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A chickpea genetic variation map based on the sequencing of 3,366 genomes.
Varshney, Rajeev K; Roorkiwal, Manish; Sun, Shuai; Bajaj, Prasad; Chitikineni, Annapurna; Thudi, Mahendar; Singh, Narendra P; Du, Xiao; Upadhyaya, Hari D; Khan, Aamir W; Wang, Yue; Garg, Vanika; Fan, Guangyi; Cowling, Wallace A; Crossa, José; Gentzbittel, Laurent; Voss-Fels, Kai Peter; Valluri, Vinod Kumar; Sinha, Pallavi; Singh, Vikas K; Ben, Cécile; Rathore, Abhishek; Punna, Ramu; Singh, Muneendra K; Tar'an, Bunyamin; Bharadwaj, Chellapilla; Yasin, Mohammad; Pithia, Motisagar S; Singh, Servejeet; Soren, Khela Ram; Kudapa, Himabindu; Jarquín, Diego; Cubry, Philippe; Hickey, Lee T; Dixit, Girish Prasad; Thuillet, Anne-Céline; Hamwieh, Aladdin; Kumar, Shiv; Deokar, Amit A; Chaturvedi, Sushil K; Francis, Aleena; Howard, Réka; Chattopadhyay, Debasis; Edwards, David; Lyons, Eric; Vigouroux, Yves; Hayes, Ben J; von Wettberg, Eric; Datta, Swapan K; Yang, Huanming.
Affiliation
  • Varshney RK; Center of Excellence in Genomics and Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India. rajeev.varshney@murdoch.edu.au.
  • Roorkiwal M; State Agricultural Biotechnology Centre, Centre for Crop and Food Innovation, Murdoch University, Murdoch, Western Australia, Australia. rajeev.varshney@murdoch.edu.au.
  • Sun S; Center of Excellence in Genomics and Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India.
  • Bajaj P; BGI-Qingdao, BGI-Shenzhen, Qingdao, China.
  • Chitikineni A; China National GeneBank, BGI-Shenzhen, Shenzhen, China.
  • Thudi M; College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China.
  • Singh NP; Center of Excellence in Genomics and Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India.
  • Du X; Center of Excellence in Genomics and Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India.
  • Upadhyaya HD; Center of Excellence in Genomics and Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India.
  • Khan AW; Institute of Crop Germplasm Resources, Shandong Academy of Agricultural Sciences (SAAS), Jinan, China.
  • Wang Y; ICAR-Indian Institute of Pulses Research, Kanpur, India.
  • Garg V; BGI-Qingdao, BGI-Shenzhen, Qingdao, China.
  • Fan G; China National GeneBank, BGI-Shenzhen, Shenzhen, China.
  • Cowling WA; Genebank, ICRISAT, Hyderabad, India.
  • Crossa J; University of Georgia, Athens, GA, USA.
  • Gentzbittel L; Center of Excellence in Genomics and Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India.
  • Voss-Fels KP; BGI-Qingdao, BGI-Shenzhen, Qingdao, China.
  • Valluri VK; China National GeneBank, BGI-Shenzhen, Shenzhen, China.
  • Sinha P; Center of Excellence in Genomics and Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India.
  • Singh VK; BGI-Qingdao, BGI-Shenzhen, Qingdao, China.
  • Ben C; China National GeneBank, BGI-Shenzhen, Shenzhen, China.
  • Rathore A; BGI-Shenzhen, Shenzhen, China.
  • Punna R; State Key Laboratory of Agricultural Genomics, BGI-Shenzhen, Shenzhen, China.
  • Singh MK; The UWA Institute of Agriculture, and School of Agriculture and Environment, The University of Western Australia, Perth, Western Australia, Australia.
  • Tar'an B; Biometrics and Statistics Unit, International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico.
  • Bharadwaj C; Digital Agriculture Laboratory, Skolkovo Institute of Science and Technology, Moscow, Russia.
  • Yasin M; Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, Queensland, Australia.
  • Pithia MS; Center of Excellence in Genomics and Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India.
  • Singh S; Center of Excellence in Genomics and Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India.
  • Soren KR; International Rice Research Institute (IRRI), South-Asia Hub, ICRISAT, Hyderabad, India.
  • Kudapa H; Center of Excellence in Genomics and Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India.
  • Jarquín D; International Rice Research Institute (IRRI), South-Asia Hub, ICRISAT, Hyderabad, India.
  • Cubry P; Digital Agriculture Laboratory, Skolkovo Institute of Science and Technology, Moscow, Russia.
  • Hickey LT; Laboratoire Ecologie Fonctionnelle et Environnement, Université de Toulouse, CNRS, Toulouse, France.
  • Dixit GP; Center of Excellence in Genomics and Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India.
  • Thuillet AC; Institute for Genomic Diversity, Cornell University, Ithaca, NY, USA.
  • Hamwieh A; Center of Excellence in Genomics and Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India.
  • Kumar S; Department of Plant Sciences, University of Saskatchewan, Saskatoon, Saskatchewan, Canada.
  • Deokar AA; ICAR-Indian Agricultural Research Institute (IARI), New Delhi, India.
  • Chaturvedi SK; Rajmata Vijayaraje Scindia Krishi Vishwa Vidyalaya, Gwalior, India.
  • Francis A; Junagadh Agricultural University, Junagadh, India.
  • Howard R; Rajasthan Agricultural Research Institute (RARI), Durgapura, India.
  • Chattopadhyay D; ICAR-Indian Institute of Pulses Research, Kanpur, India.
  • Edwards D; Center of Excellence in Genomics and Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India.
  • Lyons E; Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE, USA.
  • Vigouroux Y; DIADE (Diversity-Adaptation-Development of Plants), Université de Montpellier, Institut de Recherche pour le Développement (IRD), Montpellier, France.
  • Hayes BJ; Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, Queensland, Australia.
  • von Wettberg E; ICAR-Indian Institute of Pulses Research, Kanpur, India.
  • Datta SK; DIADE (Diversity-Adaptation-Development of Plants), Université de Montpellier, Institut de Recherche pour le Développement (IRD), Montpellier, France.
  • Yang H; International Centre for Agricultural Research in the Dry Areas (ICARDA), Cairo, Egypt.
Nature ; 599(7886): 622-627, 2021 11.
Article in En | MEDLINE | ID: mdl-34759320
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.
Subject(s)

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Genetic Variation / Sequence Analysis, DNA / Genome, Plant / Cicer Language: En Journal: Nature Year: 2021 Document type: Article Affiliation country:

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Genetic Variation / Sequence Analysis, DNA / Genome, Plant / Cicer Language: En Journal: Nature Year: 2021 Document type: Article Affiliation country: