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Washburn JD, Varela JI, Xavier A, Chen Q, Ertl D, Gage JL, Holland JB, Lima DC, Romay MC, Lopez-Cruz M, de Los Campos G, Barber W, Zimmer C, Trucillo Silva I, Rocha F, Rincent R, Ali B, Hu H, Runcie DE, Gusev K, Slabodkin A, Bax P, Aubert J, Gangloff H, Mary-Huard T, Vanrenterghem T, Quesada-Traver C, Yates S, Ariza-Suárez D, Ulrich A, Wyler M, Kick DR, Bellis ES, Causey JL, Soriano Chavez E, Wang Y, Piyush V, Fernando GD, Hu RK, Kumar R, Timon AJ, Venkatesh R, Segura Abá K, Chen H, Ranaweera T, Shiu SH, Wang P, Gordon MJ, Amos BK, Busato S, Perondi D, Gogna A, Psaroudakis D, Chen CPJ, Al-Mamun HA, Danilevicz MF, Upadhyaya SR, Edwards D, de Leon N. Global Genotype by Environment Prediction Competition Reveals That Diverse Modeling Strategies Can Deliver Satisfactory Maize Yield Estimates. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.09.13.612969. [PMID: 39345633 PMCID: PMC11429743 DOI: 10.1101/2024.09.13.612969] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/01/2024]
Abstract
Predicting phenotypes from a combination of genetic and environmental factors is a grand challenge of modern biology. Slight improvements in this area have the potential to save lives, improve food and fuel security, permit better care of the planet, and create other positive outcomes. In 2022 and 2023 the first open-to-the-public Genomes to Fields (G2F) initiative Genotype by Environment (GxE) prediction competition was held using a large dataset including genomic variation, phenotype and weather measurements and field management notes, gathered by the project over nine years. The competition attracted registrants from around the world with representation from academic, government, industry, and non-profit institutions as well as unaffiliated. These participants came from diverse disciplines include plant science, animal science, breeding, statistics, computational biology and others. Some participants had no formal genetics or plant-related training, and some were just beginning their graduate education. The teams applied varied methods and strategies, providing a wealth of modeling knowledge based on a common dataset. The winner's strategy involved two models combining machine learning and traditional breeding tools: one model emphasized environment using features extracted by Random Forest, Ridge Regression and Least-squares, and one focused on genetics. Other high-performing teams' methods included quantitative genetics, classical machine learning/deep learning, mechanistic models, and model ensembles. The dataset factors used, such as genetics; weather; and management data, were also diverse, demonstrating that no single model or strategy is far superior to all others within the context of this competition.
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Kusmec A, Yeh CT'E, Schnable PS. Data-driven identification of environmental variables influencing phenotypic plasticity to facilitate breeding for future climates. THE NEW PHYTOLOGIST 2024. [PMID: 39183371 DOI: 10.1111/nph.19937] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Accepted: 05/20/2024] [Indexed: 08/27/2024]
Abstract
Phenotypic plasticity describes a genotype's ability to produce different phenotypes in response to different environments. Breeding crops that exhibit appropriate levels of plasticity for future climates will be crucial to meeting global demand, but knowledge of the critical environmental factors is limited to a handful of well-studied major crops. Using 727 maize (Zea mays L.) hybrids phenotyped for grain yield in 45 environments, we investigated the ability of a genetic algorithm and two other methods to identify environmental determinants of grain yield from a large set of candidate environmental variables constructed using minimal assumptions. The genetic algorithm identified pre- and postanthesis maximum temperature, mid-season solar radiation, and whole season net evapotranspiration as the four most important variables from a candidate set of 9150. Importantly, these four variables are supported by previous literature. After calculating reaction norms for each environmental variable, candidate genes were identified and gene annotations investigated to demonstrate how this method can generate insights into phenotypic plasticity. The genetic algorithm successfully identified known environmental determinants of hybrid maize grain yield. This demonstrates that the methodology could be applied to other less well-studied phenotypes and crops to improve understanding of phenotypic plasticity and facilitate breeding crops for future climates.
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Affiliation(s)
- Aaron Kusmec
- Department of Agronomy, Iowa State University, Ames, IA, 50011-3650, USA
| | | | - Patrick S Schnable
- Department of Agronomy, Iowa State University, Ames, IA, 50011-3650, USA
- Plant Sciences Institute, Iowa State University, Ames, IA, 50011-3650, USA
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Tadese D, Piepho HP, Hartung J. Accuracy of prediction from multi-environment trials for new locations using pedigree information and environmental covariates: the case of sorghum (Sorghum bicolor (L.) Moench) breeding. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2024; 137:181. [PMID: 38985188 PMCID: PMC11236881 DOI: 10.1007/s00122-024-04684-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Accepted: 06/25/2024] [Indexed: 07/11/2024]
Abstract
KEY MESSAGES We investigate a method of extracting and fitting synthetic environmental covariates and pedigree information in multilocation trial data analysis to predict genotype performances in untested locations. Plant breeding trials are usually conducted across multiple testing locations to predict genotype performances in the targeted population of environments. The predictive accuracy can be increased by the use of adequate statistical models. We compared linear mixed models with and without synthetic covariates (SCs) and pedigree information under the identity, the diagonal and the factor-analytic variance-covariance structures of the genotype-by-location interactions. A comparison was made to evaluate the accuracy of different models in predicting genotype performances in untested locations using the mean squared error of predicted differences (MSEPD) and the Spearman rank correlation between predicted and adjusted means. A multi-environmental trial (MET) dataset evaluated for yield performance in the dry lowland sorghum (Sorghum bicolor (L.) Moench) breeding program of Ethiopia was used. For validating our models, we followed a leave-one-location-out cross-validation strategy. A total of 65 environmental covariates (ECs) obtained from the sorghum test locations were considered. The SCs were extracted from the ECs using multivariate partial least squares analysis and subsequently fitted in the linear mixed model. Then, the model was extended accounting for pedigree information. According to the MSEPD, models accounting for SC improve predictive accuracy of genotype performances in the three of the variance-covariance structures compared to others without SC. The rank correlation was also higher for the model with the SC. When the SC was fitted, the rank correlation was 0.58 for the factor analytic, 0.51 for the diagonal and 0.46 for the identity variance-covariance structures. Our approach indicates improvement in predictive accuracy with SC in the context of genotype-by-location interactions of a sorghum breeding in Ethiopia.
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Affiliation(s)
- Diriba Tadese
- Biostatistics Unit, Institute of Crop Science, University of Hohenheim, Fruwirthstraße 23, 70599, Stuttgart, Germany.
| | - Hans-Peter Piepho
- Biostatistics Unit, Institute of Crop Science, University of Hohenheim, Fruwirthstraße 23, 70599, Stuttgart, Germany
| | - Jens Hartung
- Biostatistics Unit, Institute of Crop Science, University of Hohenheim, Fruwirthstraße 23, 70599, Stuttgart, Germany
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Li J, Zhang D, Yang F, Zhang Q, Pan S, Zhao X, Zhang Q, Han Y, Yang J, Wang K, Zhao C. TrG2P: A transfer-learning-based tool integrating multi-trait data for accurate prediction of crop yield. PLANT COMMUNICATIONS 2024; 5:100975. [PMID: 38751121 PMCID: PMC11287160 DOI: 10.1016/j.xplc.2024.100975] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Revised: 04/14/2024] [Accepted: 05/11/2024] [Indexed: 06/24/2024]
Abstract
Yield prediction is the primary goal of genomic selection (GS)-assisted crop breeding. Because yield is a complex quantitative trait, making predictions from genotypic data is challenging. Transfer learning can produce an effective model for a target task by leveraging knowledge from a different, but related, source domain and is considered a great potential method for improving yield prediction by integrating multi-trait data. However, it has not previously been applied to genotype-to-phenotype prediction owing to the lack of an efficient implementation framework. We therefore developed TrG2P, a transfer-learning-based framework. TrG2P first employs convolutional neural networks (CNN) to train models using non-yield-trait phenotypic and genotypic data, thus obtaining pre-trained models. Subsequently, the convolutional layer parameters from these pre-trained models are transferred to the yield prediction task, and the fully connected layers are retrained, thus obtaining fine-tuned models. Finally, the convolutional layer and the first fully connected layer of the fine-tuned models are fused, and the last fully connected layer is trained to enhance prediction performance. We applied TrG2P to five sets of genotypic and phenotypic data from maize (Zea mays), rice (Oryza sativa), and wheat (Triticum aestivum) and compared its model precision to that of seven other popular GS tools: ridge regression best linear unbiased prediction (rrBLUP), random forest, support vector regression, light gradient boosting machine (LightGBM), CNN, DeepGS, and deep neural network for genomic prediction (DNNGP). TrG2P improved the accuracy of yield prediction by 39.9%, 6.8%, and 1.8% in rice, maize, and wheat, respectively, compared with predictions generated by the best-performing comparison model. Our work therefore demonstrates that transfer learning is an effective strategy for improving yield prediction by integrating information from non-yield-trait data. We attribute its enhanced prediction accuracy to the valuable information available from traits associated with yield and to training dataset augmentation. The Python implementation of TrG2P is available at https://github.com/lijinlong1991/TrG2P. The web-based tool is available at http://trg2p.ebreed.cn:81.
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Affiliation(s)
- Jinlong Li
- Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
| | - Dongfeng Zhang
- Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
| | - Feng Yang
- Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
| | - Qiusi Zhang
- Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
| | - Shouhui Pan
- Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
| | - Xiangyu Zhao
- Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
| | - Qi Zhang
- Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
| | - Yanyun Han
- Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
| | - Jinliang Yang
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE 68583, USA; Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, NE 68583, USA
| | - Kaiyi Wang
- Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China.
| | - Chunjiang Zhao
- Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China.
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5
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Wei X, Chen M, Zhang Q, Gong J, Liu J, Yong K, Wang Q, Fan J, Chen S, Hua H, Luo Z, Zhao X, Wang X, Li W, Cong J, Yu X, Wang Z, Huang R, Chen J, Zhou X, Qiu J, Xu P, Murray J, Wang H, Xu Y, Xu C, Xu G, Yang J, Han B, Huang X. Genomic investigation of 18,421 lines reveals the genetic architecture of rice. Science 2024; 385:eadm8762. [PMID: 38963845 DOI: 10.1126/science.adm8762] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Accepted: 04/29/2024] [Indexed: 07/06/2024]
Abstract
Understanding how numerous quantitative trait loci (QTL) shape phenotypic variation is an important question in genetics. To address this, we established a permanent population of 18,421 (18K) rice lines with reduced population structure. We generated reference-level genome assemblies of the founders and genotyped all 18K-rice lines through whole-genome sequencing. Through high-resolution mapping, 96 high-quality candidate genes contributing to variation in 16 traits were identified, including OsMADS22 and OsFTL1 verified as causal genes for panicle number and heading date, respectively. We identified epistatic QTL pairs and constructed a genetic interaction network with 19 genes serving as hubs. Overall, 170 masking epistasis pairs were characterized, serving as an important factor contributing to genetic background effects across diverse varieties. The work provides a basis to guide grain yield and quality improvements in rice.
