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Piepho HP, Williams E. Factor-Analytic Variance-Covariance Structures for Prediction Into a Target Population of Environments. Biom J 2024; 66:e202400008. [PMID: 39049627 DOI: 10.1002/bimj.202400008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Revised: 06/04/2024] [Accepted: 06/06/2024] [Indexed: 07/27/2024]
Abstract
Finlay-Wilkinson regression is a popular method for modeling genotype-environment interaction in plant breeding and crop variety testing. When environment is a random factor, this model may be cast as a factor-analytic variance-covariance structure, implying a regression on random latent environmental variables. This paper reviews such models with a focus on their use in the analysis of multi-environment trials for the purpose of making predictions in a target population of environments. We investigate the implication of random versus fixed effects assumptions, starting from basic analysis-of-variance models, then moving on to factor-analytic models and considering the transition to models involving observable environmental covariates, which promise to provide more accurate and targeted predictions than models with latent environmental variables.
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Affiliation(s)
- Hans-Peter Piepho
- Biostatistics Unit, Institute of Crop Science, University of Hohenheim, Stuttgart, Germany
| | - Emlyn Williams
- Statistical Support Network, Australian National University, Canberra, ACT, Australia
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Groli EL, Frascaroli E, Maccaferri M, Ammar K, Tuberosa R. Dissecting the effect of heat stress on durum wheat under field conditions. FRONTIERS IN PLANT SCIENCE 2024; 15:1393349. [PMID: 39006958 PMCID: PMC11239346 DOI: 10.3389/fpls.2024.1393349] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Accepted: 05/30/2024] [Indexed: 07/16/2024]
Abstract
Introduction Heat stress negatively affects wheat production in several ways, mainly by reducing growth rate, photosynthetic capacity and reducing spike fertility. Modeling stress response means analyzing simultaneous relationships among traits affecting the whole plant response and determinants of grain yield. The aim of this study was to dissect the diverse impacts of heat stress on key yield traits and to identify the most promising sources of alleles for heat tolerance. Methods We evaluated a diverse durum wheat panel of 183 cultivars and breeding lines from worldwide, for their response to long-term heat stress under field conditions (HS) with respect to non stress conditions (NS), considering phenological traits, grain yield (GY) and its components as a function of the timing of heat stress and climatic covariates. We investigated the relationships among plant and environmental variables by means of a structural equation model (SEM) and Genetic SEM (GSEM). Results Over two years of experiments at CENEB, CIMMYT, the effects of HS were particularly pronounced for the normalized difference vegetation index, NDVI (-51.3%), kernel weight per spike, KWS (-40.5%), grain filling period, GFP (-38.7%), and GY (-56.6%). Average temperatures around anthesis were negatively correlated with GY, thousand kernel weight TKW and test weight TWT, but also with spike density, a trait determined before heading/anthesis. Under HS, the correlation between the three major determinants of GY, i.e., fertile spike density, spike fertility and kernel size, were of noticeable magnitude. NDVI measured at medium milk-soft dough stage under HS was correlated with both spike fertility and grain weight while under NS it was less predictive of grain weight but still highly correlated with spike fertility. GSEM modeling suggested that the causal model of performance under HS directly involves genetic effects on GY, NDVI, KWS and HD. Discussion We identified consistently suitable sources of genetic resistance to heat stress to be used in different durum wheat pre-breeding programs. Among those, Desert Durums and CIMMYT'80 germplasm showed the highest degree of adaptation and capacity to yield under high temperatures and can be considered as a valuable source of alleles for adaptation to breed new HS resilient cultivars.
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Affiliation(s)
- Eder Licieri Groli
- Department of Agricultural and Food Sciences, DISTAL, University of Bologna, Bologna, Italy
| | - Elisabetta Frascaroli
- Department of Agricultural and Food Sciences, DISTAL, University of Bologna, Bologna, Italy
| | - Marco Maccaferri
- Department of Agricultural and Food Sciences, DISTAL, University of Bologna, Bologna, Italy
| | - Karim Ammar
- International Maize and Wheat Improvement Center, CIMMYT, El Batán, Mexico
| | - Roberto Tuberosa
- Department of Agricultural and Food Sciences, DISTAL, University of Bologna, Bologna, Italy
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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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Nataraj V, Gupta S, Singh KH, Choyal P, Nargund R, Shivakumar M, Agrawal N, Kumawat G, Rajesh V, Verma RK, Satpute GK, Srikanth B, Kolhe S. Envirotype-based delineation of environmental effects and genotype × environment interactions in Indian soybean (Glycine max, L.). Sci Rep 2024; 14:11629. [PMID: 38773324 PMCID: PMC11109282 DOI: 10.1038/s41598-024-62613-y] [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: 01/04/2024] [Accepted: 05/20/2024] [Indexed: 05/23/2024] Open
Abstract
Soybean is a rainfed crop grown across a wide range of environments in India. Its grain yield is a complex trait governed by many minor genes and influenced by environmental effects and genotype × environment interactions. In the current investigation, grain yield data of different sets of 41, 30 and 48 soybean genotypes evaluated during 2019, 2020 and 2021, respectively across 19 locations and twenty years' data on 19 different climatic parameters at these locations was used to study the environmental effects on grain yield, to understand the genotype × environment interactions and to identify the mega-environments. Through analysis of variance (ANOVA), it was found that predominant portion of the variation was explained by environmental effects (E) (53.89, 54.86 and 60.56% during 2019, 2020 and 2021, respectively), followed by genotype × environment interactions (GEI) (31.29, 33.72 and 28.82% during 2019, 2020 and 2021, respectively). Principal Component Analysis (PCA) revealed that grain yield was positively associated with RH (Relative humidity at 2 m height), FRUE (Effect of temperature on radiation use efficiency), WSM (Wind speed at 2 m height) and RTA (Global solar radiation based on latitude and Julian day) and negatively associated with VPD (Deficit of vapour pressure), Trange (Daily temperature range), ETP (Evapotranspiration), SW (Insolation incident on a horizontal surface), n (Actual duration of sunshine) and N (Daylight hours). Identification of mega-environments is critical in enhancing the selection gain, productivity and varietal recommendation. Through envirotyping and genotype main effect plus genotype by environment interaction (GGE) biplot methods, nineteen locations across India were grouped into four mega-environments (MEs). ME1 included five locations viz., Bengaluru, Pune, Dharwad, Kasbe Digraj and Umiam. Eight locations-Anand, Amreli, Lokbharti, Bidar, Parbhani, Ranchi, Bhawanipatna and Raipur were included in ME2. Kota and Morena constitutes ME3, while Palampur, Imphal, Mojhera and Almora were included in ME4. Locations Imphal, Bidar and Raipur were found to be both discriminative and representative; these test locations can be utilized in developing wider adaptable soybean cultivars. Pune and Amreli were found to be high-yielding locations and can be used in large scale breeder seed production.
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Affiliation(s)
- Vennampally Nataraj
- ICAR-Indian Institute of Soybean Research, Indore, Madhya Pradesh, 452001, India
| | - Sanjay Gupta
- ICAR-Indian Institute of Soybean Research, Indore, Madhya Pradesh, 452001, India.
