Evaluation of late vegetative and reproductive stage soybeans for resistance to soybean aphid (hemiptera: Aphididae)

The soybean aphid, Aphis glycines Matsmura, has become the most significant soybean [Glycine max (L.) Merrill] insect pest in the north central soybean production region of North America. The objectives of this research were to measure selected genotypes for resistance to the soybean aphid in the later vegetative and reproductive stages under field conditions, and confirm the presence of tolerance in KS4202. The results from 2007 to 2011 indicate that KS4202 can support aphid populations with minimal yield loss at levels where significant yield loss would be expected in most other genotypes. The common Nebraska cultivar, 'Asgrow 2703′, appears to show signs of tolerance as well. None of the yield parameters were significantly different between the aphid infested and noninfested treatments. Based on our results, genotypes may compensate for aphid feeding in different ways. Asgrow 2703 appears to produce a similar number of seeds as its noninfested counterpart, although the seeds produced are slightly smaller. Field evaluation of tolerance in KS4202 indicated a yield loss of only 13% at 34,585-53,508 cumulative aphid-days, when 24-36% yield loss would have been expected. © 2013 Entomological Society of America.

Overexpression of the activated form of the AtAREB1 gene (AtAREB1 Delta QT) improves soybean responses to water deficit

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Differences among origins on nutritional and quality parameters of soybean meal

Soybean meal (SBM) is the main protein source in livestock feeds. United States (USA), Brazil (BRA), and Argentine (ARG) are the major SBM exporter countries. The nutritive value of SBM varies because genetics, environment, farming conditions, and processing of the beans influence strongly the content and availability of major nutrients. The present research was conducted to determine the influence of origin (USA, BRA and ARG) on nutritive value and protein quality of SBM.

Soybean Stem Fly, Melanagromyza sojae (Diptera: Agromyzidae), in the New World: detection of high genetic diversity from soybean fields in Brazil

Soybean Stem Fly (SSF), Melanagromyza sojae (Zehntner), belongs to the family Agromyzidae and is highly polyphagous, attacking many plant species of the family Fabaceae, including soybean and other beans. SSF is regarded as one of the most important pests in soybean fields of Asia (e.g., China, India), North East Africa (e.g., Egypt), parts of Russia, and South East Asia. Despite reports of Agromyzidae flies infesting soybean fields in Rio Grande do Sul State (Brazil) in 1983 and 2009 and periodic interceptions of SSF since the 1940s by the USA quarantine authorities, SSF has not been officially reported to have successfully established in the North and South Americas. In South America, M. sojae was recently confirmed using morphology and its complete mitochondrial DNA (mtDNA) was characterized. In the present study, we surveyed the genetic diversity of M. sojae, collected directly from soybean host plants, using partial mtDNA cytochrome oxidase I (COI) gene, and provide evidence of multiple (>10) maternal lineages in SSF populations in South America, potentially representing multiple incursion events. However, a single incursion involving multiple-female founders could not be ruled out. We identified a haplotype that was common in the fields of two Brazilian states and the individuals collected from Australia in 2013. The implications of SSF incursions in southern Brazil are discussed in relation to the current soybean agricultural practices, highlighting an urgent need for better understanding of SSF population movements in the New World, which is necessary for developing effective management options for this significant soybean pest. © FUNPEC-RP.

Adaptability and phenotypic stability of soybean cultivars for grain yield and oil content.

ABSTRACT. The aim of this study was to verify the adaptability and stability of soybean cultivars with regards to yield and oil content. Data of soybean yield and oil content were used from experiments set up in six environments in the 2011/12 and 2012/13 crop seasons in the municipalities of Patos de Minas, Uberaba, Lavras, and São Gotardo, Minas Gerais, Brazil, testing 36 commercial soybean cultivars of both conventional and transgenic varieties. The Wricke method and GGE biplot analysis were used to evaluate adaptability and stability of these cultivars. Large variations were observed in grain yield in relation to the different environments studied, showing that these materials are adaptable. The cultivars exhibited significant differences in oil content. The cultivars BRSGO204 (Goiânia) and BRSMG (Garantia) exhibited the greatest average grain yield in the different environments studied, and the cultivar BRSMG 760 SRR had the greatest oil content among the cultivars evaluated. Ecovalence was adopted to identify the most stable cultivars, and the estimates were nearly uniform both for grain yield and oil content, showing a variation of 0.07 and 0.01%, respectively. The GGE biplot was efficient at identifying cultivars with high adaptability and phenotype stability.

