Pattern analysis on grain yield performance of Chinese and CIMMYT spring wheat cultivars sown in China and CIMMYT

Understanding the relationships among testing environments is essential for better targeting cultivars to production environments. To identify patterns of cultivar, environment, cultivar-by-environment interactions, and opportunities for indirect selection for grain yield, a set of 25 spring wheat cultivars from China and the International Maize and Wheat Improvement Center (CIMMYT) was evaluated in nine environments in China and four management environments at CIMMYT in Cd. Obregon, Mexico, during two wheat seasons. Genetic background and original environment were the main factors influencing grain yield performance of the cultivars. Baviacora M 92, Xinchun 2 and Xinchun 6 showed relatively more stable and higher grain yields, whereas highly photoperiod sensitive cultivars Xinkehan 9, Kefeng 6 and Longmai 19 proved consistently inferior across environments, except in Harbin and Keshan, the two high latitude environments. Longmai 26, also from high latitude environments in the northeastern Heilongjiang province, was however probably not as photoperiodicly sensitive as other cultivars; from that region, and produced much higher grain yield and expressed a broader adaptation. None of the environments reported major diseases. Pattern analyses revealed that photoperiod response and planting option on beds were the two main factors underlying the observed interactions for grain yield. The production environment of planting on the flat in Mexico grouped together with Huhhot and Urumqi in both wheat seasons, indicating an indirect response to selection for grain yield in this CIMMYT managed environment could benefit the two Chinese environments. Both the environment of planting on the flat with Chinese Hejin and Yongning, and the three CIMMYT enviromnents planting on raised beds with Chinese Yongning grouped together only in one season, showing that repeatability may not be stable in this case.

The neural correlates of reading impairment in adults with developmental dyslexia:evidence from fMRI between group analyses and an fMRI multiple-case study

The goal of this project was to investigate the neural correlates of reading impairment in dyslexia as hypothesised by the main theories – the phonological deficit, visual magnocellular deficit and cerebellar deficit theories, with emphasis on individual differences. This research took a novel approach by: 1) contrasting the predictions in one sample of participants with dyslexia (DPs); 2) using a multiple-case study (and between-group comparisons) to investigate differences in BOLD between each DP and the controls (CPs); 3) demonstrating a possible relationship between reading impairment and its hypothesised neural correlates by using fMRI and a reading task. The multiple-case study revealed that the neural correlates of reading in dyslexia in all cases are not in agreement with the predictions of a single theory. The results show striking individual differences - even, where the neural correlates of reading in two DPs are consistent with the same theory, the areas can differ. A DP can exhibit under-engagement in an area in word, but not in pseudoword reading and vice versa, demonstrating that underactivation in that area cannot be interpreted as a ‘developmental lesion’. Additional analyses revealed complex results. Within-group analyses between behavioural measures and BOLD showed correlations in the predicted regions, areas outside ROI, and lack of correlations in some predicted areas. Comparisons of subgroups which differed on Orthography Composite supported the MDT, but only for Words. The results suggest that phonological scores are not a sufficient predictor of the under-engagement of phonological areas during reading. DPs and CPs exhibited correlations between Purdue Pegboard Composite and BOLD in cerebellar areas only for Pseudowords. Future research into reading in dyslexia should use a more holistic approach, involving genetic and environmental factors, gene by environment interaction, and comorbidity with other disorders. It is argued that multidisciplinary research, within the multiple-deficit model holds significant promise here.