| | - K H Singh
- ICAR-Indian Institute of Soybean Research, Indore, Madhya Pradesh, 452001, India
| | - Prince Choyal
- ICAR-Indian Institute of Soybean Research, Indore, Madhya Pradesh, 452001, India
| | - Raghavendra Nargund
- ICAR-Indian Institute of Soybean Research, Indore, Madhya Pradesh, 452001, India
| | - M Shivakumar
- ICAR-Indian Institute of Soybean Research, Indore, Madhya Pradesh, 452001, India
| | - Nisha Agrawal
- ICAR-Indian Institute of Soybean Research, Indore, Madhya Pradesh, 452001, India
| | - Giriraj Kumawat
- ICAR-Indian Institute of Soybean Research, Indore, Madhya Pradesh, 452001, India
| | - Vangala Rajesh
- ICAR-Indian Institute of Soybean Research, Indore, Madhya Pradesh, 452001, India
| | - Rakesh Kumar Verma
- ICAR-Indian Institute of Soybean Research, Indore, Madhya Pradesh, 452001, India
| | - Gyanesh K Satpute
- ICAR-Indian Institute of Soybean Research, Indore, Madhya Pradesh, 452001, India
| | - Bairi Srikanth
- ICAR-Indian Agricultural Statistics Research Institute, Pusa, New Delhi, 110012, India
| | - Savita Kolhe
- ICAR-Indian Institute of Soybean Research, Indore, Madhya Pradesh, 452001, India
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Garin V, Diallo C, Tékété ML, Théra K, Guitton B, Dagno K, Diallo AG, Kouressy M, Leiser W, Rattunde F, Sissoko I, Touré A, Nébié B, Samaké M, Kholovà J, Berger A, Frouin J, Pot D, Vaksmann M, Weltzien E, Témé N, Rami JF. Characterization of adaptation mechanisms in sorghum using a multireference back-cross nested association mapping design and envirotyping. Genetics 2024; 226:iyae003. [PMID: 38381593 PMCID: PMC10990433 DOI: 10.1093/genetics/iyae003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Accepted: 12/20/2023] [Indexed: 02/23/2024] Open
Abstract
Identifying the genetic factors impacting the adaptation of crops to environmental conditions is of key interest for conservation and selection purposes. It can be achieved using population genomics, and evolutionary or quantitative genetics. Here we present a sorghum multireference back-cross nested association mapping population composed of 3,901 lines produced by crossing 24 diverse parents to 3 elite parents from West and Central Africa-back-cross nested association mapping. The population was phenotyped in environments characterized by differences in photoperiod, rainfall pattern, temperature levels, and soil fertility. To integrate the multiparental and multi-environmental dimension of our data we proposed a new approach for quantitative trait loci (QTL) detection and parental effect estimation. We extended our model to estimate QTL effect sensitivity to environmental covariates, which facilitated the integration of envirotyping data. Our models allowed spatial projections of the QTL effects in agro-ecologies of interest. We utilized this strategy to analyze the genetic architecture of flowering time and plant height, which represents key adaptation mechanisms in environments like West Africa. Our results allowed a better characterization of well-known genomic regions influencing flowering time concerning their response to photoperiod with Ma6 and Ma1 being photoperiod-sensitive and the region of possible candidate gene Elf3 being photoperiod-insensitive. We also accessed a better understanding of plant height genetic determinism with the combined effects of phenology-dependent (Ma6) and independent (qHT7.1 and Dw3) genomic regions. Therefore, we argue that the West and Central Africa-back-cross nested association mapping and the presented analytical approach constitute unique resources to better understand adaptation in sorghum with direct application to develop climate-smart varieties.
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Affiliation(s)
- Vincent Garin
- Crop Physiology Laboratory, International Crops Research Institute for the Semi-Arid Tropics, Patancheru, 502 324, India
- CIRAD, UMR AGAP Institut, Montpellier, F-34398, France
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, F-34398, France
| | - Chiaka Diallo
- Sorghum Program, International Crops Research Institute for the Semi-Arid Tropics, Bamako, BP 320, Mali
- Département d’Enseignement et de Recherche des Sciences et Techniques Agricoles, Institut polytechnique rural de formation et de recherche appliquée de Katibougou, Koulikoro, BP 06, Mali
| | - Mohamed Lamine Tékété
- Institut d’Economie Rurale, Bamako, BP 262, Mali
- Faculté des Sciences et Techniques, Université des Sciences des Techniques et des Technologies de Bamako, Bamako, BP E 3206, Mali
| | | | - Baptiste Guitton
- CIRAD, UMR AGAP Institut, Montpellier, F-34398, France
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, F-34398, France
| | - Karim Dagno
- Institut d’Economie Rurale, Bamako, BP 262, Mali
| | | | | | - Willmar Leiser
- Sorghum Program, International Crops Research Institute for the Semi-Arid Tropics, Bamako, BP 320, Mali
| | - Fred Rattunde
- Agronomy Department, University of Wisconsin, Madison, WI 53705, WI, USA
| | - Ibrahima Sissoko
- Sorghum Program, International Crops Research Institute for the Semi-Arid Tropics, Bamako, BP 320, Mali
| | - Aboubacar Touré
- Sorghum Program, International Crops Research Institute for the Semi-Arid Tropics, Bamako, BP 320, Mali
| | - Baloua Nébié
- Dryland Crops Program, International Maize and Wheat Improvement Center (CIMMYT-Senegal) U/C CERAAS, Thiès, Po Box 3320, Senegal
| | - Moussa Samaké
- Faculté des Sciences et Techniques, Université des Sciences des Techniques et des Technologies de Bamako, Bamako, BP E 3206, Mali
| | - Jana Kholovà
- Crop Physiology Laboratory, International Crops Research Institute for the Semi-Arid Tropics, Patancheru, 502 324, India
- Department of Information Technologies, Faculty of Economics and Management, Czech University of Life Sciences, Prague, 165 00, Czech Republic
| | - Angélique Berger
- CIRAD, UMR AGAP Institut, Montpellier, F-34398, France
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, F-34398, France
| | - Julien Frouin
- CIRAD, UMR AGAP Institut, Montpellier, F-34398, France
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, F-34398, France
| | - David Pot
- CIRAD, UMR AGAP Institut, Montpellier, F-34398, France
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, F-34398, France
| | - Michel Vaksmann
- CIRAD, UMR AGAP Institut, Montpellier, F-34398, France
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, F-34398, France
| | - Eva Weltzien
- Sorghum Program, International Crops Research Institute for the Semi-Arid Tropics, Bamako, BP 320, Mali
- Agronomy Department, University of Wisconsin, Madison, WI 53705, WI, USA
| | - Niaba Témé
- Institut d’Economie Rurale, Bamako, BP 262, Mali
| | - Jean-François Rami
- CIRAD, UMR AGAP Institut, Montpellier, F-34398, France
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, F-34398, France
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Adak A, Murray SC, Washburn JD. Deciphering temporal growth patterns in maize: integrative modeling of phenotype dynamics and underlying genomic variations. THE NEW PHYTOLOGIST 2024; 242:121-136. [PMID: 38348523 DOI: 10.1111/nph.19575] [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: 10/05/2023] [Accepted: 01/11/2024] [Indexed: 03/08/2024]
Abstract
Quantifying the temporal or longitudinal growth dynamics of crops in diverse environmental conditions is crucial for understanding plant development, requiring further modeling techniques. In this study, we analyzed the growth patterns of two different maize (Zea mays L.) populations using high-throughput phenotyping with a maize population consisting of 515 recombinant inbred lines (RILs) grown in Texas and a hybrid population containing 1090 hybrids grown in Missouri. Two models, Gaussian peak and functional principal component analysis (FPCA), were employed to study the Normalized Green-Red Difference Index (NGRDI) scores. The Gaussian peak model showed strong correlations (c. 0.94 for RILs and c. 0.97 for hybrids) between modeled and non-modeled temporal trajectories. Functional principal component analysis differentiated NGRDI trajectories in RILs under different conditions, capturing substantial variability (75%, 20%, and 5% for RILs; 88% and 12% for hybrids). By comparing these models with conventional BLUP values, common quantitative trait loci (QTLs) were identified, containing candidate genes of brd1, pin11, zcn8 and rap2. The harmony between these loci's additive effects and growing degree days, as well as the differentiation of RIL haplotypes across growth stages, underscores the significant interplay of these loci in driving plant development. These findings contribute to advancing understanding of plant-environment interactions and have implications for crop improvement strategies.
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Affiliation(s)
- Alper Adak
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX, 77843, USA
| | - Seth C Murray
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX, 77843, USA
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Crozier D, Winans ND, Hoffmann L, Patil NY, Klein PE, Klein RR, Rooney WL. Evaluating and Predicting the Performance of Sorghum Lines in an Elite by Exotic Backcross-Nested Association Mapping Population. PLANTS (BASEL, SWITZERLAND) 2024; 13:879. [PMID: 38592905 PMCID: PMC10975396 DOI: 10.3390/plants13060879] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Revised: 03/09/2024] [Accepted: 03/14/2024] [Indexed: 04/11/2024]
Abstract
Maintaining or introducing genetic diversity into plant breeding programs is necessary for continual genetic gain; however, diversity at the cost of reduced performance is not something sought by breeders. To this end, backcross-nested association mapping (BC-NAM) populations, in which the recurrent parent is an elite line, can be employed as a strategy to introgress diversity from unadapted accessions while maintaining agronomic performance. This study evaluates (i) the hybrid performance of sorghum lines from 18 BC1-NAM families and (ii) the potential of genomic prediction to screen lines from BC1-NAM families for hybrid performance prior to phenotypic evaluation. Despite the diverse geographical origins and agronomic performance of the unadapted parents for BC1-NAM families, many BC1-derived lines performed significantly better in the hybrid trials than the elite recurrent parent, R.Tx436. The genomic prediction accuracies for grain yield, plant height, and days to mid-anthesis were acceptable, but the prediction accuracies for plant height were lower than expected. While the prediction accuracies increased when including more individuals in the training set, improvements tended to plateau between two and five lines per family, with larger training sets being required for more complex traits such as grain yield. Therefore, genomic prediction models can be optimized in a large BC1-NAM population with a relatively low fraction of individuals needing to be evaluated. These results suggest that genomic prediction is an effective method of pre-screening lines within BC1-NAM families prior to evaluation in extensive hybrid field trials.
