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Fartash AH, Ben C, Mazurier M, Ebrahimi A, Ghalandar M, Gentzbittel L, Rickauer M. Medicago truncatula quantitative resistance to a new strain of Verticillium alfalfae from Iran revealed by a genome-wide association study. FRONTIERS IN PLANT SCIENCE 2023; 14:1125551. [PMID: 37123855 PMCID: PMC10140629 DOI: 10.3389/fpls.2023.1125551] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Accepted: 03/20/2023] [Indexed: 05/03/2023]
Abstract
Verticillium wilt is a major threat to many crops, among them alfalfa (Medicago sativa). The model plant Medicago truncatula, a close relative of alfalfa was used to study the genetic control of resistance towards a new Verticillium alfalfae isolate. The accidental introduction of pathogen strains through global trade is a threat to crop production and such new strains might also be better adapted to global warming. Isolates of V. alfalfae were obtained from alfalfa fields in Iran and characterized. The Iranian isolate AF1 was used in a genome-wide association study (GWAS) involving 242 accessions from the Mediterranean region. Root inoculations were performed with conidia at 25°C and symptoms were scored regularly. Maximum Symptom Score and Area under Disease Progess Curve were computed as phenotypic traits to be used in GWAS and for comparison to a previous study with French isolate V31.2 at 20°C. This comparison showed high correlation with a shift to higher susceptibility, and similar geographical distribution of resistant and susceptible accessions to AF1 at 25°C, with resistant accessions mainly in the western part. GWAS revealed 30 significant SNPs linked to resistance towards isolate AF1. None of them were common to the previous study with isolate V31.2 at 20°C. To confirm these loci, the expression of nine underlying genes was studied. All genes were induced in roots following inoculation, in susceptible and resistant plants. However, in resistant plants induction was higher and lasted longer. Taken together, the use of a new pathogen strain and a shift in temperature revealed a completely different genetic control compared to a previous study that demonstrated the existence of two major QTLs. These results can be useful for Medicago breeding programs to obtain varieties better adapted to future conditions.
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Affiliation(s)
- Amir Hossein Fartash
- Laboratoire écologie fonctionnelle et environnement, Université de Toulouse, Centre National de Recherche Scientifique, Toulouse Institut National Polytechnique, Université Toulouse 3 – Paul Sabatier (UPS), Toulouse, France
| | - Cécile Ben
- Laboratoire écologie fonctionnelle et environnement, Université de Toulouse, Centre National de Recherche Scientifique, Toulouse Institut National Polytechnique, Université Toulouse 3 – Paul Sabatier (UPS), Toulouse, France
- Project Center for Agro Technologies, Skolkovo Institute of Science and Technology, Moscow, Russia
| | - Mélanie Mazurier
- Laboratoire écologie fonctionnelle et environnement, Université de Toulouse, Centre National de Recherche Scientifique, Toulouse Institut National Polytechnique, Université Toulouse 3 – Paul Sabatier (UPS), Toulouse, France
| | - Asa Ebrahimi
- Department of Plant Breeding and Biotechnology, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Mojtaba Ghalandar
- Plant Protection Department, Markazi Agricultural and Natural Resources Research and Education Center, Arak, Iran
| | - Laurent Gentzbittel
- Laboratoire écologie fonctionnelle et environnement, Université de Toulouse, Centre National de Recherche Scientifique, Toulouse Institut National Polytechnique, Université Toulouse 3 – Paul Sabatier (UPS), Toulouse, France
- Project Center for Agro Technologies, Skolkovo Institute of Science and Technology, Moscow, Russia
| | - Martina Rickauer
- Laboratoire écologie fonctionnelle et environnement, Université de Toulouse, Centre National de Recherche Scientifique, Toulouse Institut National Polytechnique, Université Toulouse 3 – Paul Sabatier (UPS), Toulouse, France
- *Correspondence: Martina Rickauer,
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Genome-Enabled Prediction Methods Based on Machine Learning. METHODS IN MOLECULAR BIOLOGY (CLIFTON, N.J.) 2022; 2467:189-218. [PMID: 35451777 DOI: 10.1007/978-1-0716-2205-6_7] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
Growth of artificial intelligence and machine learning (ML) methodology has been explosive in recent years. In this class of procedures, computers get knowledge from sets of experiences and provide forecasts or classification. In genome-wide based prediction (GWP), many ML studies have been carried out. This chapter provides a description of main semiparametric and nonparametric algorithms used in GWP in animals and plants. Thirty-four ML comparative studies conducted in the last decade were used to develop a meta-analysis through a Thurstonian model, to evaluate algorithms with the best predictive qualities. It was found that some kernel, Bayesian, and ensemble methods displayed greater robustness and predictive ability. However, the type of study and data distribution must be considered in order to choose the most appropriate model for a given problem.
