Browsing by Subject "Genomic selection"

Now showing 1 - 7 of 7
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    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 Hussein
    Prediction 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.
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    Combating Fusarium Head Blight Resistance in Wheat with Genomic Selection and Computer Vision Technology
    (2022-01) Adeyemo, Emmanuel
    Fusarium head blight (FHB), primarily caused by Fusarium graminarum, Schwabe, is a devastating fungal disease that limits wheat production globally and can significantly reduce yield and grain quality. At the University of Minnesota, screening for FHB begins at the F5 stage and continues annually until the line is released as a cultivar. Before implementing genomic selection at the F5 stage in 2016, we evaluated ~ 3,000 F5 lines annually in field nurseries. The use of genomic selection allowed the prediction of untested lines with a training population of 500 lines selected by pedigree information. The first study showed that a set of 200 lines selected by genomic relationship led to predictive abilities of up to 0.49, whereas a larger, randomly selected subset of 500 F5 lines had a maximum predictive ability of 0.34. While the addition of parents also led to increased predictive abilities, the increments were not significant in most cases. The second study examined the merit of incorporating available germplasm into our existing genomic selection pipeline. We observed that training populations that contained historical data were less useful while those that included a subset of 200 F5 lines selected by genomic relationship, were more effective for predicting FHB. The third study demonstrated the use of computer vision to estimate the percentage of kernels damaged by Fusarium. We utilized 85 samples containing five check cultivars with varying levels of FHB susceptibility and maturity and achieved an accuracy of 90%. Additional studies should be done to assess the utilization of this technology among our experimental lines.
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    Genetic variance, transgressive segregation, and genomic selection prediction accuracy for Fusarium head blight resistance in advanced multi-parent barley breeding populations
    (2013-04) Kumar, Leticia M.
    The contemporary era of molecular breeding includes predicting breeding values based on allelic value estimations with genome-wide markers. The overarching objective of this thesis is to assess the potential use of genomic markers in predicting genetic variance, transgressive segregation, and breeding values within barley breeding populations in the context of Fusarium head blight (FHB) resistance. Chapter One investigates prediction of genetic variance and transgressive segregation using measures of phenotypic and genotypic parental dissimilarity. To a limited extent, phenotypic dissimilarity could predict transgressive segregation and genetic variance while genetic dissimilarity using a subset of FHB-associated single nucleotide polymorphism markers could predict genetic variance in both populations. Homogeneity of genomic selection prediction accuracy among families for FHB severity and deoxynivalenol concentration was examined in Chapter Two. Accuracy between predicted and observed values for both traits varied among families. Potential factors for limited ability to predict individual family performance are discussed.
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    Genome wide association mapping and genomic selection for agronomic and disease traits in soybean
    (2014-09) Bao, Yong
    Genome-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.
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    Genomewide prediction of genotypic values and genetic variances within 969 maize biparental populations
    (2014-09) Lian, Lian
    In plant breeding, selecting within biparental crosses and selecting parents to make new crosses are both important. My first study investigated the accuracy of genomewide selection (rMG) within 969 biparental maize populations (Zea mays L.). My objectives were to determine: (i) the mean and variability of rMG, (ii) if rMG can be predicted, and (iii) how training population size (N), heritability (h2), and number of markers (NM) affect rMG. I modified an equation for expected rMG [E(rMG)] to account for linkage disequilibrium (r2) between markers and quantitative trait loci. Across the 969 populations, the mean and range (in parentheses) of observed rMG was 0.45 (&minus0.59, 1.03) for yield, 0.59 (&minus0.34, 0.96) for moisture, and 0.55 (&minus0.24, 1.10) for test weight. The observed rMGvalues were centered around E(rMG) when r2 was accounted for, but had a large spread around E(rMG). The r2(Nh2)&half had the strongest association with the observed rMG. In the second study, my objective was to determine whether related populations could be used to predict the genetic variance (VG) of a segregating population from two parents (A and B). For each of 85 A/B populations, 2&ndash23 A/* and B/* populations were used as training populations, where * denotes a random parent. In the genomewide selection model, the testcross VG in A/B was predicted as the variance among the predicted genotypic values of progeny from the simulated A/B population. In the mean variance model, VG was estimated as the mean of VG in A/* and B/* populations. The correlations between observed and predicted VG were not significant (P = 0.05) for the genomewide selection model but were significant for the mean variance model (0.26 for yield, 0.46 for moisture, and 0.50 for test weight). The VG of A/B population could therefore be predicted as the mean of VG in A/* and B/* populations. Overall, the results indicated that genomewide selection can identify the best individuals within a cross, but it cannot reliably predict which parents would lead to the largest genetic variance.
