Genetic variation and environmental effects on beta-conglycinin and glycinin content in Brazilian soybean cultivars

The objective of this work was to determine genetic and environmental effects on beta-conglycinin and glycinin content in Brazilian soybean cultivars. The concentrations of these protein fractions were analyzed by scanning densitometry after electrophoresis, in 90 Brazilian soybean cultivars sown in Ponta Grossa, PR, in 2001. The effects of the sowing location were determined in the cultivar MG/BR 46 (Conquista), sown in 16 locations of Goiás and Minas Gerais states (Central Brazil), and in the cultivar IAS 5, sown in 12 locations of Paraná and São Paulo states (Southern Brazil), in 2002 soybean season. A significant variability for beta-conglycinin (7S) and glycinin (11S) protein fractions ratio was observed among the 90 Brazilian soybean cultivars. 'MS/BRS 169' (Bacuri) and 'BR-8' (Pelotas) presented the highest and the lowest 11S/7S ratios (2.76 and 1.17, respectively). Beta-conglycinin protein fractions presented more variability than glycinin protein fractions. Grouping test classified 7S proteins in seven groups, 11S proteins in four groups, and protein fraction ratios (11S/7S) in nine groups. Significant effect of sowing locations was also observed on protein fractions contents. There is a good possibility of breeding for individual protein fractions, and their subunits, without affecting protein content.

Machine learning for geospatial data : algorithms, software tools and case studies

