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Zhang X, Gao H, Zhang J, Liu L, Fu L, Zhao Y, Sun Y. Deciphering the core microbiota in open environment solid-state fermentation of Beijing rice vinegar and its correlation with environmental factors. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2024; 104:7159-7172. [PMID: 38629632 DOI: 10.1002/jsfa.13538] [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: 01/09/2024] [Revised: 04/07/2024] [Accepted: 04/11/2024] [Indexed: 04/26/2024]
Abstract
BACKGROUND Rice vinegar is a popular cereal vinegar worldwide and is typically produced in an open environment, and the ecosystem of solid-state fermentation is complicated and robust. The present study aimed to reveal the shaping force of the establishment of the ecosystem of Beijing rice vinegar, the core function microbiota and their correlation with critical environmental factors. [Correction added after first online publication on 29 May 2024; the word "worldwide" has been removed from the first sentence under the section Background.] RESULTS: The experimental findings revealed the changes in environmental factors, major metabolites and microbial patterns during Beijing rice vinegar fermentation were obtained. The major metabolites accumulated at the middle and late acetic acid fermentation (AAF) periods. Principal coordinates and t-test analyses revealed the specific bacterial and fungal species at corresponding stages. Kosakonia, Methlobacterium, Sphingomonas, unidentified Rhizobiaceae, Pseudozyma and Saccharomycopsis dorminated during saccharification and alcohol fermentation and early AAF, whereas Lactococcus, Acetobacter, Rhodotorula and Kazachstania dominated the later AAF stages. Canonical correspondence analysis of environmental factors with core microbiota. Temperature and total acid were the most significant factors correlated with the SAF bacterial profile (Pediococcus, Weissella, Enterococcus and Kosakonia). Ethanol was the most significant factor between AAF1 and AAF3, and mainly affected Acetobacter and Lactobacillus. Conversely, ethanol was the most significant factor in the SAF, AAF1 and AAF3 fungi communities; typical microorganisms were Saccharomyces and Malassezia. Furthermore, the predicted phenotypes of bacteria and their response to environmental factors were evaluated. CONCLUSION In conclusion, the present study has provided insights into the process regulation of spontaneous fermentation and distinguished the key driving forces in the microbiota of Beijing rice vinegar fermentation. © 2024 Society of Chemical Industry.
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Affiliation(s)
- Xin Zhang
- China Meat Research Center, Beijing, China
- Beijing Academy of Food Sciences, Beijing, China
| | - Hang Gao
- China Meat Research Center, Beijing, China
- Beijing Academy of Food Sciences, Beijing, China
| | - Jian Zhang
- Beijing Academy of Food Sciences, Beijing, China
| | - Li Liu
- Beijing Academy of Food Sciences, Beijing, China
| | - Lijun Fu
- Beijing Academy of Food Sciences, Beijing, China
| | - Yan Zhao
- China Meat Research Center, Beijing, China
- Beijing Academy of Food Sciences, Beijing, China
| | - Yong Sun
- Beijing Academy of Food Sciences, Beijing, China
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2
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Kusmec A, Yeh CT'E, Schnable PS. Data-driven identification of environmental variables influencing phenotypic plasticity to facilitate breeding for future climates. THE NEW PHYTOLOGIST 2024. [PMID: 39183371 DOI: 10.1111/nph.19937] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Accepted: 05/20/2024] [Indexed: 08/27/2024]
Abstract
Phenotypic plasticity describes a genotype's ability to produce different phenotypes in response to different environments. Breeding crops that exhibit appropriate levels of plasticity for future climates will be crucial to meeting global demand, but knowledge of the critical environmental factors is limited to a handful of well-studied major crops. Using 727 maize (Zea mays L.) hybrids phenotyped for grain yield in 45 environments, we investigated the ability of a genetic algorithm and two other methods to identify environmental determinants of grain yield from a large set of candidate environmental variables constructed using minimal assumptions. The genetic algorithm identified pre- and postanthesis maximum temperature, mid-season solar radiation, and whole season net evapotranspiration as the four most important variables from a candidate set of 9150. Importantly, these four variables are supported by previous literature. After calculating reaction norms for each environmental variable, candidate genes were identified and gene annotations investigated to demonstrate how this method can generate insights into phenotypic plasticity. The genetic algorithm successfully identified known environmental determinants of hybrid maize grain yield. This demonstrates that the methodology could be applied to other less well-studied phenotypes and crops to improve understanding of phenotypic plasticity and facilitate breeding crops for future climates.
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Affiliation(s)
- Aaron Kusmec
- Department of Agronomy, Iowa State University, Ames, IA, 50011-3650, USA
| | | | - Patrick S Schnable
- Department of Agronomy, Iowa State University, Ames, IA, 50011-3650, USA
- Plant Sciences Institute, Iowa State University, Ames, IA, 50011-3650, USA
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3
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Heckman RW, Pereira CG, Aspinwall MJ, Juenger TE. Physiological Responses of C 4 Perennial Bioenergy Grasses to Climate Change: Causes, Consequences, and Constraints. ANNUAL REVIEW OF PLANT BIOLOGY 2024; 75:737-769. [PMID: 38424068 DOI: 10.1146/annurev-arplant-070623-093952] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/02/2024]
Abstract
C4 perennial bioenergy grasses are an economically and ecologically important group whose responses to climate change will be important to the future bioeconomy. These grasses are highly productive and frequently possess large geographic ranges and broad environmental tolerances, which may contribute to the evolution of ecotypes that differ in physiological acclimation capacity and the evolution of distinct functional strategies. C4 perennial bioenergy grasses are predicted to thrive under climate change-C4 photosynthesis likely evolved to enhance photosynthetic efficiency under stressful conditions of low [CO2], high temperature, and drought-although few studies have examined how these species will respond to combined stresses or to extremes of temperature and precipitation. Important targets for C4 perennial bioenergy production in a changing world, such as sustainability and resilience, can benefit from combining knowledge of C4 physiology with recent advances in crop improvement, especially genomic selection.
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Affiliation(s)
- Robert W Heckman
- Rocky Mountain Research Station, US Department of Agriculture Forest Service, Cedar City, Utah, USA;
| | - Caio Guilherme Pereira
- Department of Integrative Biology, University of Texas at Austin, Austin, Texas, USA;
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | | | - Thomas E Juenger
- Department of Integrative Biology, University of Texas at Austin, Austin, Texas, USA;
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4
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Gou X, Shao Y, Wang X, Shi H, Yu J, Li X, Guo T. Evolutionary patterns of DNA base composition at polymorphic sites highlight the role of the environment in shaping barley and rice genomes. THE PLANT GENOME 2024; 17:e20456. [PMID: 38688857 DOI: 10.1002/tpg2.20456] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Revised: 03/20/2024] [Accepted: 04/01/2024] [Indexed: 05/02/2024]
Abstract
Insights into changes in genome base composition underlying crop domestication can be gained by using comparative genomics. With this approach, previous studies have reported that crop genomes during domestication accumulate more nucleotides adenine (A) and thymine (T) (termed as [AT]-increase) across polymorphic sites. However, the potential influence of the environment or its factors, for example, solar ultraviolet (UV) radiation and temperature, on the [AT]-increase has not been well elucidated. Here, we investigated the [AT]-increase in barley (Hordeum vulgare L.) and rice (Oryza sativa L.) and the association with natural environments, where accessions are distributed. With 12,798,376 and 2,861,535 single-nucleotide polymorphisms from 368 barley and 1375 rice accessions, respectively, we discovered that [AT] increases from wild accessions to improved cultivars, and genomic regions with larger [AT]-increase tend to have higher UV-related motif frequencies, suggesting solar UV radiation as a potential factor in driving genome variation. To link [AT] change with the geographic distribution, we gathered georeferenced accessions and examined their local environments. Interestingly, negative correlations between [AT] and environmental factors were observed (r = -0.39 ∼ -0.75) and modern accessions with higher [AT] values, as compared with wild relatives, are from the environments with lower solar UV radiation or lower temperature. With [AT] and environmental factors as phenotypes, genome-wide association mapping identified three candidate genes that have the potential to contribute to [AT] variation under the effect of environmental conditions. Our findings provide genomic and environmental insights into evolutionary pattern of DNA base composition and underlying mechanisms.
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Affiliation(s)
- Xiangjian Gou
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
- Hubei Hongshan Laboratory, Wuhan, China
| | - Yang Shao
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
- Hubei Hongshan Laboratory, Wuhan, China
| | - Xiao Wang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
- Hubei Hongshan Laboratory, Wuhan, China
| | - Haoran Shi
- Chengdu Academy of Agricultural and Forestry Sciences, Wenjiang, China
| | - Jianming Yu
- Department of Agronomy, Iowa State University, Ames, Iowa, USA
| | - Xianran Li
- USDA-ARS, Wheat Health, Genetics, and Quality Research Unit, Pullman, Washington, USA
- Department of Crop and Soil Sciences, Washington State University, Pullman, Washington, USA
| | - Tingting Guo
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
- Hubei Hongshan Laboratory, Wuhan, China
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5
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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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6
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Tadesse D, Yee EF, Wolabu TW, Wang H, Yun J, Grosjean N, Kumaran D, Santiago K, Kong W, Sharma A, Chen J, Paterson AH, Xie M, Tadege M. Sorghum SbGhd7 is a major regulator of floral transition and directly represses genes crucial for flowering activation. THE NEW PHYTOLOGIST 2024; 242:786-796. [PMID: 38451101 DOI: 10.1111/nph.19591] [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: 11/21/2023] [Accepted: 01/29/2024] [Indexed: 03/08/2024]
Abstract
Molecular genetic understanding of flowering time regulation is crucial for sorghum development. GRAIN NUMBER, PLANT HEIGHT AND HEADING DATE 7 (SbGhd7) is one of the six classical loci conferring photoperiod sensitivity of sorghum flowering. However, its functions remain poorly studied. The molecular functions of SbGhd7 were characterized. The gene regulatory network controlled by SbGhd7 was constructed and validated. The biological roles of SbGhd7 and its major targets were studied. SbGhd7 overexpression (OE) completely prevented sorghum flowering. Additionally, we show that SbGhd7 is a major negative regulator of flowering, binding to the promoter motif TGAATG(A/T)(A/T/C) and repressing transcription of the major florigen FLOWERING LOCUS T 10 (SbFT10) and floral activators EARLY HEADING DATE (SbEhd1), FLAVIN-BINDING, KELCH REPEAT, F-BOX1 (SbFKF1) and EARLY FLOWERING 3 (SbELF3). Reinforcing the direct effect of SbGhd7, SbEhd1 OE activated the promoters of three functional florigens (SbFT1, SbFT8 and SbFT10), dramatically accelerating flowering. Our studies demonstrate that SbGhd7 is a major repressor of sorghum flowering by directly and indirectly targeting genes for flowering activation. The mechanism appears ancient. Our study extends the current model of floral transition regulation in sorghum and provides a framework for a comprehensive understanding of sorghum photoperiod response.
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Affiliation(s)
- Dimiru Tadesse
- Biology Department, Brookhaven National Laboratory, Upton, NY, 11973, USA
- Department of Plant and Soil Sciences, Institute for Agricultural Biosciences, Oklahoma State University, Ardmore, OK, 73401, USA
| | - Estella F Yee
- Biology Department, Brookhaven National Laboratory, Upton, NY, 11973, USA
- National Synchrotron Light Source, Brookhaven National Laboratory, Upton, NY, 11973, USA
| | - Tezera W Wolabu
- Department of Plant and Soil Sciences, Institute for Agricultural Biosciences, Oklahoma State University, Ardmore, OK, 73401, USA
| | - Hui Wang
- Department of Plant and Soil Sciences, Institute for Agricultural Biosciences, Oklahoma State University, Ardmore, OK, 73401, USA
- College of Grassland Science and Technology, China Agricultural University, Beijing, 100193, China
| | - Jianfei Yun
- Department of Plant and Soil Sciences, Institute for Agricultural Biosciences, Oklahoma State University, Ardmore, OK, 73401, USA
| | - Nicolas Grosjean
- DOE Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Desigan Kumaran
- Biology Department, Brookhaven National Laboratory, Upton, NY, 11973, USA
| | - Kassandra Santiago
- Biology Department, Brookhaven National Laboratory, Upton, NY, 11973, USA
| | - Wenqian Kong
- Department of Soil and Crop Science, University of Georgia, Athens, GA, 30602, USA
| | - Ankush Sharma
- Plant Genome Mapping Laboratory, University of Georgia, Athens, GA, 30602, USA
| | - Jianghua Chen
- Key Laboratory of Tropical Plant Resources and Sustainable Use, Center for Excellence in Molecular Plant Sciences, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Kunming, Yunnan, 650223, China
| | - Andrew H Paterson
- Plant Genome Mapping Laboratory, University of Georgia, Athens, GA, 30602, USA
| | - Meng Xie
- Biology Department, Brookhaven National Laboratory, Upton, NY, 11973, USA
| | - Million Tadege
- Department of Plant and Soil Sciences, Institute for Agricultural Biosciences, Oklahoma State University, Ardmore, OK, 73401, USA
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7
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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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8
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Tan Z, Han X, Dai C, Lu S, He H, Yao X, Chen P, Yang C, Zhao L, Yang QY, Zou J, Wen J, Hong D, Liu C, Ge X, Fan C, Yi B, Zhang C, Ma C, Liu K, Shen J, Tu J, Yang G, Fu T, Guo L, Zhao H. Functional genomics of Brassica napus: Progresses, challenges, and perspectives. JOURNAL OF INTEGRATIVE PLANT BIOLOGY 2024; 66:484-509. [PMID: 38456625 DOI: 10.1111/jipb.13635] [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: 12/22/2023] [Accepted: 02/19/2024] [Indexed: 03/09/2024]
Abstract
Brassica napus, commonly known as rapeseed or canola, is a major oil crop contributing over 13% to the stable supply of edible vegetable oil worldwide. Identification and understanding the gene functions in the B. napus genome is crucial for genomic breeding. A group of genes controlling agronomic traits have been successfully cloned through functional genomics studies in B. napus. In this review, we present an overview of the progress made in the functional genomics of B. napus, including the availability of germplasm resources, omics databases and cloned functional genes. Based on the current progress, we also highlight the main challenges and perspectives in this field. The advances in the functional genomics of B. napus contribute to a better understanding of the genetic basis underlying the complex agronomic traits in B. napus and will expedite the breeding of high quality, high resistance and high yield in B. napus varieties.
