This talk presents quantitative genetic approaches that make it possible to extract knowledge on the genetic value of individual plants or animals from high-dimensional genomic data and to predict the genetic makeup of individual offspring. Statistical methods for prediction of genetic values and phenotypes from genome-wide molecular marker data will be introduced, and challenges arising from the large number of predictors and their high degree of collinearity will be addressed.
The efficiency of genome-enabled prediction will be demonstrated with experimental studies on grain yield and insect resistance in maize (Zea mays L.). Estimates of prediction accuracies achieved in these studies are encouraging with respect to the usefulness of genome-enabled prediction in practical breeding programs. On this basis, optimum scenarios for exploiting knowledge from high-dimensional molecular data in breeding schemes will be discussed.