Résumé Cette thèse est consacrée à l'analyse, la modélisation et la visualisation de données environnementales à référence spatiale à l'aide d'algorithmes d'apprentissage automatique (Machine Learning). L'apprentissage automatique peut être considéré au sens large comme une sous-catégorie de l'intelligence artificielle qui concerne particulièrement le développement de techniques et d'algorithmes permettant à une machine d'apprendre à partir de données. Dans cette thèse, les algorithmes d'apprentissage automatique sont adaptés pour être appliqués à des données environnementales et à la prédiction spatiale. Pourquoi l'apprentissage automatique ? Parce que la majorité des algorithmes d'apprentissage automatiques sont universels, adaptatifs, non-linéaires, robustes et efficaces pour la modélisation. Ils peuvent résoudre des problèmes de classification, de régression et de modélisation de densité de probabilités dans des espaces à haute dimension, composés de variables informatives spatialisées (« géo-features ») en plus des coordonnées géographiques. De plus, ils sont idéaux pour être implémentés en tant qu'outils d'aide à la décision pour des questions environnementales allant de la reconnaissance de pattern à la modélisation et la prédiction en passant par la cartographie automatique. Leur efficacité est comparable au modèles géostatistiques dans l'espace des coordonnées géographiques, mais ils sont indispensables pour des données à hautes dimensions incluant des géo-features. Les algorithmes d'apprentissage automatique les plus importants et les plus populaires sont présentés théoriquement et implémentés sous forme de logiciels pour les sciences environnementales. Les principaux algorithmes décrits sont le Perceptron multicouches (MultiLayer Perceptron, MLP) - l'algorithme le plus connu dans l'intelligence artificielle, le réseau de neurones de régression généralisée (General Regression Neural Networks, GRNN), le réseau de neurones probabiliste (Probabilistic Neural Networks, PNN), les cartes auto-organisées (SelfOrganized Maps, SOM), les modèles à mixture Gaussiennes (Gaussian Mixture Models, GMM), les réseaux à fonctions de base radiales (Radial Basis Functions Networks, RBF) et les réseaux à mixture de densité (Mixture Density Networks, MDN). Cette gamme d'algorithmes permet de couvrir des tâches variées telle que la classification, la régression ou l'estimation de densité de probabilité. L'analyse exploratoire des données (Exploratory Data Analysis, EDA) est le premier pas de toute analyse de données. Dans cette thèse les concepts d'analyse exploratoire de données spatiales (Exploratory Spatial Data Analysis, ESDA) sont traités selon l'approche traditionnelle de la géostatistique avec la variographie expérimentale et selon les principes de l'apprentissage automatique. La variographie expérimentale, qui étudie les relations entre pairs de points, est un outil de base pour l'analyse géostatistique de corrélations spatiales anisotropiques qui permet de détecter la présence de patterns spatiaux descriptible par une statistique. L'approche de l'apprentissage automatique pour l'ESDA est présentée à travers l'application de la méthode des k plus proches voisins qui est très simple et possède d'excellentes qualités d'interprétation et de visualisation. Une part importante de la thèse traite de sujets d'actualité comme la cartographie automatique de données spatiales. Le réseau de neurones de régression généralisée est proposé pour résoudre cette tâche efficacement. Les performances du GRNN sont démontrées par des données de Comparaison d'Interpolation Spatiale (SIC) de 2004 pour lesquelles le GRNN bat significativement toutes les autres méthodes, particulièrement lors de situations d'urgence. La thèse est composée de quatre chapitres : théorie, applications, outils logiciels et des exemples guidés. Une partie importante du travail consiste en une collection de logiciels : Machine Learning Office. Cette collection de logiciels a été développée durant les 15 dernières années et a été utilisée pour l'enseignement de nombreux cours, dont des workshops internationaux en Chine, France, Italie, Irlande et Suisse ainsi que dans des projets de recherche fondamentaux et appliqués. Les cas d'études considérés couvrent un vaste spectre de problèmes géoenvironnementaux réels à basse et haute dimensionnalité, tels que la pollution de l'air, du sol et de l'eau par des produits radioactifs et des métaux lourds, la classification de types de sols et d'unités hydrogéologiques, la cartographie des incertitudes pour l'aide à la décision et l'estimation de risques naturels (glissements de terrain, avalanches). Des outils complémentaires pour l'analyse exploratoire des données et la visualisation ont également été développés en prenant soin de créer une interface conviviale et facile à l'utilisation. Machine Learning for geospatial data: algorithms, software tools and case studies Abstract The thesis is devoted to the analysis, modeling and visualisation of spatial environmental data using machine learning algorithms. In a broad sense machine learning can be considered as a subfield of artificial intelligence. It mainly concerns with the development of techniques and algorithms that allow computers to learn from data. In this thesis machine learning algorithms are adapted to learn from spatial environmental data and to make spatial predictions. Why machine learning? In few words most of machine learning algorithms are universal, adaptive, nonlinear, robust and efficient modeling tools. They can find solutions for the classification, regression, and probability density modeling problems in high-dimensional geo-feature spaces, composed of geographical space and additional relevant spatially referenced features. They are well-suited to be implemented as predictive engines in decision support systems, for the purposes of environmental data mining including pattern recognition, modeling and predictions as well as automatic data mapping. They have competitive efficiency to the geostatistical models in low dimensional geographical spaces but are indispensable in high-dimensional geo-feature spaces. The most important and popular machine learning algorithms and models interesting for geo- and environmental sciences are presented in details: from theoretical description of the concepts to the software implementation. The main algorithms and models considered are the following: multi-layer perceptron (a workhorse of machine learning), general regression neural networks, probabilistic neural networks, self-organising (Kohonen) maps, Gaussian mixture models, radial basis functions networks, mixture density networks. This set of models covers machine learning tasks such as classification, regression, and density estimation. Exploratory data analysis (EDA) is initial and very important part of data analysis. In this thesis the concepts of exploratory spatial data analysis (ESDA) is considered using both traditional geostatistical approach such as_experimental variography and machine learning. Experimental variography is a basic tool for geostatistical analysis of anisotropic spatial correlations which helps to understand the presence of spatial patterns, at least described by two-point statistics. A machine learning approach for ESDA is presented by applying the k-nearest neighbors (k-NN) method which is simple and has very good interpretation and visualization properties. Important part of the thesis deals with a hot topic of nowadays, namely, an automatic mapping of geospatial data. General regression neural networks (GRNN) is proposed as efficient model to solve this task. Performance of the GRNN model is demonstrated on Spatial Interpolation Comparison (SIC) 2004 data where GRNN model significantly outperformed all other approaches, especially in case of emergency conditions. The thesis consists of four chapters and has the following structure: theory, applications, software tools, and how-to-do-it examples. An important part of the work is a collection of software tools - Machine Learning Office. Machine Learning Office tools were developed during last 15 years and was used both for many teaching courses, including international workshops in China, France, Italy, Ireland, Switzerland and for realizing fundamental and applied research projects. Case studies considered cover wide spectrum of the real-life low and high-dimensional geo- and environmental problems, such as air, soil and water pollution by radionuclides and heavy metals, soil types and hydro-geological units classification, decision-oriented mapping with uncertainties, natural hazards (landslides, avalanches) assessments and susceptibility mapping. Complementary tools useful for the exploratory data analysis and visualisation were developed as well. The software is user friendly and easy to use.