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Affiliation(s)
- Xin Wei
- Shanghai Key Laboratory of Plant Molecular Sciences, Development Center of Plant Germplasm Resources, College of Life Sciences, Shanghai Normal University, Shanghai 200234, China
| | - Mengjiao Chen
- Shanghai Key Laboratory of Plant Molecular Sciences, Development Center of Plant Germplasm Resources, College of Life Sciences, Shanghai Normal University, Shanghai 200234, China
| | - Qi Zhang
- Shanghai Key Laboratory of Plant Molecular Sciences, Development Center of Plant Germplasm Resources, College of Life Sciences, Shanghai Normal University, Shanghai 200234, China
| | - Junyi Gong
- State Key Laboratory of Rice Biology and Breeding, China National Rice Research Institute, Chinese Academy of Agricultural Sciences, Hangzhou 310006, China
| | - Jie Liu
- Shanghai Key Laboratory of Plant Molecular Sciences, Development Center of Plant Germplasm Resources, College of Life Sciences, Shanghai Normal University, Shanghai 200234, China
| | - Kaicheng Yong
- Shanghai Key Laboratory of Plant Molecular Sciences, Development Center of Plant Germplasm Resources, College of Life Sciences, Shanghai Normal University, Shanghai 200234, China
| | - Qin Wang
- Shanghai Key Laboratory of Plant Molecular Sciences, Development Center of Plant Germplasm Resources, College of Life Sciences, Shanghai Normal University, Shanghai 200234, China
| | - Jiongjiong Fan
- State Key Laboratory of Rice Biology and Breeding, China National Rice Research Institute, Chinese Academy of Agricultural Sciences, Hangzhou 310006, China
| | - Suhui Chen
- Shanghai Key Laboratory of Plant Molecular Sciences, Development Center of Plant Germplasm Resources, College of Life Sciences, Shanghai Normal University, Shanghai 200234, China
| | - Hua Hua
- Shanghai Key Laboratory of Plant Molecular Sciences, Development Center of Plant Germplasm Resources, College of Life Sciences, Shanghai Normal University, Shanghai 200234, China
| | - Zhaowei Luo
- Shanghai Key Laboratory of Plant Molecular Sciences, Development Center of Plant Germplasm Resources, College of Life Sciences, Shanghai Normal University, Shanghai 200234, China
| | - Xiaoyan Zhao
- Shanghai Key Laboratory of Plant Molecular Sciences, Development Center of Plant Germplasm Resources, College of Life Sciences, Shanghai Normal University, Shanghai 200234, China
| | - Xuan Wang
- Shanghai Key Laboratory of Plant Molecular Sciences, Development Center of Plant Germplasm Resources, College of Life Sciences, Shanghai Normal University, Shanghai 200234, China
| | - Wei Li
- Shanghai Key Laboratory of Plant Molecular Sciences, Development Center of Plant Germplasm Resources, College of Life Sciences, Shanghai Normal University, Shanghai 200234, China
| | - Jia Cong
- Shanghai Key Laboratory of Plant Molecular Sciences, Development Center of Plant Germplasm Resources, College of Life Sciences, Shanghai Normal University, Shanghai 200234, China
| | - Xiting Yu
- Shanghai Key Laboratory of Plant Molecular Sciences, Development Center of Plant Germplasm Resources, College of Life Sciences, Shanghai Normal University, Shanghai 200234, China
| | - Zhihan Wang
- Shanghai Key Laboratory of Plant Molecular Sciences, Development Center of Plant Germplasm Resources, College of Life Sciences, Shanghai Normal University, Shanghai 200234, China
| | - Ruipeng Huang
- Shanghai Key Laboratory of Plant Molecular Sciences, Development Center of Plant Germplasm Resources, College of Life Sciences, Shanghai Normal University, Shanghai 200234, China
| | - Jiaxin Chen
- Shanghai Key Laboratory of Plant Molecular Sciences, Development Center of Plant Germplasm Resources, College of Life Sciences, Shanghai Normal University, Shanghai 200234, China
| | - Xiaoyi Zhou
- Shanghai Key Laboratory of Plant Molecular Sciences, Development Center of Plant Germplasm Resources, College of Life Sciences, Shanghai Normal University, Shanghai 200234, China
| | - Jie Qiu
- Shanghai Key Laboratory of Plant Molecular Sciences, Development Center of Plant Germplasm Resources, College of Life Sciences, Shanghai Normal University, Shanghai 200234, China
| | - Ping Xu
- Shanghai Key Laboratory of Plant Molecular Sciences, Development Center of Plant Germplasm Resources, College of Life Sciences, Shanghai Normal University, Shanghai 200234, China
| | - Jeremy Murray
- CAS Center for Excellence in Molecular Plant Sciences, Chinese Academy of Sciences, Shanghai 200233, China
| | - Hai Wang
- State Key Laboratory of Plant Physiology and Biochemistry, College of Agronomy and Biotechnology, China Agricultural University, Beijing 100193, China
| | - Yang Xu
- Key Laboratory of Plant Functional Genomics of Ministry of Education, College of Agriculture, Yangzhou University, Yangzhou 225009, China
| | - Chenwu Xu
- Key Laboratory of Plant Functional Genomics of Ministry of Education, College of Agriculture, Yangzhou University, Yangzhou 225009, China
| | - Gen Xu
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE 68583, USA
| | - Jinliang Yang
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE 68583, USA
| | - Bin Han
- CAS Center for Excellence in Molecular Plant Sciences, Chinese Academy of Sciences, Shanghai 200233, China
| | - Xuehui Huang
- Shanghai Key Laboratory of Plant Molecular Sciences, Development Center of Plant Germplasm Resources, College of Life Sciences, Shanghai Normal University, Shanghai 200234, China
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Resende RT, Hickey L, Amaral CH, Peixoto LL, Marcatti GE, Xu Y. Satellite-enabled enviromics to enhance crop improvement. MOLECULAR PLANT 2024; 17:848-866. [PMID: 38637991 DOI: 10.1016/j.molp.2024.04.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Revised: 04/04/2024] [Accepted: 04/11/2024] [Indexed: 04/20/2024]
Abstract
Enviromics refers to the characterization of micro- and macroenvironments based on large-scale environmental datasets. By providing genotypic recommendations with predictive extrapolation at a site-specific level, enviromics could inform plant breeding decisions across varying conditions and anticipate productivity in a changing climate. Enviromics-based integration of statistics, envirotyping (i.e., determining environmental factors), and remote sensing could help unravel the complex interplay of genetics, environment, and management. To support this goal, exhaustive envirotyping to generate precise environmental profiles would significantly improve predictions of genotype performance and genetic gain in crops. Already, informatics management platforms aggregate diverse environmental datasets obtained using optical, thermal, radar, and light detection and ranging (LiDAR)sensors that capture detailed information about vegetation, surface structure, and terrain. This wealth of information, coupled with freely available climate data, fuels innovative enviromics research. While enviromics holds immense potential for breeding, a few obstacles remain, such as the need for (1) integrative methodologies to systematically collect field data to scale and expand observations across the landscape with satellite data; (2) state-of-the-art AI models for data integration, simulation, and prediction; (3) cyberinfrastructure for processing big data across scales and providing seamless interfaces to deliver forecasts to stakeholders; and (4) collaboration and data sharing among farmers, breeders, physiologists, geoinformatics experts, and programmers across research institutions. Overcoming these challenges is essential for leveraging the full potential of big data captured by satellites to transform 21st century agriculture and crop improvement through enviromics.
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Affiliation(s)
- Rafael T Resende
- Universidade Federal de Goiás (UFG), Agronomy Department, Plant Breeding Sector, Goiânia (GO) 74690-900, Brazil; TheCROP, a Precision-Breeding Startup: Enviromics, Phenomics, and Genomics, No Zip-code, Operating Virtually, Goiânia (GO) and Sete Lagoas (MG), Brazil.
| | - Lee Hickey
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, QLD, Australia
| | - Cibele H Amaral
- Earth Lab, Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, CO 80303, USA; Environmental Data Science Innovation & Inclusion Lab, Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, CO 80303, USA
| | - Lucas L Peixoto
- Universidade Federal de Goiás (UFG), Agronomy Department, Plant Breeding Sector, Goiânia (GO) 74690-900, Brazil
| | - Gustavo E Marcatti
- TheCROP, a Precision-Breeding Startup: Enviromics, Phenomics, and Genomics, No Zip-code, Operating Virtually, Goiânia (GO) and Sete Lagoas (MG), Brazil; Universidade Federal de São João del-Rei, Forest Engineering Department, Campus Sete Lagoas, Sete Lagoas (MG) 35701-970, Brazil
| | - Yunbi Xu
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China; Peking University Institute of Advanced Agricultural Sciences, Weifang, Shandong 261325, China; BGI Bioverse, Shenzhen 518083, China.
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7
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Alemu A, Åstrand J, Montesinos-López OA, Isidro Y Sánchez J, Fernández-Gónzalez J, Tadesse W, Vetukuri RR, Carlsson AS, Ceplitis A, Crossa J, Ortiz R, Chawade A. Genomic selection in plant breeding: Key factors shaping two decades of progress. MOLECULAR PLANT 2024; 17:552-578. [PMID: 38475993 DOI: 10.1016/j.molp.2024.03.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Revised: 01/22/2024] [Accepted: 03/08/2024] [Indexed: 03/14/2024]
Abstract
Genomic selection, the application of genomic prediction (GP) models to select candidate individuals, has significantly advanced in the past two decades, effectively accelerating genetic gains in plant breeding. This article provides a holistic overview of key factors that have influenced GP in plant breeding during this period. We delved into the pivotal roles of training population size and genetic diversity, and their relationship with the breeding population, in determining GP accuracy. Special emphasis was placed on optimizing training population size. We explored its benefits and the associated diminishing returns beyond an optimum size. This was done while considering the balance between resource allocation and maximizing prediction accuracy through current optimization algorithms. The density and distribution of single-nucleotide polymorphisms, level of linkage disequilibrium, genetic complexity, trait heritability, statistical machine-learning methods, and non-additive effects are the other vital factors. Using wheat, maize, and potato as examples, we summarize the effect of these factors on the accuracy of GP for various traits. The search for high accuracy in GP-theoretically reaching one when using the Pearson's correlation as a metric-is an active research area as yet far from optimal for various traits. We hypothesize that with ultra-high sizes of genotypic and phenotypic datasets, effective training population optimization methods and support from other omics approaches (transcriptomics, metabolomics and proteomics) coupled with deep-learning algorithms could overcome the boundaries of current limitations to achieve the highest possible prediction accuracy, making genomic selection an effective tool in plant breeding.
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Affiliation(s)
- Admas Alemu
- Department of Plant Breeding, Swedish University of Agricultural Sciences, Alnarp, Sweden.
| | - Johanna Åstrand
- Department of Plant Breeding, Swedish University of Agricultural Sciences, Alnarp, Sweden; Lantmännen Lantbruk, Svalöv, Sweden
| | | | - Julio Isidro Y Sánchez
- Centro de Biotecnología y Genómica de Plantas (CBGP, UPM-INIA), Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), Campus de Montegancedo-UPM, 28223 Madrid, Spain
| | - Javier Fernández-Gónzalez
- Centro de Biotecnología y Genómica de Plantas (CBGP, UPM-INIA), Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), Campus de Montegancedo-UPM, 28223 Madrid, Spain
| | - Wuletaw Tadesse
- International Center for Agricultural Research in the Dry Areas (ICARDA), Rabat, Morocco
| | - Ramesh R Vetukuri
- Department of Plant Breeding, Swedish University of Agricultural Sciences, Alnarp, Sweden
| | - Anders S Carlsson
- Department of Plant Breeding, Swedish University of Agricultural Sciences, Alnarp, Sweden
| | | | - José Crossa
- International Maize and Wheat Improvement Center (CIMMYT), Km 45, Carretera México-Veracruz, Texcoco, México 52640, Mexico
| | - Rodomiro Ortiz
- Department of Plant Breeding, Swedish University of Agricultural Sciences, Alnarp, Sweden.
| | - Aakash Chawade
- Department of Plant Breeding, Swedish University of Agricultural Sciences, Alnarp, Sweden
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8
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Bhoite R, Han Y, Chaitanya AK, Varshney RK, Sharma DL. Genomic approaches to enhance adaptive plasticity to cope with soil constraints amidst climate change in wheat. THE PLANT GENOME 2024; 17:e20358. [PMID: 37265088 DOI: 10.1002/tpg2.20358] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Revised: 03/09/2023] [Accepted: 05/09/2023] [Indexed: 06/03/2023]
Abstract
Climate change is varying the availability of resources, soil physicochemical properties, and rainfall events, which collectively determines soil physical and chemical properties. Soil constraints-acidity (pH < 6), salinity (pH ≤ 8.5), sodicity, and dispersion (pH > 8.5)-are major causes of wheat yield loss in arid and semiarid cropping systems. To cope with changing environments, plants employ adaptive strategies such as phenotypic plasticity, a key multifaceted trait, to promote shifts in phenotypes. Adaptive strategies for constrained soils are complex, determined by key functional traits and genotype × environment × management interactions. The understanding of the molecular basis of stress tolerance is particularly challenging for plasticity traits. Advances in sequencing and high-throughput genomics technologies have identified functional alleles in gene-rich regions, haplotypes, candidate genes, mechanisms, and in silico gene expression profiles at various growth developmental stages. Our review focuses on favorable alleles for enhanced gene expression, quantitative trait loci, and epigenetic regulation of plant responses to soil constraints, including heavy metal stress and nutrient limitations. A strategy is then described for quantitative traits in wheat by investigating significant alleles and functional characterization of variants, followed by gene validation using advanced genomic tools, and marker development for molecular breeding and genome editing. Moreover, the review highlights the progress of gene editing in wheat, multiplex gene editing, and novel alleles for smart control of gene expression. Application of these advanced genomic technologies to enhance plasticity traits along with soil management practices will be an effective tool to build yield, stability, and sustainability on constrained soils in the face of climate change.
