Combining pedigree and genomic information to improve prediction quality : an example in sorghum

• Autores: Velazco, Julio Gabriel; Malosetti, Marcos; Hunt, Colleen H.; Mace, Emma S.; Jordan, David R.; Van Eeuwijk, Fred A.
• Año de publicación: 2019
• Idioma: inglés
• Tipo de recurso: artículo
• Fuente: Theoretical and Applied Genetics 132 (7) : 2055–2067. (July 2019)
• Materia: Sorghum almum; Sorgos; Valor Genético; Genómica; Pedigrí; Pedigree livestock; Rendimiento; Evaluación; Breeding Value; Genomics; Yields; Evaluation
• Institución: Instituto Nacional de Tecnología Agropecuaria

Selection based on genome-wide markers has become an active breeding strategy in crops. Genomic prediction models can make use of pedigree information to account for the residual polygenic effects not captured by markers. Our aim was to evaluate the impact of using pedigree and genomic information on prediction quality of breeding values for different traits in sorghum. We explored BLUP models that use weighted combinations of pedigree and genomic relationship matrices. The optimal weighting factor was empirically determined in order to maximize predictive ability after evaluating a range of candidate weights. The phenotypic data consisted of testcross evaluations of sorghum parental lines across multiple environments. All lines were genotyped, and full pedigree information was available. The performance of the best predictive combined matrix was compared to that of models fitting the component matrices independently. Model performance was assessed using cross-validation technique. Fitting a combined pedigree–genomic matrix with the optimal weight always yielded the largest increases in predictive ability and the largest reductions in prediction bias relative to the simple G-BLUP. However, the weight that optimized prediction varied across traits. The benefits of including pedigree information in the genomic model were more relevant for traits with lower heritability, such as grain yield and stay-green. Our results suggest that the combination of pedigree and genomic relatedness can be used to optimize predictions of complex traits in crops when the additive variation is not fully explained by markers.
EEA Pergamino
Fil: Velazco, Julio. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Pergamino. Sección Forrajeras; Argentina. Wageningen University and Research . Biometris – Mathematical and Statistical Methods; Holanda
Fil: Malosetti, Marcos. Wageningen University and Research . Biometris – Mathematical and Statistical Methods; Holanda
Fil: Hunt, Colleen H. The University of Queensland. Hermitage Research Facility. Queensland Alliance for Agriculture and Food Innovation; Australia. Hermitage Research Facility. Department of Agriculture and Fisheries; Australia
Fil: Mace, Emma S. The University of Queensland. Hermitage Research Facility. Queensland Alliance for Agriculture and Food Innovation; Australia. Hermitage Research Facility. Department of Agriculture and Fisheries; Australia
Fil: Jordan, David R. The University of Queensland. Hermitage Research Facility. Queensland Alliance for Agriculture and Food Innovation; Australia
Fil: Eeuwijk, Fred A. van. Wageningen University and Research . Biometris – Mathematical and Statistical Methods; Holanda