Fecha/hora: 09/06/2024 19:33:31

Biblioteca(s): 	INIA La Estanzuela.	
Fecha corriente: 	06/12/2019	
Fecha de la última actualización: 	05/09/2022	
Autoria: 	BERRO, I.; LADO, B.; NALIN, R.S.; QUINCKE, M.; GUTIÉRREZ, L.	
Afiliación: 	Dep. of Agronomy, Univ. of Wisconsin, Madison, USA.; Facultad de Agronomía, Univ. de la República, Montevideo, Uruguay.; Dep. of Agronomy, Univ. of Wisconsin, Madison, USA.; MARTIN CONRADO QUINCKE WALDEN, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; Dep. of Agronomy, Univ. of Wisconsin, Madison, USA./ Facultad de Agronomía, Univ. de la República, Montevideo, Uruguay.	
Título: 	Training population optimization for genomic selection.	
Año de publicación: 	2019	
Fuente/Imprenta: 	Plant Genome, November 2019, Volume 12, Issue 3, Article number 190028. OPEN ACCESS. DOI: https://doi.org/10.3835/plantgenome2019.04.0028	
DOI: 	10.3835/plantgenome2019.04.0028	
Idioma: 	Inglés	
Notas: 	Article histoty: Received 1 Apr. 2019. /Accepted 23 Sept. 2019.	
Contenido: 	ABSTRACT :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. Genomic selection  (GS) consists of selecting individuals from a TE on the basis of genotypic values predicted from their genome-wide molecular marker scores and a statistical model adjusted with individuals that have phenotypic and genotypic information (Meuwissen et al., 2001). The group of individuals that were phenotyped and genotyped is called the TR (Heffner et al. 2009).	
Palabras-Clave: 	GENOMIC SELECTION; SELECCIÓN GENÓMICA.	
Thesagro: 	TRIGO; TRITICUM AESTIVUM.	
Categoría del tema: 	F01 Cultivo	
URL: 	http://www.ainfo.inia.uy/digital/bitstream/item/16707/1/The-Plant-Genome-2019-Berro-Training-Population-Optimization-for-Genomic-Selection.pdf
https://acsess.onlinelibrary.wiley.com/doi/epdf/10.3835/plantgenome2019.04.0028	
Registro original: 	INIA La Estanzuela (LE)	
Ejemplares: 	LE - (103063-1) - PXI - AP - DD - PP/Plant Genome/2019 -  -  - DIS