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Affiliation(s)
- Roopali Bhoite
- Department of Primary Industries and Regional Development, South Perth, Western Australia, Australia
- The UWA Institute of Agriculture, The University of Western Australia, Perth, Western Australia, Australia
| | - Yong Han
- Department of Primary Industries and Regional Development, South Perth, Western Australia, Australia
- Centre for Crop & Food Innovation, State Agricultural Biotechnology Centre, Murdoch University, Perth, Western Australia, Australia
| | - Alamuru Krishna Chaitanya
- Grains Genetics Portfolio, University of Southern Queensland, Centre for Crop Health, Toowoomba, Queensland, Australia
| | - Rajeev K Varshney
- Centre for Crop & Food Innovation, State Agricultural Biotechnology Centre, Murdoch University, Perth, Western Australia, Australia
| | - Darshan Lal Sharma
- Department of Primary Industries and Regional Development, South Perth, Western Australia, Australia
- Centre for Crop & Food Innovation, State Agricultural Biotechnology Centre, Murdoch University, Perth, Western Australia, Australia
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9
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Laitinen RAE. Importance of phenotypic plasticity in crop resilience. JOURNAL OF EXPERIMENTAL BOTANY 2024; 75:670-673. [PMID: 38307517 PMCID: PMC10837008 DOI: 10.1093/jxb/erad465] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2024]
Abstract
This article comments on:
Guo T, Wei J, Li X, Yu J. 2024. Environmental context of phenotypic plasticity in flowering time in sorghum and rice. Journal of Experimental Botany 75, 1004–1015.
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Affiliation(s)
- Roosa A E Laitinen
- Organismal and Evolutionary Research Programme, Faculty of Biological and Environmental Sciences, Viikki Plant Science Centre, University of Helsinki, Helsinki, Finland
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10
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Guo T, Wei J, Li X, Yu J. Environmental context of phenotypic plasticity in flowering time in sorghum and rice. JOURNAL OF EXPERIMENTAL BOTANY 2024; 75:1004-1015. [PMID: 37819624 PMCID: PMC10837014 DOI: 10.1093/jxb/erad398] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Accepted: 10/17/2023] [Indexed: 10/13/2023]
Abstract
Phenotypic plasticity is an important topic in biology and evolution. However, how to generate broadly applicable insights from individual studies remains a challenge. Here, with flowering time observed from a large geographical region for sorghum and rice genetic populations, we examine 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. 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. Furthermore, high accuracy through genomic prediction was obtained for reaction norm parameters of untested genotypes using models built from tested genotypes under the subsets of environments with either a large range or a large sample size. 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.
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Affiliation(s)
- Tingting Guo
- Hubei Hongshan Laboratory, Wuhan, Hubei, China
- College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, Hubei, China
| | - Jialu Wei
- Department of Agronomy, Iowa State University, Ames, IA, USA
| | - Xianran Li
- USDA, Agricultural Research Service, Wheat Health, Genetics, and Quality Research Unit, Pullman, WA, USA
| | - Jianming Yu
- Department of Agronomy, Iowa State University, Ames, IA, USA
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11
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Yamashita H, Wada KC, Inagaki N, Fujimoto Z, Yonemaru JI, Itoh H. Deciphering transcriptomic signatures explaining the phenotypic plasticity of nonheading lettuce genotypes under artificial light conditions. PLANT, CELL & ENVIRONMENT 2023; 46:3971-3985. [PMID: 37533309 DOI: 10.1111/pce.14677] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 07/11/2023] [Accepted: 07/24/2023] [Indexed: 08/04/2023]
Abstract
Elucidating the mechanisms and pathways involved in genotype-environment (G×E) interactions and phenotypic plasticity is critical for improving plant growth. Controlled environment agricultural systems allow growers to modulate the environment for particular genotypes. In this study, we evaluated the effects of interactions among 14 genotypes and four artificial light environments on leaf lettuce phenotypes and dissected the underlying molecular mechanism via transcriptome-based modeling. Variations in morphological traits and phytochemical concentrations in response to artificial light treatments revealed significant G×E interactions. The appropriate genotype and artificial light combinations for maximizing phenotypic expression were determined on the basis of a joint regression analysis and the additive main effect and multiplicative interaction model for these G×E interactions. Transcriptome-based regression modeling explained approximately 50%-90% of the G×E variations. Further analyzes indicated Red Lettuce Leaves 4 (RLL4) regulates UV-B and blue light signaling through the effects of the HY5-MBW pathway on flavonoid biosynthesis and contributes to natural variations in the light-responsive plasticity of lettuce traits. Our study represents an important step toward elucidating the phenotypic variations due to G×E interactions in nonheading lettuce under artificial light conditions.
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Affiliation(s)
- Hiroto Yamashita
- Institute of Crop Science, National Agriculture and Food Research Organization, Tsukuba, Ibaraki, Japan
| | - Kaede C Wada
- Institute of Crop Science, National Agriculture and Food Research Organization, Tsukuba, Ibaraki, Japan
| | - Noritoshi Inagaki
- Research Center for Advanced Analysis, National Agriculture and Food Research Organization, Tsukuba, Ibaraki, Japan
| | - Zui Fujimoto
- Research Center for Advanced Analysis, National Agriculture and Food Research Organization, Tsukuba, Ibaraki, Japan
| | - Jun-Ichi Yonemaru
- Institute of Crop Science, National Agriculture and Food Research Organization, Tsukuba, Ibaraki, Japan
- Research Center for Agricultural Information Technology, National Agriculture and Food Research Organization, Tsukuba, Ibaraki, Japan
| | - Hironori Itoh
- Institute of Crop Science, National Agriculture and Food Research Organization, Tsukuba, Ibaraki, Japan
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12
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Guo J, Guo J, Li L, Bai X, Huo X, Shi W, Gao L, Dai K, Jing R, Hao C. Combined linkage analysis and association mapping identifies genomic regions associated with yield-related and drought-tolerance traits in wheat (Triticum aestivum L.). TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2023; 136:250. [PMID: 37982873 DOI: 10.1007/s00122-023-04494-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Accepted: 10/26/2023] [Indexed: 11/21/2023]
Abstract
KEY MESSAGE Combined linkage analysis and association mapping identified genomic regions associated with yield and drought tolerance, providing information to assist breeding for high yield and drought tolerance in wheat. Wheat (Triticum aestivum L.) is one of the most widely grown food crops and provides adequate amounts of protein to support human health. Drought stress is the most important abiotic stress constraining yield during the flowering and grain development periods. Precise targeting of genomic regions underlying yield- and drought tolerance-responsive traits would assist in breeding programs. In this study, two water treatments (well-watered, WW, and rain-fed water stress, WS) were applied, and five yield-related agronomic traits (plant height, PH; spike length, SL; spikelet number per spike, SNPS; kernel number per spike, KNPS; thousand kernel weight, TKW) and drought response values (DRVs) were used to characterize the drought sensitivity of each accession. Association mapping was performed on an association panel of 304 accessions, and linkage analysis was applied to a doubled haploid (DH) population of 152 lines. Eleven co-localized genomic regions associated with yield traits and DRV were identified in both populations. Many previously cloned key genes were located in these regions. In particular, a TKW-associated region on chromosome 2D was identified using both association mapping and linkage analysis and a key candidate gene, TraesCS2D02G142500, was detected based on gene annotation and differences in expression levels. Exonic SNPs were analyzed by sequencing the full length of TraesCS2D02G142500 in the association panel, and a rare haplotype, Hap-2, which reduced TKW to a lesser extent than Hap-1 under drought stress, and the Hap-2 varieties presented drought-insensitive. Altogether, this study provides fundamental insights into molecular targets for high yield and drought tolerance in wheat.
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Affiliation(s)
- Jie Guo
- College of Agronomy, Key Laboratory of Sustainable Dryland Agriculture (Co-Construction By Ministry and Province), Ministry of Agriculture and Rural Affairs, Shanxi Agricultural University, Jinzhong, 030801, Shanxi, China
| | - Jiahui Guo
- College of Agronomy, Key Laboratory of Sustainable Dryland Agriculture (Co-Construction By Ministry and Province), Ministry of Agriculture and Rural Affairs, Shanxi Agricultural University, Jinzhong, 030801, Shanxi, China
- College of Agronomy, Yangzhou University, Yangzhou, 225009, Jiangsu, China
| | - Long Li
- State Key Laboratory of Crop Gene Resources and Breeding/Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Xionghui Bai
- College of Agronomy, Key Laboratory of Sustainable Dryland Agriculture (Co-Construction By Ministry and Province), Ministry of Agriculture and Rural Affairs, Shanxi Agricultural University, Jinzhong, 030801, Shanxi, China
| | - Xiaoyu Huo
- College of Agronomy, Key Laboratory of Sustainable Dryland Agriculture (Co-Construction By Ministry and Province), Ministry of Agriculture and Rural Affairs, Shanxi Agricultural University, Jinzhong, 030801, Shanxi, China
| | - Weiping Shi
- College of Agronomy, Key Laboratory of Sustainable Dryland Agriculture (Co-Construction By Ministry and Province), Ministry of Agriculture and Rural Affairs, Shanxi Agricultural University, Jinzhong, 030801, Shanxi, China
| | - Lifeng Gao
- State Key Laboratory of Crop Gene Resources and Breeding/Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Keli Dai
- College of Agronomy, Key Laboratory of Sustainable Dryland Agriculture (Co-Construction By Ministry and Province), Ministry of Agriculture and Rural Affairs, Shanxi Agricultural University, Jinzhong, 030801, Shanxi, China.
| | - Ruilian Jing
- State Key Laboratory of Crop Gene Resources and Breeding/Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China.
| | - Chenyang Hao
- College of Agronomy, Key Laboratory of Sustainable Dryland Agriculture (Co-Construction By Ministry and Province), Ministry of Agriculture and Rural Affairs, Shanxi Agricultural University, Jinzhong, 030801, Shanxi, China.
- State Key Laboratory of Crop Gene Resources and Breeding/Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China.
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13
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Lopez-Cruz M, Aguate FM, Washburn JD, de Leon N, Kaeppler SM, Lima DC, Tan R, Thompson A, De La Bretonne LW, de Los Campos G. Leveraging data from the Genomes-to-Fields Initiative to investigate genotype-by-environment interactions in maize in North America. Nat Commun 2023; 14:6904. [PMID: 37903778 PMCID: PMC10616096 DOI: 10.1038/s41467-023-42687-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Accepted: 10/18/2023] [Indexed: 11/01/2023] Open
Abstract
Genotype-by-environment (G×E) interactions can significantly affect crop performance and stability. Investigating G×E requires extensive data sets with diverse cultivars tested over multiple locations and years. The Genomes-to-Fields (G2F) Initiative has tested maize hybrids in more than 130 year-locations in North America since 2014. Here, we curate and expand this data set by generating environmental covariates (using a crop model) for each of the trials. The resulting data set includes DNA genotypes and environmental data linked to more than 70,000 phenotypic records of grain yield and flowering traits for more than 4000 hybrids. We show how this valuable data set can serve as a benchmark in agricultural modeling and prediction, paving the way for countless G×E investigations in maize. We use multivariate analyses to characterize the data set's genetic and environmental structure, study the association of key environmental factors with traits, and provide benchmarks using genomic prediction models.
