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dc.contributor.authorWard, Brian Phillipen_US
dc.date.accessioned2018-10-25T06:00:49Z
dc.date.available2018-10-25T06:00:49Z
dc.date.issued2017-05-02
dc.identifier.othervt_gsexam:11099en_US
dc.identifier.urihttp://hdl.handle.net/10919/85503
dc.description.abstractIn 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.en_US
dc.format.mediumETDen_US
dc.publisherVirginia Techen_US
dc.rightsThis item is protected by copyright and/or related rights. Some uses of this item may be deemed fair and permitted by law even without permission from the rights holder(s), or the rights holder(s) may have licensed the work for use under certain conditions. For other uses you need to obtain permission from the rights holder(s).en_US
dc.subjectwheaten_US
dc.subjectTriticum aestivumen_US
dc.subjectgenomic predictionen_US
dc.subjectgenomic selectionen_US
dc.subjectquantitative trait locusen_US
dc.subjectgenome-wide association studyen_US
dc.subjectyielden_US
dc.subjectgrain yielden_US
dc.titleGenomic Prediction and Genetic Dissection of Yield-Related Traits in Soft Red Winter Wheaten_US
dc.typeDissertationen_US
dc.contributor.departmentCrop and Soil Environmental Sciencesen_US
dc.description.degreePh. D.en_US
thesis.degree.namePh. D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen_US
thesis.degree.disciplineCrop and Soil Environmental Sciencesen_US
dc.contributor.committeechairGriffey, Carl A.en_US
dc.contributor.committeememberThomason, Wade Everetten_US
dc.contributor.committeememberSaghai-Maroof, Mohammad A.en_US
dc.contributor.committeememberHolliday, Jason A.en_US


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