Interação genótipo – ambiente no peso ao sobreano na raça Nelore

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Um estudo sobre métodos estatísticos na avaliação da interação genótipo x ambientes em genótipos de arroz (Oryza sativa L.)

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Potencial do composto flintisa anão de milho para melhoramento em condições de alta densidade populacional

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Variabilidade genética em progênies de polinização aberta de Eucalyptus urophylla

Pós-graduação em Ciência Florestal - FCA

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.

Landscapes of Poverty and Environment in Lao PDR

A key challenge for land change science is linking land cover information to human-environment interactions over larger spatial areas. Crucial information on land use types and people involved is still lacking. In Lao PDR, a country facing rapid and multilevel land change processes, this lack of information hinders evidence-based policy- and decision-making. We present a new approach for the description of landscape mosaics on national level and relate it to village level Population Census information. Results showed that swidden agricultural landscapes, involving 17% of the population, dominate 28% of the country, while permanent agricultural landscapes involve 74% of the population in 29% of the country.

Mutual Information for Testing Gene-Environment Interaction

Despite current enthusiasm for investigation of gene-gene interactions and gene-environment interactions, the essential issue of how to define and detect gene-environment interactions remains unresolved. In this report, we define gene-environment interactions as a stochastic dependence in the context of the effects of the genetic and environmental risk factors on the cause of phenotypic variation among individuals. We use mutual information that is widely used in communication and complex system analysis to measure gene-environment interactions. We investigate how gene-environment interactions generate the large difference in the information measure of gene-environment interactions between the general population and a diseased population, which motives us to develop mutual information-based statistics for testing gene-environment interactions. We validated the null distribution and calculated the type 1 error rates for the mutual information-based statistics to test gene-environment interactions using extensive simulation studies. We found that the new test statistics were more powerful than the traditional logistic regression under several disease models. Finally, in order to further evaluate the performance of our new method, we applied the mutual information-based statistics to three real examples. Our results showed that P-values for the mutual information-based statistics were much smaller than that obtained by other approaches including logistic regression models.

Concept, design and implementation of a cardiovascular gene-centric 50 k SNP array for large-scale genomic association studies.

A wealth of genetic associations for cardiovascular and metabolic phenotypes in humans has been accumulating over the last decade, in particular a large number of loci derived from recent genome wide association studies (GWAS). True complex disease-associated loci often exert modest effects, so their delineation currently requires integration of diverse phenotypic data from large studies to ensure robust meta-analyses. We have designed a gene-centric 50 K single nucleotide polymorphism (SNP) array to assess potentially relevant loci across a range of cardiovascular, metabolic and inflammatory syndromes. The array utilizes a "cosmopolitan" tagging approach to capture the genetic diversity across approximately 2,000 loci in populations represented in the HapMap and SeattleSNPs projects. The array content is informed by GWAS of vascular and inflammatory disease, expression quantitative trait loci implicated in atherosclerosis, pathway based approaches and comprehensive literature searching. The custom flexibility of the array platform facilitated interrogation of loci at differing stringencies, according to a gene prioritization strategy that allows saturation of high priority loci with a greater density of markers than the existing GWAS tools, particularly in African HapMap samples. We also demonstrate that the IBC array can be used to complement GWAS, increasing coverage in high priority CVD-related loci across all major HapMap populations. DNA from over 200,000 extensively phenotyped individuals will be genotyped with this array with a significant portion of the generated data being released into the academic domain facilitating in silico replication attempts, analyses of rare variants and cross-cohort meta-analyses in diverse populations. These datasets will also facilitate more robust secondary analyses, such as explorations with alternative genetic models, epistasis and gene-environment interactions.

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.

Impact of thermal stress on evolutionary trajectories of pathogen resistance in three-spined stickleback (Gasterosteus aculeatus), supplementary material

