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At least 19 recordsLinked to original sources
PubMed1976

The significance for breeding of linear regression analysis of genotype-environment interactions.

Methods of regression analysis of genotype-environment interaction are considered in relation to existing theory dealing with the relative efficiencies of selection for general or specific adaptation to the environment, and the choice of environments for assessment. The two alternative models is involving regression on to environmental effects (model 2) or genotypic effects (model 3) are equivalent when regression lines are concurrent, but are shown to be mutually exclusive when concurrence is absent...

Crosses, Genetic↗
PubMed1979

Allozyme genotype--environment relationships in natural populations of Drosophila buzzatii.

Allozyme frequency data from natural populations of Drosophila buzzatii were analyzed for genotype--environment relationships. Allele frequency and heterozygosity at six loci polymorphic throughout eastern Australia and a number of environmental factors (both means and variabilities) were examined by a variety of multivariate techniques. Significant genotype--environment associations were found for five of the six loci, and after correcting for geographic location significant associations remained for Est-2 and Adh-1 gene frequencies and heterozygosities and for Pgm gene frequencies. The results are discussed in relation to selection and gene flow and provide the basis for laboratory studies to disentangle confounded effects of (1) environmental means and environmental variabilities and (2) allele frequency and heterozygosity, and thus to further test for and determine the nature of any natural selection at particular allozyme loci.

Alleles↗
PubMed1977

The use of regression methods to study genotype-environment interactions: extending Griffing's model for diallel cross experiments and testing an empirical grouping method.

A model combining features of Griffing's diallel cross analysis with regression analysis for genotype-environment interactions is introduced using carp data of Moav et al. (1975) as an example. An analysis of variance based on this model provides information on the combining abilities of genetic effects and the interactions of these effects with environments from which inferences can readily be made on heterosis and heterosis-environment interactions. Applying the empirical grouping method of Lin and Thompson (1975) to these data (ignoring their diallel cross structure) established groups which were remarkably consistent with their members' crossing backgrounds.

Alleles↗
PubMed1976

A dynamic study of genotype-environment interaction with egg laying of Tribolium castaneum.

Within-line family selection was carried out at an optimum (33 degrees C) and two stress temperatures (28 degrees C and 38 degrees C) for increasing egg number laid by virgin females of Tribolium castaneum from the 7th to the 11th day after adult emergence. A control was maintained throughout the experiment. Two replicated lines were selected at each temperature and all selected lines were tested in each of the three environments. Direct and correlated responses to selection in different environments have been analysed in order to study implications of genotype-environment interaction on the selection outcome. Although selection at the optimum environment has been the most effective it did not confer the specialisation needed for performance in stress environments. However, selecting in adverse environments led to a broader range of performance over environments, which in one case (38 degrees C) included the optimum one. The degree of adaptation to adverse environments was mainly determined by the magnitude of the genetic correlation between performances in the adverse and the optimum environments. The evolution of such correlations through selection has also been investigated.

Adaptation, Physiological↗
PubMed1975

Genetic differences between the Chinese and European races of the common carp.I. Analysis of genotype-environment interactions for growth rate.

Growth rate of 12 groups of common carp was measured at five experimental environments. Three of the 12 tested groups were strains of the domesticated European race of the common carp, one group was a representative of the Big-Belly Chinese race, and the remaining eight groups were F1 crossbreds among the European strains and between the European and the Chinese races. The average growth rate over the five environments of the Chinese Big-Belly was considerably poorer than that of the European carp. All the inter-race crossbreds and the crossbreds among the European strains showed heterosis. When the genotype-environment interaction was presented as a linear function of the quality of the environment, the regression coefficient (the overall responsiveness parameter) assumed relatively low values in the Big-Belly and two to two-and-a-half fold higher values in the European carp. The overall responsiveness of crossbreds was, on the average, intermediate between the two parents. When, however, it was partitioned into a scale function of the average genotype and specific independent responsiveness, the two components showed a high degree of heterosis but in opposite directions. An explanation of this genetic system in terms of adaptive evolution to the diverse modes of carp domestication in Europe and China was given.

Animals↗
PubMed1975

An empirical method of grouping genotypes based on a linear function of the genotype-environment interaction.

The regression approach for analysing genotype-environmental interaction is extended to include the grouping of genotypes. An unweighted pair-group cluster analysis was applied to a special dissimilarly index, derived from the test statistic for differences among regressions. The resulting groups reflect the general pattern of response to the various environments. The data of Yates and Cochran (1938) were used to illustrate the clustering process.

