Progress 01/15/21 to 01/14/25
Outputs
Target Audience:The key target audience is breeders, geneticists, agronomists, plant biologists, and students who conduct experiments and data analyses indifferent crops, and scientists in the private companies. Other target audience include biologists, statisticians, agricultural scientists, and farmers who have experiences with field, greenhouse, growth chamber experiments and agriculture production. Although phenotypic plasticity can be a technical term and its underlying genetics, environmental, management, and developmental factors can be complete, people observe different growth and performance of plants, crops, and trees at different places and in different seasons can all benefit from findings of the current projects, talks given by the project personnel, and news releases associated with the publications. Changes/Problems:
Nothing Reported
What opportunities for training and professional development has the project provided?The project provided the opportunity to train three postdoctoral research associates and two graduate students. Two other graduate students were also trained in the comprehensive analysis of phenotypic plasticity. In addition, many workshop attendees and seminar attendees benefited from the presentations reporting the findings from this project. How have the results been disseminated to communities of interest?Dissemination of the results was through journal publication, scientific talks, meeting posters, news releases, general discussion, and specific discussion with breeders and quantitative geneticists. The impact of the dissemination of our scientific findings and the CERIS-JGRA tool was evident in research publications and conference presentations made by other scientists. Publications supported by this project have been cited. What do you plan to do during the next reporting period to accomplish the goals?
Nothing Reported
Impacts
What was accomplished under these goals?
Lack of a systematic understanding of the genes and environmental factors and their interactions affecting crop growth and development hinders our efforts toward precision agriculture and sustainable agriculture. In this period, we conducted extensive research across multiple traits in multiple crops to establish and refine a systematic procedure to enable researchers to identify the main genetic and environmental factors affecting the growth and development of different crops. Our findings increased knowledge on how to extract critical environmental drivers from multi-environmental trials, common garden experiments, and crop performance testing trials and how to systematically uncover the dynamic genetic effects along the primary environmental gradient. By focusing on the primary environmental gradient, we were able to quantitatively connect all tested environments, rather than treating them as isolated, discrete observation cases. Our findings provided guidance on how to deploy the most suitable crop genetics to different production environments and management systems, and guidance on how to strategically optimize the design of experiments so that major environmental patterns can be captured and revealed. In addition, our introducing the developmental dimension into the Gene-Organism-Development-Environment-Phenotype relationship provided a framework to leverage multistage measurement data, which becomes more accessible with the high throughput phenotyping. With our extensive findings across multiple traits in multiple crops (maize, sorghum, wheat, rice, and barley), our research findings are widely read by a large number of scientists working in crops. By incorporating methods from different fields, our findings and the method developed also help researchers in ecology and evolution. In addition, through invited talks and workshops, we promoted phenotypic plasticity as a synthesis framework for integrating the detailed developmental, molecular, and genomic research (gene regulatory network, co-expression network, chromatin structure, single-cell RNA-seq, protein interaction, connectivity map, genome editing, and synthetic biology) with the large-scale, natural environments, and broad topics and challenges (stress physiology, genebank germplasm, adaptation, precision agriculture, sustainable agriculture, food security, climate change, and space biology). One important impact of our research and findings is its bridging function. Objective 1. We developed the open-source CERIS-JGRA package and released it on the GitHub. We developed the stand-alone CERIS (Critical Environmental Regressor through Informed Search) procedure to identify the environmental index to quantitative connect the tested environments. We developed the combined CERIS-JGRA (Joint Genomic Regression Analysis) procedure for gene discovery underlying phenotypic plasticity and performance prediction across environments. We organized a workshop: Phenotypic Plasticity and Genotype by Environment Interaction: Dissection and Prediction, National Association of Plant Breeders Education Committee virtual, Feb 22, 2021. We organized a workshop to train students and scientists on phenotypic plasticity at the PAG 31 conference (Phenotypic Plasticity and Genotype by Environment Interaction: Dissection and Prediction Tuesday). This workshop is scheduled to repeat at the PAG 32 conference. Objective 2. Our extensive study showed that as long as there is some level of average performance differences among different environments (location by year combinations), it is very likely that there is a major environmental index (environmental variable