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Affiliation(s)
- Marco Lopez-Cruz
- Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI, 48824, USA.
- Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, 48824, USA.
| | - Fernando M Aguate
- Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI, 48824, USA
- Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, 48824, USA
| | - Jacob D Washburn
- United States Department of Agriculture, Agricultural Research Service, University of Missouri, Columbia, MO, 65211, USA
| | - Natalia de Leon
- Department of Agronomy, University of Wisconsin, Madison, WI, 53706, USA
| | - Shawn M Kaeppler
- Department of Agronomy, University of Wisconsin, Madison, WI, 53706, USA
- Wisconsin Crop Innovation Center, University of Wisconsin, Middleton, WI, 53562, USA
| | | | - Ruijuan Tan
- Department of Plant, Soil and Microbial Sciences, Michigan State University, East Lansing, MI, 48824, USA
| | - Addie Thompson
- Department of Plant, Soil and Microbial Sciences, Michigan State University, East Lansing, MI, 48824, USA
- Plant Resilience Institute, Michigan State University, East Lansing, MI, 48824, USA
| | | | - Gustavo de Los Campos
- Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI, 48824, USA.
- Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, 48824, USA.
- Department of Statistics and Probability, Michigan State University, East Lansing, MI, 48824, USA.
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14
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Ma Y, Yang W, Zhang H, Wang P, Liu Q, Li F, Du W. Genetic analysis of phenotypic plasticity identifies BBX6 as the candidate gene for maize adaptation to temperate regions. FRONTIERS IN PLANT SCIENCE 2023; 14:1280331. [PMID: 37964997 PMCID: PMC10642939 DOI: 10.3389/fpls.2023.1280331] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/20/2023] [Accepted: 10/12/2023] [Indexed: 11/16/2023]
Abstract
Introduction Climate changes pose a significant threat to crop adaptation and production. Dissecting the genetic basis of phenotypic plasticity and uncovering the responsiveness of regulatory genes to environmental factors can significantly contribute to the improvement of climate- resilience in crops. Methods We established a BC1F3:4 population using the elite inbred lines Zheng58 and PH4CV and evaluated plant height (PH) across four environments characterized by substantial variations in environmental factors. Then, we quantified the correlation between the environmental mean of PH (the mean performance in each environment) and the environmental parameters within a specific growth window. Furthermore, we performed GWAS analysis of phenotypic plasticity, and identified QTLs and candidate gene that respond to key environment index. After that, we constructed the coexpression network involving the candidate gene, and performed selective sweep analysis of the candidate gene. Results We found that the environmental parameters demonstrated substantial variation across the environments, and genotype by environment interaction contributed to the variations of PH. Then, we identified PTT(35-48) (PTT is the abbreviation for photothermal units), the mean PTT from 35 to 48 days after planting, as the pivotal environmental index that closely correlated with environmental mean of PH. Leveraging the slopes of the response of PH to both the environmental mean and PTT(35-48), we successfully pinpointed QTLs for phenotypic plasticity on chromosomes 1 and 2. Notably, the PH4CV genotypes at these two QTLs exhibited positive contributions to phenotypic plasticity. Furthermore, our analysis demonstrated a direct correlation between the additive effects of each QTL and PTT(35-48). By analyzing transcriptome data of the parental lines in two environments, we found that the 1009 genes responding to PTT(35-48) were enriched in the biological processes related to environmental sensitivity. BBX6 was the prime candidate gene among the 13 genes in the two QTL regions. The coexpression network of BBX6 contained other genes related to flowering time and photoperiod sensitivity. Our investigation, including selective sweep analysis and genetic differentiation analysis, suggested that BBX6 underwent selection during maize domestication. Discussion Th is research substantially advances our understanding of critical environmental factors influencing maize adaptation while simultaneously provides an invaluable gene resource for the development of climate-resilient maize hybrid varieties.
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Affiliation(s)
- Yuting Ma
- College of Agronomy, Shenyang Agricultural University, Shenyang, Liaoning, China
- State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Wenyan Yang
- College of Agronomy, Shenyang Agricultural University, Shenyang, Liaoning, China
- State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Hongwei Zhang
- State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Pingxi Wang
- State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Qian Liu
- State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Fenghai Li
- College of Agronomy, Shenyang Agricultural University, Shenyang, Liaoning, China
| | - Wanli Du
- College of Agronomy, Shenyang Agricultural University, Shenyang, Liaoning, China
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15
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Della Coletta R, Liese SE, Fernandes SB, Mikel MA, Bohn MO, Lipka AE, Hirsch CN. Linking genetic and environmental factors through marker effect networks to understand trait plasticity. Genetics 2023; 224:iyad103. [PMID: 37246567 DOI: 10.1093/genetics/iyad103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Revised: 05/19/2023] [Accepted: 05/24/2023] [Indexed: 05/30/2023] Open
Abstract
Understanding how plants adapt to specific environmental changes and identifying genetic markers associated with phenotypic plasticity can help breeders develop plant varieties adapted to a rapidly changing climate. Here, we propose the use of marker effect networks as a novel method to identify markers associated with environmental adaptability. These marker effect networks are built by adapting commonly used software for building gene coexpression networks with marker effects across growth environments as the input data into the networks. To demonstrate the utility of these networks, we built networks from the marker effects of ∼2,000 nonredundant markers from 400 maize hybrids across 9 environments. We demonstrate that networks can be generated using this approach, and that the markers that are covarying are rarely in linkage disequilibrium, thus representing higher biological relevance. Multiple covarying marker modules associated with different weather factors throughout the growing season were identified within the marker effect networks. Finally, a factorial test of analysis parameters demonstrated that marker effect networks are relatively robust to these options, with high overlap in modules associated with the same weather factors across analysis parameters. This novel application of network analysis provides unique insights into phenotypic plasticity and specific environmental factors that modulate the genome.
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Affiliation(s)
- Rafael Della Coletta
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN 55108, USA
| | - Sharon E Liese
- Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Samuel B Fernandes
- Department of Crop, Soil, and Environmental Sciences, University of Arkansas, Fayetteville, AR 72701, USA
| | - Mark A Mikel
- Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
- Roy J. Carver Biotechnology Center, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Martin O Bohn
- Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Alexander E Lipka
- Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Candice N Hirsch
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN 55108, USA
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16
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Ahad A, Gul A, Batool TS, Huda NU, Naseeer F, Abdul Salam U, Abdul Salam M, Ilyas M, Turkyilmaz Unal B, Ozturk M. Molecular and genetic perspectives of cold tolerance in wheat. Mol Biol Rep 2023; 50:6997-7015. [PMID: 37378744 DOI: 10.1007/s11033-023-08584-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Accepted: 06/06/2023] [Indexed: 06/29/2023]
Abstract
Environmental variation is the most crucial problem as it is causing food insecurity and negatively impacts food availability, utilization, assessment, and stability. Wheat is the largest and extensively cultivated staple food crop for fulfilling global food requirements. Abiotic stresses including salinity, heavy metal toxicity, drought, extreme temperatures, and oxidative stresses being the primary cause of productivity loss are a serious threat to agronomy. Cold stress is a foremost ecological constraint that is extremely influencing plant development, and yield. It is extremely hampering the propagative development of plant life. The structure and function of plant cells depend on the cell's immune system. The stresses due to cold, affect fluid in the plasma membrane and change it into crystals or a solid gel phase. Plants being sessile in nature have evolved progressive systems that permit them to acclimatize the cold stress at the physiological as well as molecular levels. The phenomenon of acclimatisation of plants to cold stress has been investigated for the last 10 years. Studying cold tolerance is critical for extending the adaptability zones of perennial grasses. In the present review, we have elaborated the current improvement of cold tolerance in plants from molecular and physiological viewpoints, such as hormones, the role of the posttranscriptional gene, micro RNAs, ICE-CBF-COR signaling route in cold acclimatization and how they are stimulating the expression of underlying genes encoding osmoregulatory elements and strategies to improve cold tolerance in wheat.
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Affiliation(s)
- Arzoo Ahad
- Department of Plant Biotechnology, Atta-ur-Rahman School of Applied Biosciences (ASAB), National University of Sciences and Technology (NUST), Islamabad, Pakistan
| | - Alvina Gul
- Department of Plant Biotechnology, Atta-ur-Rahman School of Applied Biosciences (ASAB), National University of Sciences and Technology (NUST), Islamabad, Pakistan.
| | - Tuba Sharf Batool
- Department of Plant Biotechnology, Atta-ur-Rahman School of Applied Biosciences (ASAB), National University of Sciences and Technology (NUST), Islamabad, Pakistan
| | - Noor-Ul Huda
- Department of Plant Biotechnology, Atta-ur-Rahman School of Applied Biosciences (ASAB), National University of Sciences and Technology (NUST), Islamabad, Pakistan
| | - Faiza Naseeer
- Department of Industrial Biotechnology, ASAB, NUST, Islamabad, Pakistan
- Shifa College of Pharmaceutical Sciences, SCPS, STMU, Islamabad, Pakistan
| | - Uzma Abdul Salam
- Department of Plant Biotechnology, Atta-ur-Rahman School of Applied Biosciences (ASAB), National University of Sciences and Technology (NUST), Islamabad, Pakistan
| | - Maria Abdul Salam
- Department of Microbiology, Quaid-I-Azam University (QAU), Islamabad, Pakistan
| | - Mahnoor Ilyas
- Department of Plant Biotechnology, Atta-ur-Rahman School of Applied Biosciences (ASAB), National University of Sciences and Technology (NUST), Islamabad, Pakistan
| | - Bengu Turkyilmaz Unal
- Department of Biotechnology, Faculty of Arts & Sciences, Niğde Ömer Halisdemir University, Niğde, Turkey
| | - Munir Ozturk
- Botany Department and Centre for Environmental Studies, Ege University, Izmir, Turkey.
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17
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Kusmec A, Attigala L, Dai X, Srinivasan S, Yeh CTE, Schnable PS. A genetic tradeoff for tolerance to moderate and severe heat stress in US hybrid maize. PLoS Genet 2023; 19:e1010799. [PMID: 37410701 DOI: 10.1371/journal.pgen.1010799] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Accepted: 05/26/2023] [Indexed: 07/08/2023] Open
Abstract
Global climate change is increasing both average temperatures and the frequencies of extreme high temperatures. Past studies have documented a strong negative effect of exposures to temperatures >30°C on hybrid maize yields. However, these studies could not disentangle genetic adaptation via artificial selection from changes in agronomic practices. Because most of the earliest maize hybrids are no longer available, side-by-side comparisons with modern hybrids under current field conditions are generally impossible. Here, we report on the collection and curation of 81 years of public yield trial records covering 4,730 maize hybrids, which enabled us to model genetic variation for temperature responses among maize hybrids. We show that selection may have indirectly and inconsistently contributed to the genetic adaptation of maize to moderate heat stress over this time period while preserving genetic variance for continued adaptation. However, our results reveal the existence of a genetic tradeoff for tolerance to moderate and severe heat stress, leading to a decrease in tolerance to severe heat stress over the same time period. Both trends are particularly conspicuous since the mid-1970s. Such a tradeoff poses challenges to the continued adaptation of maize to warming climates due to a projected increase in the frequency of extreme heat events. Nevertheless, given recent advances in phenomics, enviromics, and physiological modeling, our results offer a degree of optimism for the capacity of plant breeders to adapt maize to warming climates, assuming appropriate levels of R&D investment.
