1a.Objectives (from AD-416):
1. Select progeny based on genomic selection (GS) and phenotypic selection and compare their performance in subsequent field trials.
2. Assess the ability of GS to predict the true breeding value of a parent
3. Determine whether a trained GS model maintains accuracy over cycles of breeding.
4. Use simulations to assess scenarios for the introduction and implementation of GS in a breeding program to optimize short- and long-term success over cycles of GS.
1b.Approach (from AD-416):
Cooperating PI’s will run field experiments on sets of progeny to obtain phenotypic data; they will also extract DNA and obtain marker data. We will analyze these data using several genomic selection methods to generate predictions of breeding and genetic values based on marker data. In parallel, we will use these data and other data available from historical trials to estimate breeding and genetic values directly from phenotype and pedigree data. We will then correlate predictions with directly observed data to evaluate the accuracy of genomic selection.
Genomic selection (GS) was implemented in a Midwest U.S. barley breeding program to improve resistance to Fusarium head blight (FHB), a fungal pathogen that infects kernels and produces a mycotoxin called deoxynivalenol (DON). We generated genomic estimated breeding values (GEBV) using disease and genotype data sets from three Midwest U.S. breeding programs focused on developing six-rowed malting barley varieties with enhanced FHB resistance. We initiated GS on a set of 1,440 F2 progenies resulting from crosses among and between the Minnesota (MN) and North Dakota (ND) breeding programs. A ridge regression BLUP model using 1,536 SNP markers and a training population of 768 breeding lines was used to generate GEBVs. Three sets of 100 random lines from these F2’s corresponding to the three cross types (MN x MN, MN x ND, ND x ND), were advanced to the F4 and evaluated in four replicated disease nurseries in 2011. We used the three training populations to predict the three cross types and calculated the accuracy as the correlation between GEBV and the observed phenotype. As expected, the MN training population predicted DON for the MN x MN crosses more accurately (r = 0.60) then for the ND x ND crosses (r = 0.08). A similar trend was observed when using the ND training population. The MN & ND training population predicted the MN x ND crosses with greater accuracy (r = 0.37) than using either of the single breeding program training populations (r = 0.28, 0.15). These empirical results from one cycle of GS are promising and suggest that with properly constructed training populations we should be able to double the rate of gain from selection and substantially reduce the need for field screening of FHB in early generations.