at a critical or representative window of growth) can be extracted from the data. With this environmental index, genetic dissection of complex traits can be explicitly conducted with this environmental dimension, and performance prediction can be generated to be in-season, on target, and genome-enabled. Our efforts and findings to Identify genomic and environmental determinants underlying phenotypic plasticity of agronomically important traits from diverse populations include 1) maize diversity panel in U.S. (flowering time and plant height); 2) wheat diversity panel internationally (flowering time, plant height, and grain yield); 3) oat diversity panel (flowering time, plant height, and grain yield); 4) wheat variety performance trial in Idaho (heading date, plant height, grain yield, test weight, protein content, and falling number); 5) sorghum QTL mapping population (multi-stage measurement and modeling of plant height); 6) sorghum QTL mapping population in U.S. (flowering time and simulation); 7) rice QTL mapping population in Asia (flowering time and simulation); 8) maize nested association mapping population in U.S. (days to anthesis, days to silking, and 17 other traits); 9) wheat variety performance trial in Washington (falling number); and 10) sorghum diversity panel in U.S. (flowering time and plant height). For environmental index, we showed that it can be primarily temperature, day length, precipitation, or combinations of these three general categories. This findings agrees with the expectation that across a geographical range or across different years, patterns in the fluctuations in these environmental variables are more likely to be repeatable than other variables. Objective 3. We demonstrated the advantage of incorporating crop growth model into phenotypic plasticity research. We investigated the sorghum plant height growth curves across a QTL mapping population. Mapping with growth-curve parameters obtained from modeling the multi-stage height data across the season at individual environments and mapping with reaction-norm parameters obtained from modeling the end-of-season height data across multiple environments discovered a largely overlapping set of QTL. With the same sorghum QTL mapping population, we examined the physiological relevance of the identified environmental index by investigating the developmental trajectory of the population with multistage height measurements in four additional environments, and we conducted crop growth modeling to obtain the simulated growth curve for environments where only final plant height measurements were available. In addition, by leveraging weather information for the last few decades at multiple testing locations, we conducted crop growth simulations for the sorghum QTL mapping population and a rice QTL mapping population to investigate the environmental variability of testing sites. Objective 4. We revealed the importance of environmental context of phenotypic plasticity studies. With flowering time observed from a large geographical region for the sorghum and rice QTL mapping populations, we examined the consistency of parameter estimation for reaction norms of genotypes across different subsets of environments and searched for potential strategies to inform the study design. We found that both sample size and environmental mean range of the subset affected the consistency. The subset with either a large range of environmental means or a large sample size resulted in genetic parameters consistent with the overall pattern. With 1428 and 1674 simulated settings, our analyses suggested that the distribution of environmental index values of a site should be considered in designing experiments. Overall, we showed that environmental context was critical, and considerations should be given to better cover the intended range of the environmental variable. By comparative studies between flower time and plant height, we showed that plants can exhibit different levels of sensitivity to different environmental variables across the same set of environments. Our comprehensive analysis of 19 traits measured for the maize diversity panel demonstrated different levels of plastic response in different traits.
Publications
- Type:
Peer Reviewed Journal Articles
Status:
Published
Year Published:
2024
Citation:
Negus, K.L., X. Li, S.M. Welch, and J. Yu*. 2024. The role of artificial intelligence in crop improvement. Advances in Agronomy 184:1-66.
- Type:
Peer Reviewed Journal Articles
Status:
Published
Year Published:
2024
Citation:
Mu, Q., J. Wei, H.K. Longest, L. Hua, S.N. Char, J.T. Hinrichsen, L.E. Tibbs-Cortes, G.R. Schoenbaum, B. Yang, Bing; X. Li*, and J. Yu*. 2024. A MYB transcription factor underlying plant height in sorghum qHT7.1 and maize Brachytic 1 loci. Plant Journal.
- Type:
Peer Reviewed Journal Articles
Status:
Published
Year Published:
2024
Citation:
Zhang, B., A.L. Hauvermale, L. Amber, Z. Zhang, A. Thompson, C. Neely, A. Esser, M.O. Pumphrey, K. Garland-Campbell, J. Yu, C. Steber, and X. Li*. 2024. Harnessing enviromics to predict climate-impacted high-profile traits to assist informed decisions in agriculture. Food and Energy Security 13:e544.
- Type:
Peer Reviewed Journal Articles
Status:
Published
Year Published:
2024
Citation:
Wei, J., T. Guo, Q. Mu, M. Alladassi, R. Mural, R.E. Boyles, L. Hoffman Jr., C.M. Hayes, B. Sigmon, A.M. Thompson, M.G. Salas-Fernandez, W.L. Rooney, S. Kresovich, J.C. Schnable, X. Li, and J. Yu*. 2024. Genetic and environmental patterns underlying phenotypic plasticity in flowering time and plant height in sorghum. Plant, Cell & Environment.