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Affiliation(s)
- Daniel Crozier
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843, USA
| | - Noah D. Winans
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843, USA
| | - Leo Hoffmann
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843, USA
- Department of Horticulture Sciences, University of Florida, Gainesville, FL 32611, USA
| | - Nikhil Y. Patil
- Department of Horticultural Sciences, Texas A&M University, College Station, TX 77845, USA
- Health Sciences Center, University of Oklahoma, Oklahoma City, OK 73104, USA
| | - Patricia E. Klein
- Health Sciences Center, University of Oklahoma, Oklahoma City, OK 73104, USA
| | - Robert R. Klein
- Crop Germplasm Research Unit, United States Department of Agriculture Agricultural Research Service, College Station, TX 77843, USA;
| | - William L. Rooney
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843, USA
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Araújo MS, Chaves SFS, Dias LAS, Ferreira FM, Pereira GR, Bezerra ARG, Alves RS, Heinemann AB, Breseghello F, Carneiro PCS, Krause MD, Costa-Neto G, Dias KOG. GIS-FA: an approach to integrating thematic maps, factor-analytic, and envirotyping for cultivar targeting. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2024; 137:80. [PMID: 38472532 DOI: 10.1007/s00122-024-04579-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/15/2023] [Accepted: 02/06/2024] [Indexed: 03/14/2024]
Abstract
KEY MESSAGE We propose an "enviromics" prediction model for recommending cultivars based on thematic maps aimed at decision-makers. Parsimonious methods that capture genotype-by-environment interaction (GEI) in multi-environment trials (MET) are important in breeding programs. Understanding the causes and factors of GEI allows the utilization of genotype adaptations in the target population of environments through environmental features and factor-analytic (FA) models. Here, we present a novel predictive breeding approach called GIS-FA, which integrates geographic information systems (GIS) techniques, FA models, partial least squares (PLS) regression, and enviromics to predict phenotypic performance in untested environments. The GIS-FA approach enables: (i) the prediction of the phenotypic performance of tested genotypes in untested environments, (ii) the selection of the best-ranking genotypes based on their overall performance and stability using the FA selection tools, and (iii) the creation of thematic maps showing overall or pairwise performance and stability for decision-making. We exemplify the usage of the GIS-FA approach using two datasets of rice [Oryza sativa (L.)] and soybean [Glycine max (L.) Merr.] in MET spread over tropical areas. In summary, our novel predictive method allows the identification of new breeding scenarios by pinpointing groups of environments where genotypes demonstrate superior predicted performance. It also facilitates and optimizes cultivar recommendations by utilizing thematic maps.
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Affiliation(s)
- Maurício S Araújo
- Department of Agronomy, Federal University of Viçosa, Viçosa, Minas Gerais, Brazil
| | - Saulo F S Chaves
- Department of Agronomy, Federal University of Viçosa, Viçosa, Minas Gerais, Brazil
| | - Luiz A S Dias
- Department of Agronomy, Federal University of Viçosa, Viçosa, Minas Gerais, Brazil
| | - Filipe M Ferreira
- Department of Crop Science - College of Agricultural Sciences, São Paulo State University, Botucatu, São Paulo, Brazil
| | - Guilherme R Pereira
- Department of Agronomy, Federal University of Viçosa, Viçosa, Minas Gerais, Brazil
| | | | - Rodrigo S Alves
- Department of General Biology, Federal University of Viçosa, Viçosa, Minas Gerais, Brazil
| | - Alexandre B Heinemann
- Brazilian Agricultural Research Corporation (Embrapa Rice and Beans), Santo Antônio de Goiás, Goiás, Brazil
| | - Flávio Breseghello
- Brazilian Agricultural Research Corporation (Embrapa Rice and Beans), Santo Antônio de Goiás, Goiás, Brazil
| | - Pedro C S Carneiro
- Department of General Biology, Federal University of Viçosa, Viçosa, Minas Gerais, Brazil
| | | | | | - Kaio O G Dias
- Department of General Biology, Federal University of Viçosa, Viçosa, Minas Gerais, Brazil.
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Della Coletta R, Fernandes SB, Monnahan PJ, Mikel MA, Bohn MO, Lipka AE, Hirsch CN. Importance of genetic architecture in marker selection decisions for genomic prediction. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2023; 136:220. [PMID: 37819415 DOI: 10.1007/s00122-023-04469-w] [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: 02/28/2023] [Accepted: 09/25/2023] [Indexed: 10/13/2023]
Abstract
KEY MESSAGE We demonstrate potential for improved multi-environment genomic prediction accuracy using structural variant markers. However, the degree of observed improvement is highly dependent on the genetic architecture of the trait. Breeders commonly use genetic markers to predict the performance of untested individuals as a way to improve the efficiency of breeding programs. These genomic prediction models have almost exclusively used single nucleotide polymorphisms (SNPs) as their source of genetic information, even though other types of markers exist, such as structural variants (SVs). Given that SVs are associated with environmental adaptation and not all of them are in linkage disequilibrium to SNPs, SVs have the potential to bring additional information to multi-environment prediction models that are not captured by SNPs alone. Here, we evaluated different marker types (SNPs and/or SVs) on prediction accuracy across a range of genetic architectures for simulated traits across multiple environments. Our results show that SVs can improve prediction accuracy, but it is highly dependent on the genetic architecture of the trait and the relative gain in accuracy is minimal. When SVs are the only causative variant type, 70% of the time SV predictors outperform SNP predictors. However, the improvement in accuracy in these instances is only 1.5% on average. Further simulations with predictors in varying degrees of LD with causative variants of different types (e.g., SNPs, SVs, SNPs and SVs) showed that prediction accuracy increased as linkage disequilibrium between causative variants and predictors increased regardless of the marker type. This study demonstrates that knowing the genetic architecture of a trait in deciding what markers to use in large-scale genomic prediction modeling in a breeding program is more important than what types of markers to use.
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Affiliation(s)
- Rafael Della Coletta
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN, 55108, USA
| | - Samuel B Fernandes
- Department of Crop, Soil and Environmental Sciences at University of Arkansas, Fayetteville, AR, 72701, USA
| | - Patrick J Monnahan
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN, 55108, 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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10
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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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11
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Montesinos-López A, Rivera C, Pinto F, Piñera F, Gonzalez D, Reynolds M, Pérez-Rodríguez P, Li H, Montesinos-López OA, Crossa J. Multimodal deep learning methods enhance genomic prediction of wheat breeding. G3 (BETHESDA, MD.) 2023; 13:jkad045. [PMID: 36869747 PMCID: PMC10151399 DOI: 10.1093/g3journal/jkad045] [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: 12/14/2022] [Revised: 02/21/2023] [Accepted: 02/22/2023] [Indexed: 03/05/2023]
Abstract
While several statistical machine learning methods have been developed and studied for assessing the genomic prediction (GP) accuracy of unobserved phenotypes in plant breeding research, few methods have linked genomics and phenomics (imaging). Deep learning (DL) neural networks have been developed to increase the GP accuracy of unobserved phenotypes while simultaneously accounting for the complexity of genotype-environment interaction (GE); however, unlike conventional GP models, DL has not been investigated for when genomics is linked with phenomics. In this study we used 2 wheat data sets (DS1 and DS2) to compare a novel DL method with conventional GP models. Models fitted for DS1 were GBLUP, gradient boosting machine (GBM), support vector regression (SVR) and the DL method. Results indicated that for 1 year, DL provided better GP accuracy than results obtained by the other models. However, GP accuracy obtained for other years indicated that the GBLUP model was slightly superior to the DL. DS2 is comprised only of genomic data from wheat lines tested for 3 years, 2 environments (drought and irrigated) and 2-4 traits. DS2 results showed that when predicting the irrigated environment with the drought environment, DL had higher accuracy than the GBLUP model in all analyzed traits and years. When predicting drought environment with information on the irrigated environment, the DL model and GBLUP model had similar accuracy. The DL method used in this study is novel and presents a strong degree of generalization as several modules can potentially be incorporated and concatenated to produce an output for a multi-input data structure.
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Affiliation(s)
- Abelardo Montesinos-López
- Departamento de Matemáticas, Centro Universitario de Ciencias Exactas e Ingenierías (CUCEI), Universidad de Guadalajara, 44430, Guadalajara, Jalisco, Mexico
| | - Carolina Rivera
- International Maize and Wheat Improvement Center (CIMMYT), Carretera México- Veracruz Km. 45, El Batán, CP 56237, Texcoco, Edo. de México, Mexico
| | - Francisco Pinto
- International Maize and Wheat Improvement Center (CIMMYT), Carretera México- Veracruz Km. 45, El Batán, CP 56237, Texcoco, Edo. de México, Mexico
| | - Francisco Piñera
- International Maize and Wheat Improvement Center (CIMMYT), Carretera México- Veracruz Km. 45, El Batán, CP 56237, Texcoco, Edo. de México, Mexico
| | - David Gonzalez
- International Maize and Wheat Improvement Center (CIMMYT), Carretera México- Veracruz Km. 45, El Batán, CP 56237, Texcoco, Edo. de México, Mexico
| | - Mathew Reynolds
- International Maize and Wheat Improvement Center (CIMMYT), Carretera México- Veracruz Km. 45, El Batán, CP 56237, Texcoco, Edo. de México, Mexico
| | | | - Huihui Li
- Institute of Crop Sciences, The National Key Facility for Crop Gene Resources and Genetic Improvement and CIMMYT China office, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | | | - Jose Crossa
- International Maize and Wheat Improvement Center (CIMMYT), Carretera México- Veracruz Km. 45, El Batán, CP 56237, Texcoco, Edo. de México, Mexico
- Colegio de Postgraduados, Montecillos, Edo. de México, CP 56230, Mexico
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12
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Fradgley NS, Bacon J, Bentley AR, Costa‐Neto G, Cottrell A, Crossa J, Cuevas J, Kerton M, Pope E, Swarbreck SM, Gardner KA. Prediction of near-term climate change impacts on UK wheat quality and the potential for adaptation through plant breeding. GLOBAL CHANGE BIOLOGY 2023; 29:1296-1313. [PMID: 36482280 PMCID: PMC10108302 DOI: 10.1111/gcb.16552] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Revised: 11/17/2022] [Accepted: 11/29/2022] [Indexed: 05/26/2023]
Abstract
Wheat is a major crop worldwide, mainly cultivated for human consumption and animal feed. Grain quality is paramount in determining its value and downstream use. While we know that climate change threatens global crop yields, a better understanding of impacts on wheat end-use quality is also critical. Combining quantitative genetics with climate model outputs, we investigated UK-wide trends in genotypic adaptation for wheat quality traits. In our approach, we augmented genomic prediction models with environmental characterisation of field trials to predict trait values and climate effects in historical field trial data between 2001 and 2020. Addition of environmental covariates, such as temperature and rainfall, successfully enabled prediction of genotype by environment interactions (G × E), and increased prediction accuracy of most traits for new genotypes in new year cross validation. We then extended predictions from these models to much larger numbers of simulated environments using climate scenarios projected under Representative Concentration Pathways 8.5 for 2050-2069. We found geographically varying climate change impacts on wheat quality due to contrasting associations between specific weather covariables and quality traits across the UK. Notably, negative impacts on quality traits were predicted in the East of the UK due to increased summer temperatures while the climate in the North and South-west may become more favourable with increased summer temperatures. Furthermore, by projecting 167,040 simulated future genotype-environment combinations, we found only limited potential for breeding to exploit predictable G × E to mitigate year-to-year environmental variability for most traits except Hagberg falling number. This suggests low adaptability of current UK wheat germplasm across future UK climates. More generally, approaches demonstrated here will be critical to enable adaptation of global crops to near-term climate change.
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Affiliation(s)
| | | | - Alison R. Bentley
- NIABCambridgeUK
- International Maize and Wheat Improvement Center (CIMMYT)Carretera México‐VeracruzMexico
| | | | | | - Jose Crossa
- International Maize and Wheat Improvement Center (CIMMYT)Carretera México‐VeracruzMexico
| | - Jaime Cuevas
- Universidad Autonoma del Estado de Quintana RooChetumalQuintana RooMexico
| | | | | | | | - Keith A. Gardner
- NIABCambridgeUK
- International Maize and Wheat Improvement Center (CIMMYT)Carretera México‐VeracruzMexico
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13
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Filho CCF, Andrade MHML, Nunes JAR, Jarquin DH, Rios EF. Genomic prediction for complex traits across multiples harvests in alfalfa (Medicago sativa L.) is enhanced by enviromics. THE PLANT GENOME 2023:e20306. [PMID: 36815221 DOI: 10.1002/tpg2.20306] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Accepted: 12/17/2022] [Indexed: 06/18/2023]
Abstract
Breeding for dry matter yield and persistence in alfalfa (Medicago sativa L.) can take several years as these traits must be evaluated under multiple harvests. Therefore, genotype-by-harvest interaction should be incorporated into genomic prediction models to explore genotypes' adaptability and stability. In this study, we investigated how enviromics could help to predict the genotypic performance under multiharvest alfalfa breeding trials by evaluating 177 families across 11 harvests under four cross-validation scenarios. All scenarios were analyzed using six models in a Bayesian mixed model framework. Our results demonstrate that models accounting to the enviromics information led to an increase of genetic variance and a decrease in the error variance, indicating better biological explanation when the enviromic information was incorporated. Furthermore, models that accounted for enviromic data led to higher predictive ability (PA) in a reduced number of harvests used in the training data set. The best enviromic models (M2 and M3) outperformed the base model (GBLUP model-M0) for predicting adaptability and persistence across all cross-validation scenarios. Incorporating environmental covariates also provided higher PA for persistence compared with the base model, as predictions increased from 0 to 0.16, 0.20, 0.56, and 0.46 for CV00, CV1, CV0, and CV2. The results also demonstrate that GBLUP without enviromics term has low power to predict persistence, thus the adoption of enviromics is a cheap and efficient alternative to increase accuracy and biological meaning.
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Affiliation(s)
| | | | - José Airton Rodrigues Nunes
- Departamento de Biologia, Instituto de Ciências Naturais, Universidade Federal de Lavras, Lavras, Minas Gerais, Brazil
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14
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Nguyen VH, Morantte RIZ, Lopena V, Verdeprado H, Murori R, Ndayiragije A, Katiyar SK, Islam MR, Juma RU, Flandez-Galvez H, Glaszmann JC, Cobb JN, Bartholomé J. Multi-environment Genomic Selection in Rice Elite Breeding Lines. RICE (NEW YORK, N.Y.) 2023; 16:7. [PMID: 36752880 PMCID: PMC9908796 DOI: 10.1186/s12284-023-00623-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Accepted: 01/31/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND Assessing the performance of elite lines in target environments is essential for breeding programs to select the most relevant genotypes. One of the main complexities in this task resides in accounting for the genotype by environment interactions. Genomic prediction models that integrate information from multi-environment trials and environmental covariates can be efficient tools in this context. The objective of this study was to assess the predictive ability of different genomic prediction models to optimize the use of multi-environment information. We used 111 elite breeding lines representing the diversity of the international rice research institute breeding program for irrigated ecosystems. The lines were evaluated for three traits (days to flowering, plant height, and grain yield) in 15 environments in Asia and Africa and genotyped with 882 SNP markers. We evaluated the efficiency of genomic prediction to predict untested environments using seven multi-environment models and three cross-validation scenarios. RESULTS The elite lines were found to belong to the indica group and more specifically the indica-1B subgroup which gathered improved material originating from the Green Revolution. Phenotypic correlations between environments were high for days to flowering and plant height (33% and 54% of pairwise correlation greater than 0.5) but low for grain yield (lower than 0.2 in most cases). Clustering analyses based on environmental covariates separated Asia's and Africa's environments into different clusters or subclusters. The predictive abilities ranged from 0.06 to 0.79 for days to flowering, 0.25-0.88 for plant height, and - 0.29-0.62 for grain yield. We found that models integrating genotype-by-environment interaction effects did not perform significantly better than models integrating only main effects (genotypes and environment or environmental covariates). The different cross-validation scenarios showed that, in most cases, the use of all available environments gave better results than a subset. CONCLUSION Multi-environment genomic prediction models with main effects were sufficient for accurate phenotypic prediction of elite lines in targeted environments. These results will help refine the testing strategy to update the genomic prediction models to improve predictive ability.
