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dc.contributor.advisorGarland-Campbell, Kimberly
dc.creatorWalker, Carl Alan
dc.date.accessioned2012-10-08T22:29:12Z
dc.date.available2012-10-08T22:29:12Z
dc.date.issued2012
dc.identifier.urihttp://hdl.handle.net/2376/4088
dc.descriptionThesis (Ph.D.), Crop Science, Washington State Universityen_US
dc.description.abstractStatistical analysis has many applications ensuring the validity and reproducibility of plant breeding and genetics research. Crop plant germplasm collections are often too large to be of use regularly. A core subset with fewer accessions can increase utility while maintaining most of the genetic diversity of the complete collection. This study evaluated methods for selecting core subsets using sparse data. Cores were selected by forming clusters of accessions based on distances estimated with phenotypic data. Accessions were randomly selected relative to the number of accessions in each cluster. The method using all the available data to calculate distances, average linkage clustering, and sampling in proportion to the natural logarithm of cluster size produced the most diverse cores.Evaluations of genotypes in varied environmental conditions are referred to as multiple environment trials (MET) and often necessitate estimation of effects of genotypes within environments. Empirical best linear unbiased predictions can provide more accurate estimates of these effects, depending upon the mixed model used. An objective of this work was to simulate and analyze MET data sets to determine which models provide the most accurate estimates in varied MET conditions. Simulated MET were fit with mixed models with or without genetic relationship matrices (GRM) and with structures of varying complexity used to model relationships among environments. The model that included a GRM and a constant variance-constant correlation structure was the most accurate for the largest number of scenarios. More complex models were the most effective for a smaller subset of scenarios, most involving many genotypes and low experimental error.Statistical analyses were applied in consultation with other researchers for two projects studying Fusarium crown rot of wheat and one on cold tolerance of wheat. Heritability and genetic correlations were calculated for Fusarium resistance assays in field, growth chamber, and terrace bed settings. Factor analysis was used to estimate latent factors from field characteristic variables, which were used as predictor variables in linear mixed models and generalized linear mixed models. Cold tolerance among genotypes was assessed with logistic regression.en_US
dc.description.sponsorshipDepartment of Crop and Soil Sciences, Washington State Universityen_US
dc.language.isoEnglish
dc.rightsIn copyright
dc.rightsPublicly accessible
dc.rightsopenAccess
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/
dc.rights.urihttp://www.ndltd.org/standards/metadata
dc.rights.urihttp://purl.org/eprint/accessRights/OpenAccess
dc.subjectStatisticsen_US
dc.subjectAgronomyen_US
dc.subjectAgricultureen_US
dc.subjectCore Collectionsen_US
dc.subjectFactor Analytic Modelsen_US
dc.subjectMixed Modelsen_US
dc.subjectMultiple Environment Trialsen_US
dc.subjectPlant Breedingen_US
dc.titleStatistical Applications in Plant Breeding and Genetics
dc.typeElectronic Thesis or Dissertation


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