Theoretical model simulations for variations in thermal reaction norms

Thermal reaction norms for growth rates of six Emiliania huxleyi isolates originating from the central Atlantic (Azores, Portugal) and five isolates from the coastal North Atlantic (Bergen, Norway) were assessed. We used the template mode of variation model to decompose variations in growth rates into modes of biological interest: vertical shift, horizontal shift, and generalist-specialist variation. In line with the actual habitat conditions, isolates from Bergen (Bergen population) grew well at lower temperatures, and isolates from the Azores (Azores population) performed better at higher temperatures. The optimum growth temperature of the Azores population was significantly higher than that of the Bergen population. Neutral genetic differentiation was found between populations by microsatellite analysis. These findings indicate that E. huxleyi populations are adapted to local temperature regimes. Next to between-population variation, we also found variation within populations. Genotype-by-environment interactions resulted in the most pronounced phenotypic differences when isolates were exposed to temperatures outside the range they naturally encounter. Variation in thermal reaction norms between and within populations emphasizes the importance of using more than one isolate when studying the consequences of global change on marine phytoplankton. Phenotypic plasticity and standing genetic variation will be important in determining the potential of natural E. huxleyi populations to cope with global climate change.

The Roles of Phenotypic Plasticity and Plant-Microbe Interactions in the Evolution of Complex Traits in Boechera stricta

All organisms live in complex habitats that shape the course of their evolution by altering the phenotype expressed by a given genotype (a phenomenon known as phenotypic plasticity) and simultaneously by determining the evolutionary fitness of that phenotype. In some cases, phenotypic evolution may alter the environment experienced by future generations. This dissertation describes how genetic and environmental variation act synergistically to affect the evolution of glucosinolate defensive chemistry and flowering time in Boechera stricta, a wild perennial herb. I focus particularly on plant-associated microbes as a part of the plant’s environment that may alter trait evolution and in turn be affected by the evolution of those traits. In the first chapter I measure glucosinolate production and reproductive fitness of over 1,500 plants grown in common gardens in four diverse natural habitats, to describe how patterns of plasticity and natural selection intersect and may influence glucosinolate evolution. I detected extensive genetic variation for glucosinolate plasticity and determined that plasticity may aid colonization of new habitats by moving phenotypes in the same direction as natural selection. In the second chapter I conduct a greenhouse experiment to test whether naturally-occurring soil microbial communities contributed to the differences in phenotype and selection that I observed in the field experiment. I found that soil microbes cause plasticity of flowering time but not glucosinolate production, and that they may contribute to natural selection on both traits; thus, non-pathogenic plant-associated microbes are an environmental feature that could shape plant evolution. In the third chapter, I combine a multi-year, multi-habitat field experiment with high-throughput amplicon sequencing to determine whether B. stricta-associated microbial communities are shaped by plant genetic variation. I found that plant genotype predicts the diversity and composition of leaf-dwelling bacterial communities, but not root-associated bacterial communities. Furthermore, patterns of host genetic control over associated bacteria were largely site-dependent, indicating an important role for genotype-by-environment interactions in microbiome assembly. Together, my results suggest that soil microbes influence the evolution of plant functional traits and, because they are sensitive to plant genetic variation, this trait evolution may alter the microbial neighborhood of future B. stricta generations. Complex patterns of plasticity, selection, and symbiosis in natural habitats may impact the evolution of glucosinolate profiles in Boechera stricta.

A Biocultural Examination of Student Learning Behaviors in Large Undergraduate Lectures

Thesis (Ph.D.)--University of Washington, 2016-08

Biplot analysis of strawberry genotypes recommended for the State of Espírito Santo.

Most strawberry genotypes grown commercially in Brazil originate from breeding programs in the United States, and are therefore not adapted to the various soil and climatic conditions found in Brazil. Thus, quantifying the magnitude of genotype x environment (GE) interactions serves as a primary means for increasing average Brazilian strawberry yields, and helps provide specific recommendations for farmers on which genotypes meet high yield and phenotypic stability thresholds. The aim of this study was to use AMMI (additive main effects and multiplicative interaction) and GGE biplot (genotype main effects + genotype x environment interaction) analyses to identify high-yield, stable strawberry genotypes grown at three locations in Espírito Santo for two agricultural years. We evaluated seven strawberry genotypes (Dover, Camino Real, Ventana, Camarosa, Seascape, Diamante, and Aromas) at three locations (Domingos Martins, Iúna, and Muniz Freire) in agricultural years 2006 and 2007, totaling six study environments. Joint analysis of variance was calculated using yield data (t/ha), and AMMI and GGE biplot analysis was conducted following the detection of a significant genotypes x agricultural years x locations (G x A x L) interaction. During the two agricultural years, evaluated locations were allocated to different regions on biplot graphics using both methods, indicating distinctions among them. Based on the results obtained from the two methods used in this study to investigate the G x A x L interaction, we recommend growing the Camarosa genotype for production at the three locations assessed due to the high frequency of favorable alleles, which were expressed in all localities evaluated regardless of the agricultural year.

