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Training Population Optimization for Genomic Selection.
Berro, Inés; Lado, Bettina; Nalin, Rafael S; Quincke, Martin; Gutiérrez, Lucía.
Afiliación
  • Berro I; Dep. of Agronomy, Univ. of Wisconsin - Madison, 1575 Linden Dr., Madison, WI, 53706.
  • Lado B; Statistics Dep., Facultad de Agronomía, Univ. de la República, Garzón 780, Montevideo, 12900, Uruguay.
  • Nalin RS; Statistics Dep., Facultad de Agronomía, Univ. de la República, Garzón 780, Montevideo, 12900, Uruguay.
  • Quincke M; Dep. of Agronomy, Univ. of Wisconsin - Madison, 1575 Linden Dr., Madison, WI, 53706.
  • Gutiérrez L; Dep. of Genetics, Escola Superior de Agricultura "Luiz de Queiroz", Univ. de São Paulo, Piracicaba, São Paulo, Brazil.
Plant Genome ; 12(3): 1-14, 2019 11.
Article en En | MEDLINE | ID: mdl-33016595
CORE IDEAS: Training populations can be optimized for specific testing populations. Optimized training populations are smaller, more related, and more predictive. Stratified sampling with a relationship matrix weighted by marker effect is optimal. The effectiveness of genomic selection in breeding programs depends on the phenotypic quality and depth, the prediction model, the number and type of molecular markers, and the size and composition of the training population (TR). Furthermore, population structure and diversity have a key role in the composition of the optimal training sets. Our goal was to compare strategies for optimizing the TR for specific testing populations (TE). A total of 1353 wheat (Triticum aestivum L.) and 644 rice (Oryza sativa L.) advanced lines were evaluated for grain yield in multiple environments. Several within-TR optimization strategies were compared to identify groups of individuals with increased predictive ability. Additionally, optimization strategies to choose individuals from the TR with higher predictive ability for a specific TE were compared. There is a benefit in considering both the population structure and the relationship between the TR and the TE when designing an optimal TR for genomic selection. A weighted relationship matrix with stratified sampling is the best strategy for forward predictions of quantitative traits in populations several generations apart.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Genómica / Fitomejoramiento Tipo de estudio: Prognostic_studies Idioma: En Revista: Plant Genome Año: 2019 Tipo del documento: Article Pais de publicación: Estados Unidos
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: Genómica / Fitomejoramiento Tipo de estudio: Prognostic_studies Idioma: En Revista: Plant Genome Año: 2019 Tipo del documento: Article Pais de publicación: Estados Unidos