950 resultados para GENOTYPE AND ENVIRONMENT CONTRIBUTION


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The objective of the present work was to propose a method for testing the contribution of each level of the factors in a genotypes x environments (GxE) interaction using multi-environment trials analyses by means of an F test. The study evaluated a data set, with twenty genotypes and thirty-four environments, in a block design with four replications. The sum of squares within rows (genotypes) and columns (environments) of the GxE matrix was simulated, generating 10000 experiments to verify the empirical distribution. Results indicate a noncentral chi-square distribution for rows and columns of the GxE interaction matrix, which was also verified by the Kolmogorov-Smirnov test and Q-Q plot. Application of the F test identified the genotypes and environments that contributed the most to the GxE interaction. In this way, geneticists can select good genotypes in their studies.

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Acrylamide forms from free asparagine and reducing sugars during cooking, with asparagine concentration being the key parameter determining the formation in foods produced from wheat flour. In this study free amino acid concentrations were measured in the grain of varieties Spark and Rialto and four doubled haploid lines from a Spark x Rialto mapping population. The parental and doubled haploid lines had differing levels of total free amino acids and free asparagine in the grain, with one line consistently being lower than either parent for both of these factors. Sulfur deprivation led to huge increases in the concentrations of free asparagine and glutamine, and canonical variate analysis showed clear separation of the grain samples as a result of treatment (environment, E) and genotype (G) and provided evidence of G x E interactions. Low grain sulfur and high free asparagine concentration were closely associated with increased risk of acrylamide formation. G, E, and G x E effects were also evident in grain from six varieties of wheat grown at field locations around the United Kingdom in 2006 and 2007. The data indicate that progress in reducing the risk of acrylamide formation in processed wheat products could be made immediately through the selection and cultivation of low grain asparagme varieties and that further genetically driven improvements should be achievable. However, genotypes that are selected should also be tested under a range of environmental conditions.

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Pseudomonas aeruginosa is an opportunistic pathogen and an important cause of infection, particularly amongst cystic fibrosis (CF) patients. While specific strains capable of patient-to-patient transmission are known, many infections appear to be caused by unique and unrelated strains. There is a need to understand the relationship between strains capable of colonising the CF lung and the broader set of P. aeruginosa isolates found in natural environments. Here we report the results of a multilocus sequence typing (MLST)-based study designed to understand the genetic diversity and population structure of an extensive regional sample of P. aeruginosa isolates from South East Queensland, Australia. The analysis is based on 501 P. aeruginosa isolates obtained from environmental, animal and human (CF and non-CF) sources with particular emphasis on isolates from the Lower Brisbane River and isolates from CF patients obtained from the same geographical region. Overall, MLST identified 274 different sequence types, of which 53 were shared between one or more ecological settings. Our analysis revealed a limited association between genotype and environment and evidence of frequent recombination. We also found that genetic diversity of P. aeruginosa in Queensland, Australia was indistinguishable from that of the global P. aeruginosa population. Several CF strains were encountered frequently in multiple ecological settings; however, the most frequently encountered CF strains were confined to CF patients. Overall, our data confirm a non-clonal epidemic structure and indicate that most CF strains are a random sample of the broader P. aeruginosa population. The increased abundance of some CF strains in different geographical regions is a likely product of chance colonisation events followed by adaptation to the CF lung and horizontal transmission among patients.