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Affiliation(s)
- Aaron Kusmec
- Department of Agronomy, Iowa State University; Ames, Iowa, United States of America
| | - Lakshmi Attigala
- Department of Agronomy, Iowa State University; Ames, Iowa, United States of America
| | - Xiongtao Dai
- Department of Statistics, Iowa State University; Ames, Iowa, United States of America
| | - Srikant Srinivasan
- Plant Sciences Institute, Iowa State University; Ames, Iowa, United States of America
| | - Cheng-Ting Eddy Yeh
- Plant Sciences Institute, Iowa State University; Ames, Iowa, United States of America
| | - Patrick S Schnable
- Department of Agronomy, Iowa State University; Ames, Iowa, United States of America
- Plant Sciences Institute, Iowa State University; Ames, Iowa, United States of America
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18
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Fu R, Wang X. Modeling the influence of phenotypic plasticity on maize hybrid performance. PLANT COMMUNICATIONS 2023; 4:100548. [PMID: 36635964 DOI: 10.1016/j.xplc.2023.100548] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Revised: 12/31/2022] [Accepted: 01/10/2023] [Indexed: 05/11/2023]
Abstract
Phenotypic plasticity, the ability of an individual to alter its phenotype in response to changes in the environment, has been proposed as a target for breeding crop varieties with high environmental fitness. Here, we used phenotypic and genotypic data from multiple maize (Zea mays L.) populations to mathematically model phenotypic plasticity in response to the environment (PPRE) in inbred and hybrid lines. PPRE can be simply described by a linear model in which the two main parameters, intercept a and slope b, reflect two classes of genes responsive to endogenous (class A) and exogenous (class B) signals that coordinate plant development. Together, class A and class B genes contribute to the phenotypic plasticity of an individual in response to the environment. We also made connections between phenotypic plasticity and hybrid performance or general combining ability (GCA) of yield using 30 F1 hybrid populations generated by crossing the same maternal line with 30 paternal lines from different maize heterotic groups. We show that the parameters a and b from two given parental lines must be concordant to reach an ideal GCA of F1 yield. We hypothesize that coordinated regulation of the two classes of genes in the F1 hybrid genome is the basis for high GCA. Based on this theory, we built a series of predictive models to evaluate GCA in silico between parental lines of different heterotic groups.
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Affiliation(s)
- Ran Fu
- National Maize Improvement Center, College of Agronomy and Biotechnology, China Agricultural University, Beijing 100094, China; Frontiers Science Center for Molecular Design Breeding, College of Agronomy and Biotechnology, China Agricultural University, Beijing 100094, China
| | - Xiangfeng Wang
- National Maize Improvement Center, College of Agronomy and Biotechnology, China Agricultural University, Beijing 100094, China; Frontiers Science Center for Molecular Design Breeding, College of Agronomy and Biotechnology, China Agricultural University, Beijing 100094, China.
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19
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Kick DR, Wallace JG, Schnable JC, Kolkman JM, Alaca B, Beissinger TM, Edwards J, Ertl D, Flint-Garcia S, Gage JL, Hirsch CN, Knoll JE, de Leon N, Lima DC, Moreta DE, Singh MP, Thompson A, Weldekidan T, Washburn JD. Yield prediction through integration of genetic, environment, and management data through deep learning. G3 (BETHESDA, MD.) 2023; 13:jkad006. [PMID: 36625555 PMCID: PMC10085787 DOI: 10.1093/g3journal/jkad006] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 07/28/2022] [Accepted: 12/23/2022] [Indexed: 01/11/2023]
Abstract
Accurate prediction of the phenotypic outcomes produced by different combinations of genotypes, environments, and management interventions remains a key goal in biology with direct applications to agriculture, research, and conservation. The past decades have seen an expansion of new methods applied toward this goal. Here we predict maize yield using deep neural networks, compare the efficacy of 2 model development methods, and contextualize model performance using conventional linear and machine learning models. We examine the usefulness of incorporating interactions between disparate data types. We find deep learning and best linear unbiased predictor (BLUP) models with interactions had the best overall performance. BLUP models achieved the lowest average error, but deep learning models performed more consistently with similar average error. Optimizing deep neural network submodules for each data type improved model performance relative to optimizing the whole model for all data types at once. Examining the effect of interactions in the best-performing model revealed that including interactions altered the model's sensitivity to weather and management features, including a reduction of the importance scores for timepoints expected to have a limited physiological basis for influencing yield-those at the extreme end of the season, nearly 200 days post planting. Based on these results, deep learning provides a promising avenue for the phenotypic prediction of complex traits in complex environments and a potential mechanism to better understand the influence of environmental and genetic factors.
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Affiliation(s)
- Daniel R Kick
- United States Department of Agriculture, Agricultural Research Service Plant Genetics Research Unit, Columbia, MO 65211, USA
- Division of Plant Sciences, University of Missouri, Columbia, MO 65211, USA
| | - Jason G Wallace
- Department of Crop & Soil Science, University of Georgia, Athens, GA 30602, USA
| | - James C Schnable
- Center for Plant Science Innovation and Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE 68588, USA
| | - Judith M Kolkman
- School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
| | - Barış Alaca
- Division of Plant Breeding Methodology, Department of Crop Science, University of Goettingen, Goettingen 37073, Germany
- Center for Integrated Breeding Research, University of Goettingen, Goettingen 37073, Germany
| | - Timothy M Beissinger
- Division of Plant Breeding Methodology, Department of Crop Science, University of Goettingen, Goettingen 37073, Germany
- Center for Integrated Breeding Research, University of Goettingen, Goettingen 37073, Germany
| | - Jode Edwards
- United States Department of Agriculture, Agricultural Research Service, Ames, IA 50011, USA
| | - David Ertl
- Research and Business Development, Iowa Corn Promotion Board, Johnston, IA 50131, USA
| | - Sherry Flint-Garcia
- United States Department of Agriculture, Agricultural Research Service Plant Genetics Research Unit, Columbia, MO 65211, USA
| | - Joseph L Gage
- Department of Crop and Soil Sciences, North Carolina State University, Raleigh, NC 27695, USA
| | - Candice N Hirsch
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN 55108, USA
| | - Joseph E Knoll
- United States Department of Agriculture, Agricultural Research Service Crop Genetics and Breeding Research Unit, Tifton, GA 31793, USA
| | - Natalia de Leon
- Department of Agronomy, University of Wisconsin, Madison, WI 53706, USA
| | - Dayane C Lima
- Plant Breeding and Plant Genetics Program, University of Wisconsin, Madison, WI 53706, USA
| | - Danilo E Moreta
- School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
| | - Maninder P Singh
- Department of Plant, Soil and Microbial Sciences, Michigan State University, East Lansing, MI 48824, USA
| | - Addie Thompson
- Department of Plant, Soil and Microbial Sciences, Michigan State University, East Lansing, MI 48824, USA
| | | | - Jacob D Washburn
- United States Department of Agriculture, Agricultural Research Service Plant Genetics Research Unit, Columbia, MO 65211, USA
- Division of Plant Sciences, University of Missouri, Columbia, MO 65211, USA
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20
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Manthena V, Jarquín D, Howard R. Integrating and optimizing genomic, weather, and secondary trait data for multiclass classification. Front Genet 2023; 13:1032691. [PMID: 37065625 PMCID: PMC10090538 DOI: 10.3389/fgene.2022.1032691] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Accepted: 12/22/2022] [Indexed: 04/18/2023] Open
Abstract
Modern plant breeding programs collect several data types such as weather, images, and secondary or associated traits besides the main trait (e.g., grain yield). Genomic data is high-dimensional and often over-crowds smaller data types when naively combined to explain the response variable. There is a need to develop methods able to effectively combine different data types of differing sizes to improve predictions. Additionally, in the face of changing climate conditions, there is a need to develop methods able to effectively combine weather information with genotype data to predict the performance of lines better. In this work, we develop a novel three-stage classifier to predict multi-class traits by combining three data types-genomic, weather, and secondary trait. The method addressed various challenges in this problem, such as confounding, differing sizes of data types, and threshold optimization. The method was examined in different settings, including binary and multi-class responses, various penalization schemes, and class balances. Then, our method was compared to standard machine learning methods such as random forests and support vector machines using various classification accuracy metrics and using model size to evaluate the sparsity of the model. The results showed that our method performed similarly to or better than machine learning methods across various settings. More importantly, the classifiers obtained were highly sparse, allowing for a straightforward interpretation of relationships between the response and the selected predictors.
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Affiliation(s)
- Vamsi Manthena
- Department of Statistics, University of Nebraska-Lincoln, Lincoln, NE, United States
| | - Diego Jarquín
- Agronomy Department, University of Florida, Gainesville, FL, United States
| | - Reka Howard
- Department of Statistics, University of Nebraska-Lincoln, Lincoln, NE, United States
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21
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Guo T, Li X. Machine learning for predicting phenotype from genotype and environment. Curr Opin Biotechnol 2023; 79:102853. [PMID: 36463837 DOI: 10.1016/j.copbio.2022.102853] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Revised: 11/01/2022] [Accepted: 11/07/2022] [Indexed: 12/03/2022]
Abstract
Predicting phenotype with genomic and environmental information is critically needed and challenging. Machine learning methods have emerged as powerful tools to make accurate predictions from large and complex biological data. Here, we review the progress of phenotype prediction models enabled or improved by machine learning methods. We categorized the applications into three scenarios: prediction with genotypic information, with environmental information, and with both. In each scenario, we illustrate the practicality of prediction models, the advantages of machine learning, and the challenges of modeling complex relationships. We discuss the promising potential of leveraging machine learning and genetics theories to develop models that can predict phenotype and also interpret the biological consequences of changes in genotype and environment.
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Affiliation(s)
- Tingting Guo
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China; Hubei Hongshan Laboratory, Wuhan 430070, China.
| | - Xianran Li
- USDA, Agricultural Research Service, Wheat Health, Genetics, and Quality Research Unit, Pullman, WA 99164, USA; Department of Crop and Soil Sciences, Washington State University, Pullman, WA 99164, USA.
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22
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Napier JD, Heckman RW, Juenger TE. Gene-by-environment interactions in plants: Molecular mechanisms, environmental drivers, and adaptive plasticity. THE PLANT CELL 2023; 35:109-124. [PMID: 36342220 PMCID: PMC9806611 DOI: 10.1093/plcell/koac322] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Accepted: 11/03/2022] [Indexed: 05/13/2023]
Abstract
Plants demonstrate a broad range of responses to environmental shifts. One of the most remarkable responses is plasticity, which is the ability of a single plant genotype to produce different phenotypes in response to environmental stimuli. As with all traits, the ability of plasticity to evolve depends on the presence of underlying genetic diversity within a population. A common approach for evaluating the role of genetic variation in driving differences in plasticity has been to study genotype-by-environment interactions (G × E). G × E occurs when genotypes produce different phenotypic trait values in response to different environments. In this review, we highlight progress and promising methods for identifying the key environmental and genetic drivers of G × E. Specifically, methodological advances in using algorithmic and multivariate approaches to understand key environmental drivers combined with new genomic innovations can greatly increase our understanding about molecular responses to environmental stimuli. These developing approaches can be applied to proliferating common garden networks that capture broad natural environmental gradients to unravel the underlying mechanisms of G × E. An increased understanding of G × E can be used to enhance the resilience and productivity of agronomic systems.
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Affiliation(s)
- Joseph D Napier
- Department of Integrative Biology, The University of Texas at Austin, Austin, Texas, 78712, USA
| | - Robert W Heckman
- Department of Integrative Biology, The University of Texas at Austin, Austin, Texas, 78712, USA
| | - Thomas E Juenger
- Department of Integrative Biology, The University of Texas at Austin, Austin, Texas, 78712, USA
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23
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Cooper M, Messina CD. Breeding crops for drought-affected environments and improved climate resilience. THE PLANT CELL 2023; 35:162-186. [PMID: 36370076 PMCID: PMC9806606 DOI: 10.1093/plcell/koac321] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Accepted: 11/01/2022] [Indexed: 05/12/2023]
Abstract
Breeding climate-resilient crops with improved levels of abiotic and biotic stress resistance as a response to climate change presents both opportunities and challenges. Applying the framework of the "breeder's equation," which is used to predict the response to selection for a breeding program cycle, we review methodologies and strategies that have been used to successfully breed crops with improved levels of drought resistance, where the target population of environments (TPEs) is a spatially and temporally heterogeneous mixture of drought-affected and favorable (water-sufficient) environments. Long-term improvement of temperate maize for the US corn belt is used as a case study and compared with progress for other crops and geographies. Integration of trait information across scales, from genomes to ecosystems, is needed to accurately predict yield outcomes for genotypes within the current and future TPEs. This will require transdisciplinary teams to explore, identify, and exploit novel opportunities to accelerate breeding program outcomes; both improved germplasm resources and improved products (cultivars, hybrids, clones, and populations) that outperform and replace the products in use by farmers, in combination with modified agronomic management strategies suited to their local environments.