Latent variable models in statistical genetics

Understanding the complexities that are involved in the genetics of multifactorial diseases is still a monumental task. In addition to environmental factors that can influence the risk of disease, there is also a number of other complicating factors. Genetic variants associated with age of disease onset may be different from those variants associated with overall risk of disease, and variants may be located in positions that are not consistent with the traditional protein coding genetic paradigm. Latent Variable Models are well suited for the analysis of genetic data. A latent variable is one that we do not directly observe, but which is believed to exist or is included for computational or analytic convenience in a model. This thesis presents a mixture of methodological developments utilising latent variables, and results from case studies in genetic epidemiology and comparative genomics. Epidemiological studies have identified a number of environmental risk factors for appendicitis, but the disease aetiology of this oft thought useless vestige remains largely a mystery. The effects of smoking on other gastrointestinal disorders are well documented, and in light of this, the thesis investigates the association between smoking and appendicitis through the use of latent variables. By utilising data from a large Australian twin study questionnaire as both cohort and case-control, evidence is found for the association between tobacco smoking and appendicitis. Twin and family studies have also found evidence for the role of heredity in the risk of appendicitis. Results from previous studies are extended here to estimate the heritability of age-at-onset and account for the eect of smoking. This thesis presents a novel approach for performing a genome-wide variance components linkage analysis on transformed residuals from a Cox regression. This method finds evidence for a dierent subset of genes responsible for variation in age at onset than those associated with overall risk of appendicitis. Motivated by increasing evidence of functional activity in regions of the genome once thought of as evolutionary graveyards, this thesis develops a generalisation to the Bayesian multiple changepoint model on aligned DNA sequences for more than two species. This sensitive technique is applied to evaluating the distributions of evolutionary rates, with the finding that they are much more complex than previously apparent. We show strong evidence for at least 9 well-resolved evolutionary rate classes in an alignment of four Drosophila species and at least 7 classes in an alignment of four mammals, including human. A pattern of enrichment and depletion of genic regions in the profiled segments suggests they are functionally significant, and most likely consist of various functional classes. Furthermore, a method of incorporating alignment characteristics representative of function such as GC content and type of mutation into the segmentation model is developed within this thesis. Evidence of fine-structured segmental variation is presented.

Bayesian methodology for genetics of complex diseases

Genetic research of complex diseases is a challenging, but exciting, area of research. The early development of the research was limited, however, until the completion of the Human Genome and HapMap projects, along with the reduction in the cost of genotyping, which paves the way for understanding the genetic composition of complex diseases. In this thesis, we focus on the statistical methods for two aspects of genetic research: phenotype definition for diseases with complex etiology and methods for identifying potentially associated Single Nucleotide Polymorphisms (SNPs) and SNP-SNP interactions. With regard to phenotype definition for diseases with complex etiology, we firstly investigated the effects of different statistical phenotyping approaches on the subsequent analysis. In light of the findings, and the difficulties in validating the estimated phenotype, we proposed two different methods for reconciling phenotypes of different models using Bayesian model averaging as a coherent mechanism for accounting for model uncertainty. In the second part of the thesis, the focus is turned to the methods for identifying associated SNPs and SNP interactions. We review the use of Bayesian logistic regression with variable selection for SNP identification and extended the model for detecting the interaction effects for population based case-control studies. In this part of study, we also develop a machine learning algorithm to cope with the large scale data analysis, namely modified Logic Regression with Genetic Program (MLR-GEP), which is then compared with the Bayesian model, Random Forests and other variants of logic regression.