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Commercial environments may receive only a fraction of expected genetic gains for growth rate as predicted from the selection environment This fraction is the result of undesirable genotype-by-environment interactions (G x E) and measured by the genetic correlation (r(g)) of growth between environments. Rapid estimates of genetic correlation achieved in one generation are notoriously difficult to estimate with precision. A new design is proposed where genetic correlations can be estimated by utilising artificial mating from cryopreserved semen and unfertilised eggs stripped from a single female. We compare a traditional phenotype analysis of growth to a threshold model where only the largest fish are genotyped for sire identification. The threshold model was robust to differences in family mortality differing up to 30%. The design is unique as it negates potential re-ranking of families caused by an interaction between common maternal environmental effects and growing environment. The design is suitable for rapid assessment of G x E over one generation with a true 0.70 genetic correlation yielding standard errors as low as 0.07. Different design scenarios were tested for bias and accuracy with a range of heritability values, number of half-sib families created, number of progeny within each full-sib family, number of fish genotyped, number of fish stocked, differing family survival rates and at various simulated genetic correlation levels

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Synthetic backcrossed-derived bread wheats (SBWs) from CIMMYT were grown in the north-west of Mexico (CIANO) and sites across Australia during 3 seasons. A different set of lines was evaluated each season, as new materials became available from the CIMMYT crop enhancement program. Previously, we have evaluated both the performance of genotypes across environments and the genotype x environment interaction (G x E). The objective of this study was to interpret the G x E for yield in terms of crop attributes measured at individual sites and to identify the potential environmental drivers of this interaction. Groups of SBWs with consistent yield performance were identified, often comprising closely related lines. However, contrasting performance was also relatively common among sister lines or between a recurrent parent and its SBWs. Early flowering was a common feature among lines with broad adaptation and/or high yield in the northern Australian wheatbelt, while yields in the southern region did not show any association with the maturity type. Lines with high yields in the southern and northern regions had cooler canopies during flowering and early grain filling. Among the SBWs with Australian genetic backgrounds, lines best adapted to CIANO were tall (>100 cm), with a slightly higher ground cover. These lines also displayed a higher concentration of water-soluble carbohydrates in the stem at flowering, which was negatively correlated with stem number per unit area when evaluated in southern Australia (Horsham). Possible reasons for these patterns are discussed. Selection for yield at CIANO did not specifically identify the lines best adapted to northern Australia, although they were not the most poorly adapted either. In addition, groups of lines with specific adaptation to the south would not have been selected by choosing the highest yielding lines at CIANO. These findings suggest that selection at CIMMYT for Australian environments may be improved by either trait based selection or yield data combined with trait information. Flowering date, canopy temperature around flowering, tiller density, and water-soluble carbohydrate concentration in the stem at flowering seem likely candidates.

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Commercial environments may receive only a fraction of expected genetic gains for growth rate as predicted from the selection environment. This fraction is result of undesirable genotype-by-environment interactions (GxE) and measured by the genetic correlation (rg) of growth between environments. Rapid estimates of genetic correlation achieved in one generation are notoriously difficult to estimate with precision. A new design is proposed where genetic correlations can be estimated by utilising artificial mating from cryopreserved semen and unfertilised eggs stripped from a single female. We compare a traditional phenotype analysis of growth to a threshold model where only the largest fish are genotyped for sire identification. The threshold model was robust to differences in family mortality differing up to 30%. The design is unique as it negates potential re-ranking of families caused by an interaction between common maternal environmental effects and growing environment. The design is suitable for rapid assessment of GxE over one generation with a true 0.70 genetic correlation yielding standard errors as low as 0.07. Different design scenarios were tested for bias and accuracy with a range of heritability values, number of half-sib families created, number of progeny within each full-sib family, number of fish genotyped, number of fish stocked, differing family survival rates and at various simulated genetic correlation levels.