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Affiliation(s)
- Zengdong Tan
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, China
- Yazhouwan National Laboratory, Sanya, 572025, China
| | - Xu Han
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, China
| | - Cheng Dai
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, China
| | - Shaoping Lu
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, China
| | - Hanzi He
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, China
| | - Xuan Yao
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, China
- Yazhouwan National Laboratory, Sanya, 572025, China
| | - Peng Chen
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, China
| | - Chao Yang
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, China
| | - Lun Zhao
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, China
| | - Qing-Yong Yang
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, China
- Yazhouwan National Laboratory, Sanya, 572025, China
| | - Jun Zou
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, China
| | - Jing Wen
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, China
| | - Dengfeng Hong
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, China
- Yazhouwan National Laboratory, Sanya, 572025, China
| | - Chao Liu
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, China
| | - Xianhong Ge
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, China
| | - Chuchuan Fan
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, China
| | - Bing Yi
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, China
| | - Chunyu Zhang
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, China
| | - Chaozhi Ma
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, China
| | - Kede Liu
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, China
| | - Jinxiong Shen
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, China
| | - Jinxing Tu
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, China
| | - Guangsheng Yang
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, China
| | - Tingdong Fu
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, China
| | - Liang Guo
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, China
- Yazhouwan National Laboratory, Sanya, 572025, China
| | - Hu Zhao
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, China
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9
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Laitinen RAE. Importance of phenotypic plasticity in crop resilience. JOURNAL OF EXPERIMENTAL BOTANY 2024; 75:670-673. [PMID: 38307517 PMCID: PMC10837008 DOI: 10.1093/jxb/erad465] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2024]
Abstract
This article comments on:
Guo T, Wei J, Li X, Yu J. 2024. Environmental context of phenotypic plasticity in flowering time in sorghum and rice. Journal of Experimental Botany 75, 1004–1015.
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Affiliation(s)
- Roosa A E Laitinen
- Organismal and Evolutionary Research Programme, Faculty of Biological and Environmental Sciences, Viikki Plant Science Centre, University of Helsinki, Helsinki, Finland
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10
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Guo T, Wei J, Li X, Yu J. Environmental context of phenotypic plasticity in flowering time in sorghum and rice. JOURNAL OF EXPERIMENTAL BOTANY 2024; 75:1004-1015. [PMID: 37819624 PMCID: PMC10837014 DOI: 10.1093/jxb/erad398] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Accepted: 10/17/2023] [Indexed: 10/13/2023]
Abstract
Phenotypic plasticity is an important topic in biology and evolution. However, how to generate broadly applicable insights from individual studies remains a challenge. Here, with flowering time observed from a large geographical region for sorghum and rice genetic populations, we examine the consistency of parameter estimation for reaction norms of genotypes across different subsets of environments and searched for potential strategies to inform the study design. Both sample size and environmental mean range of the subset affected the consistency. The subset with either a large range of environmental mean or a large sample size resulted in genetic parameters consistent with the overall pattern. Furthermore, high accuracy through genomic prediction was obtained for reaction norm parameters of untested genotypes using models built from tested genotypes under the subsets of environments with either a large range or a large sample size. With 1428 and 1674 simulated settings, our analyses suggested that the distribution of environmental index values of a site should be considered in designing experiments. Overall, we showed that environmental context was critical, and considerations should be given to better cover the intended range of the environmental variable. Our findings have implications for the genetic architecture of complex traits, plant-environment interaction, and climate adaptation.
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Affiliation(s)
- Tingting Guo
- Hubei Hongshan Laboratory, Wuhan, Hubei, China
- College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, Hubei, China
| | - Jialu Wei
- Department of Agronomy, Iowa State University, Ames, IA, USA
| | - Xianran Li
- USDA, Agricultural Research Service, Wheat Health, Genetics, and Quality Research Unit, Pullman, WA, USA
| | - Jianming Yu
- Department of Agronomy, Iowa State University, Ames, IA, USA
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Lopez-Cruz M, Aguate FM, Washburn JD, de Leon N, Kaeppler SM, Lima DC, Tan R, Thompson A, De La Bretonne LW, de Los Campos G. Leveraging data from the Genomes-to-Fields Initiative to investigate genotype-by-environment interactions in maize in North America. Nat Commun 2023; 14:6904. [PMID: 37903778 PMCID: PMC10616096 DOI: 10.1038/s41467-023-42687-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Accepted: 10/18/2023] [Indexed: 11/01/2023] Open
Abstract
Genotype-by-environment (G×E) interactions can significantly affect crop performance and stability. Investigating G×E requires extensive data sets with diverse cultivars tested over multiple locations and years. The Genomes-to-Fields (G2F) Initiative has tested maize hybrids in more than 130 year-locations in North America since 2014. Here, we curate and expand this data set by generating environmental covariates (using a crop model) for each of the trials. The resulting data set includes DNA genotypes and environmental data linked to more than 70,000 phenotypic records of grain yield and flowering traits for more than 4000 hybrids. We show how this valuable data set can serve as a benchmark in agricultural modeling and prediction, paving the way for countless G×E investigations in maize. We use multivariate analyses to characterize the data set's genetic and environmental structure, study the association of key environmental factors with traits, and provide benchmarks using genomic prediction models.
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Affiliation(s)
- Marco Lopez-Cruz
- Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI, 48824, USA.
- Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, 48824, USA.
| | - Fernando M Aguate
- Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI, 48824, USA
- Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, 48824, USA
| | - Jacob D Washburn
- United States Department of Agriculture, Agricultural Research Service, University of Missouri, Columbia, MO, 65211, USA
| | - Natalia de Leon
- Department of Agronomy, University of Wisconsin, Madison, WI, 53706, USA
| | - Shawn M Kaeppler
- Department of Agronomy, University of Wisconsin, Madison, WI, 53706, USA
- Wisconsin Crop Innovation Center, University of Wisconsin, Middleton, WI, 53562, USA
| | | | - Ruijuan Tan
- Department of Plant, Soil and Microbial Sciences, Michigan State University, East Lansing, MI, 48824, USA
| | - Addie Thompson
- Department of Plant, Soil and Microbial Sciences, Michigan State University, East Lansing, MI, 48824, USA
- Plant Resilience Institute, Michigan State University, East Lansing, MI, 48824, USA
| | | | - Gustavo de Los Campos
- Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI, 48824, USA.
- Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, 48824, USA.
- Department of Statistics and Probability, Michigan State University, East Lansing, MI, 48824, USA.
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Ma Y, Yang W, Zhang H, Wang P, Liu Q, Li F, Du W. Genetic analysis of phenotypic plasticity identifies BBX6 as the candidate gene for maize adaptation to temperate regions. FRONTIERS IN PLANT SCIENCE 2023; 14:1280331. [PMID: 37964997 PMCID: PMC10642939 DOI: 10.3389/fpls.2023.1280331] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/20/2023] [Accepted: 10/12/2023] [Indexed: 11/16/2023]
Abstract
Introduction Climate changes pose a significant threat to crop adaptation and production. Dissecting the genetic basis of phenotypic plasticity and uncovering the responsiveness of regulatory genes to environmental factors can significantly contribute to the improvement of climate- resilience in crops. Methods We established a BC1F3:4 population using the elite inbred lines Zheng58 and PH4CV and evaluated plant height (PH) across four environments characterized by substantial variations in environmental factors. Then, we quantified the correlation between the environmental mean of PH (the mean performance in each environment) and the environmental parameters within a specific growth window. Furthermore, we performed GWAS analysis of phenotypic plasticity, and identified QTLs and candidate gene that respond to key environment index. After that, we constructed the coexpression network involving the candidate gene, and performed selective sweep analysis of the candidate gene. Results We found that the environmental parameters demonstrated substantial variation across the environments, and genotype by environment interaction contributed to the variations of PH. Then, we identified PTT(35-48) (PTT is the abbreviation for photothermal units), the mean PTT from 35 to 48 days after planting, as the pivotal environmental index that closely correlated with environmental mean of PH. Leveraging the slopes of the response of PH to both the environmental mean and PTT(35-48), we successfully pinpointed QTLs for phenotypic plasticity on chromosomes 1 and 2. Notably, the PH4CV genotypes at these two QTLs exhibited positive contributions to phenotypic plasticity. Furthermore, our analysis demonstrated a direct correlation between the additive effects of each QTL and PTT(35-48). By analyzing transcriptome data of the parental lines in two environments, we found that the 1009 genes responding to PTT(35-48) were enriched in the biological processes related to environmental sensitivity. BBX6 was the prime candidate gene among the 13 genes in the two QTL regions. The coexpression network of BBX6 contained other genes related to flowering time and photoperiod sensitivity. Our investigation, including selective sweep analysis and genetic differentiation analysis, suggested that BBX6 underwent selection during maize domestication. Discussion Th is research substantially advances our understanding of critical environmental factors influencing maize adaptation while simultaneously provides an invaluable gene resource for the development of climate-resilient maize hybrid varieties.
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Affiliation(s)
- Yuting Ma
- College of Agronomy, Shenyang Agricultural University, Shenyang, Liaoning, China
- State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Wenyan Yang
- College of Agronomy, Shenyang Agricultural University, Shenyang, Liaoning, China
- State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Hongwei Zhang
- State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Pingxi Wang
- State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Qian Liu
- State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Fenghai Li
- College of Agronomy, Shenyang Agricultural University, Shenyang, Liaoning, China
| | - Wanli Du
- College of Agronomy, Shenyang Agricultural University, Shenyang, Liaoning, China
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13
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Grant NP, Toy JJ, Funnell-Harris DL, Sattler SE. Deleterious mutations predicted in the sorghum (Sorghum bicolor) Maturity (Ma) and Dwarf (Dw) genes from whole-genome resequencing. Sci Rep 2023; 13:16638. [PMID: 37789045 PMCID: PMC10547693 DOI: 10.1038/s41598-023-42306-8] [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: 05/10/2023] [Accepted: 09/07/2023] [Indexed: 10/05/2023] Open
Abstract
In sorghum [Sorghum bicolor (L.) Moench] the Maturity (Ma1, Ma2, Ma3, Ma4, Ma5, Ma6) and Dwarf (Dw1, Dw2, Dw3, Dw4) loci, encode genes controlling flowering time and plant height, respectively, which are critical for designing sorghum ideotypes for a maturity timeframe and a harvest method. Publicly available whole-genome resequencing data from 860 sorghum accessions was analyzed in silico to identify genomic variants at 8 of these loci (Ma1, Ma2, Ma3, Ma5, Ma6, Dw1, Dw2, Dw3) to identify novel loss of function alleles and previously characterized ones in sorghum germplasm. From ~ 33 million SNPs and ~ 4.4 million InDels, 1445 gene variants were identified within these 8 genes then evaluated for predicted effect on the corresponding encoded proteins, which included newly identified mutations (4 nonsense, 15 frameshift, 28 missense). Likewise, most accessions analyzed contained predicted loss of function alleles (425 ma1, 22 ma2, 40 ma3, 74 ma5, 414 ma6, 289 dw1, 268 dw2 and 45 dw3) at multiple loci, but 146 and 463 accessions had no predicted ma or dw mutant alleles, respectively. The ma and dw alleles within these sorghum accessions represent a valuable source for manipulating flowering time and plant height to develop the full range of sorghum types: grain, sweet and forage/biomass.
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Affiliation(s)
- Nathan P Grant
- Wheat, Sorghum and Forage Research Unit, Agricultural Research Service, United States Department of Agriculture, Lincoln, NE, USA
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE, USA
| | - John J Toy
- Wheat, Sorghum and Forage Research Unit, Agricultural Research Service, United States Department of Agriculture, Lincoln, NE, USA
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE, USA
| | - Deanna L Funnell-Harris
- Wheat, Sorghum and Forage Research Unit, Agricultural Research Service, United States Department of Agriculture, Lincoln, NE, USA
- Department of Plant Pathology, University of Nebraska-Lincoln, Lincoln, NE, USA
| | - Scott E Sattler
- Wheat, Sorghum and Forage Research Unit, Agricultural Research Service, United States Department of Agriculture, Lincoln, NE, USA.
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE, USA.
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14
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Messina CD, Gho C, Hammer GL, Tang T, Cooper M. Two decades of harnessing standing genetic variation for physiological traits to improve drought tolerance in maize. JOURNAL OF EXPERIMENTAL BOTANY 2023; 74:4847-4861. [PMID: 37354091 PMCID: PMC10474595 DOI: 10.1093/jxb/erad231] [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: 03/06/2023] [Accepted: 06/15/2023] [Indexed: 06/26/2023]
Abstract
We review approaches to maize breeding for improved drought tolerance during flowering and grain filling in the central and western US corn belt and place our findings in the context of results from public breeding. Here we show that after two decades of dedicated breeding efforts, the rate of crop improvement under drought increased from 6.2 g m-2 year-1 to 7.5 g m-2 year-1, closing the genetic gain gap with respect to the 8.6 g m-2 year-1 observed under water-sufficient conditions. The improvement relative to the long-term genetic gain was possible by harnessing favourable alleles for physiological traits available in the reference population of genotypes. Experimentation in managed stress environments that maximized the genetic correlation with target environments was key for breeders to identify and select for these alleles. We also show that the embedding of physiological understanding within genomic selection methods via crop growth models can hasten genetic gain under drought. We estimate a prediction accuracy differential (Δr) above current prediction approaches of ~30% (Δr=0.11, r=0.38), which increases with increasing complexity of the trait environment system as estimated by Shannon information theory. We propose this framework to inform breeding strategies for drought stress across geographies and crops.