Application of Reactions Norms in Study of Genotype Environmental Interaction for Milk Yield of Buffaloes

The aim of this study was to estimate genetic parameters for milk yield (MY) in buffaloes using reaction norms. Model included the additive direct effect as random and contemporary group (herd and year of birth) were included as fixed effects and cow age classes (linear) as covariables. The animal additive direct random effect was modeled through linear Legendre polynomials on environment gradient (EG) standardized means. Mean trends were taken into account by a linear regression on Legendre polynomials of environmental group means. Residual variance was modeled trough 6 heterogeneity classes (EG). These classes of residual variance was formed : EG1: mean = 866,93 kg (621,68 kg-1011,76 kg); EG2: mean = 1193,00 kg (1011,76 kg-1251,49 kg); EG3: mean = 1309,37 kg (1251,49 kg -1393,20 kg); EG4: mean = 1497,59 kg (1393,20 kg-1593,53 kg); EG5: mean = 1664,78 kg (1593,53 kg -1727,32kg) e EG6: mean = 1973,85 kg (1727,32 kg -2422,19 kg).(Co) variance functions were estimated by restricted maximum likelihood (REML) using the GIBBS3F90 package. The heritability estimates for MY raised as the environmental gradient increased, varying from 0.20 to 0.40. However, in intermediate to favorable environments, the heritability estimates obtained with Considerable genotype-environment interaction was found for MY using reaction norms. For genetic evaluation of MY is necessary to consider heterogeneity of variances to model the residual variance.

Polymorphisms of DNA repair genes XRCC1 and XRCC3, interaction with environmental exposure and risk of chronic gastritis and gastric cancer

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Polymorphisms of DNA repair genes XRCC1 and XRCC3, interaction with environmental exposure and risk of chronic gastritis and grastic cancer

Aim: To evaluate the association between polymorphisms XRCC1 Arg194Trp and Arg399Gln and XRCC3 Thr241Met and the risk for chronic gastritis and gastric cancer, in a Southeastern Brazilian population. Methods: Genotyping by PCR-RFLP was carried out on 202 patients with chronic gastritis (CG) and 160 patients with gastric cancer (GC), matched to 202 (C1) and 150 (C2) controls, respectively. Results: No differences were observed among the studied groups with regard to the genotype distribution of XRCC1 codons 194 and 399 and of XRCC3 codon 241. However, the combined analyses of the three variant alleles (194Trp, 399Gln and 241Met) showed an increased risk for chronic gastritis when compared to the GC group. Moreover, an interaction between the polymorphic alleles and demographic and environmental factors was observed in the CG and GC groups. XRCC1 194Trp was associated with smoking in the CG group, while the variant alleles XRCC1 399Gln and XRCC3 241Met were related with gender, smoking, drinking and H pylori infection in the CG and GC groups. Conclusion: Our results showed no evidence of a rela-tionship between the polymorphisms XRCC1 Arg194Trp and Arg399Gln and XRCC3 Thr241Met and the risk of chronic gastritis and gastric cancer in the Brazilian population, but the combined effect of these variants may interact to increase the risk for chronic gastritis, considered a premalignant lesion. Our data also indicate a gene-environment interaction in the susceptibility to chronic gastritis and gastric cancer. © 2005 The WJG Press and Elsevier Inc. All rights reserved.

