Genomic prediction for general combining ability in hybrid canola (Brassica napa L.)

Abstract

Hybrid breeding is a method of selecting parental lines and determining crosses that are likely to yield the best hybrids. Genomic prediction (GP), a tool that uses genome wide markers, can be employed to predict the performance of untested hybrids based on general combining ability (GCA) of their parents. We investigated the potential of GP for GCA prediction in a commercial canola breeding program. We used female tester data, where many female lines are crossed with few male lines, to predict economically important traits in canola. Multi-year and location data for grain yield, oil, protein, days to flowering, days to maturity, total glucosinate and saturated fat were available for prediction. Three different cross-validation strategies were implemented to determine the predictive ability (PA) of each trait. In the first cross-validation scheme (CV1), a prediction model was validated using five-fold cross validation strategy. In the second cross-validation scheme (CV2), an unseen year was predicted using the previous years data as a training set. In the third cross-validation scheme (CV3), Inbred pe se performance was added to the training set to exploit the covariance between traits of inbred and hybrid trials. The highest PA was observed for CV1 while the lowest PA was seen for CV2. In CV1, PA ranged from 0.34 to 0.62. The highest PA was observed for protein (0.62) while the lowest PA was observed for days to maturity (0.34). For CV2, PA ranged from 0.16 to 0.46, while for CV3 PA ranged from 0.27 to 0.71. The highest PA for CV2 (0.46) and CV3 (0.71) was observed for total glucosinate while the lowest PA (0.16 CV2 and 0.27 -CV3) was for days to maturity. The current study demonstrates the potential of using marker information to select parents with the highest GCA to create the best hybrid combinations.