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. / Quantitative traits are those traits that can display a wide range of variability within a population of individuals. These traits are influenced by the interaction of many different genes, and are also influenced by the environment to varying degrees. Traditionally, geneticists who studied quantitative traits had to rely on statistical models, while the biological causes of variation in the expression of these traits remained largely unknown. However, the advent of DNA marker technology granted geneticists the ability to identify specific regions of the genome that highly influence quantitative traits. Many studies have since attempted to find these <i>quantitative trait loci</i> (QTLs) across a wide range of traits and species. However, we are faced with something of a paradox when we attempt to find QTLs. Theory tells us that an idealized, truly quantitative trait arises due to the effects of many genes, each with an infinitesimal effect on the trait in question. Therefore, the more quantitative a trait, the fewer QTLs we should expect to find. In addition, QTLs may not be reliable, due to the effects of different environments and different genetic backgrounds within a population. A more recent trend involves using all available marker data simultaneously to predict a particular line’s performance. This method entails ignoring the genomic underpinnings of a trait, and instead focusing solely on its expression, much like classical quantitative genetics. The obvious downside of this method is that it cannot be used to increase our understanding of what is giving rise to the variations in the trait’s expression that we observe. The studies described in this dissertation were designed to 1) test whether we could identify QTLs for highly quantitative yield-related traits in winter wheat, 2) test the reliability of identified QTLs, and 3) use the DNA marker data to instead generate predictions of line performance. The results indicate that while we can identify QTLs for highly quantitative traits in winter wheat, these QTLs may not be very reliable. Therefore, predictive models may be a good alternative to identifying QTLs, and these methods can be readily implemented within breeding programs.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/85503 |
| Date | 02 May 2017 |
| Creators | Ward, Brian Phillip |
| Contributors | Crop and Soil Environmental Sciences, Griffey, Carl A., Thomason, Wade E., Saghai-Maroof, Mohammad A., Holliday, Jason A. |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Detected Language | English |
| Type | Dissertation |
| Format | ETD, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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