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Affiliation(s)
- Mark Cooper
- Queensland Alliance for Agriculture and Food Innovation (QAAFI), The University of Queensland, Brisbane, Queensland 4072, Australia
- ARC Centre of Excellence for Plant Success in Nature and Agriculture, The University of Queensland, Brisbane, Queensland 4072, Australia
| | - Carlos D Messina
- Horticultural Sciences Department, University of Florida, Gainesville, Florida 32611, USA
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24
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Kasemsap P, Bloom AJ. Breeding for Higher Yields of Wheat and Rice through Modifying Nitrogen Metabolism. PLANTS (BASEL, SWITZERLAND) 2022; 12:85. [PMID: 36616214 PMCID: PMC9823454 DOI: 10.3390/plants12010085] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 12/21/2022] [Accepted: 12/21/2022] [Indexed: 06/17/2023]
Abstract
Wheat and rice produce nutritious grains that provide 32% of the protein in the human diet globally. Here, we examine how genetic modifications to improve assimilation of the inorganic nitrogen forms ammonium and nitrate into protein influence grain yield of these crops. Successful breeding for modified nitrogen metabolism has focused on genes that coordinate nitrogen and carbon metabolism, including those that regulate tillering, heading date, and ammonium assimilation. Gaps in our current understanding include (1) species differences among candidate genes in nitrogen metabolism pathways, (2) the extent to which relative abundance of these nitrogen forms across natural soil environments shape crop responses, and (3) natural variation and genetic architecture of nitrogen-mediated yield improvement. Despite extensive research on the genetics of nitrogen metabolism since the rise of synthetic fertilizers, only a few projects targeting nitrogen pathways have resulted in development of cultivars with higher yields. To continue improving grain yield and quality, breeding strategies need to focus concurrently on both carbon and nitrogen assimilation and consider manipulating genes with smaller effects or that underlie regulatory networks as well as genes directly associated with nitrogen metabolism.
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Affiliation(s)
- Pornpipat Kasemsap
- Department of Plant Sciences, University of California at Davis, Mailstop 3, Davis, CA 95616, USA
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25
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Costa-Neto G, Crespo-Herrera L, Fradgley N, Gardner K, Bentley AR, Dreisigacker S, Fritsche-Neto R, Montesinos-López OA, Crossa J. Envirome-wide associations enhance multi-year genome-based prediction of historical wheat breeding data. G3 (BETHESDA, MD.) 2022; 13:6861853. [PMID: 36454213 PMCID: PMC9911085 DOI: 10.1093/g3journal/jkac313] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 11/02/2022] [Accepted: 11/03/2022] [Indexed: 12/03/2022]
Abstract
Linking high-throughput environmental data (enviromics) to genomic prediction (GP) is a cost-effective strategy for increasing selection intensity under genotype-by-environment interactions (G × E). This study developed a data-driven approach based on Environment-Phenotype Association (EPA) aimed at recycling important G × E information from historical breeding data. EPA was developed in two applications: (1) scanning a secondary source of genetic variation, weighted from the shared reaction-norms of past-evaluated genotypes and (2) pinpointing weights of the similarity among trial-sites (locations), given the historical impact of each envirotyping data variable for a given site. These results were then used as a dimensionality reduction strategy, integrating historical data to feed multi-environment GP models, which led to the development of four new G × E kernels considering genomics, enviromics, and EPA outcomes. The wheat trial data used included 36 locations, 8 years, and three target populations of environments (TPEs) in India. Four prediction scenarios and six kernel models within/across TPEs were tested. Our results suggest that the conventional GBLUP, without enviromic data or when omitting EPA, is inefficient in predicting the performance of wheat lines in future years. Nevertheless, when EPA was introduced as an intermediary learning step to reduce the dimensionality of the G × E kernels while connecting phenotypic and environmental-wide variation, a significant enhancement of G × E prediction accuracy was evident. EPA revealed that the effect of seasonality makes strategies such as "covariable selection" unfeasible because G × E is year-germplasm specific. We propose that the EPA effectively serves as a "reinforcement learner" algorithm capable of uncovering the effect of seasonality over the reaction-norms, with the benefits of better forecasting the similarities between past and future trialing sites. EPA combines the benefits of dimensionality reduction while reducing the uncertainty of genotype-by-year predictions and increasing the resolution of GP for the genotype-specific level.
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Affiliation(s)
- Germano Costa-Neto
- Institute for Genomics Diversity, Cornell University, Ithaca, NY 14853, USA
| | - Leonardo Crespo-Herrera
- International Maize and Wheat Improvement Center (CIMMYT), Km 45 Carretera México-Veracruz, El Batan, Edo. de México 5623, Mexico
| | - Nick Fradgley
- NIAB, 93 Lawrence Weaver Road, Cambridge CB3 0LE, UK
| | - Keith Gardner
- International Maize and Wheat Improvement Center (CIMMYT), Km 45 Carretera México-Veracruz, El Batan, Edo. de México 5623, Mexico
| | - Alison R Bentley
- International Maize and Wheat Improvement Center (CIMMYT), Km 45 Carretera México-Veracruz, El Batan, Edo. de México 5623, Mexico
| | - Susanne Dreisigacker
- International Maize and Wheat Improvement Center (CIMMYT), Km 45 Carretera México-Veracruz, El Batan, Edo. de México 5623, Mexico
| | | | - Osval A Montesinos-López
- Corresponding authors: Facultad de Telematica, Universidad de Colima, Mexico. ; and International Maize and Wheat Improvement Center (CIMMYT) and Colegio de Post-Graduados, Mexico.
| | - Jose Crossa
- Corresponding authors: Facultad de Telematica, Universidad de Colima, Mexico. ; and International Maize and Wheat Improvement Center (CIMMYT) and Colegio de Post-Graduados, Mexico.
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26
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Han X, Tang Q, Xu L, Guan Z, Tu J, Yi B, Liu K, Yao X, Lu S, Guo L. Genome-wide detection of genotype environment interactions for flowering time in Brassica napus. FRONTIERS IN PLANT SCIENCE 2022; 13:1065766. [PMID: 36479520 PMCID: PMC9721451 DOI: 10.3389/fpls.2022.1065766] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Accepted: 10/31/2022] [Indexed: 06/17/2023]
Abstract
Flowering time is strongly related to the environment, while the genotype-by-environment interaction study for flowering time is lacking in Brassica napus. Here, a total of 11,700,689 single nucleotide polymorphisms in 490 B. napus accessions were used to associate with the flowering time and related climatic index in eight environments using a compressed variance-component mixed model, 3VmrMLM. As a result, 19 stable main-effect quantitative trait nucleotides (QTNs) and 32 QTN-by-environment interactions (QEIs) for flowering time were detected. Four windows of daily average temperature and precipitation were found to be climatic factors highly correlated with flowering time. Ten main-effect QTNs were found to be associated with these flowering-time-related climatic indexes. Using differentially expressed gene (DEG) analysis in semi-winter and spring oilseed rapes, 5,850 and 5,511 DEGs were found to be significantly expressed before and after vernalization. Twelve and 14 DEGs, including 7 and 9 known homologs in Arabidopsis, were found to be candidate genes for stable QTNs and QEIs for flowering time, respectively. Five DEGs were found to be candidate genes for main-effect QTNs for flowering-time-related climatic index. These candidate genes, such as BnaFLCs, BnaFTs, BnaA02.VIN3, and BnaC09.PRR7, were further validated by the haplotype, selective sweep, and co-expression networks analysis. The candidate genes identified in this study will be helpful to breed B. napus varieties adapted to particular environments with optimized flowering time.
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Affiliation(s)
- Xu Han
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
- Hubei Hongshan Laboratory, Wuhan, China
| | - Qingqing Tang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
- Hubei Hongshan Laboratory, Wuhan, China
| | - Liping Xu
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
- Hubei Hongshan Laboratory, Wuhan, China
| | - Zhilin Guan
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
| | - Jinxing Tu
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
- Hubei Hongshan Laboratory, Wuhan, China
| | - Bin Yi
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
- Hubei Hongshan Laboratory, Wuhan, China
| | - Kede Liu
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
| | - Xuan Yao
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
- Hubei Hongshan Laboratory, Wuhan, China
| | - Shaoping Lu
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
- Hubei Hongshan Laboratory, Wuhan, China
| | - Liang Guo
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
- Hubei Hongshan Laboratory, Wuhan, China
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27
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Genetic Dissection of Phosphorus Use Efficiency and Genotype-by-Environment Interaction in Maize. Int J Mol Sci 2022; 23:ijms232213943. [PMID: 36430424 PMCID: PMC9697416 DOI: 10.3390/ijms232213943] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 11/07/2022] [Accepted: 11/08/2022] [Indexed: 11/16/2022] Open
Abstract
Genotype-by-environment interaction (G-by-E) is a common but potentially problematic phenomenon in plant breeding. In this study, we investigated the genotypic performance and two measures of plasticity on a phenotypic and genetic level by assessing 234 maize doubled haploid lines from six populations for 15 traits in seven macro-environments with a focus on varying soil phosphorus levels. It was found intergenic regions contributed the most to the variation of phenotypic linear plasticity. For 15 traits, 124 and 31 quantitative trait loci (QTL) were identified for genotypic performance and phenotypic plasticity, respectively. Further, some genes associated with phosphorus use efficiency, such as Zm00001eb117170, Zm00001eb258520, and Zm00001eb265410, encode small ubiquitin-like modifier E3 ligase were identified. By significantly testing the main effect and G-by-E effect, 38 main QTL and 17 interaction QTL were identified, respectively, in which MQTL38 contained the gene Zm00001eb374120, and its effect was related to phosphorus concentration in the soil, the lower the concentration, the greater the effect. Differences in the size and sign of the QTL effect in multiple environments could account for G-by-E. At last, the superiority of G-by-E in genomic selection was observed. In summary, our findings will provide theoretical guidance for breeding P-efficient and broadly adaptable varieties.
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28
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Xu Y, Zhang X, Li H, Zheng H, Zhang J, Olsen MS, Varshney RK, Prasanna BM, Qian Q. Smart breeding driven by big data, artificial intelligence, and integrated genomic-enviromic prediction. MOLECULAR PLANT 2022; 15:1664-1695. [PMID: 36081348 DOI: 10.1016/j.molp.2022.09.001] [Citation(s) in RCA: 51] [Impact Index Per Article: 25.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 08/20/2022] [Accepted: 09/02/2022] [Indexed: 05/12/2023]
Abstract
The first paradigm of plant breeding involves direct selection-based phenotypic observation, followed by predictive breeding using statistical models for quantitative traits constructed based on genetic experimental design and, more recently, by incorporation of molecular marker genotypes. However, plant performance or phenotype (P) is determined by the combined effects of genotype (G), envirotype (E), and genotype by environment interaction (GEI). Phenotypes can be predicted more precisely by training a model using data collected from multiple sources, including spatiotemporal omics (genomics, phenomics, and enviromics across time and space). Integration of 3D information profiles (G-P-E), each with multidimensionality, provides predictive breeding with both tremendous opportunities and great challenges. Here, we first review innovative technologies for predictive breeding. We then evaluate multidimensional information profiles that can be integrated with a predictive breeding strategy, particularly envirotypic data, which have largely been neglected in data collection and are nearly untouched in model construction. We propose a smart breeding scheme, integrated genomic-enviromic prediction (iGEP), as an extension of genomic prediction, using integrated multiomics information, big data technology, and artificial intelligence (mainly focused on machine and deep learning). We discuss how to implement iGEP, including spatiotemporal models, environmental indices, factorial and spatiotemporal structure of plant breeding data, and cross-species prediction. A strategy is then proposed for prediction-based crop redesign at both the macro (individual, population, and species) and micro (gene, metabolism, and network) scales. Finally, we provide perspectives on translating smart breeding into genetic gain through integrative breeding platforms and open-source breeding initiatives. We call for coordinated efforts in smart breeding through iGEP, institutional partnerships, and innovative technological support.