Environment↗
PubMed1975

Open-field activity in mice as a function of ceiling height: A genotype-environment interaction.

Three inbred strains of mice, C57BL/6, DAB/2, and BALB/c, were run in the standard McClearn-type open-field with l-, 1 5/8-, and 36-inch ceiling heights. Although all strains showed increased activity as a function of lowered ceiling height, an interaction between strain and ceiling height was obtained: while BALB/c mice were the least active strain in the 36-inch-ceiling-height field, they were the most active strain in the 1-inch condition. Implications of this strain times environment interaction for the well-established low open-field activity of BALB/c and other albino strains are discussed.

Age Factors↗
PubMed2025

Prediction of Australian wheat genotype by environment interactions and mega-environments.

Latent environmental effects of genotype by environment interactions could be predicted from observed environmental covariates. Predictions into the wider target population of environments revealed greater insights. Wheat is grown across a diverse range of environments in Australia with contrasting environmental constraints. Targeted breeding to optimise genotypes in target environments is hindered by large and ubiquitous genotype by environment interactions (GEI). Common GEI in multi-environment trial experiments, which sample the target population of environments, can be efficiently modelled using latent environmental effects from factor analytic mixed models. However, generalised prediction into the full target population of environments is difficult without a clear link to observed environmental covariates (ECs) that are defined from high-resolution weather and soil data. Here, we used a large wheat multi-environment trial dataset and demonstrated that latent environmental effects can be associated with and predicted from observed ECs. We found GEI-based environment classes could be defined by combinations of key ECs. Prediction of main and latent effects in a wider set of environments covering the full TPE across the Australian grain belt over 13 years revealed the complex trends of environmental effects and GEI over regional scales demonstrating high year-to-year variability. Regional environment types often shifted year-to-year. Cross-validation of forward genomic prediction into untested year environments demonstrated that increased accuracy is possible if estimated genetic effects are also accurate and ECs of new environments are known. These findings may guide Australian wheat breeders to better target specifically adapted material to mega-environments defined by static GEI while also considering broad adaptability and non-static GEI resulting from year-to-year variability.

Triticum↗
PubMed1976

Genotype x environment interactions. IV. The effect of the background genotype.

Experimental evidence from sternopleural chaeta number and yield of offspring in Drosophila melanogaster bears out the expectation (Mather, 1975) that the value of the regression of g, measuring genotype X environment interaction, on e, measuring the overall effect of environmental change , depends on genes in which the contrasting genotypes are alike as well as on the genes in which they differ. With yield of offspring there is evidence of some genotypes reacting to the environmental changes in the opposite direction to others.

Chromosomes↗
PubMed2025

Genomic prediction of agronomic traits in perennial ryegrass (Lolium perenne L.) and genotype x environment interactions at the limit of the species distribution.

KEY MESSAGE: Perennial ryegrass shows extensive genotype x environment interactions at the limit of its ecological niche. Accounting for GxE may improve prediction even when environmental and genetic samples are highly diverse. BACKGROUND: In breeding the aim is to identify and accumulate beneficial variants. However, detection of these variants may be challenging in the presence of extensive genotype x environment interactions (GxE). METHODS: The study assesses the performance of 264 diploid perennial ryegrass accessions in a multi-environment field trial. We investigate the extent of GxE, for yield (total dry matter) and persistence traits under environmental conditions experienced in Nordic and Baltic regions at the limit of the species distribution. Two different approaches to modelling GxE were tested and validated under three different breeding scenarios. RESULTS: Our analysis documented the presence of significant GxE for all traits. Validation showed improvements in prediction accuracy when accounting for GxE: up to 4% for yield when predicting in unobserved environments, and up to 22% and 9% for spring cover and winter kill, respectively, when predicting unobserved germplasm. Genome-wide-association-studies (GWAS) were utilized to detect genetic variants with marginal effects (environment-independent effect) and conditional effects (environment-dependent effects). Results showed the presence of large-effect genetic variants with marginal effects, in addition to few Quantitative Trait Loci (QTL) whose effects were adaptive under specific environmental conditions while neutral or deleterious under different environmental conditions. CONCLUSION: This study demonstrates the usefulness and limitations of genomic prediction models for predicting GxE in highly diverse samples and describes the extent of GxE at the limit of species distribution for perennial ryegrass. Our study points towards adaptive variation which may enhance persistence of perennial ryegrass populations in Nordic and Baltic growing conditions.

Lolium↗