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Affiliation(s)
- Carlos D Messina
- Horticultural Sciences Department, University of Florida, Gainesville, FL, USA
- ARC Centre of Excellence for Plant Success in Nature and Agriculture, The University of Queensland, Brisbane, Qld 4072, Australia
| | - Carla Gho
- School of Agriculture & Food Sciences, The University of Queensland, Brisbane, Qld 4072, Australia
| | - Graeme L Hammer
- ARC Centre of Excellence for Plant Success in Nature and Agriculture, The University of Queensland, Brisbane, Qld 4072, Australia
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, Qld 4072, Australia
| | - Tom Tang
- Corteva Agrisciences, Johnston, IA, USA
| | - Mark Cooper
- ARC Centre of Excellence for Plant Success in Nature and Agriculture, The University of Queensland, Brisbane, Qld 4072, Australia
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, Qld 4072, Australia
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15
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Della Coletta R, Liese SE, Fernandes SB, Mikel MA, Bohn MO, Lipka AE, Hirsch CN. Linking genetic and environmental factors through marker effect networks to understand trait plasticity. Genetics 2023; 224:iyad103. [PMID: 37246567 DOI: 10.1093/genetics/iyad103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Revised: 05/19/2023] [Accepted: 05/24/2023] [Indexed: 05/30/2023] Open
Abstract
Understanding how plants adapt to specific environmental changes and identifying genetic markers associated with phenotypic plasticity can help breeders develop plant varieties adapted to a rapidly changing climate. Here, we propose the use of marker effect networks as a novel method to identify markers associated with environmental adaptability. These marker effect networks are built by adapting commonly used software for building gene coexpression networks with marker effects across growth environments as the input data into the networks. To demonstrate the utility of these networks, we built networks from the marker effects of ∼2,000 nonredundant markers from 400 maize hybrids across 9 environments. We demonstrate that networks can be generated using this approach, and that the markers that are covarying are rarely in linkage disequilibrium, thus representing higher biological relevance. Multiple covarying marker modules associated with different weather factors throughout the growing season were identified within the marker effect networks. Finally, a factorial test of analysis parameters demonstrated that marker effect networks are relatively robust to these options, with high overlap in modules associated with the same weather factors across analysis parameters. This novel application of network analysis provides unique insights into phenotypic plasticity and specific environmental factors that modulate the genome.
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Affiliation(s)
- Rafael Della Coletta
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN 55108, USA
| | - Sharon E Liese
- Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Samuel B Fernandes
- Department of Crop, Soil, and Environmental Sciences, University of Arkansas, Fayetteville, AR 72701, USA
| | - Mark A Mikel
- Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
- Roy J. Carver Biotechnology Center, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Martin O Bohn
- Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Alexander E Lipka
- Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Candice N Hirsch
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN 55108, USA
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16
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Kusmec A, Attigala L, Dai X, Srinivasan S, Yeh CTE, Schnable PS. A genetic tradeoff for tolerance to moderate and severe heat stress in US hybrid maize. PLoS Genet 2023; 19:e1010799. [PMID: 37410701 DOI: 10.1371/journal.pgen.1010799] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Accepted: 05/26/2023] [Indexed: 07/08/2023] Open
Abstract
Global climate change is increasing both average temperatures and the frequencies of extreme high temperatures. Past studies have documented a strong negative effect of exposures to temperatures >30°C on hybrid maize yields. However, these studies could not disentangle genetic adaptation via artificial selection from changes in agronomic practices. Because most of the earliest maize hybrids are no longer available, side-by-side comparisons with modern hybrids under current field conditions are generally impossible. Here, we report on the collection and curation of 81 years of public yield trial records covering 4,730 maize hybrids, which enabled us to model genetic variation for temperature responses among maize hybrids. We show that selection may have indirectly and inconsistently contributed to the genetic adaptation of maize to moderate heat stress over this time period while preserving genetic variance for continued adaptation. However, our results reveal the existence of a genetic tradeoff for tolerance to moderate and severe heat stress, leading to a decrease in tolerance to severe heat stress over the same time period. Both trends are particularly conspicuous since the mid-1970s. Such a tradeoff poses challenges to the continued adaptation of maize to warming climates due to a projected increase in the frequency of extreme heat events. Nevertheless, given recent advances in phenomics, enviromics, and physiological modeling, our results offer a degree of optimism for the capacity of plant breeders to adapt maize to warming climates, assuming appropriate levels of R&D investment.
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Affiliation(s)
- Aaron Kusmec
- Department of Agronomy, Iowa State University; Ames, Iowa, United States of America
| | - Lakshmi Attigala
- Department of Agronomy, Iowa State University; Ames, Iowa, United States of America
| | - Xiongtao Dai
- Department of Statistics, Iowa State University; Ames, Iowa, United States of America
| | - Srikant Srinivasan
- Plant Sciences Institute, Iowa State University; Ames, Iowa, United States of America
| | - Cheng-Ting Eddy Yeh
- Plant Sciences Institute, Iowa State University; Ames, Iowa, United States of America
| | - Patrick S Schnable
- Department of Agronomy, Iowa State University; Ames, Iowa, United States of America
- Plant Sciences Institute, Iowa State University; Ames, Iowa, United States of America
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17
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Fu R, Wang X. Modeling the influence of phenotypic plasticity on maize hybrid performance. PLANT COMMUNICATIONS 2023; 4:100548. [PMID: 36635964 DOI: 10.1016/j.xplc.2023.100548] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Revised: 12/31/2022] [Accepted: 01/10/2023] [Indexed: 05/11/2023]
Abstract
Phenotypic plasticity, the ability of an individual to alter its phenotype in response to changes in the environment, has been proposed as a target for breeding crop varieties with high environmental fitness. Here, we used phenotypic and genotypic data from multiple maize (Zea mays L.) populations to mathematically model phenotypic plasticity in response to the environment (PPRE) in inbred and hybrid lines. PPRE can be simply described by a linear model in which the two main parameters, intercept a and slope b, reflect two classes of genes responsive to endogenous (class A) and exogenous (class B) signals that coordinate plant development. Together, class A and class B genes contribute to the phenotypic plasticity of an individual in response to the environment. We also made connections between phenotypic plasticity and hybrid performance or general combining ability (GCA) of yield using 30 F1 hybrid populations generated by crossing the same maternal line with 30 paternal lines from different maize heterotic groups. We show that the parameters a and b from two given parental lines must be concordant to reach an ideal GCA of F1 yield. We hypothesize that coordinated regulation of the two classes of genes in the F1 hybrid genome is the basis for high GCA. Based on this theory, we built a series of predictive models to evaluate GCA in silico between parental lines of different heterotic groups.
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Affiliation(s)
- Ran Fu
- National Maize Improvement Center, College of Agronomy and Biotechnology, China Agricultural University, Beijing 100094, China; Frontiers Science Center for Molecular Design Breeding, College of Agronomy and Biotechnology, China Agricultural University, Beijing 100094, China
| | - Xiangfeng Wang
- National Maize Improvement Center, College of Agronomy and Biotechnology, China Agricultural University, Beijing 100094, China; Frontiers Science Center for Molecular Design Breeding, College of Agronomy and Biotechnology, China Agricultural University, Beijing 100094, China.
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18
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Jin M, Liu H, Liu X, Guo T, Guo J, Yin Y, Ji Y, Li Z, Zhang J, Wang X, Qiao F, Xiao Y, Zan Y, Yan J. Complex genetic architecture underlying the plasticity of maize agronomic traits. PLANT COMMUNICATIONS 2023; 4:100473. [PMID: 36642074 DOI: 10.1016/j.xplc.2022.100473] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Revised: 08/21/2022] [Accepted: 11/07/2022] [Indexed: 05/11/2023]
Abstract
Phenotypic plasticity is the ability of a given genotype to produce multiple phenotypes in response to changing environmental conditions. Understanding the genetic basis of phenotypic plasticity and establishing a predictive model is highly relevant to future agriculture under a changing climate. Here we report findings on the genetic basis of phenotypic plasticity for 23 complex traits using a diverse maize population planted at five sites with distinct environmental conditions. We found that latitude-related environmental factors were the main drivers of across-site variation in flowering time traits but not in plant architecture or yield traits. For the 23 traits, we detected 109 quantitative trait loci (QTLs), 29 for mean values, 66 for plasticity, and 14 for both parameters, and 80% of the QTLs interacted with latitude. The effects of several QTLs changed in magnitude or sign, driving variation in phenotypic plasticity. We experimentally validated one plastic gene, ZmTPS14.1, whose effect was likely mediated by the compensation effect of ZmSPL6 from a downstream pathway. By integrating genetic diversity, environmental variation, and their interaction into a joint model, we could provide site-specific predictions with increased accuracy by as much as 9.9%, 2.2%, and 2.6% for days to tassel, plant height, and ear weight, respectively. This study revealed a complex genetic architecture involving multiple alleles, pleiotropy, and genotype-by-environment interaction that underlies variation in the mean and plasticity of maize complex traits. It provides novel insights into the dynamic genetic architecture of agronomic traits in response to changing environments, paving a practical way toward precision agriculture.
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Affiliation(s)
- Minliang Jin
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China
| | - Haijun Liu
- Gregor Mendel Institute, Austrian Academy of Sciences, Vienna BioCenter, 1030 Vienna, Austria
| | - Xiangguo Liu
- Institute of Agricultural Biotechnology, Jilin Academy of Agricultural Sciences, Changchun 130033, China
| | - Tingting Guo
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China; Hubei Hongshan Laboratory, Wuhan 430070, China
| | - Jia Guo
- Institute of Agricultural Biotechnology, Jilin Academy of Agricultural Sciences, Changchun 130033, China
| | - Yuejia Yin
- Institute of Agricultural Biotechnology, Jilin Academy of Agricultural Sciences, Changchun 130033, China
| | - Yan Ji
- Key Laboratory of Tobacco Improvement and Biotechnology, Tobacco Research Institute, Chinese Academy of Agricultural Sciences, Qingdao 266000, China
| | - Zhenxian Li
- Institute of Agricultural Sciences of Xishuangbanna Prefecture of Yunnan Province, Jinghong 666100, China
| | - Jinhong Zhang
- Institute of Agricultural Sciences of Xishuangbanna Prefecture of Yunnan Province, Jinghong 666100, China
| | - Xiaqing Wang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China
| | - Feng Qiao
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China
| | - Yingjie Xiao
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China; Hubei Hongshan Laboratory, Wuhan 430070, China
| | - Yanjun Zan
- Umeå Plant Science Center, Department of Forestry Genetics and Plant Physiology, Swedish University of Agricultural Sciences, 90736 Umeå, Sweden; Key Laboratory of Tobacco Improvement and Biotechnology, Tobacco Research Institute, Chinese Academy of Agricultural Sciences, Qingdao 266000, China.
| | - Jianbing Yan
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China; Hubei Hongshan Laboratory, Wuhan 430070, China.
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Waters DL, van der Werf JHJ, Robinson H, Hickey LT, Clark SA. Partitioning the forms of genotype-by-environment interaction in the reaction norm analysis of stability. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2023; 136:99. [PMID: 37027025 PMCID: PMC10082108 DOI: 10.1007/s00122-023-04319-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 02/07/2023] [Indexed: 05/13/2023]
Abstract
KEY MESSAGE The reaction norm analysis of stability can be enhanced by partitioning the contribution of different types of G × E to the variation in slope. The slope of regression in a reaction norm model, where the performance of a genotype is regressed over an environmental covariable, is often used as a measure of stability of genotype performance. This method could be developed further by partitioning variation in the slope of regression into the two sources of genotype-by-environment interaction (G × E) which cause it: scale-type G × E (heterogeneity of variance) and rank-type G × E (heterogeneity of correlation). Because the two types of G × E have very different properties, separating their effect would enable a clearer understanding of stability. The aim of this paper was to demonstrate two methods which seek to achieve this in reaction norm models. Reaction norm models were fit to yield data from a multi-environment trial in Barley (Hordeum vulgare), with the adjusted mean yield from each environment used as the environmental covariable. Stability estimated from factor-analytic models, which can disentangle the two types of G × E and estimate stability based on rank-type G × E, was used for comparison. Adjusting the reaction norm slope to account for scale-type G × E using a genetic regression more than tripled the correlation with factor-analytic estimates of stability (0.24-0.26 to 0.80-0.85), indicating that it removed variation in the reaction norm slope that originated from scale-type G × E. A standardisation procedure had a more modest increase (055-0.59) but could be useful when curvilinear reaction norms are required. Analyses which use reaction norms to explore the stability of genotypes could gain additional insight into the mechanisms of stability by applying the methods outlined in this study.