- Type:
Peer Reviewed Journal Articles
Status:
Published
Year Published:
2024
Citation:
Tibbs-Cortes, L.E., T. Guo, C.M. Andorf, X. Li*, and J. Yu*. 2024. Comprehensive identification of genomic and environmental determinants of phenotypic plasticity in maize. Genome Research 34:1253-1263.
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Progress 01/15/23 to 01/14/24
Outputs
Target Audience:The target audience is breeders, geneticists, agronomists, plant biologists, and students who conductexperiments and data analyses in different crops, and scientists in the private companies. Changes/Problems:
Nothing Reported
What opportunities for training and professional development has the project provided?The project provided the opportunity to train three postdoctoral research associates and two graduate students. Two other graduate students were also trained in the comprehensive analysis of phenotypic plasticity. How have the results been disseminated to communities of interest?Dissemination of the results was through journal publication, scientific talks, meeting posters, news releases, general discussion, and specific discussion with breeders and quantitative geneticists. The impact of the dissemination of our scientific findings and the CERIS-JGRA tool was evident in research publications and conference presentations made by other scientists. What do you plan to do during the next reporting period to accomplish the goals?We plan to continue the overall research on phenotypic plasticity, study phenotypic plasticity across a set of teosinte-maize introgression lines, develop new web portal to facilitate the adoption of CERIS-JGRA, and promote phenotypic plasticity as a synthesis framework for detailed developmental and genomic research (gene regulatory network, co-expression network, chromatin structure, single-cell RNA-seq, protein interaction, connectivity map, genome editing, and synthetic biology) with broad topics (stress physiology, genebank germplasm, adaptation, precision agriculture, sustainable agriculture, food security, climate change, and space biology). We will organize a workshop to train students and scientists on phenotypic plasticity at the PAG 31 conference (Phenotypic Plasticity and Genotype by Environment Interaction: Dissection and Prediction Tuesday, January 16, 2024: 6:20 PM - 8:30 PM, Town & Country, Palm 3-4).
Impacts
What was accomplished under these goals?
Lack of a systematic understanding of the genes and environmental factors and their interactions affecting crop growth and development hinders our efforts toward precision agriculture and sustainable agriculture. In this period, we conducted research to establish and refine a systematic procedure to enable researchers to identify the main genetic and environmental factors affecting the growth and development of different crops. Our findings increased knowledge on how to extract critical information from multi-environmental trials, common garden experiments, and crop performance testing trials and how to systematically uncover the dynamic genetic effects along the primary environmental gradient. Our findings provided guidance on how to deploy the most suitable crop genetics to different production environments and management systems, and guidance on how to strategically optimize the design of experiments so that major environmental patterns can be captured and revealed. In addition, our introducing the developmental dimension into the Gene-Organism-Development-Environment-Phenotype relationship provided a framework to leverage multistage measurement data, which becomes more accessible with the high throughput phenotyping. Objective 1. We worked on expanding the capacity of the stand-alone CERIS (Critical Environmental Regressor through Informed Search) procedure to include additional environmental factors in the search process to identify the environmental index. We worked on improving the combined CERIS-JGRA (Joint Genomic Regression Analysis) procedure, such as considering nonlinear reaction norms, generating 3-D reaction norm plots to enhance our understanding of the connection between observed performance and predicted/modeled performance, and identifying new angles to analyze the data. Objective 2. With the sorghum association mapping panel evaluated across 14 natural field environments, we found that the environmental index can be obtained with the growing degree days from 32 to 59 days after planting (GDD32-59) for flowering time, and diurnal temperature range from 25 to 31 days after planting (DTR25-31) for plant height. Genome-wide association studies (GWAS) detected different sets of loci for reaction norm parameters (intercept and slope), including 10 new genomic regions in addition to known maturity (Ma1) and dwarfing genes (Dw1, Dw2, Dw3, Dw4, and qHT7.1). Cross validations under multiple scenarios showed promising results in predicting diverse germplasm in dynamic environments. Additional experiments conducted at four new environments, including one from a site outside of the geographical region of the initial environments, further validated the predictions of flowering time and plant height. Our findings indicate that identifying the environment index enriches our understanding of gene-environmental interplay underlying phenotypic plasticity, and that genomic prediction with the environmental dimension facilitates prediction-guided breeding for future environments. Objective 3. We worked on identifying ways to connect genotype-specific parameters in crop growth model with genetic mapping, genome-wide association studies, and genomic prediction. In addition, by leveraging weather information for the last few decades at multiple testing locations, we conducted crop growth simulations to investigate the environmental variability of testing sites. This helped us to come up with new ways to leverage crop growth models such as analyzing simulated data from crop growth models to see whether the environmental index can be revealed. Objective 4. We worked on revealing the importance of environmental context of phenotypic plasticity studies. With flowering time observed from a large geographical region for sorghum and rice genetic populations, we examined the consistency of parameter estimation for reaction norms of genotypes across different subsets of environments and searched for potential strategies to inform the study design. We found that both sample size and environmental mean range of the subset affected the consistency. The subset with either a large range of environmental mean or a large sample size resulted in genetic parameters consistent with the overall pattern. With 1428 and 1674 simulated settings, our analyses suggested that the distribution of environmental index values of a site should be considered in designing experiments. Overall, we showed that environmental context was critical, and considerations should be given to better cover the intended range of the environmental variable. Our findings have implications for the genetic architecture of complex traits, plant-environment interaction, and climate adaptation.
