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Exploring genetic architecture for pod-related traits in soybean using image-based phenotyping.
Chang, Fangguo; Lv, Wenhuan; Lv, Peiyun; Xiao, Yuntao; Yan, Wenliang; Chen, Shu; Zheng, Lingyi; Xie, Ping; Wang, Ling; Karikari, Benjamin; Abou-Elwafa, Salah Fatouh; Jiang, Haiyan; Zhao, Tuanjie.
Afiliação
  • Chang F; National Center for Soybean Improvement, Key Laboratory of Biology and Genetics and Breeding for Soybean, Ministry of Agriculture, State Key Laboratory for Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, 210095 China.
  • Lv W; National Center for Soybean Improvement, Key Laboratory of Biology and Genetics and Breeding for Soybean, Ministry of Agriculture, State Key Laboratory for Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, 210095 China.
  • Lv P; National Center for Soybean Improvement, Key Laboratory of Biology and Genetics and Breeding for Soybean, Ministry of Agriculture, State Key Laboratory for Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, 210095 China.
  • Xiao Y; National Center for Soybean Improvement, Key Laboratory of Biology and Genetics and Breeding for Soybean, Ministry of Agriculture, State Key Laboratory for Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, 210095 China.
  • Yan W; National Center for Soybean Improvement, Key Laboratory of Biology and Genetics and Breeding for Soybean, Ministry of Agriculture, State Key Laboratory for Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, 210095 China.
  • Chen S; College of Artificial Intelligence, Nanjing Agricultural University, Nanjing, 210095 Jiangsu China.
  • Zheng L; National Center for Soybean Improvement, Key Laboratory of Biology and Genetics and Breeding for Soybean, Ministry of Agriculture, State Key Laboratory for Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, 210095 China.
  • Xie P; National Center for Soybean Improvement, Key Laboratory of Biology and Genetics and Breeding for Soybean, Ministry of Agriculture, State Key Laboratory for Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, 210095 China.
  • Wang L; National Center for Soybean Improvement, Key Laboratory of Biology and Genetics and Breeding for Soybean, Ministry of Agriculture, State Key Laboratory for Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, 210095 China.
  • Karikari B; Department of Crop Science, Faculty of Agriculture, Food and Consumer Sciences, University for Development Studies, P. O. Box TL, 1882 Tamale, Ghana.
  • Abou-Elwafa SF; Agronomy Departments, Faculty of Agriculture, Assiut University, Asyut, 71526 Egypt.
  • Jiang H; College of Artificial Intelligence, Nanjing Agricultural University, Nanjing, 210095 Jiangsu China.
  • Zhao T; National Center for Soybean Improvement, Key Laboratory of Biology and Genetics and Breeding for Soybean, Ministry of Agriculture, State Key Laboratory for Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, 210095 China.
Mol Breed ; 41(4): 28, 2021 Apr.
Article em En | MEDLINE | ID: mdl-37309355
Mature pod color (PC) and pod size (PS) served as important characteristics are used in the soybean breeding programs. However, manual phenotyping of such complex traits is time-consuming, laborious, and expensive for breeders. Here, we collected pod images from two different populations, namely, a soybean association panel (SAP) consisting of 187 accessions and an inter-specific recombinant inbred line (RIL) population containing 284 RILs. An image-based phenotyping method was developed and used to extract the pod color- and size-related parameters from images. Genome-wide association study (GWAS) and linkage mapping were performed to decipher the genetic control of pod color- and size-related traits across 2 successive years. Both populations exhibited wide phenotypic variations and continuous distribution in pod color- and size-related traits, indicating quantitative polygenic inheritance of these traits. GWAS and linkage mapping approaches identified the two major quantitative trait loci (QTL) underlying the pod color parameters, i.e., qPC3 and qPC19, located to chromosomes 3 and 19, respectively, and 12 stable QTLs for pod size-related traits across nine chromosomes. Several genes residing within the genomic region of stable QTL were identified as potential candidates underlying these pod-related traits based on the gene annotation and expression profiling data. Our results provide the useful information for fine-mapping/map-based cloning of QTL and marker-assisted selection of elite varieties with desirable pod traits. Supplementary Information: The online version contains supplementary material available at 10.1007/s11032-021-01223-2.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2021 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2021 Tipo de documento: Article