Using modern genomic technologies to improve Montana spring wheat

Abstract

Since the development of next generation sequencing (NGS) technology, there has been an explosion in the diversity and availability of large-scale genetic data. In response, the field of plant breeding has shifted to adopt these new technologies and incorporate them into breeding programs. Specifically, the development of high-density genetic maps and crop-specific reference genomes has led to an increased capacity to obtain genome-wide marker information on many individuals. Two examples of the application of these resources are fine mapping approaches for gene discovery and assembling genomic selection models for earlier generation trait improvement. This dissertation seeks to apply both of these procedures to the Montana State University Spring Wheat Breeding Program for a multi-faceted approach to accelerate germplasm improvement. In Chapter 1, fine mapping was applied to a quantitative trait locus controlling productive tiller number, a major yield component in wheat which also contributes to phenotypic plasticity. In Chapters 2 and 3, genomic selection models were developed to predict twelve end-use quality traits (Chapter 2) and wheat stem sawfly resistance (Chapter 3). Together, these projects laid the foundation for a comprehensive adoption of NGS-based plant breeding methods into the MSU Spring Wheat Breeding Program. Adopting these methods will allow for increased genetic control and improved resource efficiency for the improvement of primary target traits, ensuring the development of the best possible wheat varieties for Montana growers.