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Unraveling the potential of phenomic selection within and among diverse breeding material of maize (Zea mays L.).
Weiß, Thea Mi; Zhu, Xintian; Leiser, Willmar L; Li, Dongdong; Liu, Wenxin; Schipprack, Wolfgang; Melchinger, Albrecht E; Hahn, Volker; Würschum, Tobias.
Afiliación
  • Weiß TM; State Plant Breeding Institute, University of Hohenheim, Stuttgart 70593, Germany.
  • Zhu X; Institute of Plant Breeding, Seed Science and Population Genetics, University of Hohenheim, Stuttgart 70593, Germany.
  • Leiser WL; State Plant Breeding Institute, University of Hohenheim, Stuttgart 70593, Germany.
  • Li D; Institute of Plant Breeding, Seed Science and Population Genetics, University of Hohenheim, Stuttgart 70593, Germany.
  • Liu W; State Plant Breeding Institute, University of Hohenheim, Stuttgart 70593, Germany.
  • Schipprack W; Key Laboratory of Crop Heterosis and Utilization, Ministry of Education, Key Laboratory of Crop Genetic Improvement, Beijing Municipality, National Maize Improvement Center, College of Agronomy and Biotechnology, China Agricultural University, Beijing 100193, China.
  • Melchinger AE; Key Laboratory of Crop Heterosis and Utilization, Ministry of Education, Key Laboratory of Crop Genetic Improvement, Beijing Municipality, National Maize Improvement Center, College of Agronomy and Biotechnology, China Agricultural University, Beijing 100193, China.
  • Hahn V; Institute of Plant Breeding, Seed Science and Population Genetics, University of Hohenheim, Stuttgart 70593, Germany.
  • Würschum T; Institute of Plant Breeding, Seed Science and Population Genetics, University of Hohenheim, Stuttgart 70593, Germany.
G3 (Bethesda) ; 12(3)2022 03 04.
Article en En | MEDLINE | ID: mdl-35100379
Genomic selection is a well-investigated approach that facilitates and supports selection decisions for complex traits and has meanwhile become a standard tool in modern plant breeding. Phenomic selection has only recently been suggested and uses the same statistical procedures to predict the targeted traits but replaces marker data with near-infrared spectroscopy data. It may represent an attractive low-cost, high-throughput alternative but has not been sufficiently studied until now. Here, we used 400 genotypes of maize (Zea mays L.) comprising elite lines of the Flint and Dent heterotic pools as well as 6 Flint landraces, which were phenotyped in multienvironment trials for anthesis-silking-interval, early vigor, final plant height, grain dry matter content, grain yield, and phosphorus concentration in the maize kernels, to compare the predictive abilities of genomic as well as phenomic prediction under different scenarios. We found that both approaches generally achieved comparable predictive abilities within material groups. However, phenomic prediction was less affected by population structure and performed better than its genomic counterpart for predictions among diverse groups of breeding material. We therefore conclude that phenomic prediction is a promising tool for practical breeding, for instance when working with unknown and rather diverse germplasm. Moreover, it may make the highly monopolized sector of plant breeding more accessible also for low-tech institutions by combining well established, widely available, and cost-efficient spectral phenotyping with the statistical procedures elaborated for genomic prediction - while achieving similar or even better results than with marker data.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Zea mays / Fitomejoramiento Idioma: En Revista: G3 (Bethesda) Año: 2022 Tipo del documento: Article País de afiliación: Alemania Pais de publicación: Reino Unido
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Zea mays / Fitomejoramiento Idioma: En Revista: G3 (Bethesda) Año: 2022 Tipo del documento: Article País de afiliación: Alemania Pais de publicación: Reino Unido