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Affiliation(s)
- Dominic L Waters
- School of Environmental and Rural Science, University of New England, Armidale, NSW, 2351, Australia.
| | - Julius H J van der Werf
- School of Environmental and Rural Science, University of New England, Armidale, NSW, 2351, Australia
| | - Hannah Robinson
- InterGrain Pty Ltd, Perth, WA, Australia
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, QLD, Australia
| | - Lee T Hickey
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, QLD, Australia
| | - Sam A Clark
- School of Environmental and Rural Science, University of New England, Armidale, NSW, 2351, Australia
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Hu Y, Wang X, Xu Y, Yang H, Tong Z, Tian R, Xu S, Yu L, Guo Y, Shi P, Huang S, Yang G, Shi S, Wei F. Molecular mechanisms of adaptive evolution in wild animals and plants. SCIENCE CHINA. LIFE SCIENCES 2023; 66:453-495. [PMID: 36648611 PMCID: PMC9843154 DOI: 10.1007/s11427-022-2233-x] [Citation(s) in RCA: 29] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/07/2022] [Accepted: 08/30/2022] [Indexed: 01/18/2023]
Abstract
Wild animals and plants have developed a variety of adaptive traits driven by adaptive evolution, an important strategy for species survival and persistence. Uncovering the molecular mechanisms of adaptive evolution is the key to understanding species diversification, phenotypic convergence, and inter-species interaction. As the genome sequences of more and more non-model organisms are becoming available, the focus of studies on molecular mechanisms of adaptive evolution has shifted from the candidate gene method to genetic mapping based on genome-wide scanning. In this study, we reviewed the latest research advances in wild animals and plants, focusing on adaptive traits, convergent evolution, and coevolution. Firstly, we focused on the adaptive evolution of morphological, behavioral, and physiological traits. Secondly, we reviewed the phenotypic convergences of life history traits and responding to environmental pressures, and the underlying molecular convergence mechanisms. Thirdly, we summarized the advances of coevolution, including the four main types: mutualism, parasitism, predation and competition. Overall, these latest advances greatly increase our understanding of the underlying molecular mechanisms for diverse adaptive traits and species interaction, demonstrating that the development of evolutionary biology has been greatly accelerated by multi-omics technologies. Finally, we highlighted the emerging trends and future prospects around the above three aspects of adaptive evolution.
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Affiliation(s)
- Yibo Hu
- CAS Key Lab of Animal Ecology and Conservation Biology, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Xiaoping Wang
- State Key Laboratory for Conservation and Utilization of Bio-Resources in Yunnan, School of Life Sciences, Yunnan University, Kunming, 650091, China
| | - Yongchao Xu
- State Key Laboratory of Systematic and Evolutionary Botany, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093, China
| | - Hui Yang
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650201, China
| | - Zeyu Tong
- Institute of Evolution and Ecology, School of Life Sciences, Central China Normal University, Wuhan, 430079, China
| | - Ran Tian
- College of Life Sciences, Nanjing Normal University, Nanjing, 210023, China
| | - Shaohua Xu
- State Key Laboratory of Biocontrol, Guangdong Key Lab of Plant Resources, School of Life Sciences, Sun Yat-sen University, Guangzhou, 510275, China
| | - Li Yu
- State Key Laboratory for Conservation and Utilization of Bio-Resources in Yunnan, School of Life Sciences, Yunnan University, Kunming, 650091, China.
| | - Yalong Guo
- State Key Laboratory of Systematic and Evolutionary Botany, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093, China.
| | - Peng Shi
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650201, China.
| | - Shuangquan Huang
- Institute of Evolution and Ecology, School of Life Sciences, Central China Normal University, Wuhan, 430079, China.
| | - Guang Yang
- Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Guangzhou, 511458, China.
- College of Life Sciences, Nanjing Normal University, Nanjing, 210023, China.
| | - Suhua Shi
- State Key Laboratory of Biocontrol, Guangdong Key Lab of Plant Resources, School of Life Sciences, Sun Yat-sen University, Guangzhou, 510275, China.
| | - Fuwen Wei
- CAS Key Lab of Animal Ecology and Conservation Biology, Chinese Academy of Sciences, Beijing, 100101, China.
- Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Guangzhou, 511458, China.
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Yu S, Kusmec AM, Wang L, Nettleton D. Fusion Learning of Functional Linear Regression with Application to Genotype-by-Environment Interaction Studies. JOURNAL OF AGRICULTURAL, BIOLOGICAL AND ENVIRONMENTAL STATISTICS 2023. [DOI: 10.1007/s13253-023-00529-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
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de Ronne M, Légaré G, Belzile F, Boyle B, Torkamaneh D. 3D-GBS: a universal genotyping-by-sequencing approach for genomic selection and other high-throughput low-cost applications in species with small to medium-sized genomes. PLANT METHODS 2023; 19:13. [PMID: 36740716 PMCID: PMC9899395 DOI: 10.1186/s13007-023-00990-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Accepted: 01/31/2023] [Indexed: 06/18/2023]
Abstract
Despite the increased efficiency of sequencing technologies and the development of reduced-representation sequencing (RRS) approaches allowing high-throughput sequencing (HTS) of multiplexed samples, the per-sample genotyping cost remains the most limiting factor in the context of large-scale studies. For example, in the context of genomic selection (GS), breeders need genome-wide markers to predict the breeding value of large cohorts of progenies, requiring the genotyping of thousands candidates. Here, we introduce 3D-GBS, an optimized GBS procedure, to provide an ultra-high-throughput and ultra-low-cost genotyping solution for species with small to medium-sized genome and illustrate its use in soybean. Using a combination of three restriction enzymes (PstI/NsiI/MspI), the portion of the genome that is captured was reduced fourfold (compared to a "standard" ApeKI-based protocol) while reducing the number of markers by only 40%. By better focusing the sequencing effort on limited set of restriction fragments, fourfold more samples can be genotyped at the same minimal depth of coverage. This GBS protocol also resulted in a lower proportion of missing data and provided a more uniform distribution of SNPs across the genome. Moreover, we investigated the optimal number of reads per sample needed to obtain an adequate number of markers for GS and QTL mapping (500-1000 markers per biparental cross). This optimization allows sequencing costs to be decreased by ~ 92% and ~ 86% for GS and QTL mapping studies, respectively, compared to previously published work. Overall, 3D-GBS represents a unique and affordable solution for applications requiring extremely high-throughput genotyping where cost remains the most limiting factor.
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Affiliation(s)
- Maxime de Ronne
- Département de Phytologie, Université Laval, Quebec, Canada
- Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, Quebec, Canada
- Centre de recherche et d'innovation sur les végétaux (CRIV), Université Laval, Quebec, Canada
| | - Gaétan Légaré
- Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, Quebec, Canada
| | - François Belzile
- Département de Phytologie, Université Laval, Quebec, Canada
- Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, Quebec, Canada
- Centre de recherche et d'innovation sur les végétaux (CRIV), Université Laval, Quebec, Canada
| | - Brian Boyle
- Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, Quebec, Canada
| | - Davoud Torkamaneh
- Département de Phytologie, Université Laval, Quebec, Canada.
- Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, Quebec, Canada.
- Centre de recherche et d'innovation sur les végétaux (CRIV), Université Laval, Quebec, Canada.
- Institut intelligence et données (IID), Université Laval, Quebec, Canada.
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Guo T, Li X. Machine learning for predicting phenotype from genotype and environment. Curr Opin Biotechnol 2023; 79:102853. [PMID: 36463837 DOI: 10.1016/j.copbio.2022.102853] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Revised: 11/01/2022] [Accepted: 11/07/2022] [Indexed: 12/03/2022]
Abstract
Predicting phenotype with genomic and environmental information is critically needed and challenging. Machine learning methods have emerged as powerful tools to make accurate predictions from large and complex biological data. Here, we review the progress of phenotype prediction models enabled or improved by machine learning methods. We categorized the applications into three scenarios: prediction with genotypic information, with environmental information, and with both. In each scenario, we illustrate the practicality of prediction models, the advantages of machine learning, and the challenges of modeling complex relationships. We discuss the promising potential of leveraging machine learning and genetics theories to develop models that can predict phenotype and also interpret the biological consequences of changes in genotype and environment.
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Affiliation(s)
- Tingting Guo
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China; Hubei Hongshan Laboratory, Wuhan 430070, China.
| | - Xianran Li
- USDA, Agricultural Research Service, Wheat Health, Genetics, and Quality Research Unit, Pullman, WA 99164, USA; Department of Crop and Soil Sciences, Washington State University, Pullman, WA 99164, USA.
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Subedi M, Ghimire B, Bagwell JW, Buck JW, Mergoum M. Wheat end-use quality: State of art, genetics, genomics-assisted improvement, future challenges, and opportunities. Front Genet 2023; 13:1032601. [PMID: 36685944 PMCID: PMC9849398 DOI: 10.3389/fgene.2022.1032601] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Accepted: 12/20/2022] [Indexed: 01/06/2023] Open
Abstract
Wheat is the most important source of food, feed, and nutrition for humans and livestock around the world. The expanding population has increasing demands for various wheat products with different quality attributes requiring the development of wheat cultivars that fulfills specific demands of end-users including millers and bakers in the international market. Therefore, wheat breeding programs continually strive to meet these quality standards by screening their improved breeding lines every year. However, the direct measurement of various end-use quality traits such as milling and baking qualities requires a large quantity of grain, traits-specific expensive instruments, time, and an expert workforce which limits the screening process. With the advancement of sequencing technologies, the study of the entire plant genome is possible, and genetic mapping techniques such as quantitative trait locus mapping and genome-wide association studies have enabled researchers to identify loci/genes associated with various end-use quality traits in wheat. Modern breeding techniques such as marker-assisted selection and genomic selection allow the utilization of these genomic resources for the prediction of quality attributes with high accuracy and efficiency which speeds up crop improvement and cultivar development endeavors. In addition, the candidate gene approach through functional as well as comparative genomics has facilitated the translation of the genomic information from several crop species including wild relatives to wheat. This review discusses the various end-use quality traits of wheat, their genetic control mechanisms, the use of genetics and genomics approaches for their improvement, and future challenges and opportunities for wheat breeding.
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Affiliation(s)
- Madhav Subedi
- Institute of Plant Breeding, Genetics and Genomics, University of Georgia, Griffin Campus, Griffin, GA, United States
| | - Bikash Ghimire
- Department of Plant Pathology, University of Georgia, Griffin Campus, Griffin, GA, United States
| | - John White Bagwell
- Institute of Plant Breeding, Genetics and Genomics, University of Georgia, Griffin Campus, Griffin, GA, United States
| | - James W. Buck
- Department of Plant Pathology, University of Georgia, Griffin Campus, Griffin, GA, United States
| | - Mohamed Mergoum
- Department of Crop and Soil Sciences, University of Georgia, Griffin Campus, Griffin, GA, United States
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Voelker WG, Krishnan K, Chougule K, Alexander LC, Lu Z, Olson A, Ware D, Songsomboon K, Ponce C, Brenton ZW, Boatwright JL, Cooper EA. Ten new high-quality genome assemblies for diverse bioenergy sorghum genotypes. FRONTIERS IN PLANT SCIENCE 2023; 13:1040909. [PMID: 36684744 PMCID: PMC9846640 DOI: 10.3389/fpls.2022.1040909] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Accepted: 12/09/2022] [Indexed: 06/17/2023]
Abstract
Introduction Sorghum (Sorghum bicolor (L.) Moench) is an agriculturally and economically important staple crop that has immense potential as a bioenergy feedstock due to its relatively high productivity on marginal lands. To capitalize on and further improve sorghum as a potential source of sustainable biofuel, it is essential to understand the genomic mechanisms underlying complex traits related to yield, composition, and environmental adaptations. Methods Expanding on a recently developed mapping population, we generated de novo genome assemblies for 10 parental genotypes from this population and identified a comprehensive set of over 24 thousand large structural variants (SVs) and over 10.5 million single nucleotide polymorphisms (SNPs). Results We show that SVs and nonsynonymous SNPs are enriched in different gene categories, emphasizing the need for long read sequencing in crop species to identify novel variation. Furthermore, we highlight SVs and SNPs occurring in genes and pathways with known associations to critical bioenergy-related phenotypes and characterize the landscape of genetic differences between sweet and cellulosic genotypes. Discussion These resources can be integrated into both ongoing and future mapping and trait discovery for sorghum and its myriad uses including food, feed, bioenergy, and increasingly as a carbon dioxide removal mechanism.
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Affiliation(s)
- William G. Voelker
- Dept. of Bioinformatics & Genomics, University of North Carolina at Charlotte, Charlotte, NC, United States
- North Carolina Research Campus, Kannapolis, NC, United States
| | - Krittika Krishnan
- Dept. of Bioinformatics & Genomics, University of North Carolina at Charlotte, Charlotte, NC, United States
- North Carolina Research Campus, Kannapolis, NC, United States
| | - Kapeel Chougule
- Cold Spring Harbor Research Laboratory, Cold Spring Harbor, NY, United States
| | - Louie C. Alexander
- Dept. of Bioinformatics & Genomics, University of North Carolina at Charlotte, Charlotte, NC, United States
- North Carolina Research Campus, Kannapolis, NC, United States
| | - Zhenyuan Lu
- Cold Spring Harbor Research Laboratory, Cold Spring Harbor, NY, United States
| | - Andrew Olson
- Cold Spring Harbor Research Laboratory, Cold Spring Harbor, NY, United States
| | - Doreen Ware
- Cold Spring Harbor Research Laboratory, Cold Spring Harbor, NY, United States
- United States Department of Agriculture - Agricultural Research Service in the North Atlantic Area (USDA-ARS NAA), Robert W. Holley Center for Agriculture and Health, Ithaca, NY, United States
| | - Kittikun Songsomboon
- Dept. of Bioinformatics & Genomics, University of North Carolina at Charlotte, Charlotte, NC, United States
- North Carolina Research Campus, Kannapolis, NC, United States
| | - Cristian Ponce
- Dept. of Bioinformatics & Genomics, University of North Carolina at Charlotte, Charlotte, NC, United States
- North Carolina Research Campus, Kannapolis, NC, United States
| | - Zachary W. Brenton
- Carolina Seed Systems, Darlington, SC, United States
- Advanced Plant Technology, Clemson University, Clemson, SC, United States
| | - J. Lucas Boatwright
- Advanced Plant Technology, Clemson University, Clemson, SC, United States
- Dept. of Plant and Environmental Sciences, Clemson University, Clemson, SC, United States
| | - Elizabeth A. Cooper
- Dept. of Bioinformatics & Genomics, University of North Carolina at Charlotte, Charlotte, NC, United States
- North Carolina Research Campus, Kannapolis, NC, United States
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26
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Napier JD, Heckman RW, Juenger TE. Gene-by-environment interactions in plants: Molecular mechanisms, environmental drivers, and adaptive plasticity. THE PLANT CELL 2023; 35:109-124. [PMID: 36342220 PMCID: PMC9806611 DOI: 10.1093/plcell/koac322] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Accepted: 11/03/2022] [Indexed: 05/13/2023]
Abstract
Plants demonstrate a broad range of responses to environmental shifts. One of the most remarkable responses is plasticity, which is the ability of a single plant genotype to produce different phenotypes in response to environmental stimuli. As with all traits, the ability of plasticity to evolve depends on the presence of underlying genetic diversity within a population. A common approach for evaluating the role of genetic variation in driving differences in plasticity has been to study genotype-by-environment interactions (G × E). G × E occurs when genotypes produce different phenotypic trait values in response to different environments. In this review, we highlight progress and promising methods for identifying the key environmental and genetic drivers of G × E. Specifically, methodological advances in using algorithmic and multivariate approaches to understand key environmental drivers combined with new genomic innovations can greatly increase our understanding about molecular responses to environmental stimuli. These developing approaches can be applied to proliferating common garden networks that capture broad natural environmental gradients to unravel the underlying mechanisms of G × E. An increased understanding of G × E can be used to enhance the resilience and productivity of agronomic systems.
