EVALUATING GENOMIC SELECTION FOR APPLIED PLANT BREEDING
Plant, Soil and Nutrition Research
2013 Annual Report
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.
Short-term success of genomic selection (GS) depends in part on optimizing investment in genotyping and phenotyping. Given that genotypes can be used to model relatedness among individuals, they can also improve evaluation of individuals that have also been phenotyped. We modeled the optimization problem of determining what fraction, if any, of genotyped experimental lines should not be phenotyped but assessed on prediction only. This problem is being addressed in the context of biparental populations. The optimization differs depending on whether the objective is to increase the mean of a group of lines that will be intermated or to identify, within a population, the single individual with the highest performance.
From long-term selection experiments in model systems, we know that mutation can play an important role in long-term response. However, GS will not adequately capture contributions from new mutations. We have run simulation experiments to assess what impact this issue might have on medium and long-term gain, and whether approaches can be developed to improve GS capture of mutational events. Application of simple GS methods shows that over the short term, GS outperforms phenotypic selection after a program transitions to GS, but that as variation from novel mutation becomes more important, the performance of GS falls below that of phenotypic selection.