Breeding for β-glucan content in elite North American oat (Avena sativa L.) using molecular markers

This dissertation explored genomewide association study (GWAS) and conducted actual breeding program in oat using different selection methods to identify molecular markers and improve beta-glucan content- a trait with positive health benefits. Results from GWAS suggested that beta-glucan content in elite oat is controlled by many QTL with small effects. Some of the important markers in our study co-localized with QTL in previous linkage studies. For the selection study, results demonstrated that after two cycles of selection the population means from marker-assisted selection and genomic selection methods were higher than BLUP phenotypic selection. The study also showed that the top performing lines came from marker-based methods indicating superiority of these methods in terms of cultivar development. The top lines in this study were also submitted to National Small Grains Collection for germplasm preservation and distribution purposes. We also found that the genetic variance for beta-glucan is mainly controlled by additive genetic component. However, the genetic variance decreased after two cycles of selection but the magnitude of decrease was different between selection methods. Particularly, the greatest reduction in genetic variance was detected for populations undergoing BLUP phenotypic selection. This could be attributed to higher chance of co-selection of sibs. On the other hand, populations under genomic selection had the lowest reduction in genetic variance which could be attributed to ability of markers to detect segregation in the estimation of breeding values. Among the three methods, only genomic selection can be conducted atleast twice a year which can result to doubling of genetic gain. Our experiments also demonstrated empirically that the accuracy of genomic selection can be increased by larger training population size, higher marker density and if selection candidates are genetically related to the training population

Post Financial Crisis Securitization: Can (EU) 2017/2402 Make any Difference? : A critical Analysis of Regulation (EU) 2017/2402

Following the devastating effect of the financial crisis, the securitization in Europe is still largely impaired and yet to get to the pre-crisis level. There are have been many regulatory interventions to revive the securitization market, with the latest being the regulation (EU) 2017/2402 that will be in force by January 2019. This thesis thus critically examines the regulation in the light of its ability to make a difference in European securitization, and possibly preventing another securitization-induced financial crisis. First, the rationale and motivations for securitization are highlighted, and possible drawbacks noted. In the same vein, the positives of having the regulation (EU) 2017/2402 are far reaching. For example, with the regulation coming to force, there is the expectation for more transparency, simplicity and standardization of securitization in Europe. The regulation (EU) 2017/2402, while replacing the laws on securitization in Europe, also creates the general framework for securitization and specific framework for simple, transparent and standardized (STS) securitization. The motivation for European securitizes to get the STS tag is that it makes them eligible for differentiated capital requirement of regulation (EU) 2017/2401. The benefits of the regulation (EU) 2017/2402, however, there are valid concerns about the overall impact on securitization in Europe. This thesis categorizes the concerns to two – sundry concerns and discrepancy concerns. The former relates to the general concerns about the regulation, while the latter expresses the concerns that show differences between what the regulation seeks (as marketed by the authorities), and what is realistically available. The thesis finally concludes on the note that the European securitization market will be greatly impacted by the regulation (EU) 2017/2402 - there will hopefully be a lot of simplicity, transparency, and standardization or comparability, going forward. In addition, parties in securitization transactions now have more clearly defined roles, e.g. investors now have the responsibility of doing their due diligence before and after holding securitization positions, as well as originators and sponsors providing material information about securitization transactions

Accuracy and Training Population Design for Genomic Selection on Quantitative Traits in Elite North American Oats

Genomic selection (GS) is a method to estimate the breeding values of individuals by using markers throughout the genome. We evaluated the accuracies of GS using data from five traits on 446 oat (Avena sativa L.) lines genotyped with 1005 Diversity Array Technology (DArT) markers and two GS methods (ridge regression–best linear unbiased prediction [RR-BLUP] and BayesCπ) under various training designs. Our objectives were to (i) determine accuracy under increasing marker density and training population size, (ii) assess accuracies when data is divided over time, and (iii) examine accuracy in the presence of population structure. Accuracy increased as the number of markers and training size become larger. Including older lines in the training population increased or maintained accuracy, indicating that older generations retained information useful for predicting validation populations. The presence of population structure affected accuracy: when training and validation subpopulations were closely related accuracy was greater than when they were distantly related, implying that linkage disequilibrium (LD) relationships changed across subpopulations. Across many scenarios involving large training populations, the accuracy of BayesCπ and RR-BLUP did not differ. This empirical study provided evidence regarding the application of GS to hasten the delivery of cultivars through the use of inexpensive and abundant molecular markers available to the public sector

Genomic, Marker-Assisted, and Pedigree-BLUP Selection Methods for β-Glucan Concentration in Elite Oat

β-glucan, a soluble fiber found in oat (Avena sativa L.) grain, is good for human health, and selection for higher levels of this compound is regarded as an important breeding objective. Recent advances in oat DNA markers present an opportunity to investigate new selection methods for polygenic traits such as β-glucan concentration. Our objectives in this study were to compare genomic, marker-assisted, and best linear unbiased prediction (BLUP)–based phenotypic selection for short-term response to selection and ability to maintain genetic variance for β-glucan concentration. Starting with a collection of 446 elite oat lines from North America, each method was conducted for two cycles. The average β-glucan concentration increased from 4.57 g/100 g in Cycle 0 to between 6.66 and 6.88 g/100 g over the two cycles. The averages of marker-based selection methods in Cycle 2 were greater than those of phenotypic selection (P \u3c 0.08). Progenies with the highest β-glucan came from the marker-based selection methods. Marker-assisted selection (MAS) for higher β-glucan concentration resulted in a later heading date. We also found that marker-based selection methods maintained greater genetic variance than did BLUP phenotypic selection, potentially enabling greater future selection gains. Overall, the results of these experiments suggest that genomic selection is a superior method for selecting a polygenic complex trait like β-glucan concentration