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Affiliation(s)
- Yunbi Xu
- Institute of Crop Sciences, CIMMYT-China, Chinese Academy of Agricultural Sciences, Beijing 100081, China; CIMMYT-China Tropical Maize Research Center, School of Food Science and Engineering, Foshan University, Foshan, Guangdong 528231, China; Peking University Institute of Advanced Agricultural Sciences, Weifang, Shandong 261325, China.
| | - Xingping Zhang
- Peking University Institute of Advanced Agricultural Sciences, Weifang, Shandong 261325, China
| | - Huihui Li
- Institute of Crop Sciences, CIMMYT-China, Chinese Academy of Agricultural Sciences, Beijing 100081, China; National Nanfan Research Institute (Sanya), Chinese Academy of Agricultural Sciences, Sanya, Hainan 572024, China
| | - Hongjian Zheng
- CIMMYT-China Specialty Maize Research Center, Shanghai Academy of Agricultural Sciences, Shanghai 201400, China
| | - Jianan Zhang
- MolBreeding Biotechnology Co., Ltd., Shijiazhuang, Hebei 050035, China
| | - Michael S Olsen
- CIMMYT (International Maize and Wheat Improvement Center), ICRAF Campus, United Nations Avenue, Nairobi, Kenya
| | - Rajeev K Varshney
- State Agricultural Biotechnology Centre, Centre for Crop and Food Innovation, Food Futures Institute, Murdoch University, Murdoch, Australia
| | - Boddupalli M Prasanna
- CIMMYT (International Maize and Wheat Improvement Center), ICRAF Campus, United Nations Avenue, Nairobi, Kenya
| | - Qian Qian
- Institute of Crop Sciences, CIMMYT-China, Chinese Academy of Agricultural Sciences, Beijing 100081, China
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29
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Egan LM, Stiller WN. The Past, Present, and Future of Host Plant Resistance in Cotton: An Australian Perspective. FRONTIERS IN PLANT SCIENCE 2022; 13:895877. [PMID: 35873986 PMCID: PMC9297922 DOI: 10.3389/fpls.2022.895877] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Accepted: 06/06/2022] [Indexed: 05/24/2023]
Abstract
Cotton is a key global fiber crop. However, yield potential is limited by the presence of endemic and introduced pests and diseases. The introduction of host plant resistance (HPR), defined as the purposeful use of resistant crop cultivars to reduce the impact of pests and diseases, has been a key breeding target for the Commonwealth Scientific and Industrial Research Organisation (CSIRO) cotton breeding program. The program has seen success in releasing cultivars resistant to Bacterial blight, Verticillium wilt, Fusarium wilt, and Cotton bunchy top. However, emerging biotic threats such as Black root rot and secondary pests, are becoming more frequent in Australian cotton production systems. The uptake of tools and breeding methods, such as genomic selection, high throughput phenomics, gene editing, and landscape genomics, paired with the continued utilization of sources of resistance from Gossypium germplasm, will be critical for the future of cotton breeding. This review celebrates the success of HPR breeding activities in the CSIRO cotton breeding program and maps a pathway for the future in developing resistant cultivars.
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30
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Piepho HP. Prediction of and for new environments: What's your model? MOLECULAR PLANT 2022; 15:581-582. [PMID: 35104675 DOI: 10.1016/j.molp.2022.01.018] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Revised: 01/19/2022] [Accepted: 01/19/2022] [Indexed: 06/14/2023]
Affiliation(s)
- Hans-Peter Piepho
- University of Hohenheim, Institute of Crop Science, Biostatistics Unit, Fruwirthstrasse 23, 70599 Stuttgart, Germany.
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31
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Li M, Zhang YW, Zhang ZC, Xiang Y, Liu MH, Zhou YH, Zuo JF, Zhang HQ, Chen Y, Zhang YM. A compressed variance component mixed model for detecting QTNs and QTN-by-environment and QTN-by-QTN interactions in genome-wide association studies. MOLECULAR PLANT 2022; 15:630-650. [PMID: 35202864 DOI: 10.1016/j.molp.2022.02.012] [Citation(s) in RCA: 50] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/04/2021] [Revised: 01/26/2022] [Accepted: 02/19/2022] [Indexed: 05/25/2023]
Abstract
Although genome-wide association studies are widely used to mine genes for quantitative traits, the effects to be estimated are confounded, and the methodologies for detecting interactions are imperfect. To address these issues, the mixed model proposed here first estimates the genotypic effects for AA, Aa, and aa, and the genotypic polygenic background replaces additive and dominance polygenic backgrounds. Then, the estimated genotypic effects are partitioned into additive and dominance effects using a one-way analysis of variance model. This strategy was further expanded to cover QTN-by-environment interactions (QEIs) and QTN-by-QTN interactions (QQIs) using the same mixed-model framework. Thus, a three-variance-component mixed model was integrated with our multi-locus random-SNP-effect mixed linear model (mrMLM) method to establish a new methodological framework, 3VmrMLM, that detects all types of loci and estimates their effects. In Monte Carlo studies, 3VmrMLM correctly detected all types of loci and almost unbiasedly estimated their effects, with high powers and accuracies and a low false positive rate. In re-analyses of 10 traits in 1439 rice hybrids, detection of 269 known genes, 45 known gene-by-environment interactions, and 20 known gene-by-gene interactions strongly validated 3VmrMLM. Further analyses of known genes showed more small (67.49%), minor-allele-frequency (35.52%), and pleiotropic (30.54%) genes, with higher repeatability across datasets (54.36%) and more dominance loci. In addition, a heteroscedasticity mixed model in multiple environments and dimension reduction methods in quite a number of environments were developed to detect QEIs, and variable selection under a polygenic background was proposed for QQI detection. This study provides a new approach for revealing the genetic architecture of quantitative traits.
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Affiliation(s)
- Mei Li
- Crop Information Center, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Ya-Wen Zhang
- Crop Information Center, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China; State Key Laboratory of Cotton Biology, Anyang 455000, China
| | - Ze-Chang Zhang
- Crop Information Center, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Yu Xiang
- Crop Information Center, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Ming-Hui Liu
- Crop Information Center, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Ya-Hui Zhou
- Crop Information Center, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Jian-Fang Zuo
- Crop Information Center, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Han-Qing Zhang
- Crop Information Center, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Ying Chen
- Crop Information Center, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Yuan-Ming Zhang
- Crop Information Center, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China.
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Abstract
Winter wheat growing areas in the Northern hemisphere are regularly exposed to heavy frost. Due to the negative impact on yield, the identification of genetic factors controlling frost tolerance (FroT) and development of tools for breeding is of prime importance. Here, we detected QTL associated with FroT by genome wide association studies (GWAS) using a diverse panel of 276 winter wheat genotypes that was phenotyped at five locations in Germany and Russia in three years. The panel was genotyped using the 90 K iSelect array and SNPs in FroT candidate genes. In total, 17,566 SNPs were used for GWAS resulting in the identification of 53 markers significantly associated (LOD ≥ 4) to FroT, corresponding to 23 QTL regions located on 11 chromosomes (1A, 1B, 2A, 2B, 2D, 3A, 3D, 4A, 5A, 5B and 7D). The strongest QTL effect confirmed the importance of chromosome 5A for FroT. In addition, to our best knowledge, eight FroT QTLs were discovered for the first time in this study comprising one QTL on chromosomes 3A, 3D, 4A, 7D and two on chromosomes 1B and 2D. Identification of novel FroT candidate genes will help to better understand the FroT mechanism in wheat and to develop more effective combating strategies.
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Zhou YH, Li G, Zhang YM. A compressed variance component mixed model framework for detecting small and linked QTL-by-environment interactions. Brief Bioinform 2022; 23:6527275. [DOI: 10.1093/bib/bbab596] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Revised: 12/07/2021] [Accepted: 12/23/2021] [Indexed: 12/22/2022] Open
Abstract
Abstract
Detecting small and linked quantitative trait loci (QTLs) and QTL-by-environment interactions (QEIs) for complex traits is a difficult issue in immortalized F2 and F2:3 design, especially in the era of global climate change and environmental plasticity research. Here we proposed a compressed variance component mixed model. In this model, a parametric vector of QTL genotype and environment combination effects replaced QTL effects, environmental effects and their interaction effects, whereas the combination effect polygenic background replaced the QTL and QEI polygenic backgrounds. Thus, the number of variance components in the mixed model was greatly reduced. The model was incorporated into our genome-wide composite interval mapping (GCIM) to propose GCIM-QEI-random and GCIM-QEI-fixed, respectively, under random and fixed models of genetic effects. First, potentially associated QTLs and QEIs were selected from genome-wide scanning. Then, significant QTLs and QEIs were identified using empirical Bayes and likelihood ratio test. Finally, known and candidate genes around these significant loci were mined. The new methods were validated by a series of simulation studies and real data analyses. Compared with ICIM, GCIM-QEI-random had 29.77 ± 18.20% and 24.33 ± 10.15% higher average power, respectively, in 0.5–3.0% QTL and QEI detection, 43.44 ± 9.53% and 51.47 ± 15.70% higher average power, respectively, in linked QTL and QEI detection, and identified 30 more known genes for four rice yield traits, because GCIM-QEI-random identified more small genes/loci, being 2.69 ± 2.37% for additional genes. GCIM-QEI-random was slightly better than GCIM-QEI-fixed. In addition, the new methods may be extended into backcross and genome-wide association studies. This study provides effective methods for detecting small-effect and linked QTLs and QEIs.
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Affiliation(s)
- Ya-Hui Zhou
- College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Guo Li
- College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
- State Key Laboratory of Cotton Biology, Anyang 455000, China
| | - Yuan-Ming Zhang
- College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
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Li X, Guo T, Bai G, Zhang Z, See D, Marshall J, Garland-Campbell KA, Yu J. Genetics-inspired data-driven approaches explain and predict crop performance fluctuations attributed to changing climatic conditions. MOLECULAR PLANT 2022; 15:203-206. [PMID: 34999020 DOI: 10.1016/j.molp.2022.01.001] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/11/2021] [Revised: 12/17/2021] [Accepted: 01/04/2022] [Indexed: 06/14/2023]
Affiliation(s)
- Xianran Li
- USDA, Agricultural Research Service, Wheat Health, Genetics, and Quality Research Unit, Pullman, WA 99164, USA.
| | - Tingting Guo
- Department of Agronomy, Iowa State University, Ames, IA 50011, USA
| | - Guihua Bai
- USDA, Agricultural Research Service, Hard Winter Wheat Genetics Research Unit, Manhattan, KS 66506, USA
| | - Zhiwu Zhang
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA 99164, USA
| | - Deven See
- USDA, Agricultural Research Service, Wheat Health, Genetics, and Quality Research Unit, Pullman, WA 99164, USA
| | - Juliet Marshall
- Department of Plant Sciences, University of Idaho Research and Extension, Idaho Falls, ID 83402, USA
| | - Kimberly A Garland-Campbell
- USDA, Agricultural Research Service, Wheat Health, Genetics, and Quality Research Unit, Pullman, WA 99164, USA
| | - Jianming Yu
- Department of Agronomy, Iowa State University, Ames, IA 50011, USA
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Mu Q, Guo T, Li X, Yu J. Phenotypic plasticity in plant height shaped by interaction between genetic loci and diurnal temperature range. THE NEW PHYTOLOGIST 2022; 233:1768-1779. [PMID: 34870847 DOI: 10.1111/nph.17904] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Accepted: 11/21/2021] [Indexed: 06/13/2023]
Abstract
Phenotypic plasticity is observed widely in plants and often studied with reaction norms for adult plant or end-of-season traits. Uncovering genetic, environmental and developmental patterns behind the observed phenotypic variation under natural field conditions is needed. Using a sorghum (Sorghum bicolor) genetic population evaluated for plant height in seven natural field conditions, we investigated the major pattern that differentiated these environments. We then 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 conducting crop growth modelling. We found that diurnal temperature range (DTR) during the rapid growth period of sorghum development was an effective environmental index. Three genetic loci (Dw1, Dw3 and qHT7.1) were consistently detected for individual environments, reaction-norm parameters across environments and growth-curve parameters through 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. Beyond genomic and environmental analyses, further integration of development and physiology at the whole-plant and molecular levels into complex trait dissection would enhance our understanding of mechanisms underlying phenotypic variation.