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The soil-plant transfer factors for Cs and Sr were analyzed in relationship to soil properties, crops, and varieties of crops. Two crops and two varieties of each crop: lettuce (Lactuca sativa L.), cv. Salad Bowl Green and cv. Lobjoits Green Cos, and radish (Raphanus sativus L.), cv. French Breakfast 3 and cv. Scarlet Globe, were grown on five different soils amended with Cs and Sr to give concentrations of 1 mg kg(-1) and 50 mg kg(-1) of each element. Soil-plant transfer coefficients ranged between 0.12-19.10 (Cs) and 1.48-146.10 (Sr) for lettuce and 0.09-13.24 (Cs) and 2.99-93.00 (Sr) for radish. Uptake of Cs and Sr by plants depended on both plant and soil properties. There were significant (P less than or equal to 0.05) differences between soil-plant transfer factors for each plant type at the two soil concentrations. At each soil concentration about 60% of the variance in the uptake of the Cs and Sr was due to soil properties. For a given concentration of Cs or Sr in soil, the most important factor effecting soil-plant transfer of these elements was the soil properties rather than the crops or varieties of crops. Therefore, for the varieties considered here, soil-plant transfer of Cs and Sr would be best regulated through the management of soil properties. At each concentration of Cs and Sr, the main soil properties effecting the uptake of Cs and Sr by lettuce and radish were the concentrations of K and Ca, pH and CEC. Together with the concentrations of contaminants in soils, they explained about 80% of total data variance, and were the best predictors for soil-plant transfer. The different varieties of lettuce and radish gave different responses in soil-plant transfer of Cs and Sr in different soil conditions, i.e. genotype x environment interaction caused about 30% of the variability in the uptake of Cs and Sr by plants. This means that a plant variety with a low soil-plant transfer of Cs and Sr in one soil could have an increased soil-plant transfer factor in other soils. The broad implications of this work are that in contaminated agricultural lands still used for plant growing, contaminant-excluding crop varieties may not be a reliable method for decreasing contaminant transfer to foodstuffs. Modification of soil properties would be a more reliable technique. This is particularly relevant to agricultural soils in the former USSR still affected by fallout from the Chernobyl disaster.

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Objectives: To investigate the impact of apolipoprotein E (apoE) genotype on the response of the plasma lipoprotein profile to eicosapentaenoic acid (EPA) versus docosahexaenoic acid (DHA) intervention in humans. Methods and results: 38 healthy normolipidaemic males, prospectively recruited on the basis of apoE genotype (n = 20 E3/E3 and n = 18 E3/E4), completed a double-blind placebo-controlled cross-over trial, consisting of 3 × 4 week intervention arms of either control oil, EPA-rich oil (ERO, 3.3 g EPA/day) or DHA-rich oil (DRO, 3.7 g DHA/day) in random order, separated by 10 week wash-out periods. A significant genotype-independent 28% and 19% reduction in plasma triglycerides in response to ERO and DRO was observed. For total cholesterol (TC), no significant treatment effects were evident; however a significant genotype by treatment interaction emerged (P = 0.045), with a differential response to ERO and DRO in E4 carriers. Although the genotype × treatment interaction for LDL-cholesterol (P = 0.089) did not reach significance, within DRO treatment analysis indicated a 10% increase in LDL (P = 0.029) in E4 carriers with a non-significant 4% reduction in E3/E3 individuals. A genotype-independent increase in LDL mass was observed following DRO intervention (P = 0.018). Competitive uptake studies in HepG2 cells using plasma very low density lipoproteins (VLDL) from the human trial, indicated that following DRO treatment, VLDL2 fractions obtained from E3/E4 individuals resulted in a significant 32% (P = 0.002) reduction in LDL uptake relative to the control. Conclusions: High dose DHA supplementation is associated with increases in total cholesterol in E4 carriers, which appears to be due to an increase in LDL-C and may in part negate the cardioprotective action of DHA in this population subgroup.

