Browsing by Subject "Plant breeding"

Now showing 1 - 4 of 4
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    Elucidation of the extended pedigree of ‘Honeycrisp’ apple and genetic architecture of its susceptibility to soft scald and soggy breakdown postharvest fruit disorders and zonal leaf chlorosis disorder
    (2017-06) Howard, Nicholas
    The apple (Malus × domestica) cultivar Honeycrisp has become important economically and as a breeding parent due to its ultra-crisp fruit texture, its ability to retain this high level of fruit crispness in storage, and its resistance to apple scab. However, ‘Honeycrisp’ has several detrimental traits that have not been satisfactorily evaluated genetically. Additionally, the original pedigree records for ‘Honeycrisp’ were previously determined as incorrect and this lack of pedigree information has impeded thorough genetic analyses in studies involving ‘Honeycrisp’. The objectives of the research in my dissertation were to identify and genetically describe the parents and grandparents of ‘Honeycrisp’ and to use this new pedigree information in pedigree-based analyses to examine the genetic architecture of its susceptibility to fruit soft scald and soggy breakdown postharvest fruit disorders and zonal leaf chlorosis disorder. Towards these objectives, a high quality genetic map was created using single nucleotide polymorphism data from the apple 8K Illumina Infinium® SNP array and five large families with ‘Honeycrisp’ as a common parent. ‘Keepsake’ was verified as one parent of ‘Honeycrisp’ and ‘Duchess of Oldenburg’ and ‘Golden Delicious’ were identified as grandparents through a previously unknown parent that was identified to be the University of Minnesota selection MN1627. Two quantitative trait loci (QTL) were consistently identified on linkage groups (LGs) 2 and 16 for both soft scald and soggy breakdown. ‘Honeycrisp’ is homozygous for an identical by state haplotype identified at the LG2 QTL that was consistently associated with increased incidences of soft scald and soggy breakdown. ‘Honeycrisp’ inherited the deleterious haplotypes at the LG2 QTL from grandparent ‘Keepsake’ and great-grandparent ‘Grimes Golden’. A large effect QTL for zonal leaf chlorosis was identified on LG9 and a recombinant haplotype that ‘Honeycrisp’ inherited from ‘Duchess of Oldenburg’ at this QTL was associated with increased ZLC in offspring of ‘Honeycrisp’. The LG9 QTL was located approximately between 5 and 10 cM away from a major QTL for skin over color. ‘Honeycrisp’ is heterozygous for skin over color at this location. In ‘Honeycrisp’, the haplotype associated with increased zonal leaf chlorosis at the LG9 QTL is in coupling phase with the haplotype associated with red color at the LG9 skin over color QTL. All of these major QTL were consistently identified across all years of analysis. These new discoveries will be useful in apple breeding efforts involving ‘Honeycrisp’, its ancestors, and its progeny.
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    Genetic analysis to improve drought and low nitrogen tolerance of corn in monoculture and in a kura clover intercropping system
    (2012-12) Ziyomo, Cathrine
    Direct selection for grain yield under stress conditions is often inefficient because the heritability for grain yield is greatly reduced under stress. The objectives of the first study described in this thesis were to determine the efficiency of indirect selection for corn (Zea mays L.) grain yield under drought and low N conditions using secondary traits or molecular markers. Testcrosses of 238 intermated B73 x Mo17 recombinant inbreds were evaluated under drought and low N stress conditions. Results indicated that direct selection for grain yield in the targeted stress environment is more efficient than using secondary traits under both drought stress and low N stress. Using significant markers only was not more efficient than direct phenotypic selection for grain yield. The relative efficiency of genomewide selection was significantly greater than 1.0 for grain yield under drought stress but not for grain yield under low N stress. The results suggest that selection based on molecular markers is more efficient than phenotypic selection alone for the improvement of grain yield under drought stress, while for grain yield under low N stress, selection based on markers alone can only be more efficient if gains per unit time and cost are considered. In addition to the genetic improvement of corn for stress tolerance, the use of drought tolerant corn in a kura clover (Trifolium ambiguum M. Bieb.) intercropping system can reduce the competition for moisture between the grain crop and the cover crop. The objectives of the second study were to determine if drought tolerant corn can minimize the yield losses incurred when corn is intercropped with kura clover. Results indicate that drought-tolerant corn can maintain high yields and allow sufficient regrowth of kura clover and therefore significantly reduce the risk associated with intercropping corn with living mulch.
