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The use of genotypic markers in plant breeding has greatly increased in the last few decades. In this dissertation, I report on three topics that illustrate how genotypic marker information can be applied in maize breeding to increase genetic gain. In the fi rst chapter1, I describe how genotypic and phenotypic data can be used to predict the mean, variance, and superior progeny mean of virtual biparental populations. I use these predictions to identify optimal breeding crosses out of a commercially relevant collection of North American dent inbreds. In the second chapter, within the context of early generation maize inbred development, and using a hybrid testcross data set, I report on the change in genomic prediction accuracy as the size of the training set increases and compare the accuracy of different genomic selection models. In the third chapter2, I used a multi-variable linear regression approach known as genomewide association (GWA) analysis to identify particular genetic locations, known as quantitative trait loci (QTL), that are associated with maize in orescence traits. |
