Prediction of genetic values of quantitative traits in plant breeding using pedigree and molecular markers

Abstract

The availability of dense molecular markers has made possible the use of genomic selection (GS) for plant breeding. However, the evaluation of models for GS in real plant populations is very limited. This article evaluates the performance of parametric and semiparametric models for GS using wheat (Triticum aestivum L.) and maize (Zea mays) data in which different traits were measured in several environmental conditions. The findings, based on extensive cross-validations, indicate that models including marker information had higher predictive ability than pedigree-based models. In the wheat data set, and relative to a pedigree model, gains in predictive ability due to inclusion of markers ranged from 7.7 to 35.7%. Correlation between observed and predictive values in the maize data set achieved values up to 0.79. Estimates of marker effects were different across environmental conditions, indicating that genotype × environment interaction is an important component of genetic variability. These results indicate that GS in plant breeding can be an effective strategy for selecting among lines whose phenotypes have yet to be observed.

Figure 1.—

Biplot of the first two principal components (Comp. 1 and Comp. 2) of estimates of marker effects on grain yield (GY) in wheat evaluated in four environments (E1–E4). Marker effects were obtained from a full-data analysis and using a pedigree plus marker model (PM-BL). Only the effects of 17 markers that are located farthest from the center of the biplot were identified with their corresponding marker's name (solid circles).

Figure A1.—

Prior density of the regularization parameter, p(), used to fit the Bayesian LASSO.

Figure C1.—

Biplot of the first two principal components (Comp. 1 and Comp. 2) of estimates of marker effects for female flowering (FFL), male flowering (MFL), and the FFL-MFL interval (ASI) evaluated under well-watered (WW) and drought-stress (SS) conditions. Estimates of marker effects were obtained from a full-data analysis and using a pedigree plus marker model (PM-BL). Only the effects of the 19 markers that are located farthest from the center of the biplot were identified with their corresponding marker's name (solid circles).