Background: Pathogens are a major regulatory force for host populations, especially under stressful conditions. Elevated temperatures may enhance the development of pathogens, increase the number of transmission stages, and can negatively influence host susceptibility depending on host thermal tolerance. As a net result, this can lead to a higher prevalence of epidemics during summer months. These conditions also apply to marine ecosystems, where possible ecological impacts and the population-specific potential for evolutionary responses to changing environments and increasing disease prevalence are, however, less known. Therefore, we investigated the influence of thermal stress on the evolutionary trajectories of disease resistance in three marine populations of three-spined sticklebacks Gasterosteus aculeatus by combining the effects of elevated temperature and infection with a bacterial strain of Vibrio sp. using a common garden experiment. Results: We found that thermal stress had an impact on fish weight and especially on survival after infection after only short periods of thermal acclimation. Environmental stress reduced genetic differentiation (QST) between populations by releasing cryptic within-population variation. While life history traits displayed positive genetic correlations across environments with relatively weak genotype by environment interactions (GxE), environmental stress led to negative genetic correlations across environments in pathogen resistance. This reversal of genetic effects governing resistance is probably attributable to changing environment-dependent virulence mechanisms of the pathogen interacting differently with host genotypes, i.e. GPathogenxGHostxE or (GPathogenxE)x(GHostxE) interactions, rather than to pure host genetic effects, i.e. GHostxE interactions. Conclusion: To cope with climatic changes and the associated increase in pathogen virulence, host species require wide thermal tolerances and pathogen-resistant genotypes. The higher resistance we found for some families at elevated temperatures showed that there is evolutionary potential for resistance to Vibrio sp. in both thermal environments. The negative genetic correlation of pathogen resistance between thermal environments, on the other hand, indicates that adaptation to current conditions can be a weak predictor for performance in changing environments. The observed feedback on selective gradients exerted on life history traits may exacerbate this effect, as it can also modify the response to selection for other vital components of fitness.

Mapeamento associativo para múltiplos ambientes e múltiplos locos, visando tolerância à seca em milho

A seca é um dos estresses abióticos mais importantes na cultura do milho, o qual ocasiona reduções significativas na produção de grãos. A arquitetura genética da tolerância à seca é complexa, fazendo-se necessária a melhor compreensão desse caráter. Estudos envolvendo mapeamento associativo são úteis por explorarem a variação genética de caracteres quantitativos e, adicionalmente, levam em conta informações acerca de genótipos, ambientes e interações genótipo por ambiente (G × E). Ao considerar efeitos de G × E em modelos de mapeamento associativo há possibilidade de identificar regiões no genoma associadas à condições e ambientes específicos. Este trabalho teve como objetivo detectar associações relacionadas à tolerância à seca em milho por meio de um modelo de mapeamento associativo para múltiplos ambientes e múltiplos locos, o qual permitiu distinguir associações com efeitos ambiente-específico daquelas com efeitos principais e de interação associação por ambiente (QEI). O painel associativo foi composto por 190 linhagens, classificadas de acordo com os grupos heteróticos quanto ao tipo de grão. Marcadores SNPs (∼500k) foram utilizados para a genotipagem do painel associativo. Duas linhagens (L228-3 e L3) foram usadas como testadores comuns e os híbridos obtidos foram avaliados em duas localidades (Janaúba-MG e Teresina-PI), dois anos agrícolas (2010 e 2011), sob duas condições de tratamento (irrigado e não irrigado). Ao total, consideraram-se seis caracteres: peso de grãos, intervalo de florescimento, florescimento feminino e masculino, altura de planta e de espiga. Consideraram-se dois grupos de mapeamento, agrupados de acordo com os testadores utilizados. SNPs foram úteis para testar associações ao longo do genoma do milho e investigar o relacionamento genético entre indivíduos. O modelo de mapeamento associativo, com inclusão de informações sobre interação G × E, detectou o total de 179 associações, e o maior número de associações foram relacionadas aos caracteres de florescimento. A maioria das associações (168) apresentaram QEI significativo, sendo que o tamanho e a magnitude desses efeitos distinguiram-se de acordo com o ambiente em avaliação. Apenas o caráter florescimento feminino não apresentou associações com efeitos estáveis ao longo dos ambientes em estudo. A detecção de algumas associações em posições próximas do genoma evidenciam possíveis efeitos de pleiotropia. Algumas associações foram co-localizadas em regiões do genoma do milho relacionadas à tolerância à seca, sendo que algumas dessas associações estavam envolvidas a fatores pertencentes à vias metabólicas de interesse. O presente estudo forneceu informações úteis para a compreensão da base genética da tolerância à seca em milho sob os ambientes específicos em avaliação.