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Affiliation(s)
- Joseph D Napier
- Department of Integrative Biology, The University of Texas at Austin, Austin, Texas, 78712, USA
| | - Robert W Heckman
- Department of Integrative Biology, The University of Texas at Austin, Austin, Texas, 78712, USA
| | - Thomas E Juenger
- Department of Integrative Biology, The University of Texas at Austin, Austin, Texas, 78712, USA
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Matzel LD, Sauce B. A multi-faceted role of dual-state dopamine signaling in working memory, attentional control, and intelligence. Front Behav Neurosci 2023; 17:1060786. [PMID: 36873775 PMCID: PMC9978119 DOI: 10.3389/fnbeh.2023.1060786] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Accepted: 01/25/2023] [Indexed: 02/18/2023] Open
Abstract
Genetic evidence strongly suggests that individual differences in intelligence will not be reducible to a single dominant cause. However, some of those variations/changes may be traced to tractable, cohesive mechanisms. One such mechanism may be the balance of dopamine D1 (D1R) and D2 (D2R) receptors, which regulate intrinsic currents and synaptic transmission in frontal cortical regions. Here, we review evidence from human, animal, and computational studies that suggest that this balance (in density, activity state, and/or availability) is critical to the implementation of executive functions such as attention and working memory, both of which are principal contributors to variations in intelligence. D1 receptors dominate neural responding during stable periods of short-term memory maintenance (requiring attentional focus), while D2 receptors play a more specific role during periods of instability such as changing environmental or memory states (requiring attentional disengagement). Here we bridge these observations with known properties of human intelligence. Starting from theories of intelligence that place executive functions (e.g., working memory and attentional control) at its center, we propose that dual-state dopamine signaling might be a causal contributor to at least some of the variation in intelligence across individuals and its change by experiences/training. Although it is unlikely that such a mechanism can account for more than a modest portion of the total variance in intelligence, our proposal is consistent with an array of available evidence and has a high degree of explanatory value. We suggest future directions and specific empirical tests that can further elucidate these relationships.
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Affiliation(s)
- Louis D Matzel
- Department of Psychology, Rutgers University, Piscataway, NJ, United States
| | - Bruno Sauce
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
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28
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Adak A, Murray SC, Anderson SL. Temporal phenomic predictions from unoccupied aerial systems can outperform genomic predictions. G3 (BETHESDA, MD.) 2022; 13:6851143. [PMID: 36445027 PMCID: PMC9836347 DOI: 10.1093/g3journal/jkac294] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Accepted: 10/21/2022] [Indexed: 11/30/2022]
Abstract
A major challenge of genetic improvement and selection is to accurately predict individuals with the highest fitness in a population without direct measurement. Over the last decade, genomic predictions (GP) based on genome-wide markers have become reliable and routine. Now phenotyping technologies, including unoccupied aerial systems (UAS also known as drones), can characterize individuals with a data depth comparable to genomics when used throughout growth. This study, for the first time, demonstrated that the prediction power of temporal UAS phenomic data can achieve or exceed that of genomic data. UAS data containing red-green-blue (RGB) bands over 15 growth time points and multispectral (RGB, red-edge and near infrared) bands over 12 time points were compared across 280 unique maize hybrids. Through cross-validation of untested genotypes in tested environments (CV2), temporal phenomic prediction (TPP), outperformed GP (0.80 vs 0.71); TPP and GP performed similarly in 3 other cross-validation scenarios. Genome-wide association mapping using area under temporal curves of vegetation indices (VIs) revealed 24.5% of a total of 241 discovered loci (59 loci) had associations with multiple VIs, explaining up to 51% of grain yield variation, less than GP and TPP predicted. This suggests TPP, like GP, integrates small effect loci well improving plant fitness predictions. More importantly, TPP appeared to work successfully on unrelated individuals unlike GP.
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Affiliation(s)
- Alper Adak
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843-2474, USA
| | - Seth C Murray
- Corresponding author: Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843-2474, USA.
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29
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Santiago E, Moreno DF, Acar M. Phenotypic plasticity as a facilitator of microbial evolution. ENVIRONMENTAL EPIGENETICS 2022; 8:dvac020. [PMID: 36465837 PMCID: PMC9709823 DOI: 10.1093/eep/dvac020] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Revised: 09/27/2022] [Accepted: 11/16/2022] [Indexed: 06/17/2023]
Abstract
Tossed about by the tides of history, the inheritance of acquired characteristics has found a safe harbor at last in the rapidly expanding field of epigenetics. The slow pace of genetic variation and high opportunity cost associated with maintaining a diverse genetic pool are well-matched by the flexibility of epigenetic traits, which can enable low-cost exploration of phenotypic space and reactive tuning to environmental pressures. Aiding in the generation of a phenotypically plastic population, epigenetic mechanisms often provide a hotbed of innovation for countering environmental pressures, while the potential for genetic fixation can lead to strong epigenetic-genetic evolutionary synergy. At the level of cells and cellular populations, we begin this review by exploring the breadth of mechanisms for the storage and intergenerational transmission of epigenetic information, followed by a brief review of common and exotic epigenetically regulated phenotypes. We conclude by offering an in-depth coverage of recent papers centered around two critical issues: the evolvability of epigenetic traits through Baldwinian adaptive phenotypic plasticity and the potential for synergy between epigenetic and genetic evolution.
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Affiliation(s)
- Emerson Santiago
- Department of Molecular Cellular and Developmental Biology, Yale University, 219 Prospect Street, New Haven, CT 06511, USA
| | - David F Moreno
- Department of Molecular Cellular and Developmental Biology, Yale University, 219 Prospect Street, New Haven, CT 06511, USA
- Systems Biology Institute, Yale University, 850 West Campus Drive, West Haven, CT 06516, USA
| | - Murat Acar
- *Correspondence address. Department of Molecular Cellular and Developmental Biology, Yale University, 219 Prospect Street, New Haven, CT 06511, USA. Tel: +90 (543) 304-0388; E-mail:
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Hudzenko VM, Buniak NM, Tsentylo LV, Demydov OA, Fedorenko IV, Fedorenko MV, Ishchenko VA, Kozelets HM, Khudolii LV, Lashuk SO, Syplyva NO. Evaluation of grain yield performance and its stability in various spring barley accessions under condition of different agroclimatic zones of Ukraine. BIOSYSTEMS DIVERSITY 2022. [DOI: 10.15421/012240] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/10/2023] Open
Abstract
Two extremely urgent problems of biological and agronomic research nowadays are ensuring an optimal balance between usage of natural resources to meet rapidly growing needs for food production and preservation of biodiversity. It is also important to extend the genetic diversity of the main crop varieties in agroecosystems. At the same time, modern varieties should be characterized by a combination of high yield and preserving yield stability under variable conditions. Solving the outlined tasks requires comprehensive research and involvement in breeding process of the genetical diversity concentrated in genebanks of the world. Barley (Hordeum vulgare L.) is one of the most important crops that satisfy the various needs of humanity. In respect to this, in 2020–2022, a multi-environment trial was conducted in three agroclimatic zones of Ukraine (Forest-Steppe, Polissia, and Northern Steppe). We studied 44 spring barley collection accessions of different ecological and geographical origin, different subspecies and groups of botanical varieties which were obtained from the National Center for Plant Genetic Resources of Ukraine. Statistical indices (Hom, Sc) and graphical models (GGE biplot, AMMI) were used to interpret the yield performance and its stability. Both individual ecological sites in different years and combinations of different sites and years of trials were characterized for productivity, discriminating power and representativeness. The environments differed quite strongly among themselves in terms of these indicators. It was established that most of the genotypes were characterized by higher adaptability to individual environmental conditions (stability in different years), compared to adaptability for all agroclimatic zones (wide adaptation). A strong cross-over genotype by environment interaction was found for most studied accessions. Nevertheless, both genotypes with very high stability in only one agroclimatic zone (Amil (UKR), Gateway (CAN)) and genotypes with a combination of high adaptability to one or two ecological niches and relatively higher wide adaptability (Stymul (UKR), Ly-1064 (UKR), Rannij (KAZ), Shedevr (UKR), and Arthur (CZE)) were identified. There were also the accessions which did not show maximum performance in the individual sites, but had relatively higher wide adaptability (Ly-1059 (UKR), Ly-1120 (UKR), Diantus (UKR), and Danielle (CZE)). In general, the naked barley genotypes were inferior to the covered ones in terms of yield potential and wide adaptability, but at the same time, some of them (CDC ExPlus (CAN), CDC Gainer (CAN), and Roseland (CAN)), accordingly to the statistical indicators, had increased stability in certain ecological sites. Among naked barley accessions relatively better wide adaptability according to the graphical analysis was found in the accession CDC McGwire (CAN), and by the statistical parameters CDC ExPlus (CAN) was better than standard. The peculiarities of yield manifestation and its variability in different spring barley genotypes in the multi-environment trial revealed in this study will contribute to the complementation and deepening of existing data in terms of the genotype by environment interaction. Our results can be used in further studies for developing spring barley variety models both with specific and wide adaptation under conditions of different agroclimatic zones of Ukraine. The disitnguished accessions of different origin and botanical affiliation are recommended for creating a new breeding material with the aim of simultaneously increasing yield potential and stability, as well as widening the genetic basis of spring barley varieties.
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Genetic Dissection of Phosphorus Use Efficiency and Genotype-by-Environment Interaction in Maize. Int J Mol Sci 2022; 23:ijms232213943. [PMID: 36430424 PMCID: PMC9697416 DOI: 10.3390/ijms232213943] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 11/07/2022] [Accepted: 11/08/2022] [Indexed: 11/16/2022] Open
Abstract
Genotype-by-environment interaction (G-by-E) is a common but potentially problematic phenomenon in plant breeding. In this study, we investigated the genotypic performance and two measures of plasticity on a phenotypic and genetic level by assessing 234 maize doubled haploid lines from six populations for 15 traits in seven macro-environments with a focus on varying soil phosphorus levels. It was found intergenic regions contributed the most to the variation of phenotypic linear plasticity. For 15 traits, 124 and 31 quantitative trait loci (QTL) were identified for genotypic performance and phenotypic plasticity, respectively. Further, some genes associated with phosphorus use efficiency, such as Zm00001eb117170, Zm00001eb258520, and Zm00001eb265410, encode small ubiquitin-like modifier E3 ligase were identified. By significantly testing the main effect and G-by-E effect, 38 main QTL and 17 interaction QTL were identified, respectively, in which MQTL38 contained the gene Zm00001eb374120, and its effect was related to phosphorus concentration in the soil, the lower the concentration, the greater the effect. Differences in the size and sign of the QTL effect in multiple environments could account for G-by-E. At last, the superiority of G-by-E in genomic selection was observed. In summary, our findings will provide theoretical guidance for breeding P-efficient and broadly adaptable varieties.
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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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Pham H, Reisner J, Swift A, Olafsson S, Vardeman S. Crop phenotype prediction using biclustering to explain genotype-by-environment interactions. FRONTIERS IN PLANT SCIENCE 2022; 13:975976. [PMID: 36204056 PMCID: PMC9530907 DOI: 10.3389/fpls.2022.975976] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Accepted: 08/25/2022] [Indexed: 06/16/2023]
Abstract
Phenotypic variation in plants is attributed to genotype (G), environment (E), and genotype-by-environment interaction (GEI). Although the main effects of G and E are typically larger and easier to model, the GEI interaction effects are important and a critical factor when considering such issues as to why some genotypes perform consistently well across a range of environments. In plant breeding, a major challenge is limited information, including a single genotype is tested in only a small subset of all possible test environments. The two-way table of phenotype responses will therefore commonly contain missing data. In this paper, we propose a new model of GEI effects that only requires an input of a two-way table of phenotype observations, with genotypes as rows and environments as columns that do not assume the completeness of data. Our analysis can deal with this scenario as it utilizes a novel biclustering algorithm that can handle missing values, resulting in an output of homogeneous cells with no interactions between G and E. In other words, we identify subsets of genotypes and environments where phenotype can be modeled simply. Based on this, we fit no-interaction models to predict phenotypes of a given crop and draw insights into how a particular cultivar will perform in the unused test environments. Our new methodology is validated on data from different plant species and phenotypes and shows superior performance compared to well-studied statistical approaches.