BAYESIAN STATISTICAL METHODS IN GENE-ENVIRONMENT AND GENE-GENE INTERACTION STUDIES

Complex diseases such as cancer result from multiple genetic changes and environmental exposures. Due to the rapid development of genotyping and sequencing technologies, we are now able to more accurately assess causal effects of many genetic and environmental factors. Genome-wide association studies have been able to localize many causal genetic variants predisposing to certain diseases. However, these studies only explain a small portion of variations in the heritability of diseases. More advanced statistical models are urgently needed to identify and characterize some additional genetic and environmental factors and their interactions, which will enable us to better understand the causes of complex diseases. In the past decade, thanks to the increasing computational capabilities and novel statistical developments, Bayesian methods have been widely applied in the genetics/genomics researches and demonstrating superiority over some regular approaches in certain research areas. Gene-environment and gene-gene interaction studies are among the areas where Bayesian methods may fully exert its functionalities and advantages. This dissertation focuses on developing new Bayesian statistical methods for data analysis with complex gene-environment and gene-gene interactions, as well as extending some existing methods for gene-environment interactions to other related areas. It includes three sections: (1) Deriving the Bayesian variable selection framework for the hierarchical gene-environment and gene-gene interactions; (2) Developing the Bayesian Natural and Orthogonal Interaction (NOIA) models for gene-environment interactions; and (3) extending the applications of two Bayesian statistical methods which were developed for gene-environment interaction studies, to other related types of studies such as adaptive borrowing historical data. We propose a Bayesian hierarchical mixture model framework that allows us to investigate the genetic and environmental effects, gene by gene interactions (epistasis) and gene by environment interactions in the same model. It is well known that, in many practical situations, there exists a natural hierarchical structure between the main effects and interactions in the linear model. Here we propose a model that incorporates this hierarchical structure into the Bayesian mixture model, such that the irrelevant interaction effects can be removed more efficiently, resulting in more robust, parsimonious and powerful models. We evaluate both of the 'strong hierarchical' and 'weak hierarchical' models, which specify that both or one of the main effects between interacting factors must be present for the interactions to be included in the model. The extensive simulation results show that the proposed strong and weak hierarchical mixture models control the proportion of false positive discoveries and yield a powerful approach to identify the predisposing main effects and interactions in the studies with complex gene-environment and gene-gene interactions. We also compare these two models with the 'independent' model that does not impose this hierarchical constraint and observe their superior performances in most of the considered situations. The proposed models are implemented in the real data analysis of gene and environment interactions in the cases of lung cancer and cutaneous melanoma case-control studies. The Bayesian statistical models enjoy the properties of being allowed to incorporate useful prior information in the modeling process. Moreover, the Bayesian mixture model outperforms the multivariate logistic model in terms of the performances on the parameter estimation and variable selection in most cases. Our proposed models hold the hierarchical constraints, that further improve the Bayesian mixture model by reducing the proportion of false positive findings among the identified interactions and successfully identifying the reported associations. This is practically appealing for the study of investigating the causal factors from a moderate number of candidate genetic and environmental factors along with a relatively large number of interactions. The natural and orthogonal interaction (NOIA) models of genetic effects have previously been developed to provide an analysis framework, by which the estimates of effects for a quantitative trait are statistically orthogonal regardless of the existence of Hardy-Weinberg Equilibrium (HWE) within loci. Ma et al. (2012) recently developed a NOIA model for the gene-environment interaction studies and have shown the advantages of using the model for detecting the true main effects and interactions, compared with the usual functional model. In this project, we propose a novel Bayesian statistical model that combines the Bayesian hierarchical mixture model with the NOIA statistical model and the usual functional model. The proposed Bayesian NOIA model demonstrates more power at detecting the non-null effects with higher marginal posterior probabilities. Also, we review two Bayesian statistical models (Bayesian empirical shrinkage-type estimator and Bayesian model averaging), which were developed for the gene-environment interaction studies. Inspired by these Bayesian models, we develop two novel statistical methods that are able to handle the related problems such as borrowing data from historical studies. The proposed methods are analogous to the methods for the gene-environment interactions on behalf of the success on balancing the statistical efficiency and bias in a unified model. By extensive simulation studies, we compare the operating characteristics of the proposed models with the existing models including the hierarchical meta-analysis model. The results show that the proposed approaches adaptively borrow the historical data in a data-driven way. These novel models may have a broad range of statistical applications in both of genetic/genomic and clinical studies.

