Source: IOWA STATE UNIVERSITY submitted to
UNDERSTANDING GENOMIC AND ENVIRONMENTAL FACTORS UNDERLYING PHENOTYPIC PLASTICITY FOR CROP IMPROVEMENT
Sponsoring Institution
National Institute of Food and Agriculture
Project Status
NEW
Funding Source
Reporting Frequency
Annual
Accession No.
1025337
Grant No.
2021-67013-33833
Project No.
IOW05642
Proposal No.
2020-03604
Multistate No.
(N/A)
Program Code
A1141
Project Start Date
Jan 15, 2021
Project End Date
Jan 14, 2024
Grant Year
2021
Project Director
Yu, J.
Recipient Organization
IOWA STATE UNIVERSITY
2229 Lincoln Way
AMES,IA 50011
Performing Department
Agronomy
Non Technical Summary
One of the major challenges in plant breeding is the differential response of genotypes from one environment to another, or phenotypic plasticity. Although many methods have been developed, these methods lack the forecasting capacity, and our understanding of genomic and environmental determinants is still limited. We have recently established an analytical framework to answer long-standing questions in phenotypic plasticity and genotype by environment interaction. The essence of this framework is to combine knowledge from physiology, genetics, and statistics to identify the determinants rather than model fitting. It is about identifying hidden patterns and biological insights and enhancing forecasting capacities and gene effect continuum profiling.Our long-term goal is to significantly enrich our understanding of genes and environmental factors underlying phenotypic plasticity and translate this understanding to application by enabling the in-season, on-target crop performance prediction and informing plant breeders about exploring genetic and environmental space. We hypothesize that the long-desired independent environmental indices can be identified to exploit the patterns underlying crop performance variation. Toward this goal, we designed four components of this project. 1) Develop an open-source package and host workshops to facilitate the community to dissect flowering-time plasticity. These workshops will not only provide lectures to explain the background knowledge and new insights in phenotypic plasticity, but also train attendees to practice with prepared datasets and the open-source package. 2) Identify genomic and environmental determinants underlying phenotypic plasticity of agronomically important traits from diverse populations. For multi-environmental trial data for different traits, an environmental index will be sought to quantify the overall environment and approximate what is reflected by the overall performance of the population of genotypes. With the obtained environmental index, performance data of genotypes are then regressed to extract two parameters to connect with genome-wide genetic variants for genetic determinants identification. 3) Integrate crop growth model to quantify environments and dissect phenotypic plasticity. Sensitivity analysis will be conducted to identify major environmental factors that affect the performance of genotypes. Performance prediction by combining the strengths of crop growth model and the phenotypic-plasticity approach will be pursued. 4) Explore ways to understand the environmental factors underlying the performance trial and optimize breeder's design of testing and reference set. Performance data obtained directly from two collaboration breeders will be analyzed and specific recommendations will be made about how each testing location captures the range of major environmental index and the distribution, and what are the possible representative subset of testing locations when resource is limited.This project is expected togain an improvedunderstanding of genes and environmental factors underlying phenotypic plasticity; achieve an enhanced ability to make in-season, on-target crop performance prediction; and develop an Improved analytics to helpplant breeders optimize the process of exploring genetic and environmental space.
Animal Health Component
0%
Research Effort Categories
Basic
70%
Applied
30%
Developmental
0%
Classification

Knowledge Area (KA)Subject of Investigation (SOI)Field of Science (FOS)Percent
2011510108130%
2011549108130%
2011560108120%
2011820108120%
Goals / Objectives
One of the major challenges in plant breeding is the differential response of genotypes from one environment to another, or phenotypic plasticity. Although many methods have been developed, these methods lack the forecasting capacity, and our understanding of genomic and environmental determinants is still limited. We have recently established an analytical framework to answer long-standing questions in phenotypic plasticity and genotype by environment interaction. The essence of this framework is to combine knowledge from physiology, genetics, and statistics to identify the major genetic and environmental determinants, rather than only model fitting of the data. This framework focuses on identifying the hidden patterns and biological insights and enabling forecasting capacities and gene effect continuum profiling.Our long-term goal is to significantly enrich our understanding of genes and environmental factors underlying phenotypic plasticity and translate this understanding to breeding applications by enabling the in-season, on-target crop performance prediction and informing plant breeders about exploring genetic and environmental space.We hypothesize that the long-desired independent environmental indices can be identified to exploit the patterns underlying crop performance variation. Specific objectives are to:Develop an open-source package and host workshops to facilitate the community to dissect flowering-time plasticity;Identify genomic and environmental determinants underlying phenotypic plasticity of agronomically important traits from diverse populations;Integrate crop growth model to quantify environments and dissect phenotypic plasticity; andExplore ways to understand the environmental factors underlying the performance trial and optimize breeder's design of testing and reference set.
Project Methods
In active plant breeding programs, hundreds and thousands of test plots across multiple years are conducted at different sites before an elite variety is selected and released to market. The "brute-force" variety testing strategy is implemented because of the need for cultivars with stable performance and desired adaptation assurance. This is also partly due to the limited understanding of phenotypic plasticity, the varied performance of a genotype among different environments. Compared with the advancement in genetics/genomics, our understanding of the dynamic environment and its interaction with genetics needs to be improved to harness the full potential.We have recently made some progress in establishing an integrated framework to upgrade our understanding of phenotypic plasticity. Our proof-of-concept study was recently published. The proposed project will leverage this framework to dissect phenotypic plasticity of agronomically important traits measured for different germplasm panels (maize, wheat, and oat) with broad genetic backgrounds.Objective 1. Develop an open-source package and host workshops to facilitate the community to dissect flowering-time plasticity. Flowering time is a key agronomic and life-cycle trait. A large amount of flowering time records from multi-environment trial (MET) have been accumulated. We believe an open-source package automating the analysis pipeline will greatly facilitate the mechanistic dissection of flowering-time plasticity by many researchers. Findings will inform us of how different crops respond to diverse environmental conditions.Objective 2. Identify genomic and environmental determinants underlying phenotypic plasticity of agronomically important traits from diverse populations. We will move beyond the bi-parental population and flowering time to explore additional traits in multiple diversity panels. Our research team has already gathered 35,338 phenotypic records of multiple traits evaluated at 53 environments from 1,004 diverse accessions genotyped with genome-wide SNPs from three crops (maize, wheat, and oat). With promising preliminary results, we plan to conduct dedicated research to achieve a comprehensive understanding about environment, genetics, and interaction.Objective 3. Integrate crop growth model to quantify environments and dissect phenotypic plasticity. The Agricultural Production Systems sIMulator (APSIM) is an open-source, widely-used simulator with many crop models for different species, environmental conditions, and management capabilities. The APSIM models have been extensively tested in the US Midwest. However, insights from genetics and genomics need to be incorporated to achieve a better integration. We will integrate what we found from Objective 2 with APSIM models to gain a better understanding of phenotypic plasticity.Objective 4. Explore ways to understand the environmental factors underlying the performance trial and optimize breeder's design of testing and reference set. We will extend research to the actual elite crop varieties. Both actual crop performance trial data as well as computer simulation will be conducted to answer a set of questions that directly benefit plant breeders in their decision making.

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.