Browsing by Subject "Association mapping"
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Item Assessing genomic selection prediction accuracy in a dynamic barley breeding population and comparing gain between genomic and phenotypic selection in barley(2014-11) Sallam, Ahmad HusseinPrediction accuracy of genomic selection has been previously evaluated through simulation and cross-validation; however validation based on progeny performance in a plant breeding program has not been investigated thoroughly. We evaluated several prediction models in a dynamic barley breeding population comprised of 647 six-row lines using four traits differing in genetic architecture and 1,536 SNP markers. The breeding lines were divided into six sets designated as one parent set and five consecutive progeny sets comprised of representative samples of breeding lines over a five-year period. We used these data sets to investigate the effect of model and training population composition on prediction accuracy over time. We found little difference in prediction accuracy among the models confirming prior studies that found the simplest model, RR-BLUP, to be accurate across a range of situations. In general, we found that using the parent set was sufficient to predict progeny sets with little to no gain in accuracy from generating larger training populations by combining the parent set with subsequent progeny sets. The prediction accuracy ranged from 0.03 to 0.99 across the four traits and five progeny sets. We explored characteristics of the training and validation populations (marker allele frequency, population structure, and linkage disequilibrium) as well as characteristics of the trait (genetic architecture and heritability). Fixation of markers associated with a trait over time was most clearly associated with reduced prediction accuracy for the mycotoxin trait DON. Higher trait heritability in the training population and simpler trait architecture were associated with greater prediction accuracy.Genomic selection is a marker based selection method that promises to improve and accelerate the breeding process in plants and animals. Numerous studies have investigated the gain per unit time; however very limited studies have directly compared gains from genomic and phenotypic selection using empirical data. In this study, we used five consecutive sets of breeding lines to compare the gain between genomic and phenotypic selection. In each set, about ninety six barley breeding lines were phenotypically evaluated for yield, FHB resistance, and DON accumulation. A set 168 historic parental lines were used as a training population to predict the performance of the selection candidate sets using RR-BLUP. All lines were genotyped using 1,536 SNP markers (BOPA1) for all seven barley chromosomes. The best performing 10% of the breeding lines in each year were selected using the two schemes and revaluated together in several trials in Minnesota and North Dakota. We made direct comparison between genomic and phenotypic selection in two selection candidate sets for yield and five sets for FHB resistance and DON accumulation. We assessed the relative efficiency of genomic over phenotypic selection and changes in the genetic similarity using the two selection schemes. Results showed similar response to selection between genomic and phenotypic selection in most cases. Genomic selection resulted in more genetic similarity only for FHB resistance; however, for yield and DON concentration no changes in the genetic similarity were detected between genomic and phenotypic selection. In addition, we assessed the use of phenotypic selection for FHB, genomic selection for FHB, and genomic selection for DON as indirect selection methods to select for low DON concentration. We did not find significant differences between direct and indirect selection methods.Item Association Mapping and Genome-Wide Prediction of Fusarium Head Blight Resistance in Minnesota Wheat Lines(2021-12) Conley, EmilyTriticum aestivum L. (common wheat) is one of the top three global staple crops, with both record production and consumption forecasted by the USDA for 2021-22. The University of Minnesota’s Wheat Breeding program has developed wheat cultivars and conducted wheat breeding and genetics research for over a century. Breeding for disease resistance is a major priority. Fusarium head blight (FHB), or scab, is a fungal disease of wheat and other small grain crops causing significant yield and quality reduction. FHB has been a major focus for wheat breeding and research. Quantitative trait locus (QTL) mapping studies have identified hundreds of QTL, while only a handful have been successfully deployed in breeding. The University of Minnesota introduced resistant germplasm from Asia in the late 1980s. Genome-wide association mapping (GWAS) uses high density genetic markers and historic linkage disequilibrium to uncover genetic associations between genotypes and phenotypes. Through GWAS, this study identified QTL maintained in the program over decades of phenotypic and marker-assisted selection. Genome-wide selection (GS), a molecular marker-based method for improving quantitative traits, has shown promise for FHB resistance breeding. This study investigated strategies to implement genome-wide selection for FHB resistance during cultivar development. Genome-wide selection has the potential to reduce time and cost and accelerate the rate of genetic gain.Item Association Mapping for Net Blotch Resistance in Barley and a Study of Barley/Cereal Yellow Dwarf Virus in Minnesota(2017-08) Adhikari, AnilGenome wide association studies (GWAS) were conducted to identify net blotch (net form: caused by Pyrenophora teres f. teres) resistance loci in Ethiopia and Eritrea (EEBC) barley landraces and the elite germplasm of the Barley Coordinated Agricultural Project (Barley CAP). Two quantitative trait loci (chromosomes 5H and 6H) were identified in the EEBC. Resistance QTL were found in chromosomes 4H and 6H in the two-row panel, chromosomes 3H and 6H in the six-row panel and in chromosomes 3H, 4H, 6H and at an unmapped location in the combined Barley CAP germplasm. The distribution of Barley yellow dwarf (BYD), caused by Barley/cereal