Analysis of line x environment interactions for yield in navy beans. 2. Pattern analysis of lines and environment within years

Seven years of multi-environment yield trials of navy bean (Phaseolus vulgaris L.) grown in Queensland were examined. As is common with plant breeding evaluation trials, test entries and locations varied between years. Grain yield data were analysed for each year using cluster and ordination analyses (pattern analyses). These methods facilitate descriptions of genotype performance across environments and the discrimination among genotypes provided by the environments. The observed trends for genotypic yield performance across environments were partly consistent with agronomic and disease reactions at specific environments and also partly explainable by breeding and selection history. In some cases, similarities in discrimination among environments were related to geographic proximity, in others management practices, and in others similarities occurred between geographically widely separated environments which differed in management practices. One location was identified as having atypical line discrimination. The analysis indicated that the number of test locations was below requirements for adequate representation of line x environment interaction. The pattern analyses methods used were an effective aid in describing the patterns in data for each year and illustrated the variations in adaptive patterns from year to year. The study has implications for assessing the number and location of test sites for plant breeding multi-environment trials, and for the understanding of genetic traits contributing to line x environment interactions.

The journey of becoming involved : the experience of participation in urban spaces by children with diverse mobility

Despite considerable discussion regarding the virtues of participation in urban spaces, the urban experience of children with disabilities has been largely ignored. This intensive study reported on the everyday experience of urban participation on the part of children with conditions such as cerebral palsy, muscular dystrophy, and juvenile arthritis, contributing new insights into their experience of journeys central to becoming involved in settings such as schools, neighbourhoods and shopping centres. The study identified problems in body – space – context relationships as points of intervention in our urban settings that promise to make a significant difference to their everyday journeys.

Pattern analysis of the diversity of morphological plant attributes and herbage yield in a world collection of white clover (Trifolium repens L.) germplasm characterised in a summer moisture stress environment of Australia

Information on the variation available for different plant attributes has enabled germplasm collections to be effectively utilised in plant breeding. A world sourced collection of white clover germplasm has been developed at the White Clover Resource Centre at Glen Innes, New South Wales. This collection of 439 accessions was characterised under field conditions as a preliminary study of the genotypic variation for morphological attributes; stolon density, stolon branching, number of nodes. number of rooted nodes, stolon thickness, internode length, leaf length, plant height and plant spread, together with seasonal herbage yield. Characterisation was conducted on different batches of germplasm (subsets of accessions taken from the complete collection) over a period of five years. Inclusion of two check cultivars, Haifa and Huia, in each batch enabled adjustment of the characterisation data for year effects and attribute-by-year interaction effects. The component of variance for seasonal herbage yield among batches was large relative to that for accessions. Accession-by-experiment and accession-by-season interactions for herbage yield were not detected. Accession mean repeatability for herbage yield across seasons was intermediate (0.453). The components of genotypic variance among accessions for all attributes, except plant height, were larger than their respective standard errors. The estimates of accession mean repeatability for the attributes ranged from low (0.277 for plant height) to intermediate (0.544 for internode length). Multivariate techniques of clustering and ordination were used to investigate the diversity present among the accessions in the collection. Both cluster analysis and principal component analysis suggested that seven groups of accessions existed. It was also proposed from the pattern analysis results that accessions from a group characterised by large leaves, tall plants and thick stolons could be crossed with accessions from a group that had above average stolon density and stolon branching. This material could produce breeding populations to be used in recurrent selection for the development of white clover cultivars for dryland summer moisture stress environments in Australia. The germplasm collection was also found to be deficient in genotypes with high stolon density, high number of branches high number of rooted nodes and large leaves. This warrants addition of new germplasm accessions possessing these characteristics to the present germplasm collection.

