Selection and Characterization of Previously Plant-Variety-Protected Commercial Maize Inbreds
Abstract
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 first chapter, 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 chapter, I used a multi-variable linear regression approach known as genome-wide association (GWA) analysis to identify particular genetic locations, known as quantitative trait loci (QTL), that are associated with maize inflorescence traits.
Degree
Ph.D.
Advisors
Rocheford, Purdue University.
Subject Area
Agriculture|Agronomy|Genetics|Plant sciences
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