You are here
Project abstract for group bernardo
Genomewide Prediction of Maize Performance
The availability of cheap and abundant DNA markers in plants has allowed the development of methods to predict the performance of maize lines and hybrids on the basis of DNA fingerprint data. In particular, genomewide prediction entails the development of prediction equations from a training data set of maize lines or hybrids that have been genotyped and evaluated for field performance (i.e., phenotyped). The prediction models are then used to assess the performance of new candidates that have been genotyped but not phenotyped. In 2011, this research group gained access to genotypic and phenotypic data, worth millions of dollars, from a maize breeding company. Such data are allowing them to empirically test different genomewide-prediction models and breeding schemes. Resources at MSI are vital for accomplishing this research goal. Results to date indicate that while genomewide predictions can be routinely used in maize, the accuracy of prediction is variable and one cannot determine in advance whether the predictions will be accurate or not.