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Field-based high throughput phenotyping rapidly identifies genomic regions controlling yield components in rice.
Tanger, Paul; Klassen, Stephen; Mojica, Julius P; Lovell, John T; Moyers, Brook T; Baraoidan, Marietta; Naredo, Maria Elizabeth B; McNally, Kenneth L; Poland, Jesse; Bush, Daniel R; Leung, Hei; Leach, Jan E; McKay, John K.
Afiliação
  • Tanger P; Bioagricultural Sciences and Pest Management, Colorado State University, Fort Collins, CO, USA.
  • Klassen S; International Rice Research Institute (IRRI), Los Baños, Philippines.
  • Mojica JP; Bioagricultural Sciences and Pest Management, Colorado State University, Fort Collins, CO, USA.
  • Lovell JT; Department of Biology, Duke University, Durham, NC, USA.
  • Moyers BT; Department of Integrative Biology, University of Texas, Austin, Austin, TX, USA.
  • Baraoidan M; Bioagricultural Sciences and Pest Management, Colorado State University, Fort Collins, CO, USA.
  • Naredo ME; International Rice Research Institute (IRRI), Los Baños, Philippines.
  • McNally KL; International Rice Research Institute (IRRI), Los Baños, Philippines.
  • Poland J; International Rice Research Institute (IRRI), Los Baños, Philippines.
  • Bush DR; Departments of Plant Pathology and Agronomy, Kansas State University, Manhattan, KS, USA.
  • Leung H; Department of Biology, Colorado State University, Fort Collins, CO, USA.
  • Leach JE; International Rice Research Institute (IRRI), Los Baños, Philippines.
  • McKay JK; Bioagricultural Sciences and Pest Management, Colorado State University, Fort Collins, CO, USA.
Sci Rep ; 7: 42839, 2017 02 21.
Article em En | MEDLINE | ID: mdl-28220807
ABSTRACT
To ensure food security in the face of population growth, decreasing water and land for agriculture, and increasing climate variability, crop yields must increase faster than the current rates. Increased yields will require implementing novel approaches in genetic discovery and breeding. Here we demonstrate the potential of field-based high throughput phenotyping (HTP) on a large recombinant population of rice to identify genetic variation underlying important traits. We find that detecting quantitative trait loci (QTL) with HTP phenotyping is as accurate and effective as traditional labor-intensive measures of flowering time, height, biomass, grain yield, and harvest index. Genetic mapping in this population, derived from a cross of an modern cultivar (IR64) with a landrace (Aswina), identified four alleles with negative effect on grain yield that are fixed in IR64, demonstrating the potential for HTP of large populations as a strategy for the second green revolution.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Oryza / Genoma de Planta Tipo de estudo: Prognostic_studies Idioma: En Revista: Sci Rep Ano de publicação: 2017 Tipo de documento: Article País de afiliação: Estados Unidos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Oryza / Genoma de Planta Tipo de estudo: Prognostic_studies Idioma: En Revista: Sci Rep Ano de publicação: 2017 Tipo de documento: Article País de afiliação: Estados Unidos