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Affiliation(s)
- Van Hieu Nguyen
- CIRAD, UMR AGAP Institut, 34398, Montpellier, France
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France
- Rice Breeding Innovation Platform, International Rice Research Institute, DAPO, Box7777, Metro Manila, Philippines
- Institute of Crop Science, College of Agriculture and Food Science, University of the Philippines, Los Baños, Laguna, Philippines
| | - Rose Imee Zhella Morantte
- Rice Breeding Innovation Platform, International Rice Research Institute, DAPO, Box7777, Metro Manila, Philippines
| | - Vitaliano Lopena
- Rice Breeding Innovation Platform, International Rice Research Institute, DAPO, Box7777, Metro Manila, Philippines
| | - Holden Verdeprado
- Rice Breeding Innovation Platform, International Rice Research Institute, DAPO, Box7777, Metro Manila, Philippines
| | - Rosemary Murori
- Rice Breeding Innovation Platform, International Rice Research Institute, DAPO, Box7777, Metro Manila, Philippines
| | - Alexis Ndayiragije
- Rice Breeding Innovation Platform, International Rice Research Institute, DAPO, Box7777, Metro Manila, Philippines
| | - Sanjay Kumar Katiyar
- Rice Breeding Innovation Platform, International Rice Research Institute, DAPO, Box7777, Metro Manila, Philippines
| | - Md Rafiqul Islam
- Rice Breeding Innovation Platform, International Rice Research Institute, DAPO, Box7777, Metro Manila, Philippines
| | - Roselyne Uside Juma
- Rice Breeding Innovation Platform, International Rice Research Institute, DAPO, Box7777, Metro Manila, Philippines
| | - Hayde Flandez-Galvez
- Institute of Crop Science, College of Agriculture and Food Science, University of the Philippines, Los Baños, Laguna, Philippines
| | - Jean-Christophe Glaszmann
- CIRAD, UMR AGAP Institut, 34398, Montpellier, France
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France
| | - Joshua N Cobb
- Rice Breeding Innovation Platform, International Rice Research Institute, DAPO, Box7777, Metro Manila, Philippines
- RiceTec. Inc, PO Box 1305, Alvin, TX, 77512, USA
| | - Jérôme Bartholomé
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France.
- CIRAD, UMR AGAP Institut, Cali, Colombia.
- Alliance Bioversity-CIAT, Cali, Colombia.
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15
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Gevartosky R, Carvalho HF, Costa-Neto G, Montesinos-López OA, Crossa J, Fritsche-Neto R. Enviromic-based kernels may optimize resource allocation with multi-trait multi-environment genomic prediction for tropical Maize. BMC PLANT BIOLOGY 2023; 23:10. [PMID: 36604618 PMCID: PMC9814176 DOI: 10.1186/s12870-022-03975-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Accepted: 11/24/2022] [Indexed: 06/17/2023]
Abstract
BACKGROUND Success in any genomic prediction platform is directly dependent on establishing a representative training set. This is a complex task, even in single-trait single-environment conditions and tends to be even more intricated wherein additional information from envirotyping and correlated traits are considered. Here, we aimed to design optimized training sets focused on genomic prediction, considering multi-trait multi-environment trials, and how those methods may increase accuracy reducing phenotyping costs. For that, we considered single-trait multi-environment trials and multi-trait multi-environment trials for three traits: grain yield, plant height, and ear height, two datasets, and two cross-validation schemes. Next, two strategies for designing optimized training sets were conceived, first considering only the genomic by environment by trait interaction (GET), while a second including large-scale environmental data (W, enviromics) as genomic by enviromic by trait interaction (GWT). The effective number of individuals (genotypes × environments × traits) was assumed as those that represent at least 98% of each kernel (GET or GWT) variation, in which those individuals were then selected by a genetic algorithm based on prediction error variance criteria to compose an optimized training set for genomic prediction purposes. RESULTS The combined use of genomic and enviromic data efficiently designs optimized training sets for genomic prediction, improving the response to selection per dollar invested by up to 145% when compared to the model without enviromic data, and even more when compared to cross validation scheme with 70% of training set or pure phenotypic selection. Prediction models that include G × E or enviromic data + G × E yielded better prediction ability. CONCLUSIONS Our findings indicate that a genomic by enviromic by trait interaction kernel associated with genetic algorithms is efficient and can be proposed as a promising approach to designing optimized training sets for genomic prediction when the variance-covariance matrix of traits is available. Additionally, great improvements in the genetic gains per dollar invested were observed, suggesting that a good allocation of resources can be deployed by using the proposed approach.
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Affiliation(s)
- Raysa Gevartosky
- Department of Genetics, Luiz de Queiroz College of Agriculture, University of São Paulo, Piracicaba, São Paulo, Brazil.
| | - Humberto Fanelli Carvalho
- Centro de Biotecnología y Genómica de Plantas (CBGP, UPM-INIA), Universidad Politécnica de Madrid (UPM), Madrid, Spain
| | - Germano Costa-Neto
- Department of Genetics, Luiz de Queiroz College of Agriculture, University of São Paulo, Piracicaba, São Paulo, Brazil
- Institute for Genomics Diversity, Cornell University, Ithaca, NY, USA
| | | | - José Crossa
- International Maize and Wheat Improvement Center (CIMMYT), Km 45, Carretera Mexico-Veracruz, CP 52640, Texcoco, Edo. de México, Mexico
- Colegio de Postgraduados, CP 56230, Montecillos, Edo. de México, Mexico
| | - Roberto Fritsche-Neto
- Department of Genetics, Luiz de Queiroz College of Agriculture, University of São Paulo, Piracicaba, São Paulo, Brazil
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16
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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: 10] [Impact Index Per Article: 10.0] [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)
| | - Carlos D Messina
- Horticultural Sciences Department, University of Florida, Gainesville, Florida 32611, USA
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17
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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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18
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Yue H, Olivoto T, Bu J, Li J, Wei J, Xie J, Chen S, Peng H, Nardino M, Jiang X. Multi-trait selection for mean performance and stability of maize hybrids in mega-environments delineated using envirotyping techniques. FRONTIERS IN PLANT SCIENCE 2022; 13:1030521. [PMID: 36452111 PMCID: PMC9702090 DOI: 10.3389/fpls.2022.1030521] [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: 08/29/2022] [Accepted: 10/26/2022] [Indexed: 06/17/2023]
Abstract
Under global climate changes, understanding climate variables that are most associated with environmental kinships can contribute to improving the success of hybrid selection, mainly in environments with high climate variations. The main goal of this study is to integrate envirotyping techniques and multi-trait selection for mean performance and the stability of maize genotypes growing in the Huanghuaihai plain in China. A panel of 26 maize hybrids growing in 10 locations in two crop seasons was evaluated for 9 traits. Considering 20 years of climate information and 19 environmental covariables, we identified four mega-environments (ME) in the Huanghuaihai plain which grouped locations that share similar long-term weather patterns. All the studied traits were significantly affected by the genotype × mega-environment × year interaction, suggesting that evaluating maize stability using single-year, multi-environment trials may provide misleading recommendations. Counterintuitively, the highest yields were not observed in the locations with higher accumulated rainfall, leading to the hypothesis that lower vapor pressure deficit, minimum temperatures, and high relative humidity are climate variables that -under no water restriction- reduce plant transpiration and consequently the yield. Utilizing the multi-trait mean performance and stability index (MTMPS) prominent hybrids with satisfactory mean performance and stability across cultivation years were identified. G23 and G25 were selected within three out of the four mega-environments, being considered the most stable and widely adapted hybrids from the panel. The G5 showed satisfactory yield and stability across contrasting years in the drier, warmer, and with higher vapor pressure deficit mega-environment, which included locations in the Hubei province. Overall, this study opens the door to a more systematic and dynamic characterization of the environment to better understand the genotype-by-environment interaction in multi-environment trials.