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    The oat-crown rust pathosystem: an interaction of a plant, a pathogen, and time
    (2020-12) McNish, Ian
    Plant diseases are often described as the interaction of a plant, a pathogen, and the environment. For a disease to develop, there must be a susceptible plant, a virulent pathogen, and an environment amenable to disease. This concept is useful to explain the presence or absence of a disease, but many important questions and ideas in plant pathology, plant genomics, plant phenomics, and plant cultivar development are also dependent on time. A pathogen population changes over time, by a process of selection, to defeat the resistances deployed in crop cultivars. The genetic architecture of disease resistance changes as a plant grows from a seedling to an adult plant, matures, and dies. The visual and spectral signature of plant stress and disease also changes as the plant grows and the disease develops. Finally, plant breeders attempt to limit the damage diseases cause by quickly improving plant populations and deploying disease resistant plant cultivars. The dimension of time has been well-explored in some areas of plant science such as gene expression, but time is often overlooked in plant breeding, quantitative genetics, and phenomics. Crown rust, caused by the fungal pathogen Puccinia coronata f. sp. avenae Erikss. (Pca), is a dynamic and devastating disease of cultivated oat (Avena sativa L.). In this research, I found that the North American Pca population has gained many virulences over the past thirty years and that the Pca isolates collected in recent years are capable of defeating a surprisingly high number of crown rust resistance genes. I found that the genetic architecture of crown rust resistance changed throughout the growing season. Many resistance loci were detected briefly, sometimes just for a couple of days, and few loci were detected at many points in time. I found that the spectral signature of disease and plant stress changed throughout the season and that the predictive value of the collected data was greatest for adult plants before senescence. Finally, I found that quantitative resistance to crown rust could be rapidly improved in an oat population, but the race-specificity of that resistance was difficult to determine. If plant breeders understand how time influences the composition of pathogen populations, the observations they make, the analyses they perform, and the technologies they develop, then they will be more capable of improving complex plant traits like disease resistance.
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    Reducing And Exploiting Genotype By Environment Interaction In The Context Of Genomewide Prediction In 969 Maize Biparental Populations
    (2018-03) Ames, Nicholas
    Multi-environment testing remains crucial in genomewide selection, and environmental effects (Ej) complicate selection. We aimed to: 1) determine if past year’s data on previous populations can be used to eliminate environments for a current training population; 2) assess if genomewide predictions can reduce the number of environments used in subsequent phenotypic selection; 3) identify which statistical models and environmental factors are best for estimating Ej; and 4) determine the predictive ability in models that include and exclude genotype × environment interaction effects. A total of 969 Monsanto maize (Zea mays L.) populations were genotyped and phenotyped at multiple U.S. locations from 2000 to 2008. Environmental data from the National Oceanic and Atmospheric Administration were gathered and interpolated. The data included 154,000 lines, 448 million marker data points, 3.2 million phenotypic observations, 1395 unique environments, and 1.3 million environmental covariable data points. For 27 biparental crosses that we chose as test populations, environmental stability and an index that used genomewide predictions and phenotypic data could replace one out of four environments in phenotypic evaluation. Correlations between predicted and observed Ej were between 0.25 and 0.35 even when only two environmental factors (precipitation and heat units) were used. A nonfactorial model for line performance in a given environment effectively combined both the line genetic effect and Ej, doubling prediction ability for grain yield and test weight. We speculate that this model can be combined with crop modelling for additional prediction ability in predicting plant performance in a given environment.