Genetic variability of Brazilian rice landraces determined by SSR markers

The objective of this study was to evaluate the genetic variability of rice (Oryza sativa) landraces collected in Brazilian small farms. Twelve simple sequence repeat (SSR) markers characterized 417 landraces collected in 1986, 1987 and 2003, in the state of Goiás, Brazil. The number of landraces with long and thin grain type increased in the evaluated period, probably due to market demand. Based on the molecular data, the genetic variability increased during this period and, as per to the factorial correspondence analysis, most of the accessions were grouped according to the year of collection. The incorporation of modern rice cultivars in landrace cultivation areas and the selection carried out by small farmers are the most probable factors responsible for increasing landrace genetic variability, during the evaluated period. Genotype exchange between farmers, selection practice and local environmental adaptation are able to generate novel adapted allele combinations, which can be used by breeding programs, to reinitiate the process.

In situ and genetic characterization of Gossypium barbadense populations from the states of Pará and Amapá, Brazil

The objective of this work was to characterize the populations of Gossypium barbadense in the states of Amapá and Pará, Brazil. In situ characterization was conducted through interviews with the owners of the plants and environmental observations. Leaf or petal tissue as well as seed samples were collected for genetic characterization by single sequence repeats markers and for storage in germplasm banks, respectively. The plants were maintained in dooryards and used mainly for medical purposes. The genetic analysis showed no heterozygous plants at the loci tested (f = 1), indicating that reproduction occurs mainly through selfing. The total genetic diversity was high (He = 0.39); and a high level of differentiation was observed between cotton plants from the two states (F ST = 0.36). Conventional methods of in situ maintenance of G. barbadense populations are not applicable. The conservation of the genetic variability of populations present in the two states could be achieved through germplasm collection and establishing of ex situ seed banks.

Common genetic loci influencing plasma homocysteine concentrations and their effect on risk of coronary artery disease.

BACKGROUND: The strong observational association between total homocysteine (tHcy) concentrations and risk of coronary artery disease (CAD) and the null associations in the homocysteine-lowering trials have prompted the need to identify genetic variants associated with homocysteine concentrations and risk of CAD. OBJECTIVE: We tested whether common genetic polymorphisms associated with variation in tHcy are also associated with CAD. DESIGN: We conducted a meta-analysis of genome-wide association studies (GWAS) on tHcy concentrations in 44,147 individuals of European descent. Polymorphisms associated with tHcy (P < 10(-8)) were tested for association with CAD in 31,400 cases and 92,927 controls. RESULTS: Common variants at 13 loci, explaining 5.9% of the variation in tHcy, were associated with tHcy concentrations, including 6 novel loci in or near MMACHC (2.1 Ã- 10(-9)), SLC17A3 (1.0 Ã- 10(-8)), GTPB10 (1.7 Ã- 10(-8)), CUBN (7.5 Ã- 10(-10)), HNF1A (1.2 Ã- 10(-12)), and FUT2 (6.6 Ã- 10(-9)), and variants previously reported at or near the MTHFR, MTR, CPS1, MUT, NOX4, DPEP1, and CBS genes. Individuals within the highest 10% of the genotype risk score (GRS) had 3-μmol/L higher mean tHcy concentrations than did those within the lowest 10% of the GRS (P = 1 Ã- 10(-36)). The GRS was not associated with risk of CAD (OR: 1.01; 95% CI: 0.98, 1.04; P = 0.49). CONCLUSIONS: We identified several novel loci that influence plasma tHcy concentrations. Overall, common genetic variants that influence plasma tHcy concentrations are not associated with risk of CAD in white populations, which further refutes the causal relevance of moderately elevated tHcy concentrations and tHcy-related pathways for CAD.

Interference of genotypes x environments interaction in the genetic control of resistance to Asian rust soybean

The objectives of this work were to identify parents resistant to Asian soybean rust using diallel crosses, obtain information on the genetic control of soybean resistance to the pathogen and verify whether the combining ability estimates interact with the environment (year or time of assessment). The F1 generation was obtained in a greenhouse from crosses between five contrasting parents for the trait resistance to soybean rust, in a complete diallel without reciprocals. Two rust-severity assessments were carried out on individual soybean plants of 25 treatments (parents and F2 and F3 populations) in 2006/2007 and 2007/2008, in an experimental field at Embrapa Soja, Londrina, PR, Brazil. Additive effects predominated in the genetic control of soybean resistance to Asian rust, and the interaction of the segregant populations with the environment, although significant, did not alter the genetic parameter's general combining ability (GCA) and specific combining ability estimates, indicating that estimates obtained in one year and one assessment can be extrapolated to others. BR01-18437 inbred line is resistant to Asian rust and showed high GCA effects. This line should be used as parent if the objective is the resistance to Phakopsora pachyrhizi.

