Reinforcing the hexaploid wheat breeding pipeline with genomic prediction of quantitative traits

Thesis (Ph.D.), Molecular Plant Sciences, Washington State UniversityNatural, artificial, and genomic selection are the driving forces of quantitative trait improvement in plants. Since the domestication of wheat over 10,000 years ago, steady gains in yield potential have resulted in a crop that provides nearly one-fifth of the human population’s caloric intake. Thus, maintaining its genetic integrity is a priority for ensuring global food security. But volatile climatic trends and rapidly evolving plant pathogens threaten sustainable farming systems, warranting resource allocation to the research and development of improved cultivars. Here we describe a breeder’s manual for the discovery, characterization, and practical introgression of alleles that confer resistance to wheat rusts (Puccinia spp.). In addition to being the most economically damaging pathogens of wheat, they are an excellent model for studying quantitative genetics and molecular plant-pathogen interactions. Recent advances in molecular genetics and high-performance computing have provided access to a massive toolbox of breeding methods that can help solve complex agricultural problems. The following chapters attempt to describe the most efficient ways of translating these technical resources into tangible outcomes.Washington State University, Molecular Plant Science