Markers of genetic susceptibility in human environmental hygiene and toxicology : the role of selected CYP, NAT and GST genes

Inherited genetic traits co-determine the susceptibility of an individual to a toxic chemical. Special emphasis has been put on individual responses to environmental and industrial carcinogens, but other chronic diseases are of increasing interest. Polymorphisms of relevant xenobiotic metabolising enzymes may be used as toxicological susceptibility markers. A growing number of genes encoding enzymes involved in biotransformation of toxicants and in cellular defence against toxicant-induced damage to the cells has been identified and cloned, leading to increased knowledge of allelic variants of genes and genetic defects that may result in a differential susceptibility toward environmental toxicants. "Low penetrating" polymorphisms in metabolism genes tend to be much more common in the population than allelic variants of "high penetrating" cancer genes, and are therefore of considerable importance from a public health point of view. Positive associations between cancer and CYP1A1 alleles, in particular the *2C I462V allele, were found for tissues following the aerodigestive tract. Again, in most cases, the effect of the variant CYP1A1 allele becomes apparent or clearer in connection with the GSTM1 null allele. The CYP1B1 codon 432 polymorphism (CYP1B1*3) has been identified as a susceptibility factor in smoking-related head-and-neck squameous cell cancer. The impact of this polymorphic variant of CYP1B1 on cancer risk was also reflected by an association with the frequency of somatic mutations of the p53 gene. Combined genotype analysis of CYP1B1 and the glutathione transferases GSTM1 or GSTT1 has also pointed to interactive effects. Of particular interest for the industrial and environmental field is the isozyme CYP2E1. Several genotypes of this isozyme have been characterised which seem to be associated with different levels of expression of enzyme activity. The acetylator status for NAT2 can be determined by genotyping or by phenotyping. In the pathogenesis of human bladder cancer due to occupational exposure to "classical" aromatic amines (benzidine, 4-aminodiphenyl, 1-naphthylamine) acetylation by NAT2 is regarded as a detoxication step. Interestingly, the underlying European findings of a higher susceptibility of slow acetylators towards aromatic amines are in contrast to findings in Chinese workers occupationally exposed to aromatic amines which points to different mechanisms of susceptibility between European and Chinese populations. Regarding human bladder cancer, the hypothesis has been put forward that genetic polymorphism of GSTM1 might be linked with the occurrence of this tumour type. This supports the hypothesis that exposure to PAH might causally be involved in urothelial cancers. The human polymorphic GST catalysing conjugation of halomethanes, dihalomethanes, ethylene oxide and a number of other industrial compounds could be characterised as a class theta enzyme (GSTT1) by means of molecular biology. "Conjugator" and "non-conjugator" phenotypes are coincident with the presence and absence of the GSTT1 gene. There are wide variations in the frequencies of GSTT1 deletion (GSTT1 *0/0) among different ethnicities. Human phenotyping is facilitated by the GST activity towards methyl bromide or ethylene oxide in erythrocytes which is representative of the metabolic GSTT1 competence of the entire organism. Inter-individual variations in xenobiotic metabolism capacities may be due to polymorphisms of the genes coding for the enzymes themselves or of the genes coding for the receptors or transcription factors which regulate the expression of the enzymes. Also, polymorphisms in several regions of genes may cause altered ligand affinity, transactivation activity or expression levels of the receptor subsequently influencing the expression of the downstream target genes. Studies of individual susceptibility to toxicants and gene-environment interaction are now emerging as an important component of molecular epidemiology.