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Affiliation(s)
- Haiwang Yue
- Hebei Provincial Key Laboratory of Crops Drought Resistance Research, Dryland Farming Institute, Hebei Academy of Agriculture and Forestry Sciences, Hengshui, China
| | - Tiago Olivoto
- Department of Plant Science, Center of Agrarian Sciences, Federal University of Santa Catarina, Florianópolis, SC, Brazil
| | - Junzhou Bu
- Hebei Provincial Key Laboratory of Crops Drought Resistance Research, Dryland Farming Institute, Hebei Academy of Agriculture and Forestry Sciences, Hengshui, China
| | - Jie Li
- Hebei Provincial Key Laboratory of Crops Drought Resistance Research, Dryland Farming Institute, Hebei Academy of Agriculture and Forestry Sciences, Hengshui, China
| | - Jianwei Wei
- Hebei Provincial Key Laboratory of Crops Drought Resistance Research, Dryland Farming Institute, Hebei Academy of Agriculture and Forestry Sciences, Hengshui, China
| | - Junliang Xie
- Hebei Provincial Key Laboratory of Crops Drought Resistance Research, Dryland Farming Institute, Hebei Academy of Agriculture and Forestry Sciences, Hengshui, China
| | - Shuping Chen
- Hebei Provincial Key Laboratory of Crops Drought Resistance Research, Dryland Farming Institute, Hebei Academy of Agriculture and Forestry Sciences, Hengshui, China
| | - Haicheng Peng
- Hebei Provincial Key Laboratory of Crops Drought Resistance Research, Dryland Farming Institute, Hebei Academy of Agriculture and Forestry Sciences, Hengshui, China
| | - Maicon Nardino
- Department of Agronomy, Federal University of Viçosa, Viçosa, MG, Brazil
| | - Xuwen Jiang
- Maize Research Institute, Qingdao Agricultural University, Qingdao, China
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19
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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: 43] [Impact Index Per Article: 21.5] [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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20
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Seyum EG, Bille NH, Abtew WG, Munyengwa N, Bell JM, Cros D. Genomic selection in tropical perennial crops and plantation trees: a review. MOLECULAR BREEDING : NEW STRATEGIES IN PLANT IMPROVEMENT 2022; 42:58. [PMID: 37313015 PMCID: PMC10248687 DOI: 10.1007/s11032-022-01326-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Accepted: 09/06/2022] [Indexed: 06/15/2023]
Abstract
To overcome the multiple challenges currently faced by agriculture, such as climate change and soil deterioration, more efficient plant breeding strategies are required. Genomic selection (GS) is crucial for the genetic improvement of quantitative traits, as it can increase selection intensity, shorten the generation interval, and improve selection accuracy for traits that are difficult to phenotype. Tropical perennial crops and plantation trees are of major economic importance and have consequently been the subject of many GS articles. In this review, we discuss the factors that affect GS accuracy (statistical models, linkage disequilibrium, information concerning markers, relatedness between training and target populations, the size of the training population, and trait heritability) and the genetic gain expected in these species. The impact of GS will be particularly strong in tropical perennial crops and plantation trees as they have long breeding cycles and constrained selection intensity. Future GS prospects are also discussed. High-throughput phenotyping will allow constructing of large training populations and implementing of phenomic selection. Optimized modeling is needed for longitudinal traits and multi-environment trials. The use of multi-omics, haploblocks, and structural variants will enable going beyond single-locus genotype data. Innovative statistical approaches, like artificial neural networks, are expected to efficiently handle the increasing amounts of heterogeneous multi-scale data. Targeted recombinations on sites identified from profiles of marker effects have the potential to further increase genetic gain. GS can also aid re-domestication and introgression breeding. Finally, GS consortia will play an important role in making the best of these opportunities. Supplementary Information The online version contains supplementary material available at 10.1007/s11032-022-01326-4.
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Affiliation(s)
- Essubalew Getachew Seyum
- Department of Plant Biology and Physiology, Faculty of Sciences, University of Yaoundé I, Yaoundé, Cameroon
- Department of Horticulture and Plant Sciences, College of Agriculture and Veterinary Medicine, Jimma University, P.O. Box 307, Jimma, Ethiopia
| | - Ngalle Hermine Bille
- Department of Plant Biology and Physiology, Faculty of Sciences, University of Yaoundé I, Yaoundé, Cameroon
| | - Wosene Gebreselassie Abtew
- Department of Horticulture and Plant Sciences, College of Agriculture and Veterinary Medicine, Jimma University, P.O. Box 307, Jimma, Ethiopia
| | - Norman Munyengwa
- Queensland Alliance for Agriculture and Food Innovation, University of Queensland, Brisbane, QLD 4072 Australia
| | - Joseph Martin Bell
- Department of Plant Biology and Physiology, Faculty of Sciences, University of Yaoundé I, Yaoundé, Cameroon
| | - David Cros
- CIRAD, UMR AGAP Institut, 34398 Montpellier, France
- UMR AGAP Institut, CIRAD, INRAE, Univ. Montpellier, Institut Agro, 34398 Montpellier, France
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21
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Westhues CC, Simianer H, Beissinger TM. learnMET: an R package to apply machine learning methods for genomic prediction using multi-environment trial data. G3 GENES|GENOMES|GENETICS 2022; 12:6705235. [PMID: 36124944 PMCID: PMC9635651 DOI: 10.1093/g3journal/jkac226] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Accepted: 07/29/2022] [Indexed: 12/04/2022]
Abstract
We introduce the R-package learnMET, developed as a flexible framework to enable a collection of analyses on multi-environment trial breeding data with machine learning-based models. learnMET allows the combination of genomic information with environmental data such as climate and/or soil characteristics. Notably, the package offers the possibility of incorporating weather data from field weather stations, or to retrieve global meteorological datasets from a NASA database. Daily weather data can be aggregated over specific periods of time based on naive (for instance, nonoverlapping 10-day windows) or phenological approaches. Different machine learning methods for genomic prediction are implemented, including gradient-boosted decision trees, random forests, stacked ensemble models, and multilayer perceptrons. These prediction models can be evaluated via a collection of cross-validation schemes that mimic typical scenarios encountered by plant breeders working with multi-environment trial experimental data in a user-friendly way. The package is published under an MIT license and accessible on GitHub.
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Affiliation(s)
- Cathy C Westhues
- Division of Plant Breeding Methodology, Department of Crop Sciences, University of Goettingen , 37075 Goettingen, Germany
- Center for Integrated Breeding Research, University of Goettingen , 37075 Goettingen, Germany
| | - Henner Simianer
- Center for Integrated Breeding Research, University of Goettingen , 37075 Goettingen, Germany
- Animal Breeding and Genetics Group, Department of Animal Sciences, University of Gottingen , 37075 Gottingen, Germany
| | - Timothy M Beissinger
- Division of Plant Breeding Methodology, Department of Crop Sciences, University of Goettingen , 37075 Goettingen, Germany
- Center for Integrated Breeding Research, University of Goettingen , 37075 Goettingen, Germany
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22
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Montesinos-López OA, Montesinos-López A, Cano-Paez B, Hernández-Suárez CM, Santana-Mancilla PC, Crossa J. A Comparison of Three Machine Learning Methods for Multivariate Genomic Prediction Using the Sparse Kernels Method (SKM) Library. Genes (Basel) 2022; 13:genes13081494. [PMID: 36011405 PMCID: PMC9407886 DOI: 10.3390/genes13081494] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Revised: 08/10/2022] [Accepted: 08/19/2022] [Indexed: 11/30/2022] Open
Abstract
Genomic selection (GS) changed the way plant breeders select genotypes. GS takes advantage of phenotypic and genotypic information to training a statistical machine learning model, which is used to predict phenotypic (or breeding) values of new lines for which only genotypic information is available. Therefore, many statistical machine learning methods have been proposed for this task. Multi-trait (MT) genomic prediction models take advantage of correlated traits to improve prediction accuracy. Therefore, some multivariate statistical machine learning methods are popular for GS. In this paper, we compare the prediction performance of three MT methods: the MT genomic best linear unbiased predictor (GBLUP), the MT partial least squares (PLS) and the multi-trait random forest (RF) methods. Benchmarking was performed with six real datasets. We found that the three investigated methods produce similar results, but under predictors with genotype (G) and environment (E), that is, E + G, the MT GBLUP achieved superior performance, whereas under predictors E + G + genotype × environment (GE) and G + GE, random forest achieved the best results. We also found that the best predictions were achieved under the predictors E + G and E + G + GE. Here, we also provide the R code for the implementation of these three statistical machine learning methods in the sparse kernel method (SKM) library, which offers not only options for single-trait prediction with various statistical machine learning methods but also some options for MT predictions that can help to capture improved complex patterns in datasets that are common in genomic selection.
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Affiliation(s)
| | - Abelardo Montesinos-López
- Centro Universitario de Ciencias Exactas e Ingenierías (CUCEI), Universidad de Guadalajara, Guadalajara 44100, Mexico
- Correspondence: (A.M.-L.); (J.C.)
| | - Bernabe Cano-Paez
- Facultad de Ciencias, Universidad Nacional Autónoma de México (UNAM), México City 04510, Mexico
| | - Carlos Moisés Hernández-Suárez
- Instituto de Ciencias Tecnología e Innovación, Universidad Francisco Gavidia, El Progreso St., No. 2748, Colonia Flor Blanca, San Salvador CP 1101, El Salvador
| | | | - José Crossa
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco 56237, Mexico
- Colegio de Postgraduados, Montecillo 56230, Mexico
- Correspondence: (A.M.-L.); (J.C.)
