondary traits involving the capture and allocation of environmental resources during the growing season. This hierarchy...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2019-12-01
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| Series: | Frontiers in Plant Science |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/article/10.3389/fpls.2019.01540/full |
| _version_ | 1811273426789203968 |
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| author | Daniela Bustos-Korts Marcos Malosetti Karine Chenu Scott Chapman Scott Chapman Martin P. Boer Bangyou Zheng Fred A. van Eeuwijk |
| author_facet | Daniela Bustos-Korts Marcos Malosetti Karine Chenu Scott Chapman Scott Chapman Martin P. Boer Bangyou Zheng Fred A. van Eeuwijk |
| author_sort | Daniela Bustos-Korts |
| collection | DOAJ |
| description | Genotype by environment interaction (G×E) for the target trait, e.g. yield, is an emerging property of agricultural systems and results from the interplay between a hierarchy of secondary traits involving the capture and allocation of environmental resources during the growing season. This hierarchy of secondary traits ranges from basic traits that correspond to response mechanisms/sensitivities, to intermediate traits that integrate a larger number of processes over time and therefore show a larger amount of G×E. Traits underlying yield differ in their contribution to adaptation across environmental conditions and have different levels of G×E. Here, we provide a framework to study the performance of genotype to phenotype (G2P) modeling approaches. We generate and analyze response surfaces, or adaptation landscapes, for yield and yield related traits, emphasizing the organization of the traits in a hierarchy and their development and interactions over time. We use the crop growth model APSIM-wheat with genotype-dependent parameters as a tool to simulate non-linear trait responses over time with complex trait dependencies and apply it to wheat crops in Australia. For biological realism, APSIM parameters were given a genetic basis of 300 QTLs sampled from a gamma distribution whose shape and rate parameters were estimated from real wheat data. In the simulations, the hierarchical organization of the traits and their interactions over time cause G×E for yield even when underlying traits do not show G×E. Insight into how G×E arises during growth and development helps to improve the accuracy of phenotype predictions within and across environments and to optimize trial networks. We produced a tangible simulated adaptation landscape for yield that we first investigated for its biological credibility by statistical models for G×E that incorporate genotypic and environmental covariables. Subsequently, the simulated trait data were used to evaluate statistical genotype-to-phenotype models for multiple traits and environments and to characterize relationships between traits over time and across environments, as a way to identify traits that could be useful to select for specific adaptation. Designed appropriately, these types of simulated landscapes might also serve as a basis to train other, more deep learning methodologies in order to transfer such network models to real-world situations. |
| first_indexed | 2024-04-12T22:58:57Z |
| format | Article |
| id | doaj.art-4286ca24e1924360a19f0952433aedcb |
| institution | Directory Open Access Journal |
| issn | 1664-462X |
| language | English |
| last_indexed | 2024-04-12T22:58:57Z |
| publishDate | 2019-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj.art-4286ca24e1924360a19f0952433aedcb2022-12-22T03:13:06ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2019-12-011010.3389/fpls.2019.01540481582From QTLs to Adaptation Landscapes: Using Genotype-To-Phenotype Models to Characterize G×E Over TimeDaniela Bustos-Korts0Marcos Malosetti1Karine Chenu2Scott Chapman3Scott Chapman4Martin P. Boer5Bangyou Zheng6Fred A. van Eeuwijk7Biometris, Wageningen University and Research Centre, Wageningen, NetherlandsBiometris, Wageningen University and Research Centre, Wageningen, NetherlandsQueensland Alliance for Agriculture and Food Innovation, The University of Queensland, Toowoomba, QLD, AustraliaAgriculture and Food, CSIRO, Queensland Bioscience Precinct, St Lucia, QLD, AustraliaSchool of Agriculture and Food Sciences, The University of Queensland, Gatton, QLD, AustraliaBiometris, Wageningen University and Research Centre, Wageningen, NetherlandsAgriculture and Food, CSIRO, Queensland Bioscience Precinct, St Lucia, QLD, AustraliaBiometris, Wageningen University and Research Centre, Wageningen, NetherlandsGenotype by environment interaction (G×E) for the target trait, e.g. yield, is an emerging property of agricultural systems and results from the interplay between a hierarchy of secondary traits involving the capture and allocation of environmental resources during the growing season. This hierarchy of secondary traits ranges from basic traits that correspond to response mechanisms/sensitivities, to intermediate traits that integrate a larger number of processes over time and therefore show a larger amount of G×E. Traits underlying yield differ in their contribution to adaptation across environmental conditions and have different levels of G×E. Here, we provide a framework to study the performance of genotype to phenotype (G2P) modeling approaches. We generate and analyze response surfaces, or adaptation landscapes, for yield and yield related traits, emphasizing the organization of the traits in a hierarchy and their development and interactions over time. We use the crop growth model APSIM-wheat with genotype-dependent parameters as a tool to simulate non-linear trait responses over time with complex trait dependencies and apply it to wheat crops in Australia. For biological realism, APSIM parameters were given a genetic basis of 300 QTLs sampled from a gamma distribution whose shape and rate parameters were estimated from real wheat data. In the simulations, the hierarchical organization of the traits and their interactions over time cause G×E for yield even when underlying traits do not show G×E. Insight into how G×E arises during growth and development helps to improve the accuracy of phenotype predictions within and across environments and to optimize trial networks. We produced a tangible simulated adaptation landscape for yield that we first investigated for its biological credibility by statistical models for G×E that incorporate genotypic and environmental covariables. Subsequently, the simulated trait data were used to evaluate statistical genotype-to-phenotype models for multiple traits and environments and to characterize relationships between traits over time and across environments, as a way to identify traits that could be useful to select for specific adaptation. Designed appropriately, these types of simulated landscapes might also serve as a basis to train other, more deep learning methodologies in order to transfer such network models to real-world situations.https://www.frontiersin.org/article/10.3389/fpls.2019.01540/fullQTL (quantitative trait loci)crop growth modeladaptationG×E interactionwheatAPSIM model |
| spellingShingle | Daniela Bustos-Korts Marcos Malosetti Karine Chenu Scott Chapman Scott Chapman Martin P. Boer Bangyou Zheng Fred A. van Eeuwijk From QTLs to Adaptation Landscapes: Using Genotype-To-Phenotype Models to Characterize G×E Over Time Frontiers in Plant Science QTL (quantitative trait loci) crop growth model adaptation G×E interaction wheat APSIM model |
| title | From QTLs to Adaptation Landscapes: Using Genotype-To-Phenotype Models to Characterize G×E Over Time |
| title_full | From QTLs to Adaptation Landscapes: Using Genotype-To-Phenotype Models to Characterize G×E Over Time |
| title_fullStr | From QTLs to Adaptation Landscapes: Using Genotype-To-Phenotype Models to Characterize G×E Over Time |
| title_full_unstemmed | From QTLs to Adaptation Landscapes: Using Genotype-To-Phenotype Models to Characterize G×E Over Time |
| title_short | From QTLs to Adaptation Landscapes: Using Genotype-To-Phenotype Models to Characterize G×E Over Time |
| title_sort | from qtls to adaptation landscapes using genotype to phenotype models to characterize g e over time |
| topic | QTL (quantitative trait loci) crop growth model adaptation G×E interaction wheat APSIM model |
| url | https://www.frontiersin.org/article/10.3389/fpls.2019.01540/full |
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