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Affiliation(s)
- Hieu Pham
- Department of Information Systems, Supply Chain, and Analytics, College of Business, The University of Alabama in Huntsville, Huntsville, AL, United States
| | - John Reisner
- Department of Statistics, Iowa State University, Ames, IA, United States
| | - Ashley Swift
- Department of Industrial and Manufacturing Systems Engineering, Iowa State University, Ames, IA, United States
| | - Sigurdur Olafsson
- Department of Industrial and Manufacturing Systems Engineering, Iowa State University, Ames, IA, United States
| | - Stephen Vardeman
- Department of Statistics, Iowa State University, Ames, IA, United States
- Department of Industrial and Manufacturing Systems Engineering, Iowa State University, Ames, IA, United States
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Schneider HM. Characterization, costs, cues and future perspectives of phenotypic plasticity. ANNALS OF BOTANY 2022; 130:131-148. [PMID: 35771883 PMCID: PMC9445595 DOI: 10.1093/aob/mcac087] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Accepted: 06/28/2022] [Indexed: 06/09/2023]
Abstract
BACKGROUND Plastic responses of plants to the environment are ubiquitous. Phenotypic plasticity occurs in many forms and at many biological scales, and its adaptive value depends on the specific environment and interactions with other plant traits and organisms. Even though plasticity is the norm rather than the exception, its complex nature has been a challenge in characterizing the expression of plasticity, its adaptive value for fitness and the environmental cues that regulate its expression. SCOPE This review discusses the characterization and costs of plasticity and approaches, considerations, and promising research directions in studying plasticity. Phenotypic plasticity is genetically controlled and heritable; however, little is known about how organisms perceive, interpret and respond to environmental cues, and the genes and pathways associated with plasticity. Not every genotype is plastic for every trait, and plasticity is not infinite, suggesting trade-offs, costs and limits to expression of plasticity. The timing, specificity and duration of plasticity are critical to their adaptive value for plant fitness. CONCLUSIONS There are many research opportunities to advance our understanding of plant phenotypic plasticity. New methodology and technological breakthroughs enable the study of phenotypic responses across biological scales and in multiple environments. Understanding the mechanisms of plasticity and how the expression of specific phenotypes influences fitness in many environmental ranges would benefit many areas of plant science ranging from basic research to applied breeding for crop improvement.
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Fortuna MA. The phenotypic plasticity of an evolving digital organism. ROYAL SOCIETY OPEN SCIENCE 2022; 9:220852. [PMID: 36117864 PMCID: PMC9470259 DOI: 10.1098/rsos.220852] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Accepted: 08/17/2022] [Indexed: 06/15/2023]
Abstract
Climate change will fundamentally reshape life on Earth in the coming decades. Therefore, understanding the extent to which species will cope with rising temperatures is of paramount importance. Phenotypic plasticity is the ability of an organism to change the morphological and functional traits encoded by its genome in response to the environment. I show here that plasticity pervades not only natural but also artificial systems that mimic the developmental process of biological organisms, such as self-replicating and evolving computer programs-digital organisms. Specifically, the environment can modify the sequence of instructions executed from a digital organism's genome (i.e. its transcriptome), which results in changes in its phenotype (i.e. the ability of the digital organism to perform Boolean logic operations). This genetic-based pathway for plasticity comes at a fitness cost to an organism's viability and generation time: the longer the transcriptome (higher fitness cost), the more chances for the environment to modify the genetic execution flow control, and the higher the likelihood for the genome to encode novel phenotypes. By studying to what extent a digital organism's phenotype is influenced by both its genome and the environment, I make a parallelism between natural and artificial evolving systems on how natural selection might slide trait regulation anywhere along a continuum from total environmental control to total genomic control, which harbours lessons not only for designing evolvable artificial systems, but also for synthetic biology.
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Affiliation(s)
- Miguel A. Fortuna
- Computational Biology Lab, Estación Biológica de Doñana (EBD), Spanish National Research Council (CSIC), Seville, Spain
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36
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Perkins ML, Gandara L, Crocker J. A synthetic synthesis to explore animal evolution and development. Philos Trans R Soc Lond B Biol Sci 2022; 377:20200517. [PMID: 35634925 PMCID: PMC9149795 DOI: 10.1098/rstb.2020.0517] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
Identifying the general principles by which genotypes are converted into phenotypes remains a challenge in the post-genomic era. We still lack a predictive understanding of how genes shape interactions among cells and tissues in response to signalling and environmental cues, and hence how regulatory networks generate the phenotypic variation required for adaptive evolution. Here, we discuss how techniques borrowed from synthetic biology may facilitate a systematic exploration of evolvability across biological scales. Synthetic approaches permit controlled manipulation of both endogenous and fully engineered systems, providing a flexible platform for investigating causal mechanisms in vivo. Combining synthetic approaches with multi-level phenotyping (phenomics) will supply a detailed, quantitative characterization of how internal and external stimuli shape the morphology and behaviour of living organisms. We advocate integrating high-throughput experimental data with mathematical and computational techniques from a variety of disciplines in order to pursue a comprehensive theory of evolution. This article is part of the theme issue ‘Genetic basis of adaptation and speciation: from loci to causative mutations’.
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Affiliation(s)
- Mindy Liu Perkins
- Developmental Biology Unit, European Molecular Biology Laboratory, 69117 Heidelberg, Germany
| | - Lautaro Gandara
- Developmental Biology Unit, European Molecular Biology Laboratory, 69117 Heidelberg, Germany
| | - Justin Crocker
- Developmental Biology Unit, European Molecular Biology Laboratory, 69117 Heidelberg, Germany
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dos Santos CL, Abendroth LJ, Coulter JA, Nafziger ED, Suyker A, Yu J, Schnable PS, Archontoulis SV. Maize Leaf Appearance Rates: A Synthesis From the United States Corn Belt. FRONTIERS IN PLANT SCIENCE 2022; 13:872738. [PMID: 35481150 PMCID: PMC9037294 DOI: 10.3389/fpls.2022.872738] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Accepted: 03/21/2022] [Indexed: 06/09/2023]
Abstract
The relationship between collared leaf number and growing degree days (GDD) is crucial for predicting maize phenology. Biophysical crop models convert GDD accumulation to leaf numbers by using a constant parameter termed phyllochron (°C-day leaf-1) or leaf appearance rate (LAR; leaf oC-day-1). However, such important parameter values are rarely estimated for modern maize hybrids. To fill this gap, we sourced and analyzed experimental datasets from the United States Corn Belt with the objective to (i) determine phyllochron values for two types of models: linear (1-parameter) and bilinear (3-parameters; phase I and II phyllochron, and transition point) and (ii) explore whether environmental factors such as photoperiod and radiation, and physiological variables such as plant growth rate can explain variability in phyllochron and improve predictability of maize phenology. The datasets included different locations (latitudes between 48° N and 41° N), years (2009-2019), hybrids, and management settings. Results indicated that the bilinear model represented the leaf number vs. GDD relationship more accurately than the linear model (R 2 = 0.99 vs. 0.95, n = 4,694). Across datasets, first phase phyllochron, transition leaf number, and second phase phyllochron averaged 57.9 ± 7.5°C-day, 9.8 ± 1.2 leaves, and 30.9 ± 5.7°C-day, respectively. Correlation analysis revealed that radiation from the V3 to the V9 developmental stages had a positive relationship with phyllochron (r = 0.69), while photoperiod was positively related to days to flowering or total leaf number (r = 0.89). Additionally, a positive nonlinear relationship between maize LAR and plant growth rate was found. Present findings provide important parameter values for calibration and optimization of maize crop models in the United States Corn Belt, as well as new insights to enhance mechanisms in crop models.
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Affiliation(s)
| | - Lori J. Abendroth
- Cropping Systems and Water Quality Research Unit, USDA-ARS, Columbia, MO, United States
| | - Jeffrey A. Coulter
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN, United States
| | - Emerson D. Nafziger
- Department of Crop Sciences, College of Agricultural, Consumer and Environmental Sciences, University of Illinois at Urbana–Champaign, Urbana, IL, United States
| | - Andy Suyker
- School of Natural Resources, University of Nebraska-Lincoln, Lincoln, NE, United States
| | - Jianming Yu
- Department of Agronomy, Iowa State University, Ames, IA, United States
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38
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Dias KOG, Dos Santos JPR, Krause MD, Piepho HP, Guimarães LJM, Pastina MM, Garcia AAF. Leveraging probability concepts for cultivar recommendation in multi-environment trials. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2022; 135:1385-1399. [PMID: 35192008 DOI: 10.1007/s00122-022-04041-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Accepted: 01/07/2022] [Indexed: 06/14/2023]
Abstract
We propose using probability concepts from Bayesian models to leverage a more informed decision-making process toward cultivar recommendation in multi-environment trials. Statistical models that capture the phenotypic plasticity of a genotype across environments are crucial in plant breeding programs to potentially identify parents, generate offspring, and obtain highly productive genotypes for target environments. In this study, our aim is to leverage concepts of Bayesian models and probability methods of stability analysis to untangle genotype-by-environment interaction (GEI). The proposed method employs the posterior distribution obtained with the No-U-Turn sampler algorithm to get Hamiltonian Monte Carlo estimates of adaptation and stability probabilities. We applied the proposed models in two empirical tropical datasets. Our findings provide a basis to enhance our ability to consider the uncertainty of cultivar recommendation for global or specific adaptation. We further demonstrate that probability methods of stability analysis in a Bayesian framework are a powerful tool for unraveling GEI given a defined intensity of selection that results in a more informed decision-making process toward cultivar recommendation in multi-environment trials.
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Affiliation(s)
- Kaio O G Dias
- Department of Genetics, Luiz de Queiroz College of Agriculture, University of São Paulo, Piracicaba, SP, Brazil
- Department of General Biology, Federal University of Viçosa, Viçosa, Brazil
| | - Jhonathan P R Dos Santos
- Department of Genetics, Luiz de Queiroz College of Agriculture, University of São Paulo, Piracicaba, SP, Brazil
| | | | | | | | | | - Antonio A F Garcia
- Department of Genetics, Luiz de Queiroz College of Agriculture, University of São Paulo, Piracicaba, SP, Brazil.
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39
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Zhao H, Savin KW, Li Y, Breen EJ, Maharjan P, Tibbits JF, Kant S, Hayden MJ, Daetwyler HD. Genome-wide association studies dissect the G × E interaction for agronomic traits in a worldwide collection of safflowers ( Carthamus tinctorius L.). MOLECULAR BREEDING : NEW STRATEGIES IN PLANT IMPROVEMENT 2022; 42:24. [PMID: 37309464 PMCID: PMC10248593 DOI: 10.1007/s11032-022-01295-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Accepted: 03/30/2022] [Indexed: 06/14/2023]
Abstract
Genome-wide association studies were conducted using a globally diverse safflower (Carthamus tinctorius L.) Genebank collection for grain yield (YP), days to flowering (DF), plant height (PH), 500 seed weight (SW), seed oil content (OL), and crude protein content (PR) in four environments (sites) that differed in water availability. Phenotypic variation was observed for all traits. YP exhibited low overall genetic correlations (rGoverall) across sites, while SW and OL had high rGoverall and high pairwise genetic correlations (rGij) across all pairwise sites. In total, 92 marker-trait associations (MTAs) were identified using three methods, single locus genome-wide association studies (GWAS) using a mixed linear model (MLM), the Bayesian multi-locus method (BayesR), and meta-GWAS. MTAs with large effects across all sites were detected for OL, SW, and PR, and MTAs specific for the different water stress sites were identified for all traits. Five MTAs were associated with multiple traits; 4 of 5 MTAs were variously associated with the three traits of SW, OL, and PR. This study provided insights into the phenotypic variability and genetic architecture of important safflower agronomic traits under different environments. Supplementary Information The online version contains supplementary material available at 10.1007/s11032-022-01295-8.
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Affiliation(s)
- Huanhuan Zhao
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC 3083 Australia
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC 3083 Australia
| | - Keith W. Savin
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC 3083 Australia
| | - Yongjun Li
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC 3083 Australia
| | - Edmond J. Breen
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC 3083 Australia
| | - Pankaj Maharjan
- Agriculture Victoria, Grains Innovation Park, Horsham, VIC 3400 Australia
| | - Josquin F. Tibbits
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC 3083 Australia
| | - Surya Kant
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC 3083 Australia
- Agriculture Victoria, Grains Innovation Park, Horsham, VIC 3400 Australia
| | - Matthew J. Hayden
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC 3083 Australia
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC 3083 Australia
| | - Hans D. Daetwyler
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC 3083 Australia
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC 3083 Australia
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40
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Stuart KC, Sherwin WB, Cardilini AP, Rollins LA. Genetics and Plasticity Are Responsible for Ecogeographical Patterns in a Recent Invasion. Front Genet 2022; 13:824424. [PMID: 35360868 PMCID: PMC8963341 DOI: 10.3389/fgene.2022.824424] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Accepted: 02/02/2022] [Indexed: 12/02/2022] Open
Abstract
Patterns of covariation between phenotype and environment are presumed to be reflective of local adaptation, and therefore translate to a meaningful influence on an individual's overall fitness within that specific environment. However, these environmentally driven patterns may be the result of numerous and interacting processes, such as genetic variation, epigenetic variation, or plastic non-heritable variation. Understanding the relative importance of different environmental variables on underlying genetic patterns and resulting phenotypes is fundamental to understanding adaptation. Invasive systems are excellent models for such investigations, given their propensity for rapid evolution. This study uses reduced representation sequencing data paired with phenotypic data to examine whether important phenotypic traits in invasive starlings (Sturnus vulgaris) within Australia appear to be highly heritable (presumably genetic) or appear to vary with environmental gradients despite underlying genetics (presumably non-heritable plasticity). We also sought to determine which environmental variables, if any, play the strongest role shaping genetic and phenotypic patterns. We determined that environmental variables-particularly elevation-play an important role in shaping allelic trends in Australian starlings and may also reinforce neutral genetic patterns resulting from historic introduction regime. We examined a range of phenotypic traits that appear to be heritable (body mass and spleen mass) or negligibly heritable (e.g. beak surface area and wing length) across the starlings' Australian range. Using SNP variants associated with each of these phenotypes, we identify key environmental variables that correlate with genetic patterns, specifically that temperature and precipitation putatively play important roles shaping phenotype in this species. Finally, we determine that overall phenotypic variation is correlated with underlying genetic variation, and that these interact positively with the level of vegetation variation within a region, suggesting that ground cover plays an important role in shaping selection and plasticity of phenotypic traits within the starlings of Australia.