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23
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Cortés AJ, López-Hernández F, Blair MW. Genome–Environment Associations, an Innovative Tool for Studying Heritable Evolutionary Adaptation in Orphan Crops and Wild Relatives. Front Genet 2022; 13:910386. [PMID: 35991553 PMCID: PMC9389289 DOI: 10.3389/fgene.2022.910386] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Accepted: 05/30/2022] [Indexed: 11/23/2022] Open
Abstract
Leveraging innovative tools to speed up prebreeding and discovery of genotypic sources of adaptation from landraces, crop wild relatives, and orphan crops is a key prerequisite to accelerate genetic gain of abiotic stress tolerance in annual crops such as legumes and cereals, many of which are still orphan species despite advances in major row crops. Here, we review a novel, interdisciplinary approach to combine ecological climate data with evolutionary genomics under the paradigm of a new field of study: genome–environment associations (GEAs). We first exemplify how GEA utilizes in situ georeferencing from genotypically characterized, gene bank accessions to pinpoint genomic signatures of natural selection. We later discuss the necessity to update the current GEA models to predict both regional- and local- or micro-habitat–based adaptation with mechanistic ecophysiological climate indices and cutting-edge GWAS-type genetic association models. Furthermore, to account for polygenic evolutionary adaptation, we encourage the community to start gathering genomic estimated adaptive values (GEAVs) for genomic prediction (GP) and multi-dimensional machine learning (ML) models. The latter two should ideally be weighted by de novo GWAS-based GEA estimates and optimized for a scalable marker subset. We end the review by envisioning avenues to make adaptation inferences more robust through the merging of high-resolution data sources, such as environmental remote sensing and summary statistics of the genomic site frequency spectrum, with the epigenetic molecular functionality responsible for plastic inheritance in the wild. Ultimately, we believe that coupling evolutionary adaptive predictions with innovations in ecological genomics such as GEA will help capture hidden genetic adaptations to abiotic stresses based on crop germplasm resources to assist responses to climate change. “I shall endeavor to find out how nature’s forces act upon one another, and in what manner the geographic environment exerts its influence on animals and plants. In short, I must find out about the harmony in nature” Alexander von Humboldt—Letter to Karl Freiesleben, June 1799.
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Affiliation(s)
- Andrés J. Cortés
- Corporacion Colombiana de Investigacion Agropecuaria AGROSAVIA, C.I. La Selva, Rionegro, Colombia
- *Correspondence: Andrés J. Cortés, ; Matthew W. Blair,
| | - Felipe López-Hernández
- Corporacion Colombiana de Investigacion Agropecuaria AGROSAVIA, C.I. La Selva, Rionegro, Colombia
| | - Matthew W. Blair
- Department of Agricultural & Environmental Sciences, Tennessee State University, Nashville, TN, United States
- *Correspondence: Andrés J. Cortés, ; Matthew W. Blair,
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24
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Resende RT, Chenu K, Rasmussen SK, Heinemann AB, Fritsche-Neto R. Editorial: Enviromics in Plant Breeding. FRONTIERS IN PLANT SCIENCE 2022; 13:935380. [PMID: 35845710 PMCID: PMC9280691 DOI: 10.3389/fpls.2022.935380] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Accepted: 05/19/2022] [Indexed: 05/26/2023]
Affiliation(s)
| | - Karine Chenu
- Queensland Alliance for Agriculture and Food Innovation, University of Queensland, St Lucia, QLD, Australia
| | - Soren K. Rasmussen
- Section for Plant Biochemistry, Department of Plant and Environmental Sciences, Faculty of Natural and Life Sciences, University of Copenhagen, Frederiksberg, Denmark
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25
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Martini JWR, Gao N, Crossa J. Incorporating Omics Data in Genomic Prediction. Methods Mol Biol 2022; 2467:341-357. [PMID: 35451782 DOI: 10.1007/978-1-0716-2205-6_12] [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] [Indexed: 06/14/2023]
Abstract
In this chapter, we discuss the motivation for integrating other types of omics data into genomic prediction methods. We give an overview of literature investigating the performance of omics-enhanced predictions, and highlight potential pitfalls when applying these methods in breeding. We emphasize that the statistical methods available for genomic data can be transferred to the general omics case. However, when using a framework of omic relationship matrices, the standardization of the variables may be more relevant than it is for a genomic relationship matrix based on single-nucleotide polymorphisms.
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Affiliation(s)
- Johannes W R Martini
- International Maize and Wheat Improvement Center (CIMMYT), Veracruz, CP, Mexico.
| | - Ning Gao
- School of Life Sciences, Sun Yat-Sen University, Guangzhou, China
| | - José Crossa
- International Maize and Wheat Improvement Center (CIMMYT), Veracruz, CP, Mexico
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26
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Crossa J, Montesinos-López OA, Pérez-Rodríguez P, Costa-Neto G, Fritsche-Neto R, Ortiz R, Martini JWR, Lillemo M, Montesinos-López A, Jarquin D, Breseghello F, Cuevas J, Rincent R. Genome and Environment Based Prediction Models and Methods of Complex Traits Incorporating Genotype × Environment Interaction. Methods Mol Biol 2022; 2467:245-283. [PMID: 35451779 DOI: 10.1007/978-1-0716-2205-6_9] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Genomic-enabled prediction models are of paramount importance for the successful implementation of genomic selection (GS) based on breeding values. As opposed to animal breeding, plant breeding includes extensive multienvironment and multiyear field trial data. Hence, genomic-enabled prediction models should include genotype × environment (G × E) interaction, which most of the time increases the prediction performance when the response of lines are different from environment to environment. In this chapter, we describe a historical timeline since 2012 related to advances of the GS models that take into account G × E interaction. We describe theoretical and practical aspects of those GS models, including the gains in prediction performance when including G × E structures for both complex continuous and categorical scale traits. Then, we detailed and explained the main G × E genomic prediction models for complex traits measured in continuous and noncontinuous (categorical) scale. Related to G × E interaction models this review also examine the analyses of the information generated with high-throughput phenotype data (phenomic) and the joint analyses of multitrait and multienvironment field trial data that is also employed in the general assessment of multitrait G × E interaction. The inclusion of nongenomic data in increasing the accuracy and biological reliability of the G × E approach is also outlined. We show the recent advances in large-scale envirotyping (enviromics), and how the use of mechanistic computational modeling can derive the crop growth and development aspects useful for predicting phenotypes and explaining G × E.
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Affiliation(s)
- José Crossa
- International Maize and Wheat Improvement Center (CIMMYT), Carretera México-Veracruz, Mexico
- Colegio de Postgraduados, Montecillos, Mexico
| | | | | | - Germano Costa-Neto
- Departamento de Genética, Escola Superior de Agricultura "Luiz de Queiroz" (ESALQ/USP), São Paulo, Brazil
| | - Roberto Fritsche-Neto
- Departamento de Genética, Escola Superior de Agricultura "Luiz de Queiroz" (ESALQ/USP), São Paulo, Brazil
| | - Rodomiro Ortiz
- Department of Plant Breeding, Swedish University of Agricultural Sciences (SLU), Alnarp, Sweden
| | - Johannes W R Martini
- International Maize and Wheat Improvement Center (CIMMYT), Carretera México-Veracruz, Mexico
| | - Morten Lillemo
- Department of Plant Sciences, Norwegian University of Life Sciences, IHA/CIGENE, Ås, Norway
| | - Abelardo Montesinos-López
- Departamento de Matemáticas, Centro Universitario de Ciencias Exactas e Ingenierías (CUCEI), Universidad de Guadalajara, Guadalajara, Jalisco, Mexico
| | | | | | - Jaime Cuevas
- Universidad de Quintana Roo, Chetumal, Quintana Roo, Mexico.
| | - Renaud Rincent
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, Génétique Quantitative et Evolution - Le Moulon, Gif-sur-Yvette, France.