Gasterosteus aculeatus egg traits and fish lengths vs. experiment setup

1) Our study addresses the role of non-genetic and genetic inheritance in shaping the adaptive potential of populations under a warming ocean scenario. We used a combined experimental approach (transgenerational plasticity and quantitative genetics) to partition the relative contribution of maternal vs. paternal (additive genetic) effects to offspring body size (a key component of fitness), and investigated a potential physiological mechanism (mitochondrial respiration capacities) underlying whole organism growth/size responses. 2) In very early stages of growth (up to 30 days), offspring body size of marine sticklebacks benefited from maternal transgenerational plasticity (TGP): offspring of mothers acclimated to17°C were larger when reared at 17°C, and offspring of mothers acclimated to 21°C were larger when reared at 21°C. The benefits of maternal TGP on body size were stronger and persisted longer (up to 60 days) for offspring reared in the warmer (21°C) environment, suggesting that maternal effects will be highly relevant for climate change scenarios in this system. 3) Mitochondrial respiration capacities measured on mature offspring (F1 adults) matched the pattern of TGP for juvenile body size, providing an intuitive mechanistic basis for the maternal acclimation persisting into adulthood. Size differences between temperatures seen at early growth stages remained in the F1 adults, linking offspring body size to maternal inheritance of mitochondria. 4) Lower maternal variance components in the warmer environment were mostly driven by mothers acclimated to ambient (colder) conditions, further supporting our tenet that maternal effects were stronger at elevated temperature. Importantly, all parent-offspring temperature combination groups showed genotype x environment (GxE) interactions, suggesting that reaction norms have the potential to evolve. 5) To summarise, transgenerational plasticity and genotype x environment interactions work in concert to mediate impacts of ocean warming on metabolic capacity and early growth of marine sticklebacks. TGP can buffer short-term detrimental effects of climate warming and may buy time for genetic adaptation to catch up, therefore markedly contributing to the evolutionary potential and persistence of populations under climate change.

Antidepressant Use and Risk of Dementia and Cognitive Decline

Thesis (Ph.D.)--University of Washington, 2016-06

Case-only study of interactions between genetic polymorphisms of GSTM1, P1, T1 and Z1 and smoking in Parkinson's disease

Current opinion contends that complex interactions between genetic and environmental factors play a role in the etiology of Parkinson's disease (PD). Cigarette smoking is thought to reduce risk of PD, and emerging evidence suggests that genetic factors may modulate smoking's effect. We used a case-only design, an approach not previously used to study gene-environment interactions in PD, specifically to study interactions between glutathione-S-transferase (GST) gene polymorphisms and smoking in relation to PD. Four-hundred PD cases (age at onset: 60.0 +/- 10.7 years) were genotyped for common polymorphisms in GSTM1, PI, T1 and Z1 using well-established methods. Smoking exposure data were collected in face-to-face interviews. The independence of the studied GST genotypes and smoking exposure was confirmed by studying 402 healthy, aged individuals. No differences were observed in the distributions of GSTM1, T1 or Z1 polymorphisms between ever-smoked and never-smoked PD cases using logistic regression (all P > 0.43). However, GSTP1 *C haplotypes were over-represented among PD cases who ever smoked (odds ratio for interaction (ORi) = 2.00 (95% Cl: 1.11-3.60, P = 0.03)). Analysis revealed that ORi between smoking and the GSTP1-114Val carrier status increased with increasing smoking dose (P = 0.02 for trend). These data suggest that one or more GSTP1 polymorphisms may interact with cigarette smoking to influence the risk for PD. (C) 2004 Elsevier Ireland Ltd. All rights reserved.

Application of DNA parentage analyses for determining relative growth rates of Penaeus japonicus families reared in commercial ponds

The ability to track large numbers of individuals and families is a key determinant of the power and precision of breeding programs, including the capacity to quantify interactions between genotypes and their environment. Until recently, most family based selective breeding programs for shrimp, and other highly fecund aquaculture species, have been restricted by the number of animals that can be physically tagged and individually selected. Advances in the development of molecular markers, such as microsatellite loci, are now providing the means to track large numbers of individuals and families in commercial production systems. In this study microsatellites, coupled with DNA parentage analyses, were used to determine the relative performance of 22 families of R japonicus reared in commercial production ponds. In the experimental design 6000 post-larvae from each of 22 families, whose maternal parents had been genotyped at 8 microsatellite loci, were stocked into each of four I ha ponds. After 6 months the ponds were harvested and a total of 6000 individuals were randomly weighed from each pond. Mean wet weight of the shrimp from one pond was significantly lower than that of the other three ponds demonstrating a possible pond effect on growth rate. The representation of families in the top 10% of each pond's weight distribution was then determined by randomly genotyping up to 300 individuals from this upper weight class. Parentage analyses based on individual genotypic data demonstrated that some families were over-represented in the top 10% in all ponds, while others were under-represented due to slower growth rates. The results also revealed some weak, but significant, male genotype x environment (G x E) interactions in the expression of shrimp growth for some families. This indicates that G x E effects may need to be factored into future R japonicus selective breeding programs. This study demonstrated the utility of DNA parentage analyses for tracking individual family performance in communally stocked shrimp pond populations and, its application to examining G x E effects on trait expression under commercial culture conditions. Crown Copyright (c) 2005 Published by Elsevier B.V. All rights reserved.