Download or read book Leveraging Genetic Diversity in Maize Breeding written by Alden Perkins and published by . This book was released on 2024 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Maize (Zea mays L.) cultivars grown in the United States are not closely related to cultivars found in many other parts of the world, and pedigree records suggest that there has been little use of germplasm from other countries in US maize breeding. Exotic germplasm could be a useful source of alleles for improving traits such as grain quality, disease resistance, and abiotic stress tolerance in US populations. Exotic cultivars may also contain alleles that confer adaptation to different climate conditions and management practices, however, which could be detrimental in US growing environments. This dissertation contains a review article about the importance of genetic diversity in breeding and three research projects that involve the incorporation of exotic diversity into US maize germplasm. In the first research project, we investigated the effects of introgressions from open-pollinated populations that originated in Latin America on the performance of maize hybrids in US environments. We compared the phenotypic stability of exotic-derived hybrids and US-adapted hybrids, and we found that the two groups had significantly different stability values for grain yield and flowering time. In the second project, we estimated the effects of exotic introgressions contained in maize populations created by a public-private partnership on grain protein content, kernel vitreousness, and other agronomic traits. One introgression was estimated to increase grain protein content by 0.78%, although it was also associated with detrimental changes in the time of flowering and plant height. Finally, we compared models that could be used to predict the phenotypes of untested hybrids in diverse populations, which could make the process of incorporating new diversity into US germplasm more efficient. We found that models using near-infrared absorbance data collected on inbred seeds as predictors were inferior to models based on genotypic data in most cases, although the difference between the methods was small for traits such as grain moisture and test weight. An alternative to the standard genomic best linear unbiased prediction (GBLUP) method that included additional regularization, called the sparse selection index method, was superior to GBLUP models when large training populations were available. Collectively, the results from these projects provide new insights into how exotic germplasm can be used in US maize breeding most effectively.