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Affiliation(s)
- Qi Mu
- Department of Agronomy, Iowa State University, Ames, IA, 50011, USA
| | - Tingting Guo
- Department of Agronomy, Iowa State University, Ames, IA, 50011, USA
| | - Xianran Li
- Department of Agronomy, Iowa State University, Ames, IA, 50011, USA
| | - Jianming Yu
- Department of Agronomy, Iowa State University, Ames, IA, 50011, USA
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36
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Rogers AR, Holland JB. Environment-specific genomic prediction ability in maize using environmental covariates depends on environmental similarity to training data. G3 (BETHESDA, MD.) 2021; 12:6486423. [PMID: 35100364 PMCID: PMC9245610 DOI: 10.1093/g3journal/jkab440] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Accepted: 12/06/2021] [Indexed: 12/30/2022]
Abstract
Technology advances have made possible the collection of a wealth of genomic, environmental, and phenotypic data for use in plant breeding. Incorporation of environmental data into environment-specific genomic prediction is hindered in part because of inherently high data dimensionality. Computationally efficient approaches to combining genomic and environmental information may facilitate extension of genomic prediction models to new environments and germplasm, and better understanding of genotype-by-environment (G × E) interactions. Using genomic, yield trial, and environmental data on 1,918 unique hybrids evaluated in 59 environments from the maize Genomes to Fields project, we determined that a set of 10,153 SNP dominance coefficients and a 5-day temporal window size for summarizing environmental variables were optimal for genomic prediction using only genetic and environmental main effects. Adding marker-by-environment variable interactions required dimension reduction, and we found that reducing dimensionality of the genetic data while keeping the full set of environmental covariates was best for environment-specific genomic prediction of grain yield, leading to an increase in prediction ability of 2.7% to achieve a prediction ability of 80% across environments when data were masked at random. We then measured how prediction ability within environments was affected under stratified training-testing sets to approximate scenarios commonly encountered by plant breeders, finding that incorporation of marker-by-environment effects improved prediction ability in cases where training and test sets shared environments, but did not improve prediction in new untested environments. The environmental similarity between training and testing sets had a greater impact on the efficacy of prediction than genetic similarity between training and test sets.
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Affiliation(s)
- Anna R Rogers
- Program in Genetics, North Carolina State University, Raleigh, NC
27695, USA
| | - James B Holland
- Program in Genetics, North Carolina State University, Raleigh, NC
27695, USA,USDA-ARS Plant Science Research Unit, North Carolina State
University, Raleigh, NC 27695, USA,Department of Crop and Soil Sciences, North Carolina State
University, Raleigh, NC 27695, USA,Corresponding author: Department of Agriculture—Agriculture
Research Service, Box 7620 North Carolina State University, Raleigh, NC 27695-7620, USA.
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37
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Washburn JD, Cimen E, Ramstein G, Reeves T, O'Briant P, McLean G, Cooper M, Hammer G, Buckler ES. Predicting phenotypes from genetic, environment, management, and historical data using CNNs. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2021; 134:3997-4011. [PMID: 34448888 DOI: 10.1007/s00122-021-03943-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Accepted: 08/18/2021] [Indexed: 06/13/2023]
Abstract
Convolutional Neural Networks (CNNs) can perform similarly or better than standard genomic prediction methods when sufficient genetic, environmental, and management data are provided. Predicting phenotypes from genetic (G), environmental (E), and management (M) conditions is a long-standing challenge with implications to agriculture, medicine, and conservation. Most methods reduce the factors in a dataset (feature engineering) in a subjective and potentially oversimplified manner. Deep neural networks such as Multilayer Perceptrons (MPL) and Convolutional Neural Networks (CNN) can overcome this by allowing the data itself to determine which factors are most important. CNN models were developed for predicting agronomic yield from a combination of replicated trials and historical yield survey data. The results were more accurate than standard methods when tested on held-out G, E, and M data (r = 0.50 vs. r = 0.43), and performed slightly worse than standard methods when only G was held out (r = 0.74 vs. r = 0.80). Pre-training on historical data increased accuracy compared to trial data alone. Saliency map analysis indicated the CNN has "learned" to prioritize many factors of known agricultural importance.
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Affiliation(s)
- Jacob D Washburn
- United States Department of Agriculture, Agricultural Research Service, Columbia, MO, 65211, USA.
| | - Emre Cimen
- Institute for Genomic Diversity, Cornell University, Ithaca, NY, 14853, USA
- Computational Intelligence and Optimization Laboratory, Industrial Engineering Department, Eskisehir Technical University, Eskisehir, Turkey
| | - Guillaume Ramstein
- Institute for Genomic Diversity, Cornell University, Ithaca, NY, 14853, USA
- Center for Quantitative Genetics and Genomics, Aarhus University, 8000, Aarhus, Denmark
| | - Timothy Reeves
- Institute for Genomic Diversity, Cornell University, Ithaca, NY, 14853, USA
| | - Patrick O'Briant
- Institute for Genomic Diversity, Cornell University, Ithaca, NY, 14853, USA
| | - Greg McLean
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St. Lucia, Brisbane, QLD, 4072, Australia
| | - Mark Cooper
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St. Lucia, Brisbane, QLD, 4072, Australia
| | - Graeme Hammer
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St. Lucia, Brisbane, QLD, 4072, Australia
| | - Edward S Buckler
- Institute for Genomic Diversity, Cornell University, Ithaca, NY, 14853, USA
- Department of Agriculture, Agricultural Research Service, Ithaca, NY, 14850, USA
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38
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Maurer A, Pillen K. Footprints of Selection Derived From Temporal Heterozygosity Patterns in a Barley Nested Association Mapping Population. FRONTIERS IN PLANT SCIENCE 2021; 12:764537. [PMID: 34721490 PMCID: PMC8551860 DOI: 10.3389/fpls.2021.764537] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Accepted: 09/22/2021] [Indexed: 06/13/2023]
Abstract
Nowadays, genetic diversity more than ever represents a key driver of adaptation to climate challenges like drought, heat, and salinity. Therefore, there is a need to replenish the limited elite gene pools with favorable exotic alleles from the wild progenitors of our crops. Nested association mapping (NAM) populations represent one step toward exotic allele evaluation and enrichment of the elite gene pool. We investigated an adaptive selection strategy in the wild barley NAM population HEB-25 based on temporal genomic data by studying the fate of 214,979 SNP loci initially heterozygous in individual BC1S3 lines after five cycles of selfing and field propagation. We identified several loci exposed to adaptive selection in HEB-25. In total, 48.7% (104,725 SNPs) of initially heterozygous SNP calls in HEB-25 were fixed in BC1S3:8 generation, either toward the wild allele (19.9%) or the cultivated allele (28.8%). Most fixed SNP loci turned out to represent gene loci involved in domestication and flowering time as well as plant height, for example, btr1/btr2, thresh-1, Ppd-H1, and sdw1. Interestingly, also unknown loci were found where the exotic allele was fixed, hinting at potentially useful exotic alleles for plant breeding.
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Varshney RK, Bohra A, Yu J, Graner A, Zhang Q, Sorrells ME. Designing Future Crops: Genomics-Assisted Breeding Comes of Age. TRENDS IN PLANT SCIENCE 2021; 26:631-649. [PMID: 33893045 DOI: 10.1016/j.tplants.2021.03.010] [Citation(s) in RCA: 162] [Impact Index Per Article: 54.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/11/2020] [Revised: 03/16/2021] [Accepted: 03/17/2021] [Indexed: 05/18/2023]
Abstract
Over the past decade, genomics-assisted breeding (GAB) has been instrumental in harnessing the potential of modern genome resources and characterizing and exploiting allelic variation for germplasm enhancement and cultivar development. Sustaining GAB in the future (GAB 2.0) will rely upon a suite of new approaches that fast-track targeted manipulation of allelic variation for creating novel diversity and facilitate their rapid and efficient incorporation in crop improvement programs. Genomic breeding strategies that optimize crop genomes with accumulation of beneficial alleles and purging of deleterious alleles will be indispensable for designing future crops. In coming decades, GAB 2.0 is expected to play a crucial role in breeding more climate-smart crop cultivars with higher nutritional value in a cost-effective and timely manner.
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Affiliation(s)
- Rajeev K Varshney
- Center of Excellence in Genomics and Systems Biology (CEGSB), International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, India; State Agricultural Biotechnology Centre, Centre for Crop and Food Innovation, Food Futures Institute, Murdoch University, Murdoch, Western Australia, Australia.
| | - Abhishek Bohra
- Crop Improvement Division, ICAR- Indian Institute of Pulses Research (ICAR- IIPR), Kanpur, India
| | - Jianming Yu
- Department of Agronomy, Iowa State University, Ames, IA, USA
| | - Andreas Graner
- Leibniz Institute of Plant Genetics and Crops Plant Research (IPK), Gatersleben, Germany
| | - Qifa Zhang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
| | - Mark E Sorrells
- Department of Plant Breeding and Genetics, Cornell University, Ithaca, NY, USA
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40
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Cooper M, Messina CD. Can We Harness "Enviromics" to Accelerate Crop Improvement by Integrating Breeding and Agronomy? FRONTIERS IN PLANT SCIENCE 2021; 12:735143. [PMID: 34567047 PMCID: PMC8461239 DOI: 10.3389/fpls.2021.735143] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Accepted: 08/16/2021] [Indexed: 05/02/2023]
Abstract
The diverse consequences of genotype-by-environment (GxE) interactions determine trait phenotypes across levels of biological organization for crops, challenging our ambition to predict trait phenotypes from genomic information alone. GxE interactions have many implications for optimizing both genetic gain through plant breeding and crop productivity through on-farm agronomic management. Advances in genomics technologies have provided many suitable predictors for the genotype dimension of GxE interactions. Emerging advances in high-throughput proximal and remote sensor technologies have stimulated the development of "enviromics" as a community of practice, which has the potential to provide suitable predictors for the environment dimension of GxE interactions. Recently, several bespoke examples have emerged demonstrating the nascent potential for enhancing the prediction of yield and other complex trait phenotypes of crop plants through including effects of GxE interactions within prediction models. These encouraging results motivate the development of new prediction methods to accelerate crop improvement. If we can automate methods to identify and harness suitable sets of coordinated genotypic and environmental predictors, this will open new opportunities to upscale and operationalize prediction of the consequences of GxE interactions. This would provide a foundation for accelerating crop improvement through integrating the contributions of both breeding and agronomy. Here we draw on our experience from improvement of maize productivity for the range of water-driven environments across the US corn-belt. We provide perspectives from the maize case study to prioritize promising opportunities to further develop and automate "enviromics" methodologies to accelerate crop improvement through integrated breeding and agronomic approaches for a wider range of crops and environmental targets.
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Affiliation(s)
- Mark Cooper
- Queensland Alliance for Agriculture and Food Innovation (QAAFI), The University of Queensland, Brisbane, QLD, Australia
- *Correspondence: Mark Cooper,
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