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Multi-trait multi-environment genomic prediction of preliminary yield trial in pulse crop.
Saludares, Rica Amor; Atanda, Sikiru Adeniyi; Piche, Lisa; Worral, Hannah; Dariva, Francoise; McPhee, Kevin; Bandillo, Nonoy.
Afiliación
  • Saludares RA; Department of Plant Sciences, North Dakota State University, Fargo, North Dakota, USA.
  • Atanda SA; Department of Plant Sciences, North Dakota State University, Fargo, North Dakota, USA.
  • Piche L; Department of Plant Sciences, North Dakota State University, Fargo, North Dakota, USA.
  • Worral H; North Central Research Extension Center, Minot, North Dakota, USA.
  • Dariva F; Department of Plant Sciences, North Dakota State University, Fargo, North Dakota, USA.
  • McPhee K; Department of Plant Science and Plant Pathology, Montana State University, Bozeman, Montana, USA.
  • Bandillo N; Department of Plant Sciences, North Dakota State University, Fargo, North Dakota, USA.
Plant Genome ; : e20496, 2024 Aug 04.
Article en En | MEDLINE | ID: mdl-39099220
ABSTRACT
Phenotypic selection of complex traits such as seed yield and protein in the preliminary yield trial (PYT) is often constrained by limited seed availability, resulting in trials with few environments and minimal to no replications. Multi-trait multi-environment enabled genomic prediction (MTME-GP) offers a valuable alternative to predict missing phenotypes of selection candidates for multiple traits and diverse environments. In this study, we assessed the efficiency of MTME-GP for improving seed protein and seed yield in field pea, the top two breeding targets but highly antagonistic traits in pulse crop. We utilized a set of 300 selection candidates in the PYT that virtually represented all possible families of the North Dakota State University field pea breeding program. Selection candidates were evaluated in three diverse, contrasting environments, as indicated by a range of heritability. Using whole- and split-environment cross validation schemes, MTME-GP had higher predictive ability than a standard additive G-BLUP model. Integrating a range of overlapping genotypes in between environments showed improvement on the predictive ability of the MTME-GP model but tends to plateau at 50%-80% training set size. Regardless of the cross-validation scheme, accuracy was among the lowest in stressed environments, presumably due to low heritability for seed protein and yield. This study provided insights into the potential of MTME-GP in a public pulse crop breeding program. The MTME-GP framework can be further improved with more testing environments and integration of additional orthogonal information in the early stages of the breeding pipeline.
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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