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Thudi M, Palakurthi R, Schnable JC, Chitikineni A, Dreisigacker S, Mace E, Srivastava RK, Satyavathi CT, Odeny D, Tiwari VK, Lam HM, Hong YB, Singh VK, Li G, Xu Y, Chen X, Kaila S, Nguyen H, Sivasankar S, Jackson SA, Close TJ, Shubo W, Varshney RK. Genomic resources in plant breeding for sustainable agriculture. JOURNAL OF PLANT PHYSIOLOGY 2021; 257:153351. [PMID: 33412425 PMCID: PMC7903322 DOI: 10.1016/j.jplph.2020.153351] [Citation(s) in RCA: 45] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Revised: 12/14/2020] [Accepted: 12/14/2020] [Indexed: 05/19/2023]
Abstract
Climate change during the last 40 years has had a serious impact on agriculture and threatens global food and nutritional security. From over half a million plant species, cereals and legumes are the most important for food and nutritional security. Although systematic plant breeding has a relatively short history, conventional breeding coupled with advances in technology and crop management strategies has increased crop yields by 56 % globally between 1965-85, referred to as the Green Revolution. Nevertheless, increased demand for food, feed, fiber, and fuel necessitates the need to break existing yield barriers in many crop plants. In the first decade of the 21st century we witnessed rapid discovery, transformative technological development and declining costs of genomics technologies. In the second decade, the field turned towards making sense of the vast amount of genomic information and subsequently moved towards accurately predicting gene-to-phenotype associations and tailoring plants for climate resilience and global food security. In this review we focus on genomic resources, genome and germplasm sequencing, sequencing-based trait mapping, and genomics-assisted breeding approaches aimed at developing biotic stress resistant, abiotic stress tolerant and high nutrition varieties in six major cereals (rice, maize, wheat, barley, sorghum and pearl millet), and six major legumes (soybean, groundnut, cowpea, common bean, chickpea and pigeonpea). We further provide a perspective and way forward to use genomic breeding approaches including marker-assisted selection, marker-assisted backcrossing, haplotype based breeding and genomic prediction approaches coupled with machine learning and artificial intelligence, to speed breeding approaches. The overall goal is to accelerate genetic gains and deliver climate resilient and high nutrition crop varieties for sustainable agriculture.
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Affiliation(s)
- Mahendar Thudi
- Center of Excellence in Genomics & Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India; University of Southern Queensland, Toowoomba, Australia
| | - Ramesh Palakurthi
- Center of Excellence in Genomics & Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India
| | | | - Annapurna Chitikineni
- Center of Excellence in Genomics & Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India
| | | | - Emma Mace
- Agri-Science Queensland, Department of Agriculture & Fisheries (DAF), Warwick, Australia
| | - Rakesh K Srivastava
- Center of Excellence in Genomics & Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India
| | - C Tara Satyavathi
- Indian Council of Agricultural Research (ICAR)- Indian Agricultural Research Institute (IARI), New Delhi, India
| | - Damaris Odeny
- International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Nairobi, Kenya
| | | | - Hon-Ming Lam
- Center for Soybean Research of the State Key Laboratory of Agrobiotechnology and School of Life Sciences, The Chinese University of Hong Kong, Shatin, Hong Kong Special Administrative Region
| | - Yan Bin Hong
- Crops Research Institute, Guangdong Academy of Agricultural Sciences, Guangzhou, China
| | - Vikas K Singh
- South Asia Hub, International Rice Research Institute (IRRI), Hyderabad, India
| | - Guowei Li
- Shandong Academy of Agricultural Sciences, Jinan, China
| | - Yunbi Xu
- International Maize and Wheat Improvement Center (CYMMIT), Mexico DF, Mexico; Chinese Academy of Agricultural Sciences, Beijing, China
| | - Xiaoping Chen
- Crops Research Institute, Guangdong Academy of Agricultural Sciences, Guangzhou, China
| | - Sanjay Kaila
- Department of Biotechnology, Ministry of Science and Technology, Government of India, India
| | - Henry Nguyen
- National Centre for Soybean Research, University of Missouri, Columbia, USA
| | - Sobhana Sivasankar
- Joint FAO/IAEA Division of Nuclear Techniques in Food and Agriculture, Vienna, Austria
| | | | | | - Wan Shubo
- Shandong Academy of Agricultural Sciences, Jinan, China
| | - Rajeev K Varshney
- Center of Excellence in Genomics & Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India.
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A chickpea genetic variation map based on the sequencing of 3,366 genomes. Nature 2021; 599:622-627. [PMID: 34759320 PMCID: PMC8612933 DOI: 10.1038/s41586-021-04066-1] [Citation(s) in RCA: 87] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2020] [Accepted: 09/28/2021] [Indexed: 11/17/2022]
Abstract
Zero hunger and good health could be realized by 2030 through effective conservation, characterization and utilization of germplasm resources1. So far, few chickpea (Cicer arietinum) germplasm accessions have been characterized at the genome sequence level2. Here we present a detailed map of variation in 3,171 cultivated and 195 wild accessions to provide publicly available resources for chickpea genomics research and breeding. We constructed a chickpea pan-genome to describe genomic diversity across cultivated chickpea and its wild progenitor accessions. A divergence tree using genes present in around 80% of individuals in one species allowed us to estimate the divergence of Cicer over the last 21 million years. Our analysis found chromosomal segments and genes that show signatures of selection during domestication, migration and improvement. The chromosomal locations of deleterious mutations responsible for limited genetic diversity and decreased fitness were identified in elite germplasm. We identified superior haplotypes for improvement-related traits in landraces that can be introgressed into elite breeding lines through haplotype-based breeding, and found targets for purging deleterious alleles through genomics-assisted breeding and/or gene editing. Finally, we propose three crop breeding strategies based on genomic prediction to enhance crop productivity for 16 traits while avoiding the erosion of genetic diversity through optimal contribution selection (OCS)-based pre-breeding. The predicted performance for 100-seed weight, an important yield-related trait, increased by up to 23% and 12% with OCS- and haplotype-based genomic approaches, respectively.
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Paape T, Heiniger B, Santo Domingo M, Clear MR, Lucas MM, Pueyo JJ. Genome-Wide Association Study Reveals Complex Genetic Architecture of Cadmium and Mercury Accumulation and Tolerance Traits in Medicago truncatula. FRONTIERS IN PLANT SCIENCE 2021; 12:806949. [PMID: 35154199 PMCID: PMC8832151 DOI: 10.3389/fpls.2021.806949] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Accepted: 12/13/2021] [Indexed: 05/15/2023]
Abstract
Heavy metals are an increasing problem due to contamination from human sources that and can enter the food chain by being taken up by plants. Understanding the genetic basis of accumulation and tolerance in plants is important for reducing the uptake of toxic metals in crops and crop relatives, as well as for removing heavy metals from soils by means of phytoremediation. Following exposure of Medicago truncatula seedlings to cadmium (Cd) and mercury (Hg), we conducted a genome-wide association study using relative root growth (RRG) and leaf accumulation measurements. Cd and Hg accumulation and RRG had heritability ranging 0.44 - 0.72 indicating high genetic diversity for these traits. The Cd and Hg trait associations were broadly distributed throughout the genome, indicated the traits are polygenic and involve several quantitative loci. For all traits, candidate genes included several membrane associated ATP-binding cassette transporters, P-type ATPase transporters, oxidative stress response genes, and stress related UDP-glycosyltransferases. The P-type ATPase transporters and ATP-binding cassette protein-families have roles in vacuole transport of heavy metals, and our findings support their wide use in physiological plant responses to heavy metals and abiotic stresses. We also found associations between Cd RRG with the genes CAX3 and PDR3, two linked adjacent genes, and leaf accumulation of Hg associated with the genes NRAMP6 and CAX9. When plant genotypes with the most extreme phenotypes were compared, we found significant divergence in genomic regions using population genomics methods that contained metal transport and stress response gene ontologies. Several of these genomic regions show high linkage disequilibrium (LD) among candidate genes suggesting they have evolved together. Minor allele frequency (MAF) and effect size of the most significant SNPs was negatively correlated with large effect alleles being most rare. This is consistent with purifying selection against alleles that increase toxicity and abiotic stress. Conversely, the alleles with large affect that had higher frequencies that were associated with the exclusion of Cd and Hg. Overall, macroevolutionary conservation of heavy metal and stress response genes is important for improvement of forage crops by harnessing wild genetic variants in gene banks such as the Medicago HapMap collection.
