The Beavis Group

Marker Assisted Breeding (MAB) is now routinely applied to simple and oligogenic traits by commercial plant breeders working on corn and soybean. For these types of traits MAB has been shown to significantly increase genetic gain per unit time ( ΔGt) by enabling effective selection in winter nurseries. To be of use for crop improvement for complex and quantitative traits, MAB must improve the accuracy of decisions in a breeding program. Unfortunately, the amount of marker information that can be generated for multiple complex traits, whether from DNA, RNA, protein or metabolite biomarkers, is so large that it can overwhelm breeder’s ability to process it. Thus, there is a need for Decision Support Systems (DSS) consisting of methods to a) identify functional alleles, b) predict their breeding value and c) to explore multiple breeding strategies for optimal genetic improvement. The technical goals of this program are to develop and evaluate such DSS.

While MAB has been shown to increase ΔGt, it may also limit the genetic potential of a breeding population through unintended elimination of desirable alleles. Of course, MAB is not the only process that can limit the genetic potential of a breeding population. The processes of domestication, adaptation, founding and phenotypic selection can likewise limit genetic potential through unintended elimination of desirable alleles. Indeed, the success of any future genetic improvements to crops, whether through MAB or traditional breeding, will depend on the history of the breeding population. Despite these theoretical considerations of limits to genetic potential we do not know: 1) whether desirable allelic variants have been eliminated during adaptation, founding and phenotypic selection, 2) how to distinguish desirable and undesirable allelic variants in unadapted germplasm and 3) if desirable alleles can be identified, how do we recover them without sacrificing genetic gains made by previous generations of breeders? The scientific goals of this project are to address these questions through the use of genomics, bioinformatics, population genetics, quantitative genetics and simulation modeling.

The impact of combining the technical goals of MAB with the scientific goals of understanding the genomic signatures of breeding populations with limited genetic potential will assure that the full genetic potential of domesticated crop species can be realized using the most effective genetic resources and efficient breeding methods.