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Affiliation(s)
- Katarina C. Stuart
- Evolution and Ecology Research Centre, School of Biological, Earth and Environmental Sciences, UNSW Sydney, Sydney, NSW, Australia
| | - William B. Sherwin
- Evolution and Ecology Research Centre, School of Biological, Earth and Environmental Sciences, UNSW Sydney, Sydney, NSW, Australia
| | - Adam P.A. Cardilini
- School of Life and Environmental Sciences, Deakin University, Waurn Ponds, VIC, Australia
| | - Lee A. Rollins
- Evolution and Ecology Research Centre, School of Biological, Earth and Environmental Sciences, UNSW Sydney, Sydney, NSW, Australia
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41
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Faye JM, Akata EA, Sine B, Diatta C, Cisse N, Fonceka D, Morris GP. Quantitative and population genomics suggest a broad role of stay-green loci in the drought adaptation of sorghum. THE PLANT GENOME 2022; 15:e20176. [PMID: 34817118 DOI: 10.1002/tpg2.20176] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Accepted: 10/08/2021] [Indexed: 06/13/2023]
Abstract
Drought is a major constraint on plant productivity globally. Sorghum [Sorghum bicolor (L.) Moench] landraces have evolved in drought-prone regions, but the genetics of their adaptation is poorly understood. Here we sought to identify novel drought-tolerance loci and test hypotheses on the role of known loci including those underlying stay-green (Stg) postflowering drought tolerance. We phenotyped 590 diverse sorghum accessions from West Africa in 10 environments, under field-based managed drought stress [preflowering water stress (WS1), postflowering water stress (WS2), and well-watered (WW)] and rainfed (RF) conditions over 4 yr. Days to 50% flowering (DFLo), aboveground dry biomass (DBM), plant height (PH), and plant grain yield components (including grain weight [GrW], panicle weight [PW] and grain number [GrN] per plant, and 1000-grain weight [TGrW]) were measured, and genome-wide association studies (GWAS) was conducted. Broad-sense heritability for biomass and plant grain yield was high (33-92%) across environments. There was a significant correlation between stress tolerance index (STI) for GrW per plant across WS1 and WS2. Genome-wide association studies revealed that SbZfl1 and SbCN12, orthologs of maize (Zea mays L.) flowering genes, likely underlie flowering time variation under these conditions. Genome-wide association studies further identified associations (n = 134; common between two GWAS models) for STI and drought effects on plant yield components including 16 putative pleiotropic associations. Thirty of the associations colocalized with Stg1, Stg2, Stg3, and Stg4 loci and had large effects. Seven lead associations, including some within Stg1, overlapped with positive selection outliers. Our findings reveal previously undescribed natural genetic variation for drought-tolerance-related traits and suggest a broad role of Stg loci in drought adaptation of sorghum.
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Affiliation(s)
- Jacques M Faye
- Dep. of Agronomy, Kansas State Univ., Manhattan, KS, USA
- Centre d'Étude Régional pour l'Amélioration de l'Adaptation à la Sécheresse, Institut Sénégalais de Recherches Agricoles, Thiès, Senegal
| | - Eyanawa A Akata
- Centre d'Étude Régional pour l'Amélioration de l'Adaptation à la Sécheresse, Institut Sénégalais de Recherches Agricoles, Thiès, Senegal
- Institut Togolais de Recherche Agronomique, Kara, Togo
| | - Bassirou Sine
- Centre d'Étude Régional pour l'Amélioration de l'Adaptation à la Sécheresse, Institut Sénégalais de Recherches Agricoles, Thiès, Senegal
| | - Cyril Diatta
- Centre d'Étude Régional pour l'Amélioration de l'Adaptation à la Sécheresse, Institut Sénégalais de Recherches Agricoles, Thiès, Senegal
| | - Ndiaga Cisse
- Centre d'Étude Régional pour l'Amélioration de l'Adaptation à la Sécheresse, Institut Sénégalais de Recherches Agricoles, Thiès, Senegal
| | - Daniel Fonceka
- Centre d'Étude Régional pour l'Amélioration de l'Adaptation à la Sécheresse, Institut Sénégalais de Recherches Agricoles, Thiès, Senegal
- AGAP, Univ. Montpellier, CIRAD, INRA, Montpellier SupAgro, Montpellier, France
| | - Geoffrey P Morris
- Dep. of Agronomy, Kansas State Univ., Manhattan, KS, USA
- Dep. of Soil & Crop Science, Colorado State Univ., Ft. Collins, CO, 80523, USA
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Li Y, Ruperao P, Batley J, Edwards D, Martin W, Hobson K, Sutton T. Genomic prediction of preliminary yield trials in chickpea: Effect of functional annotation of SNPs and environment. THE PLANT GENOME 2022; 15:e20166. [PMID: 34786880 DOI: 10.1002/tpg2.20166] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Accepted: 09/14/2021] [Indexed: 06/13/2023]
Abstract
Achieving yield potential in chickpea (Cicer arietinum L.) is limited by many constraints that include biotic and abiotic stresses. Combining next-generation sequencing technology with advanced statistical modeling has the potential to increase genetic gain efficiently. Whole genome resequencing data was obtained from 315 advanced chickpea breeding lines from the Australian chickpea breeding program resulting in more than 298,000 single nucleotide polymorphisms (SNPs) discovered. Analysis of population structure revealed a distinct group of breeding lines with many alleles that are absent from recently released Australian cultivars. Genome-wide association studies (GWAS) using these Australian breeding lines identified 20 SNPs significantly associated with grain yield in multiple field environments. A reduced level of nucleotide diversity and extended linkage disequilibrium suggested that some regions in these chickpea genomes may have been through selective breeding for yield or other traits. A large introgression segment that introduced from C. echinospermum for phytophthora root rot resistance was identified on chromosome 6, yet it also has unintended consequences of reducing yield due to linkage drag. We further investigated the effect of genotype by environment interaction on genomic prediction of yield. We found that the training set had better prediction accuracy when phenotyped under conditions relevant to the targeted environments. We also investigated the effect of SNP functional annotation on prediction accuracy using different subsets of SNPs based on their genomic locations: regulatory regions, exome, and alternative splice sites. Compared with the whole SNP dataset, a subset of SNPs did not significantly decrease prediction accuracy for grain yield despite consisting of a smaller number of SNPs.
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Affiliation(s)
- Yongle Li
- School of Agriculture, Food and Wine, The Univ. of Adelaide, Adelaide, SA, 5064, Australia
| | - Pradeep Ruperao
- Statistics, Bioinformatics and Data Management, ICRISAT, Hyderabad, 502324, India
| | - Jacqueline Batley
- School of Biological Sciences, The Univ. of Western Australia, Perth, WA, 6001, Australia
| | - David Edwards
- School of Biological Sciences, The Univ. of Western Australia, Perth, WA, 6001, Australia
| | - William Martin
- Dep. of Agriculture and Fisheries, Warwick, Qld, 4370, Australia
| | - Kristy Hobson
- NSW Dep. of Primary Industries, Tamworth, NSW, 2340, Australia
| | - Tim Sutton
- School of Agriculture, Food and Wine, The Univ. of Adelaide, Adelaide, SA, 5064, Australia
- South Australian Research and Development Institute, Adelaide, SA, 5064, Australia
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Li X, Guo T, Bai G, Zhang Z, See D, Marshall J, Garland-Campbell KA, Yu J. Genetics-inspired data-driven approaches explain and predict crop performance fluctuations attributed to changing climatic conditions. MOLECULAR PLANT 2022; 15:203-206. [PMID: 34999020 DOI: 10.1016/j.molp.2022.01.001] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/11/2021] [Revised: 12/17/2021] [Accepted: 01/04/2022] [Indexed: 06/14/2023]
Affiliation(s)
- Xianran Li
- USDA, Agricultural Research Service, Wheat Health, Genetics, and Quality Research Unit, Pullman, WA 99164, USA.
| | - Tingting Guo
- Department of Agronomy, Iowa State University, Ames, IA 50011, USA
| | - Guihua Bai
- USDA, Agricultural Research Service, Hard Winter Wheat Genetics Research Unit, Manhattan, KS 66506, USA
| | - Zhiwu Zhang
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA 99164, USA
| | - Deven See
- USDA, Agricultural Research Service, Wheat Health, Genetics, and Quality Research Unit, Pullman, WA 99164, USA
| | - Juliet Marshall
- Department of Plant Sciences, University of Idaho Research and Extension, Idaho Falls, ID 83402, USA
| | - Kimberly A Garland-Campbell
- USDA, Agricultural Research Service, Wheat Health, Genetics, and Quality Research Unit, Pullman, WA 99164, USA
| | - Jianming Yu
- Department of Agronomy, Iowa State University, Ames, IA 50011, USA
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44
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Diepenbrock CH, Tang T, Jines M, Technow F, Lira S, Podlich D, Cooper M, Messina C. Can we harness digital technologies and physiology to hasten genetic gain in US maize breeding? PLANT PHYSIOLOGY 2022; 188:1141-1157. [PMID: 34791474 PMCID: PMC8825268 DOI: 10.1093/plphys/kiab527] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Accepted: 10/01/2021] [Indexed: 05/26/2023]
Abstract
Plant physiology can offer invaluable insights to accelerate genetic gain. However, translating physiological understanding into breeding decisions has been an ongoing and complex endeavor. Here we demonstrate an approach to leverage physiology and genomics to hasten crop improvement. A half-diallel maize (Zea mays) experiment resulting from crossing 9 elite inbreds was conducted at 17 locations in the USA corn belt and 6 locations at managed stress environments between 2017 and 2019 covering a range of water environments from 377 to 760 mm of evapotranspiration and family mean yields from 542 to 1,874 g m-2. Results from analyses of 35 families and 2,367 hybrids using crop growth models linked to whole-genome prediction (CGM-WGP) demonstrated that CGM-WGP offered a predictive accuracy advantage compared to BayesA for untested genotypes evaluated in untested environments (r = 0.43 versus r = 0.27). In contrast to WGP, CGMs can deal effectively with time-dependent interactions between a physiological process and the environment. To facilitate the selection/identification of traits for modeling yield, an algorithmic approach was introduced. The method was able to identify 4 out of 12 candidate traits known to explain yield variation in maize. The estimation of allelic and physiological values for each genotype using the CGM created in silico phenotypes (e.g. root elongation) and physiological hypotheses that could be tested within the breeding program in an iterative manner. Overall, the approach and results suggest a promising future to fully harness digital technologies, gap analysis, and physiological knowledge to hasten genetic gain by improving predictive skill and definition of breeding goals.
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Affiliation(s)
| | - Tom Tang
- Research & Development, Corteva Agriscience, Johnston, Iowa 50131, USA
| | - Michael Jines
- Research & Development, Corteva Agriscience, Windfall, Indiana 46076, USA
| | - Frank Technow
- Research & Development, Corteva Agriscience, Tavistock, ON N4S 7W1, Canada
| | - Sara Lira
- Research & Development, Corteva Agriscience, Johnston, Iowa 50131, USA
| | - Dean Podlich
- Research & Development, Corteva Agriscience, Johnston, Iowa 50131, USA
| | - Mark Cooper
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, Queensland 4072, Australia
| | - Carlos Messina
- Research & Development, Corteva Agriscience, Johnston, Iowa 50131, USA
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45
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Mu Q, Guo T, Li X, Yu J. Phenotypic plasticity in plant height shaped by interaction between genetic loci and diurnal temperature range. THE NEW PHYTOLOGIST 2022; 233:1768-1779. [PMID: 34870847 DOI: 10.1111/nph.17904] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Accepted: 11/21/2021] [Indexed: 06/13/2023]
Abstract
Phenotypic plasticity is observed widely in plants and often studied with reaction norms for adult plant or end-of-season traits. Uncovering genetic, environmental and developmental patterns behind the observed phenotypic variation under natural field conditions is needed. Using a sorghum (Sorghum bicolor) genetic population evaluated for plant height in seven natural field conditions, we investigated the major pattern that differentiated these environments. We then examined the physiological relevance of the identified environmental index by investigating the developmental trajectory of the population with multistage height measurements in four additional environments and conducting crop growth modelling. We found that diurnal temperature range (DTR) during the rapid growth period of sorghum development was an effective environmental index. Three genetic loci (Dw1, Dw3 and qHT7.1) were consistently detected for individual environments, reaction-norm parameters across environments and growth-curve parameters through the season. Their genetic effects changed dynamically along the environmental gradient and the developmental stage. A conceptual model with three-dimensional reaction norms was proposed to showcase the interconnecting components: genotype, environment and development. Beyond genomic and environmental analyses, further integration of development and physiology at the whole-plant and molecular levels into complex trait dissection would enhance our understanding of mechanisms underlying phenotypic variation.
