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

Julio G. Velazco, Marcos Malosetti, Colleen H. Hunt, Emma S. Mace, David R. Jordan, Fred A. van Eeuwijk

Theoretical and Applied Genetics; July 2019, Volume 132, Issue 7, pp 2055–2067

Key message

The 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.

Abstract

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.

See https://link.springer.com/article/10.1007/s00122-019-03337-w

Fig. 1

Predictive abilities, regression coefficients and MSEP from BLUP models using different weights (w) to construct the combined matrix K for grain yield (GY), stay-green (SG), plant height (PH) and flowering time (FT) predictions within (blue) and among (green) families. The weight w = 0 corresponds to the simple G-BLUP model. The horizontal lines indicate a regression coefficient b = 1 (color figure online)