Transgene inheritances and genetic similarities of near isogenic lines of genetically modified common beans

The objective of the present work was to determine the inheritance and stability of transgenes of a transgenic bean line expressing the genes rep-trap-ren from Bean golden mosaic virus and the bar gene. Crosses were done between the transgenic line and four commercial bean cultivars, followed by four backcrosses to the commercial cultivars. Progenies from each cross were evaluated for the presence of the transgenes by brushing the leaves with glufosinate ammonium and by polymerase chain reaction using specific oligonucleotides. Advanced generations were rub-inoculated with an isolate of Bean common mosaic necrosis virus (BCMNV). The transgenes were inherited consistently in a Mendelian pattern in the four crosses studied. The analyzed lines recovered close to 80% of the characteristics of the recurrent parent, as determined by the random amplified DNA markers used, besides maintaining important traits such as resistance to BCMNV. The presence of the transgene did not cause any detectable undesirable effect in the evaluated progenies.

Environmental and genetic variation of isoflavone content of soybean seeds grown in Brazil

The objective of this work was to evaluate isoflavone concentrations in seeds of different Brazilian soybean cultivars grown in a range of locations and environmental conditions in Brazil. Seeds of 233 cultivars grown in Ponta Grossa, PR, Brazil, during the 2001/2002 soybean season, and of 22 cultivars sown in different locations of Brazilian Northeast, Southeast on South regions were analyzed for total isoflavones, including daidzin, glycitin, genistin and acetylgenistin. The total isoflavones ranged from 12 mg 100 g-1 (cv. Embrapa 48) to 461 mg 100 g-1 (cv. CS 305) among the 233 cultivars grown in Ponta Grossa, and the differences among them are due to genetic effects since all cultivars were grown and collected at the same locatation and year. This is an indication of the possibility of breeding for isoflavone content. Differences in isoflavone content observed in the cultivars grown in different locations permit the selection of locations for optimum isoflavone content (low or high), depending on the uses of soybean. In the Northeast region (5-8°S), higher concentrations of total isoflavones were observed at São Raimundo das Mangabeiras (232 mg 100 g-1) and Tasso Fragoso (284 mg 100 g-1) municipalities, and in the South (23-30°S), isoflavones were higher in Guarapuava, Canoinhas, Vacaria and Campos Novos municipalities, ranging from 130 to 409 mg 100 g-1.

Genetic polymorphisms related to meat traits in purebred and crossbred Nelore cattle

The objective of this work was to estimate the allelic and genotypic frequencies of CAST/XmnI, a calpastatin gene polymorphism, and CAPN530, a calpain 1 large subunit gene polymorphism, in different beef genetic groups (Nelore and Nelore x Bos taurus), and to investigate associations between these polymorphisms and carcass and meat traits. Three hundred animals - comprising 114 Nelore, 67 Angus x Nelore, 44 Rubia Gallega x Nelore, 41 Canchim, 19 Brangus three-way cross and 15 Braunvieh three-way cross- were genotyped by PCR-RFLP and phenotyped for rib-eye area (REA), back-fat thickness (BT), intramuscular fat (IF), shear force (SF) and myofibrillar fragmentation index (MFI). The occurrence of the two alleles of the CAST/XmnI and CAPN530 single nucleotide polymorphisms (SNPs) in a B. indicus breed, which permitted association studies in purebred and crossbred Nelore cattle, was first shown in the present work. No relationship was found between the CAST or CAPN1 SNPs and growth-related traits (REA) or fat deposition (BT and IF), since calpastatin and µ-calpain are not physiologically involved with these traits. Moreover, the association results between genotypes and aged meat tenderness (assessed by SF and MFI) showed that these markers are useless in assisted selection for purebred Nelore and their crosses with B. taurus.

Divergence and genetic variability among superior rubber tree genotypes

The objective of this work was to estimate the genetic variability and divergence among 22 superior rubber tree (Hevea sp.) genotypes of the IAC 400 series. Univariate and multivariate analyses were performed using eight quantitative traits (descriptors), including yield. In the univariate analyses, the estimated parameters were: genetic and environmental variances; genetic and environmental coefficients of variation; and the variation index. The Mahalanobis generalized distance, the Tocher agglomerative method and canonical variables were used for the multivariate analyses. In the univariate analyses, variability was verified among the genotypes for all the variables evaluated. The Tocher method grouped the genotypes into 11 clusters of dissimilarity. The first four canonical variables explained 87.93% of the cumulative variation. The highest genetic variability was found in rubber yield-related traits, which contributed the most to the genetic divergence. The most divergent pairs of genotypes are suggested for crossbreeding. The genotypes evaluated are suitable for breeding and may be used to continue the IAC rubber tree breeding program.