Publications
- Type:
Journal Articles
Status:
Published
Year Published:
2023
Citation:
Guo, T, J. Wei, X. Li, and J. Yu. 2023. Environmental context of phenotypic plasticity in flowering time in sorghum and rice. Journal Experimental Botany 75, https://doi.org/10.1093/jxb/erad398
- Type:
Journal Articles
Status:
Accepted
Year Published:
2023
Citation:
Dzievit, M.J., X. Li, and J. Yu. 2023. Genetic mapping of dynamic control of leaf angle across multiple canopy levels in maize. The Plant Genome (accepted)
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Progress 01/15/22 to 01/14/23
Outputs
Target Audience:The target audience is breeders, geneticists, agronomists, plant biologists, and students who conduct field experiments on different crops. Changes/Problems:
Nothing Reported
What opportunities for training and professional development has the project provided?The project provided the opportunity to train one postdoctoral research associate and one graduate student. Four othergraduate students were also trained in the comprehensive analysis ofphenotypic plasticity. How have the results been disseminated to communities of interest?Dissemination of the results was through journal publication, scientific talks, news releases, and general discussion. What do you plan to do during the next reporting period to accomplish the goals?We planto continue the overall research on phenotypic plasticity, examine ways to optimize the design of sampling the environmental space to study phenotypic plasticity, and seekopportunities to organize workshops to trainstudents on phenotypic plasticity,
Impacts
What was accomplished under these goals?
Lack of a systematic understanding of the genes and environmental factors and their interactions affecting crop growth and development hinders our efforts toward precision agriculture and sustainable agriculture. In this period, we conducted research to establish and refine a systematic procedure to enable researchers to identify the main genetic and environmental factors affecting the growth and development of different crops. Our findings increased knowledge on how to extract critical information from multi-environmental trials and crop performance testing trials and how to systematically uncover the dynamic genetic effects alongthe primary environmental gradient. Our findingsprovidedguidance on how to deploy the most suitable crop genetics to different production environments and management systems. In addition, our introducing the developmental dimension into the Genoype-Enviroment-Development-Phenotyperelationship provided a framework to leveragemultistage measurement data, which becomes more accessible with the high throughput phenotyping. Objective 1. We have been working on expanding the capacity of the stand-aloneCERIS (Critical Environmental Regressor through Informed Search) procedureso that it can be used to probe the connection between phenotypic data and environmental data. Additional environmental factors were considered in the search process to identify the environmental index. We have also been working on improving the combined CERIS-JGRA (Joint Genomic Regression Analysis)procedure. Objective 2. With thesorghum genetic mapping population, we found that the diurnal temperature range (DTR) during the rapid growth period of sorghum development was an effective environmental index for plant height. Three genetic loci (Dw1,Dw3,andqHT7.1) were consistently detected for individual environments, reaction-norm parameters across environments, and growth-curve parameters throughout the season. Their genetic effects changed dynamically along the environmental gradient and the developmental stage. A conceptual model with three-dimensional reaction norms was proposed to showcase the interconnecting components: genotype, environment, and development. Objective 3. With the sorghum genetic mapping population, weexamined the physiological relevance of the identified environmental index by investigating the developmental trajectory of the population with multistage height measurements in four additional environments, and we conductedcrop growth modeling to obtain the simulated growth curve for environments where only final plant height measurements were available. Objective 4. We analyzed a wheat performance trial spanning 16 years. This data set includes15 varietiestested in five locationseach year. Grain yield, twograin-quality traits (test weight and protein content), and twoagronomic traits (heading date and plant height) were measured consecutively from 2005 to 2020, while fallingnumbers, a quality trait measuring starch degradation byendogenous a-amylases, were evaluated from 2013 to 2020. CERIS identified environmental indices for different traits. Using climatic variables to build an environmental relationship, we were able to build a performance prediction model with data from training data from 2005 to 2015 to predict the performance from 2016 to 2020.
