Our current research focuses on four areas: genomewide selection, breeding schemes, targeted recombination, and sweet corn.
Genomewide selection (or genomic selection) exploits the availability of cheap and abundant markers. Phenotypic and single nucleotide polymorphism (SNP) marker data in a training population are used to develop a statistical model to predict performance for traits such as grain yield, moisture, and lodging. The accuracy of the genomewide predictions is assessed from a validation population, which has been phenotyped and genotyped.
If the predictions are sufficiently accurate, the statistical model is used to predict the performance of candidates in a test population, which has been genotyped but not yet phenotyped. We have found that wide-scale genomewide selection for hybrid yield among individual F2 plants leads to about 85% of the gains eventually achieved via phenotypic selection, but at less than 25% of the cost.