Abstract
Key messageThe use of a kinship matrix integrating pedigree- and marker-based relationships optimized the performance of genomic prediction in sorghum, especially for traits of lower heritability.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.
Highlights
Selection based on dense genome-wide markers has become a revolutionary alternative to traditional genetic evaluations for improving quantitative traits in crops (Jannink et al.Communicated by Chris Carolin Schön.2010; Crossa et al 2017)
We considered four productivity and adaptability traits routinely measured by the program: grain yield (GY), stay-green (SG), plant height (PH) and flowering time (FT)
This paper provides a first empirical evidence based on sorghum breeding data that the use of genomic relationships alone, even with relatively low marker density, can give better predictions of parental breeding value (BV) than the pedigreebased model
Summary
Selection based on dense genome-wide markers has become a revolutionary alternative to traditional genetic evaluations for improving quantitative traits in crops (Jannink et al.Communicated by Chris Carolin Schön.2010; Crossa et al 2017). Genomic prediction (GP) is expected to increase accuracy of evaluations by capturing large and small allelic effects across the genome simultaneously (Meuwissen et al 2001). These effects are estimated using phenotypic and genotypic data from a reference breeding population and integrated to predict genome-assisted BVs of untested selection candidates that have been only genotyped. The implementation of this prediction method can potentially lead to higher rates of genetic gain and lower phenotyping costs compared to classical phenotypic or pedigree-based selection
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