Genome-wide association study for oat (Avena sativa L.) beta-glucan concentration using germplasm of worldwide origin

Detection of quantitative trait loci (QTL) controlling complex traits followed by selection has become a common approach for selection in crop plants. The QTL are most often identified by linkage mapping using experimental F2, backcross, advanced inbred, or doubled haploid families. An alternative approach for QTL detection are genome-wide association studies (GWAS) that use pre-existing lines such as those found in breeding programs. We explored the implementation of GWAS in oat (Avena sativa L.) to identify QTL affecting β-glucan concentration, a soluble dietary fiber with several human health benefits when consumed as a whole grain. A total of 431 lines of worldwide origin were tested over 2 years and genotyped using Diversity Array Technology (DArT) markers. A mixed model approach was used where both population structure fixed effects and pair-wise kinship random effects were included. Various mixed models that differed with respect to population structure and kinship were tested for their ability to control for false positives. As expected, given the level of population structure previously described in oat, population structure did not play a large role in controlling for false positives. Three independent markers were significantly associated with β-glucan concentration. Significant marker sequences were compared with rice and one of the three showed sequence homology to genes localized on rice chromosome seven adjacent to the CslFgene family, known to have β-glucan synthase function. Results indicate that GWAS in oat can be a successful option for QTL detection, more so with future development of higher-density markers

Nanoalloying in real time: a high resolution STEM and computer simulation study

Bimetallic nanoparticles constitute a promising type of catalysts, mainly because their physical and chemical properties may be tuned by varying their chemical composition, atomic ordering, and size. Today, the design of novel nanocatalysts is possible through a combination of virtual lab simulations on massive parallel computing and modern electron microscopy with picometre resolution on one hand, and the capability of chemical analysis at the atomic scale on the other. In this work we show how the combination of theoretical calculations and characterization can solve some of the paradoxes reported about nanocatalysts: Au-Pd bimetallic nanoparticles. In particular, we demonstrate the key role played by adsorbates, such as carbon monoxide (CO), on the structure of nanoalloys. Our results imply that surface condition of nanoparticles during synthesis is a parameter of paramount importance.Fil: Mariscal, Marcelo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Investigaciones en Físico-química de Córdoba. Universidad Nacional de Córdoba. Facultad de Ciencias Químicas. Instituto de Investigaciones en Físico-química de Córdoba; ArgentinaFil: Mayoral, Alba. Universidad de Zaragoza. Instituto de Nanociencia de Aragón; EspañaFil: Olmos Asar, Jimena Anahí. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Investigaciones en Físico-química de Córdoba. Universidad Nacional de Córdoba. Facultad de Ciencias Químicas. Instituto de Investigaciones en Físico-química de Córdoba; ArgentinaFil: Magen, César. Universidad de Zaragoza. Instituto de Nanociencia de Aragón; EspañaFil: Mejia Rosales, Sergio Javier. Universidad Autónoma de Nuevo León; MéxicoFil: Pérez Tijerina, Eduardo. Universidad Autónoma de Nuevo León; MéxicoFil: José Yacamán, Miguel. University of Texas; Estados Unido

Haplotype based genotyping-by-sequencing in oat genome research

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Genome-based trait prediction in multi- environment breeding trials in groundnut

Genomic selection (GS) can be an efficient and cost-effective breeding approach which captures both small- and large-effect genetic factors and therefore promises to achieve higher genetic gains for complex traits such as yield and oil content in groundnut. A training population was constituted with 340 elite lines followed by genotyping with 58 K ‘Axiom_Arachis’ SNP array and phenotyping for key agronomic traits at three locations in India. Four GS models were tested using three different random cross-validation schemes (CV0, CV1 and CV2). These models are: (1) model 1 (M1 = E + L) which includes the main effects of environment (E) and line (L); (2) model 2 (M2 = E + L + G) which includes the main effects of markers (G) in addition to E and L; (3) model 3 (M3 = E + L + G + GE), a naïve interaction model; and (4) model 4 (E + L + G + LE + GE), a naïve and informed interaction model. Prediction accuracy estimated for four models indicated clear advantage of the inclusion of marker information which was reflected in better prediction accuracy achieved with models M2, M3 and M4 as compared to M1 model. High prediction accuracies (> 0.600) were observed for days to 50% flowering, days to maturity, hundred seed weight, oleic acid, rust@90 days, rust@105 days and late leaf spot@90 days, while medium prediction accuracies (0.400–0.600) were obtained for pods/plant, shelling %, and total yield/plant. Assessment of comparative prediction accuracy for different GS models to perform selection for untested genotypes, and unobserved and unevaluated environments provided greater insights on potential application of GS breeding in groundnut