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Affiliation(s)
- Qi Mu
- Department of Agronomy, Iowa State University, Ames, IA, 50011, USA
| | - Tingting Guo
- Department of Agronomy, Iowa State University, Ames, IA, 50011, USA
| | - Xianran Li
- Department of Agronomy, Iowa State University, Ames, IA, 50011, USA
| | - Jianming Yu
- Department of Agronomy, Iowa State University, Ames, IA, 50011, USA
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46
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Onogi A, Sekine D, Kaga A, Nakano S, Yamada T, Yu J, Ninomiya S. A Method for Identifying Environmental Stimuli and Genes Responsible for Genotype-by-Environment Interactions From a Large-Scale Multi-Environment Data Set. Front Genet 2022; 12:803636. [PMID: 35027920 PMCID: PMC8751104 DOI: 10.3389/fgene.2021.803636] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Accepted: 12/06/2021] [Indexed: 11/24/2022] Open
Abstract
It has not been fully understood in real fields what environment stimuli cause the genotype-by-environment (G × E) interactions, when they occur, and what genes react to them. Large-scale multi-environment data sets are attractive data sources for these purposes because they potentially experienced various environmental conditions. Here we developed a data-driven approach termed Environmental Covariate Search Affecting Genetic Correlations (ECGC) to identify environmental stimuli and genes responsible for the G × E interactions from large-scale multi-environment data sets. ECGC was applied to a soybean (Glycine max) data set that consisted of 25,158 records collected at 52 environments. ECGC illustrated what meteorological factors shaped the G × E interactions in six traits including yield, flowering time, and protein content and when these factors were involved in the interactions. For example, it illustrated the relevance of precipitation around sowing dates and hours of sunshine just before maturity to the interactions observed for yield. Moreover, genome-wide association mapping on the sensitivities to the identified stimuli discovered candidate and known genes responsible for the G × E interactions. Our results demonstrate the capability of data-driven approaches to bring novel insights on the G × E interactions observed in fields.
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Affiliation(s)
- Akio Onogi
- Department of Plant Life Science, Faculty of Agriculture, Ryukoku University, Otsu, Japan
| | - Daisuke Sekine
- Institute of Vegetable and Floriculture Science, National Agriculture and Food Research Organization, Tsukuba, Japan.,Institute of Crop Science, National Agriculture and Food Research Organization, Tsukuba, Japan
| | - Akito Kaga
- Institute of Crop Science, National Agriculture and Food Research Organization, Tsukuba, Japan
| | - Satoshi Nakano
- Institute for Agro-Environmental Sciences, National Agriculture and Food Research Organization, Tsukuba, Japan
| | - Tetsuya Yamada
- Institute of Crop Science, National Agriculture and Food Research Organization, Tsukuba, Japan
| | - Jianming Yu
- Department of Agronomy, Iowa State University, Ames, IA, United States
| | - Seishi Ninomiya
- Graduate School of Agricultural and Life Science, The University of Tokyo, Nishitokyo, Japan
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47
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Bari MAA, Zheng P, Viera I, Worral H, Szwiec S, Ma Y, Main D, Coyne CJ, McGee RJ, Bandillo N. Harnessing Genetic Diversity in the USDA Pea Germplasm Collection Through Genomic Prediction. Front Genet 2022; 12:707754. [PMID: 35003202 PMCID: PMC8740293 DOI: 10.3389/fgene.2021.707754] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Accepted: 10/26/2021] [Indexed: 11/13/2022] Open
Abstract
Phenotypic evaluation and efficient utilization of germplasm collections can be time-intensive, laborious, and expensive. However, with the plummeting costs of next-generation sequencing and the addition of genomic selection to the plant breeder's toolbox, we now can more efficiently tap the genetic diversity within large germplasm collections. In this study, we applied and evaluated genomic prediction's potential to a set of 482 pea (Pisum sativum L.) accessions-genotyped with 30,600 single nucleotide polymorphic (SNP) markers and phenotyped for seed yield and yield-related components-for enhancing selection of accessions from the USDA Pea Germplasm Collection. Genomic prediction models and several factors affecting predictive ability were evaluated in a series of cross-validation schemes across complex traits. Different genomic prediction models gave similar results, with predictive ability across traits ranging from 0.23 to 0.60, with no model working best across all traits. Increasing the training population size improved the predictive ability of most traits, including seed yield. Predictive abilities increased and reached a plateau with increasing number of markers presumably due to extensive linkage disequilibrium in the pea genome. Accounting for population structure effects did not significantly boost predictive ability, but we observed a slight improvement in seed yield. By applying the best genomic prediction model (e.g., RR-BLUP), we then examined the distribution of genotyped but nonphenotyped accessions and the reliability of genomic estimated breeding values (GEBV). The distribution of GEBV suggested that none of the nonphenotyped accessions were expected to perform outside the range of the phenotyped accessions. Desirable breeding values with higher reliability can be used to identify and screen favorable germplasm accessions. Expanding the training set and incorporating additional orthogonal information (e.g., transcriptomics, metabolomics, physiological traits, etc.) into the genomic prediction framework can enhance prediction accuracy.
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Affiliation(s)
- Md Abdullah Al Bari
- Department of Plant Sciences, North Dakota State University, Fargo, ND, United States
| | - Ping Zheng
- Department of Horticulture, Washington State University, Pullman, WA, United States
| | - Indalecio Viera
- Department of Plant Sciences, North Dakota State University, Fargo, ND, United States
| | - Hannah Worral
- NDSU North Central Research Extension Center, Minot, ND, United States
| | - Stephen Szwiec
- NDSU North Central Research Extension Center, Minot, ND, United States
| | - Yu Ma
- Department of Horticulture, Washington State University, Pullman, WA, United States
| | - Dorrie Main
- Department of Horticulture, Washington State University, Pullman, WA, United States
| | - Clarice J Coyne
- USDA-ARS Plant Germplasm Introduction and Testing, Washington State University, Pullman, WA, United States
| | - Rebecca J McGee
- USDA-ARS Grain Legume Genetics and Physiology Research, Pullman, WA, United States
| | - Nonoy Bandillo
- Department of Plant Sciences, North Dakota State University, Fargo, ND, United States
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48
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Rogers AR, Holland JB. Environment-specific genomic prediction ability in maize using environmental covariates depends on environmental similarity to training data. G3 (BETHESDA, MD.) 2021; 12:6486423. [PMID: 35100364 PMCID: PMC9245610 DOI: 10.1093/g3journal/jkab440] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Accepted: 12/06/2021] [Indexed: 12/30/2022]
Abstract
Technology advances have made possible the collection of a wealth of genomic, environmental, and phenotypic data for use in plant breeding. Incorporation of environmental data into environment-specific genomic prediction is hindered in part because of inherently high data dimensionality. Computationally efficient approaches to combining genomic and environmental information may facilitate extension of genomic prediction models to new environments and germplasm, and better understanding of genotype-by-environment (G × E) interactions. Using genomic, yield trial, and environmental data on 1,918 unique hybrids evaluated in 59 environments from the maize Genomes to Fields project, we determined that a set of 10,153 SNP dominance coefficients and a 5-day temporal window size for summarizing environmental variables were optimal for genomic prediction using only genetic and environmental main effects. Adding marker-by-environment variable interactions required dimension reduction, and we found that reducing dimensionality of the genetic data while keeping the full set of environmental covariates was best for environment-specific genomic prediction of grain yield, leading to an increase in prediction ability of 2.7% to achieve a prediction ability of 80% across environments when data were masked at random. We then measured how prediction ability within environments was affected under stratified training-testing sets to approximate scenarios commonly encountered by plant breeders, finding that incorporation of marker-by-environment effects improved prediction ability in cases where training and test sets shared environments, but did not improve prediction in new untested environments. The environmental similarity between training and testing sets had a greater impact on the efficacy of prediction than genetic similarity between training and test sets.
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Affiliation(s)
- Anna R Rogers
- Program in Genetics, North Carolina State University, Raleigh, NC
27695, USA
| | - James B Holland
- Program in Genetics, North Carolina State University, Raleigh, NC
27695, USA,USDA-ARS Plant Science Research Unit, North Carolina State
University, Raleigh, NC 27695, USA,Department of Crop and Soil Sciences, North Carolina State
University, Raleigh, NC 27695, USA,Corresponding author: Department of Agriculture—Agriculture
Research Service, Box 7620 North Carolina State University, Raleigh, NC 27695-7620, USA.
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49
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Brun-Usan M, Rago A, Thies C, Uller T, Watson RA. Development and selective grain make plasticity 'take the lead' in adaptive evolution. BMC Ecol Evol 2021; 21:205. [PMID: 34800979 PMCID: PMC8605539 DOI: 10.1186/s12862-021-01936-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Accepted: 11/10/2021] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND Biological evolution exhibits an extraordinary capability to adapt organisms to their environments. The explanation for this often takes for granted that random genetic variation produces at least some beneficial phenotypic variation in which natural selection can act. Such genetic evolvability could itself be a product of evolution, but it is widely acknowledged that the immediate selective gains of evolvability are small on short timescales. So how do biological systems come to exhibit such extraordinary capacity to evolve? One suggestion is that adaptive phenotypic plasticity makes genetic evolution find adaptations faster. However, the need to explain the origin of adaptive plasticity puts genetic evolution back in the driving seat, and genetic evolvability remains unexplained. RESULTS To better understand the interaction between plasticity and genetic evolvability, we simulate the evolution of phenotypes produced by gene-regulation network-based models of development. First, we show that the phenotypic variation resulting from genetic and environmental perturbation are highly concordant. This is because phenotypic variation, regardless of its cause, occurs within the relatively specific space of possibilities allowed by development. Second, we show that selection for genetic evolvability results in the evolution of adaptive plasticity and vice versa. This linkage is essentially symmetric but, unlike genetic evolvability, the selective gains of plasticity are often substantial on short, including within-lifetime, timescales. Accordingly, we show that selection for phenotypic plasticity can be effective in promoting the evolution of high genetic evolvability. CONCLUSIONS Without overlooking the fact that adaptive plasticity is itself a product of genetic evolution, we show how past selection for plasticity can exercise a disproportionate effect on genetic evolvability and, in turn, influence the course of adaptive evolution.
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Affiliation(s)
- Miguel Brun-Usan
- Institute for Life Sciences/Electronics and Computer Sciences, University of Southampton, Southampton, UK.
- Department of Biology, Lund University, 22362, Lund, Sweden.
| | - Alfredo Rago
- Institute for Life Sciences/Electronics and Computer Sciences, University of Southampton, Southampton, UK
- Department of Biology, Lund University, 22362, Lund, Sweden
| | - Christoph Thies
- Institute for Life Sciences/Electronics and Computer Sciences, University of Southampton, Southampton, UK
| | - Tobias Uller
- Department of Biology, Lund University, 22362, Lund, Sweden
| | - Richard A Watson
- Institute for Life Sciences/Electronics and Computer Sciences, University of Southampton, Southampton, UK
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50
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Kaya A, Phua CZJ, Lee M, Wang L, Tyshkovskiy A, Ma S, Barre B, Liu W, Harrison BR, Zhao X, Zhou X, Wasko BM, Bammler TK, Promislow DEL, Kaeberlein M, Gladyshev VN. Evolution of natural lifespan variation and molecular strategies of extended lifespan in yeast. eLife 2021; 10:e64860. [PMID: 34751131 PMCID: PMC8612763 DOI: 10.7554/elife.64860] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2020] [Accepted: 11/04/2021] [Indexed: 01/29/2023] Open
Abstract
To understand the genetic basis and selective forces acting on longevity, it is useful to examine lifespan variation among closely related species, or ecologically diverse isolates of the same species, within a controlled environment. In particular, this approach may lead to understanding mechanisms underlying natural variation in lifespan. Here, we analyzed 76 ecologically diverse wild yeast isolates and discovered a wide diversity of replicative lifespan (RLS). Phylogenetic analyses pointed to genes and environmental factors that strongly interact to modulate the observed aging patterns. We then identified genetic networks causally associated with natural variation in RLS across wild yeast isolates, as well as genes, metabolites, and pathways, many of which have never been associated with yeast lifespan in laboratory settings. In addition, a combined analysis of lifespan-associated metabolic and transcriptomic changes revealed unique adaptations to interconnected amino acid biosynthesis, glutamate metabolism, and mitochondrial function in long-lived strains. Overall, our multiomic and lifespan analyses across diverse isolates of the same species shows how gene-environment interactions shape cellular processes involved in phenotypic variation such as lifespan.
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Affiliation(s)
- Alaattin Kaya
- Department of Biology, Virginia Commonwealth UniversityRichmondUnited States
| | - Cheryl Zi Jin Phua
- Genetics, Genome Institute of Singapore, Agency for Science, Technology and Research (A*STAR)SingaporeSingapore
| | - Mitchell Lee
- Department of Laboratory Medicine and Pathology, University of WashingtonSeattleUnited States
| | - Lu Wang
- Department of Environmental and Occupational Health Sciences, University of WashingtonSeattleUnited States
| | - Alexander Tyshkovskiy
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical SchoolBostonUnited States
- Belozersky Institute of Physico-Chemical Biology, Moscow State UniversityMoscowRussian Federation
| | - Siming Ma
- Genetics, Genome Institute of Singapore, Agency for Science, Technology and Research (A*STAR)SingaporeSingapore
| | - Benjamin Barre
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical SchoolBostonUnited States
| | - Weiqiang Liu
- Key Laboratory of Animal Ecology and Conservation Biology, Chinese Academy of Sciences, Institute of ZoologyBeijingChina
| | - Benjamin R Harrison
- Department of Laboratory Medicine and Pathology, University of WashingtonSeattleUnited States
| | - Xiaqing Zhao
- Department of Laboratory Medicine and Pathology, University of WashingtonSeattleUnited States
| | - Xuming Zhou
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical SchoolBostonUnited States
| | - Brian M Wasko
- Department of Biology, University of Houston - Clear LakeHoustonUnited States
| | - Theo K Bammler
- Department of Environmental and Occupational Health Sciences, University of WashingtonSeattleUnited States
| | - Daniel EL Promislow
- Department of Laboratory Medicine and Pathology, University of WashingtonSeattleUnited States
- Department of Biology, University of WashingtonSeattleUnited States
| | - Matt Kaeberlein
- Department of Laboratory Medicine and Pathology, University of WashingtonSeattleUnited States
| | - Vadim N Gladyshev
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical SchoolBostonUnited States
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