yellow dwarf virus (B/CYDV) in the Luteovirus family, in Minnesota was studied using 243 cereals and grass samples collected from 2013 to 2015. Reverse transcription polymerase chain reaction using virus strain specific primers revealed that BYDV-PAV was the most prevalent strain of B/CYDV.Item Genome wide association mapping and genomic selection for agronomic and disease traits in soybean(2014-09) Bao, YongGenome-wide association mapping and genomic selection are two emerging genomic approaches for investigating genetic architecture and improving breeding efficiency for complex traits in crop species. The objectives of our study were to: 1) dissect the genetic basis of resistance to soybean cyst nematode (SCN) and sudden death syndrome (SDS) through association mapping (AM) and 2) evaluate genomic selection (GS) as an improved marker-based selection tool for predicting agronomic and disease traits in a public soybean breeding program. For AM, we genotyped 282 common breeding parents from the University of Minnesota soybean breeding program using a genome-wide panel of 1,536 single nucleotide polymorphism (SNP) markers and evaluated plant responses to SCN and SDS in the greenhouse. AM rediscovered reported resistance genes (rhg1 and FGAM1 for SCN resistance; cqSDS001, cqRfs4, and SDS11-2 for SDS resistance) and also identified novel loci. For GS, average prediction accuracy through cross-validation studies was 0.67 for SCN resistance and 0.64 for root lesion severity associated with SDS resistance. We also empirically assessed the prediction accuracy and responses to GS for agronomic traits. Soybean lines in the AM panel were used as a training set and a validation set consisting of 273 breeding lines were selected from the ongoing breeding program. Existing historical trial data were used to train the GS model. GS was then conducted to select the top 20% individuals from the validation set based on a comprehensive consideration including genomic estimated breeding values. Our GS model predicted yield with a significant positive accuracy in only two MN x MN crosses, while the prediction accuracy was near to zero or negative for protein and oil, and for the rest of crosses. Moreover, one generation of GS didn't significantly change the population mean of yield, seed protein and oil content. Overall, our study suggested AM holds promise to be used as an alternative approach for mapping QTL in soybean breeding germplasm, and GS deserves further investigation prior to implementation in genetic improvement in existing soybean breeding programs.Item Molecular diversity, linkage disequilbrium and genetic mapping in East Africa wheat(2013-04) Macharia, Godwin Kamau.Despite recognition as key bread wheat producers in sub-Saharan Africa, for decades Ethiopia and Kenya have relied on imports to meet local consumption needs. The challenges posed by pests and diseases as exemplified by repeated attacks by the Russian Wheat Aphid, and the new highly virulent race TTKSK (Ug99) of stem rust, have been strong yield barriers. To change this trend, it is important that breeding populations that combine desirable alleles for yield, disease, and pest resistance are developed followed by selection, promotion and adoption of superior cultivars. In the first chapter of this thesis, SNP- based characterization of population structure and diversity in a comprehensive panel of 297, mainly East Africa lines are described. Four to seven subpopulations were identified largely consistent with line breeding era. The oldest East Africa germplasm was found to be most diverse based on several summary statistics. Present day germplasm including that in commercial production was associated with relatively high allelic diversity too. Methods used to detect signatures of past selection successfully identified outlier SNPs possibly under positive selection which could have played an important role in increasing the adaptive range of bread wheat in East Africa. Long range linkage disequilibrium consistent with past observations for this species, was detected and is described in chapter two. Over 50 QTL for adult plant resistance (APR) to stem, leaf and stripe rusts are reported from mixed model association mapping. Similarly, APR previously observed in the cultivar Kenya- Nyangumi was dissected leading to the detection of 15 minor QTL across all rusts. These results are presented in chapter three. In chapter four, results for high and low molecular weight glutenin subunit alleles, puroindoline proteins, grain hardness and other bread making quality-determining traits that were characterized amongst 216 Ethiopia and Kenya bread wheat lines are discussed.Item Using near isogenic barley lines to validate Deoxynivalenol (DON) QTL previously identified through association analysis(2013-01) Navara, Stephanie LynnFusarium head blight (FHB) is a serious disease of cereal grains caused by the fungal pathogen Fusarium graminearum. Deoxynivalenol (or DON), the associated trichothecene mycotoxin is of special concern to barley producers and consumers. A recent association mapping (AM) study of U.S. six-row spring barley identified several modest effect quantitative trait loci (QTL) for DON and FHB. To date, few studies have attempted to verify the results of association analyses, particularly for complex traits such as FHB and DON resistance in barley. Despite control measures used to mitigate the effects of population structure and multiple testing in AM, false positives may still occur. To verify previously reported associations we evaluated the effects of nine DON QTL using near isogenic lines (NILs) for each QTL region. Families of contrasting homozygous haplotypes for each region were derived from lines in the original AM populations that were heterozygous for DON QTL. Seventeen NIL families were evaluated for FHB and DON in three field experiments. Significant differences between contrasting NIL haplotypes were detected for three QTL across environments and/or genetic backgrounds, thereby confirming QTL from the original AM study. Several explanations for those QTL that were not confirmed are discussed, including the effect of genetic background and incomplete sampling of relevant haplotypes.