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    Genomewide selection: prediction accuracy, marker Imputation, and introgression of semidwarf corn germplasm
    (2012-12) Combs, Emily Elizabeth
    I present here three studies on genomewide selection, a marker based selection procedure with the potential to accelerate genetic gain while decreasing costs. For the first study, I looked at factors that have been previously derived by other researchers as determining the accuracy of genomewide selection: training population size (N), trait heritability (h2), and effective number of loci or chromosome segments underlying the trait (Me). My objective was to determine if prediction accuracy is equal across traits if N, h2 and marker number (NM) are kept constant. Cross validations indicated that the traits predicted most accurately did not always stay the same across changes to h2, N, and NM. For the second study, I investigated the use of marker imputation to reduce costs by genotyping the training population with many markers (MTotal), genotyping the validation population with fewer markers (MLow), and predicting the genotypes at the MTotal – MLow markers in the validation population. My objective was to determine if genomewide prediction with imputed markers can be as accurate as genomewide prediction with non-imputed markers in inbred collections. With imputation, many combinations of MTotal and MLow led to prediction accuracy that was as high as the accuracy with MTotal non-imputed markers. For the third study, I used a semidwarf corn (Zea mays L.) line that could potentially be grown in new areas of production or in alternative crop rotations. My objectives were to determine: (i) if genomewide selection is useful for the rapid improvement of an adapted × exotic cross; and (ii) if 4 cycles of genomewide selection are more effective than phenotypic backcrossing to the BC4 for a trait with major genes. Genomewide selection from Cycle 1 until Cycle 5 either maintained or improved upon the gains from phenotypic selection achieved in Cycle 1. Compared with phenotypic backcrossing, genomewide selection led to better mean performance and a higher proportion of exotic germplasm introgressed. To my knowledge, this is the first empirical study on genomewide selection to improve an exotic × adapted cross.
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    Implementing association mapping and genomic selection using germplasm from Midwest barley breeding programs
    (2014-01) Vikram, Vikas
    Given the stringent requirements set by the malting and brewing industries and the complex nature of traits, marker based breeding will become increasingly important for the improvement of malting barley (Hordeum vulgare ssp. vulgare). Identification and exploitation of useful and novel alleles at quantitative trait loci (QTL) is crucial to improve genetic gains. We identified marker trait associations and assessed the prediction accuracy of genomic selection for agronomic and disease traits using a collection of 768 breeding lines from two closely related breeding programs in the Upper Midwest. Three hundred progeny lines derived from crosses among 14 parents from the two programs were used as a validation mapping panel in association mapping and to assess genomic prediction accuracy. In general, we found that different sets of QTL were segregating in the two breeding programs. The difference in QTL detected could be due to different genes segregating in the two programs, but could also be affected by differences in marker allele frequencies and linkage disequilibrium between adjacent markers. The genomic prediction accuracies of progeny for six traits were moderate to moderately high, indicating that genomic selection could be successfully implemented for agronomic, disease and quality traits with a range of heritabilities. Our results also indicated that the prediction accuracies were better when training and prediction panels were closely related. The information gained from this study will be valuable to design sound and cost-effective breeding strategies for malting barley whose genetic base has become narrower over time.