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

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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The objective was to determine whether there is a genotype x environment interaction for age at first calving (AFC) in Holstein cattle in Brazil and Colombia. Data included 51,239 and 25,569 first-lactation records from Brazil and Colombia, respectively. Of 4230 sires in the data, 530 were North American sires used in both countries. Analyses were done using the REML bi-trait animal model, and AFC was considered as a distinct characteristic in each country. Fixed effects of contemporary group (herd-calving year), sire genetic group, and cow genetic group, and random effects of animal and residual variation were included in the model. Average AFC in Brazil and Colombia were 29.5 ± 4.0 and 32.1 ± 3.5 mo, respectively. Additive and residual genetic components and heritability coefficient for AFC in Brazil were 2.21 mo 2, 9.41 mo 2, and 0.19, respectively, whereas for Colombia, they were 1.02 mo 2, 6.84 mo 2, and 0.13, respectively. The genetic correlation of AFC between Brazil and Colombia was 0.78, indicating differences in ranking of sires consistent with a genotype x environment interaction. Therefore, in countries with differing environments, progeny of Holstein sires may calve at relatively younger or older ages compared with contemporary herdmates in one environment versus another.

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Descriptive herd variables (DVHE) were used to explain genotype by environment interactions (G x E) for milk yield (MY) in Brazilian and Colombian production environments and to develop a herd-cluster model to estimate covariance components and genetic parameters for each herd environment group. Data consisted of 180,522 lactation records of 94,558 Holstein cows from 937 Brazilian and 400 Colombian herds. Herds in both countries were jointly grouped in thirds according to 8 DVHE: production level, phenotypic variability, age at first calving, calving interval, percentage of imported semen, lactation length, and herd size. For each DVHE, REML bivariate animal model analyses were used to estimate genetic correlations for MY between upper and lower thirds of the data. Based on estimates of genetic correlations, weights were assigned to each DVHE to group herds in a cluster analysis using the FASTCLUS procedure in SAS. Three clusters were defined, and genetic and residual variance components were heterogeneous among herd clusters. Estimates of heritability in clusters 1 and 3 were 0.28 and 0.29, respectively, but the estimate was larger (0.39) in Cluster 2. The genetic correlations of MY from different clusters ranged from 0.89 to 0.97. The herd-cluster model based on DVHE properly takes into account G x E by grouping similar environments accordingly and seems to be an alternative to simply considering country borders to distinguish between environments.

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The objective of this study was to determine whether there is a genotype by environment interaction (GxE) for dairy buffaloes in Brazil and Colombia. The (co)variance components were estimated by using a bi-trait repeatability animal model with the REML method. Each trait consisted in the milk yield obtained in both countries. Contemporary group (herd, year and season of parity) and age at parity (linear and quadratic covariate) fixed effects, along with the additive genetic, permanent environment, and the residual random effects were included in the model. Genetic, permanent environmental and residual variance and heritabilities were different for both countries. The genetic correlations for milk yield between Brazil and Colombia were low (between 0.10 and 0.13), indicating a GxE interaction between both countries. Knowing that this interaction influences the genetic progress of buffalo populations in Brazil and Colombia, we recommend choosing sires tested in the country they will be used, along with conducting joint genetic evaluations that consider GxE interaction effects.

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The objective of this study was to evaluate the effect of genotype by environment interaction (GEI) on the weight of Tabapuã cattle at 240 (W240), 365 (W365) and 450 (W450) days of age. In total, 35,732 records of 8,458 Tabapuã animalswhich were born in the state of Bahia, Brazil, from 1975 to 2001, from 167 sires and 3,707 dams, were used. Two birth seasons were tested as for the environment effect: the dry (D) and rainy (R) ones. The covariance components were obtainedby a multiple-trait analysis using Bayesian inference, in which each trait was considered as being different in each season. Covariance components were estimated by software gibbs2f90. As for W240, the model was comprised of contemporary groups and cow age (in classes) as fixed effects; animal and maternal genetic additive, maternal permanent environmental and residual were considered as random effects. Concerning W365 and W450, the model included only the contemporary aged cow groups as fixed effects and the genetic additive and residual effects of the animal as the random ones. The GEI was assessed considering the genetic correlation, in which values below 0.80 indicated the presence of GEI. Regarding W365 and W450, the GEI was found in both seasons. As for post-weaning weight (W240), the effect of such interaction was not observed. ©2012 Sociedade Brasileira de Zootecnia.