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Affiliation(s)
- Timothy Paape
- Brookhaven National Laboratory, Upton, NY, United States
- *Correspondence: Tim Paape,
| | - Benjamin Heiniger
- Department of Evolutionary Biology and Environmental Studies, University of Zurich, Zurich, Switzerland
| | - Miguel Santo Domingo
- Department of Soil, Plant and Environmental Quality, Institute of Agricultural Sciences, ICA-CSIC, Madrid, Spain
| | | | - M. Mercedes Lucas
- Department of Soil, Plant and Environmental Quality, Institute of Agricultural Sciences, ICA-CSIC, Madrid, Spain
| | - José J. Pueyo
- Department of Soil, Plant and Environmental Quality, Institute of Agricultural Sciences, ICA-CSIC, Madrid, Spain
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Renzi JP, Duchoslav M, Brus J, Hradilová I, Pechanec V, Václavek T, Machalová J, Hron K, Verdier J, Smýkal P. Physical Dormancy Release in Medicago truncatula Seeds Is Related to Environmental Variations. PLANTS (BASEL, SWITZERLAND) 2020; 9:E503. [PMID: 32295289 PMCID: PMC7238229 DOI: 10.3390/plants9040503] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/30/2020] [Revised: 04/13/2020] [Accepted: 04/13/2020] [Indexed: 12/26/2022]
Abstract
Seed dormancy and timing of its release is an important developmental transition determining the survival of individuals, populations, and species in variable environments. Medicago truncatula was used as a model to study physical seed dormancy at the ecological and genetics level. The effect of alternating temperatures, as one of the causes releasing physical seed dormancy, was tested in 178 M. truncatula accessions over three years. Several coefficients of dormancy release were related to environmental variables. Dormancy varied greatly (4-100%) across accessions as well as year of experiment. We observed overall higher physical dormancy release under more alternating temperatures (35/15 °C) in comparison with less alternating ones (25/15 °C). Accessions from more arid climates released dormancy under higher experimental temperature alternations more than accessions originating from less arid environments. The plasticity of physical dormancy can probably distribute the germination through the year and act as a bet-hedging strategy in arid environments. On the other hand, a slight increase in physical dormancy was observed in accessions from environments with higher among-season temperature variation. Genome-wide association analysis identified 136 candidate genes related to secondary metabolite synthesis, hormone regulation, and modification of the cell wall. The activity of these genes might mediate seed coat permeability and, ultimately, imbibition and germination.
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Affiliation(s)
- Juan Pablo Renzi
- Instituto Nacional de Tecnología Agropecuaria, Hilario Ascasubi 8142, Argentina;
| | - Martin Duchoslav
- Department of Botany, Palacký University, Šlechtitelů 27, 783 71 Olomouc, Czech Republic; (M.D.); (I.H.)
| | - Jan Brus
- Department of Geoinformatics, Palacký University, 17. listopadu 50, 771 46 Olomouc, Czech Republic; (J.B.); (V.P.)
| | - Iveta Hradilová
- Department of Botany, Palacký University, Šlechtitelů 27, 783 71 Olomouc, Czech Republic; (M.D.); (I.H.)
| | - Vilém Pechanec
- Department of Geoinformatics, Palacký University, 17. listopadu 50, 771 46 Olomouc, Czech Republic; (J.B.); (V.P.)
| | - Tadeáš Václavek
- Department of Mathematical Analysis and Applications of Mathematics, Palacký University, 17. listopadu 12, 771 46 Olomouc, Czech Republic; (T.V.); (J.M.); (K.H.)
| | - Jitka Machalová
- Department of Mathematical Analysis and Applications of Mathematics, Palacký University, 17. listopadu 12, 771 46 Olomouc, Czech Republic; (T.V.); (J.M.); (K.H.)
| | - Karel Hron
- Department of Mathematical Analysis and Applications of Mathematics, Palacký University, 17. listopadu 12, 771 46 Olomouc, Czech Republic; (T.V.); (J.M.); (K.H.)
| | - Jerome Verdier
- UMR 1345 Institut de Recherche en Horticulture et Semences, Agrocampus Ouest, INRA, Université d’Angers, SFR 4207 QUASAV, 49070 Beaucouzé, France;
| | - Petr Smýkal
- Department of Botany, Palacký University, Šlechtitelů 27, 783 71 Olomouc, Czech Republic; (M.D.); (I.H.)
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