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27
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Fritsche-Neto R, Galli G, Borges KLR, Costa-Neto G, Alves FC, Sabadin F, Lyra DH, Morais PPP, Braatz de Andrade LR, Granato I, Crossa J. Optimizing Genomic-Enabled Prediction in Small-Scale Maize Hybrid Breeding Programs: A Roadmap Review. FRONTIERS IN PLANT SCIENCE 2021; 12:658267. [PMID: 34276721 PMCID: PMC8281958 DOI: 10.3389/fpls.2021.658267] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Accepted: 05/10/2021] [Indexed: 06/13/2023]
Abstract
The usefulness of genomic prediction (GP) for many animal and plant breeding programs has been highlighted for many studies in the last 20 years. In maize breeding programs, mostly dedicated to delivering more highly adapted and productive hybrids, this approach has been proved successful for both large- and small-scale breeding programs worldwide. Here, we present some of the strategies developed to improve the accuracy of GP in tropical maize, focusing on its use under low budget and small-scale conditions achieved for most of the hybrid breeding programs in developing countries. We highlight the most important outcomes obtained by the University of São Paulo (USP, Brazil) and how they can improve the accuracy of prediction in tropical maize hybrids. Our roadmap starts with the efforts for germplasm characterization, moving on to the practices for mating design, and the selection of the genotypes that are used to compose the training population in field phenotyping trials. Factors including population structure and the importance of non-additive effects (dominance and epistasis) controlling the desired trait are also outlined. Finally, we explain how the source of the molecular markers, environmental, and the modeling of genotype-environment interaction can affect the accuracy of GP. Results of 7 years of research in a public maize hybrid breeding program under tropical conditions are discussed, and with the great advances that have been made, we find that what is yet to come is exciting. The use of open-source software for the quality control of molecular markers, implementing GP, and envirotyping pipelines may reduce costs in an efficient computational manner. We conclude that exploring new models/tools using high-throughput phenotyping data along with large-scale envirotyping may bring more resolution and realism when predicting genotype performances. Despite the initial costs, mostly for genotyping, the GP platforms in combination with these other data sources can be a cost-effective approach for predicting the performance of maize hybrids for a large set of growing conditions.
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Affiliation(s)
- Roberto Fritsche-Neto
- Laboratory of Allogamous Plant Breeding, Genetics Department, Luiz de Queiroz College of Agriculture, University of São Paulo, Piracicaba, Brazil
| | - Giovanni Galli
- Laboratory of Allogamous Plant Breeding, Genetics Department, Luiz de Queiroz College of Agriculture, University of São Paulo, Piracicaba, Brazil
| | - Karina Lima Reis Borges
- Laboratory of Allogamous Plant Breeding, Genetics Department, Luiz de Queiroz College of Agriculture, University of São Paulo, Piracicaba, Brazil
| | - Germano Costa-Neto
- Laboratory of Allogamous Plant Breeding, Genetics Department, Luiz de Queiroz College of Agriculture, University of São Paulo, Piracicaba, Brazil
| | - Filipe Couto Alves
- Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI, United States
| | - Felipe Sabadin
- Laboratory of Allogamous Plant Breeding, Genetics Department, Luiz de Queiroz College of Agriculture, University of São Paulo, Piracicaba, Brazil
| | - Danilo Hottis Lyra
- Department of Computational and Analytical Sciences, Rothamsted Research, Harpenden, United Kingdom
| | | | | | - Italo Granato
- Laboratoire d'Ecophysiologie des Plantes sous Stress Environnementaux (LEPSE), Institut National de la Recherche Agronomique (INRA), Univ. Montpellier, SupAgro, Montpellier, France
| | - Jose Crossa
- Biometrics and Statistics Unit, International Maize and Wheat Improvement Center (CIMMYT), Carretera México - Veracruz, Texcoco, Mexico
- Colegio de Posgraduado, Montecillo, Mexico
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28
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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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29
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Mohanta TK, Mishra AK, Mohanta YK, Al-Harrasi A. Space Breeding: The Next-Generation Crops. FRONTIERS IN PLANT SCIENCE 2021; 12:771985. [PMID: 34777452 PMCID: PMC8579881 DOI: 10.3389/fpls.2021.771985] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Accepted: 10/04/2021] [Indexed: 05/03/2023]
Abstract
Since the beginning of space exploration, researchers have been exploring the role of microgravity, cosmic radiation, and other aspects of the space environment on plant growth and development. To create superior crop varieties and achieve noticeable success in the space environment, several types of research have been conducted thus far. Space-grown plants have been exposed to cosmic radiation and microgravity, which has led to the generation of crop varieties with diverse genotypes and phenotypes arising from different cellular, subcellular, genomic, chromosomal, and biochemical changes. DNA damage and chromosomal aberrations due to cosmic radiation are the major factors responsible for genetic polymorphism and the generation of crops with modified genetic combinations. These changes can be used to produce next-generation crop varieties capable of surviving diverse environmental conditions. This review aims to elucidate the detailed molecular mechanisms and genetic mutations found in plants used in recent space crop projects and how these can be applied in space breeding programmes in the future.
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Affiliation(s)
- Tapan Kumar Mohanta
- Natural and Medical Sciences Research Center, University of Nizwa, Nizwa, Oman
- *Correspondence: Tapan Kumar Mohanta, ;
| | | | - Yugal Kishore Mohanta
- Department of Applied Biology, School of Biological Science, University of Science and Technology, Ri-Bhoi, India
| | - Ahmed Al-Harrasi
- Natural and Medical Sciences Research Center, University of Nizwa, Nizwa, Oman
- Ahmed Al-Harrasi,
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Costa-Neto G, Crossa J, Fritsche-Neto R. Enviromic Assembly Increases Accuracy and Reduces Costs of the Genomic Prediction for Yield Plasticity in Maize. FRONTIERS IN PLANT SCIENCE 2021; 12:717552. [PMID: 34691099 PMCID: PMC8529011 DOI: 10.3389/fpls.2021.717552] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Accepted: 09/03/2021] [Indexed: 05/21/2023]
Abstract
Quantitative genetics states that phenotypic variation is a consequence of the interaction between genetic and environmental factors. Predictive breeding is based on this statement, and because of this, ways of modeling genetic effects are still evolving. At the same time, the same refinement must be used for processing environmental information. Here, we present an "enviromic assembly approach," which includes using ecophysiology knowledge in shaping environmental relatedness into whole-genome predictions (GP) for plant breeding (referred to as enviromic-aided genomic prediction, E-GP). We propose that the quality of an environment is defined by the core of environmental typologies and their frequencies, which describe different zones of plant adaptation. From this, we derived markers of environmental similarity cost-effectively. Combined with the traditional additive and non-additive effects, this approach may better represent the putative phenotypic variation observed across diverse growing conditions (i.e., phenotypic plasticity). Then, we designed optimized multi-environment trials coupling genetic algorithms, enviromic assembly, and genomic kinships capable of providing in-silico realization of the genotype-environment combinations that must be phenotyped in the field. As proof of concept, we highlighted two E-GP applications: (1) managing the lack of phenotypic information in training accurate GP models across diverse environments and (2) guiding an early screening for yield plasticity exerting optimized phenotyping efforts. Our approach was tested using two tropical maize sets, two types of enviromics assembly, six experimental network sizes, and two types of optimized training set across environments. We observed that E-GP outperforms benchmark GP in all scenarios, especially when considering smaller training sets. The representativeness of genotype-environment combinations is more critical than the size of multi-environment trials (METs). The conventional genomic best-unbiased prediction (GBLUP) is inefficient in predicting the quality of a yet-to-be-seen environment, while enviromic assembly enabled it by increasing the accuracy of yield plasticity predictions. Furthermore, we discussed theoretical backgrounds underlying how intrinsic envirotype-phenotype covariances within the phenotypic records can impact the accuracy of GP. The E-GP is an efficient approach to better use environmental databases to deliver climate-smart solutions, reduce field costs, and anticipate future scenarios.
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Affiliation(s)
- Germano Costa-Neto
- Department of Genetics, “Luiz de Queiroz” Agriculture College, University of São Paulo (ESALQ/USP), Piracicaba, Brazil
- Institute for Genomic Diversity, Cornell University, Ithaca, NY, United States
- *Correspondence: Germano Costa-Neto
| | - Jose Crossa
- Biometrics and Statistics Unit, International Maize and Wheat Improvement Center (CIMMYT), Mexico City, Mexico
- Colegio de Posgraduado, Mexico City, Mexico
| | - Roberto Fritsche-Neto
- Department of Genetics, “Luiz de Queiroz” Agriculture College, University of São Paulo (ESALQ/USP), Piracicaba, Brazil
- Breeding Analytics and Data Management Unit, International Rice Research Institute (IRRI), Los Baños, Philippines
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