Publications
- Type:
Journal Articles
Status:
Published
Year Published:
2022
Citation:
Mu Q., T. Guo, X. Li, and J. Yu. 2022. Phenotypic plasticity in plant height shaped by interaction between genetic loci and diurnal temperature range. New Phytol, 233(4): 1768-1779
- Type:
Journal Articles
Status:
Published
Year Published:
2022
Citation:
Li X., T. Guo , G. Bai, Z. Zhang, D. See, J. Marshall, K.A. Garland-Campbell, and J. Yu. 2022. Genetics-inspired data-driven approaches explain and predict crop performance fluctuations attributed to changing climatic conditions. Mol Plant, 15(2): 203-206.
- Type:
Journal Articles
Status:
Published
Year Published:
2022
Citation:
Li X., and J. Yu. 2022. Unraveling the sorghum domestication. Mol Plant, 15(5): 791-792.
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Progress 01/15/21 to 01/14/22
Outputs
Target Audience:Breeders, geneticists, agronmists,plant biologists, and students who conduct field experiments in different crops. Changes/Problems:
Nothing Reported
What opportunities for training and professional development has the project provided?The project provided the opportunity to trainone postdoctoral research associate. Four graduate students were also trained on phenothpic plasticity. How have the results been disseminated to communities of interest?Dissemination of the results was through journal publication, scientific talk, workshop, news release, and general discussion. What do you plan to do during the next reporting period to accomplish the goals?We will host additional training workshops andcontinue research.
Impacts
What was accomplished under these goals?
An enriched understanding of genes and environmental factors and their interactions is critical to precision agriculture and sustainable production. It provides guidance on how to deploy the most suitable crop genetics to different production environments and management systems. Leveraging existing data of diverse germplasmfrom three majorcrops and previous findings fromgenetic mapping populations, we conducted further investigation to identify the major genetic and environmental factors underlying the varied crop performance and connect the end-of-season performance with growth trajectories of different genotypes. This project's research findings and associated analysis pipelines are expected to greatly facilitate gene identification, genetic effect quantification, testing site selection, and genomic prediction for cultivar development. Objective 1. We developed the open-sourceCERIS-JGRA package and released it on the GitHub. We organized a workshop: Phenotypic Plasticity and Genotype by Environment Interaction: Dissection and Prediction, National Association of Plant Breeders Education Committee virtual, Feb 22, 2021. Objective 2. We developed the integrated framework to reinstate the environmental dimension in genome-wide association studies (GWAS) and genomic selection (GS). GWAS and GS are two common research topics across crops. Our integrated frameworkfacilitates biologically informed dissection of complex traits, enhanced performance prediction in breeding for future climates, and coordinated efforts to enrich our understanding of mechanisms underlying phenotypic variation. This has been demonstrated with maize, wheat, and oat diversity panels. We continue to work on a diversity panel in sorghum. Objective 3. We investigated the sorghum plant height growth curves across a genetic mapping population. Mapping with growth-curve parameters obtained from modeling the multi-stage height data across the season atindividual environments and mapping with reaction-norm parameters obtained from modeling the end-of-season height data acrossmultiple environments discovered a largelyoverlapping set of quantitative trait loci (QTL). Objective 4. We started the investigation of environmental factors underlying the performance trial and testing sites design
Publications
- Type:
Journal Articles
Status:
Published
Year Published:
2021
Citation:
Li, X., T. Guo, J. Wang, W.A. Bekele, S. Sukumaran, A.E. Vanous, J.P. McNellie, L. Tibbs Cortes, M.S. Lopes, K. Lamkey, M.E. Westgate, J. McKay, S.V. Archontoulis, M.P. Reynolds, N.A. Tinker, P.S. Schnable, and J. Yu. 2021. An integrated framework reinstating the environmental dimension for GWAS and genomic selection in crops. Molecular Plant 14:874-887.
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