| Summary: | In multiple species, genome-wide association (GWA) studies have become an increasingly
prevalent method of identifying the quantitative trait loci (QTLs) that underlie complex traits.
Despite this, relatively few GWA analyses using high-density genomic markers have been
carried out on highly quantitative traits in wheat. We utilized single-nucleotide polymorphism
(SNP) data generated via a genotyping-by-sequencing (GBS) protocol to perform GWA on
multiple yield-related traits using a panel of 329 soft red winter wheat genotypes grown in four
environments. In addition, the SNP data was used to examine linkage disequilibrium and
population structure within the testing panel. The results indicated that an alien translocation
from the species Triticum timopheevii was responsible for the majority of observed population
structure. In addition, a total of 50 significant marker-trait associations were identified. However,
a subsequent study cast some doubt upon the reproducibility and reliability of plant QTLs
identified via GWA analyses. We used two highly-related panels of different genotypes grown in
different sets of environments to attempt to identify highly stable QTLs. No QTLs were shared
across panels for any trait, suggesting that QTL-by-environment and QTL-by-genetic
background interaction effects are significant, even when testing across many environments. In
light of the challenges involved in QTL mapping, prediction of phenotypes using whole-genome
marker data is an attractive alternative. However, many evaluations of genomic prediction in
crop species have utilized univariate models adapted from animal breeding. These models cannot
directly account for genotype-by-environment interaction, and hence are often not suitable for
use with lower-heritability traits assessed in multiple environments. We sought to test genomic
prediction models capable of more ad-hoc analyses, utilizing highly unbalanced experimental
designs consisting of individuals with varying degrees of relatedness. The results suggest that
these designs can successfully be used to generate reasonably accurate phenotypic predictions. In addition, multivariate models can dramatically increase predictive accuracy for some traits,
though this depends upon the quantity and characteristics of genotype-by-environment
interaction. === Ph. D.
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