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Genomic selection optimization in blueberry: Data-driven methods for marker and training population design.
Adunola, Paul; Ferrão, Luis Felipe V; Benevenuto, Juliana; Azevedo, Camila F; Munoz, Patricio R.
Afiliación
  • Adunola P; Blueberry Breeding and Genomics Lab, Horticultural Sciences Department, University of Florida, Gainesville, Florida, USA.
  • Ferrão LFV; Blueberry Breeding and Genomics Lab, Horticultural Sciences Department, University of Florida, Gainesville, Florida, USA.
  • Benevenuto J; Blueberry Breeding and Genomics Lab, Horticultural Sciences Department, University of Florida, Gainesville, Florida, USA.
  • Azevedo CF; Blueberry Breeding and Genomics Lab, Horticultural Sciences Department, University of Florida, Gainesville, Florida, USA.
  • Munoz PR; Statistics Department, Federal University of Viçosa, Viçosa, Minas Gerais, Brazil.
Plant Genome ; : e20488, 2024 Aug 01.
Article en En | MEDLINE | ID: mdl-39087863
ABSTRACT
Genomic prediction is a modern approach that uses genome-wide markers to predict the genetic merit of unphenotyped individuals. With the potential to reduce the breeding cycles and increase the selection accuracy, this tool has been designed to rank genotypes and maximize genetic gains. Despite this importance, its practical implementation in breeding programs requires critical allocation of resources for its application in a predictive framework. In this study, we integrated genetic and data-driven methods to allocate resources for phenotyping and genotyping tailored to genomic prediction. To this end, we used a historical blueberry (Vaccinium corymbosun L.) breeding dataset containing more than 3000 individuals, genotyped using probe-based target sequencing and phenotyped for three fruit quality traits over several years. Our contribution in this study is threefold (i) for the genotyping resource allocation, the use of genetic data-driven methods to select an optimal set of markers slightly improved prediction results for all the traits; (ii) for the long-term implication, we carried out a simulation study and emphasized that data-driven method results in a slight improvement in genetic gain over 30 cycles than random marker sampling; and (iii) for the phenotyping resource allocation, we compared different optimization algorithms to select training population, showing that it can be leveraged to increase predictive performances. Altogether, we provided a data-oriented decision-making approach for breeders by demonstrating that critical breeding decisions associated with resource allocation for genomic prediction can be tackled through a combination of statistics and genetic methods.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Plant Genome Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos 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 Idioma: En Revista: Plant Genome Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos