Tiede, Tyler2016-08-192016-08-192016-04https://hdl.handle.net/11299/181675University of Minnesota Ph.D. dissertation. April 2016. Major: Applied Plant Sciences. Advisor: Kevin Smith. 1 computer file (PDF); iv, 106 pages.The genetic improvement of plants for human use is the primary goal of plant breeding. Through repeated processes of population development and selection, breeders have produced highly productive plants that are adapted to a vast array of environments and cultivation practices. The challenges that drove plant breeders in the past, such as increasing production, novel or increasingly prevalent abiotic and biotic stresses, and evolving end-user demands persist today and are compounded with unprecedented population growth. Genomic selection (GS), a genomewide marker-based selection method, has been shown to be an efficient and effective breeding tool. Its general applicability to plant breeding and principles guiding its use have been established by simulation and empirical cross-validation studies. More recently, studies have demonstrated genetic gains over multiple cycles of selection in a variety of crop species. In the first chapter we provide additional evidence for the effectiveness of GS in an actual breeding program by demonstrating significant gains of 164.74 kg ha-1 and -1.41 ppm for grain yield and DON, respectively, two unfavorably correlated quantitative traits, across three cycles of selection in a spring six-row barley breeding population. With its general effectiveness established, the next step is to increase the accuracy of GS and thereby increase genetic gains. For this, we first showed that updating the training population (TP) with phenotyped lines from recent breeding cycles, specifically selected lines, had an overall positive effect on prediction accuracy. Additionally, we investigated four recently-proposed algorithms that seek to optimize the composition of a TP. Overall the optimization algorithms improved prediction accuracy when compared to a randomly selected TP subset of the same size, but which algorithm performed best was dependent on the trait being predicted and other factors discussed within. This retrospective investigation highlighted the importance of maintaining and optimizing the TP when using GS in real breeding situations to maximize prediction accuracy, thereby maximizing gain from selection and resource utilization. Furthermore, genetic gains depend on genetic variation. Exotic germplasm can be exploited to introduce genetic variability into elite breeding populations to drive genetic gains and address new or changing breeding targets. Wide crosses between elite and exotic germplasm have been widely used to identify large-effect QTL and breed for improved disease or insect resistance. The utility of exotic germplasm to improve quantitative traits, which includes many important agronomic traits, has not been tested as widely. In the second chapter we select parents from an advanced backcross population constructed from 25 wild barley (Hordeum vulgare L. spp spontaneum) accessions crossed to the common high-yielding malting barley cultivar, Rasmusson. We extended the genomic selection framework to identify parent combinations that, with ideal recombination, should produce progeny with large numbers of exotic introgressions with favorable effects. We compared the marker-based crossing strategy to the traditional methods of crossing the topmost performing parents or the most genetically diverse parents. After one round of marker-based progeny selection from these crosses we identified breeding lines, harboring exotic introgressions, which consistently yielded higher than Rasmusson across five trial locations. While none of these lines were statistically better than Rasmusson, there is compelling evidence that the introgression of wild alleles contributed to increased yield. The three parent selection strategies were not significantly different for their ability to identify superior progeny.enbarleygenomewidegenomic selectionplant breedingThesis or Dissertation