Effects of genotype-environment interactions on genetic correlations

The objective of the work presented here was to investigate the influence of genotype-environment interaction on genetic correlations. In our theoretical models we have considered plant populations consisting of random samples of lines from chromosome-doubled haploids produced from F₁ gametes, highly inbred SSD-lines, and clones of randomly breeding populations grown in two and multiple environments. The results of our theoretical considerations are that if genotype-environment interaction exists, great differences are expected to occur in the estimates of genetic correlation coefficients obtained in different environments. Based on the variance and covariance components for genotype-environment interaction we suggest a new type of correlation coefficient, called genotype-environment correlation, r_ge. Our theory has been applied to several series of experiments. Estimates are presented from two series, both of which demonstrate clearly the consequences of genotype-environment interaction on the genetic correlations.     

Relationships between genotype x environment interaction and genetic correlation of the same trait measured in different environments

Summary Relationships between genotype x environment interaction and genetic correlation of the same trait measured in different fixed environments are derived by comparing the variance-covariance structures of observations between a one-way multiple-trait linear model and a two-way single-trait mixed linear model. In the latter model, heterogeneity of interaction variances among environments and non-zero covariances among interactions are assumed, in addition to the heterogeneity of error variances and non-zero covariances between genetic-group effects and interactions that were accommodated in earlier work. The results are applicable to more than two environments and to unbalanced data. This paper is a generalization and a correction of earlier works.     

Genotype x environment interaction in QTL analysis of an intervarietal almond cross by means of genetic markers

Besides QTL location and the estimation of gene effects, QTL analysis based on genetic markers could be used to comprehensively investigate quantitative trait-related phenomena such as pleiotropy, gene interactions, heterosis, and genotype-by-environment interaction (G x E). Given that the G x E interaction is of great relevance in tree improvement, the objective of the research presented here was to study the effect of years on QTL detection for 15 quantitative traits by means of isozymatic markers in a large progeny group of an intervarietal cross of almond. At least 17 putative QTLs were detected, 3 of which had alleles with opposite effects to those predicted from the parental genotypes. Only 3 QTLs behaved homogeneously over the years. Three possible causes are discussed in relation to this lack of stability: the power of the test statistic being used, the low contribution of the QTL to the genetic variation of the trait, and a differential gene expression dependent on the year (G x E). Most cases showing lack of stability involved traits whose heritability estimates change drastically from year to year and/or whose correlation coefficients between years are low, suggesting the presence of G x E as the most likely cause. A marker-assisted selection scheme to improve late flowering and short flowering duration is suggested for an early and wide screening of the progeny.     

Genotype by environment interactions in viability and developmental time in populations of cactophilic Drosophila

The genetic and ecological basis of viability and developmental time differences between Drosophila buzzatii and D. koepferae were analysed using the isofemale line technique. Several isofemale lines were sampled from pairs of allopatric/sympatric populations of each species. Flies were reared in media prepared with decaying tissues of two of the main natural cactus hosts of each species. This experimental design enabled us to evaluate the relative contribution of phenotypic plasticity, genetic variation and genotype by environment interaction (G x E) to total phenotypic variation for two fitness traits, viability and developmental time. Our results revealed significant G x E in both traits, suggesting that the maintenance of genetic variation can be explained, at least in part, by diversifying selection in different patches of a heterogeneous environment in both species. However, the relative importance of the factors involved in the G x E varied between traits and populations within species. For viability, the G x E can be mainly attributed to changes in the rank order of lines across cacti. However, the pattern was different for developmental time. In D. buzzatii the G x E can be mainly accounted for by changes in among line variance across cacti, whereas changes in the rank order of lines across cacti was the main component in D. koepferae. These dissimilar patterns of variation between traits and species suggest that the evolutionary forces shaping genetic variation for developmental time and viability vary between populations within species and between species.     

Factor regression for interpreting genotype-environment interaction in bread-wheat trials

Summary The French INRA wheat (Triticum aestivum L. em Thell.) breeding program is based on multilocation trials to produce high-yielding, adapted lines for a wide range of environments. Differential genotypic responses to variable environment conditions limit the accuracy of yield estimations. Factor regression was used to partition the genotype-environment (GE) interaction into four biologically interpretable terms. Yield data were analyzed from 34 wheat genotypes grown in four environments using 12 auxiliary agronomic traits as genotypic and environmental covariates. Most of the GE interaction (91%) was explained by the combination of only three traits: 1,000-kernel weight, lodging susceptibility and spike length. These traits are easily measured in breeding programs, therefore factor regression model can provide a convenient and useful prediction method of yield.     

Genotype x strain interactions for resistance to Fusarium head blight caused by Fusarium culmorum in winter wheat

Summary In 3 consecutive years, a set of 17 winter wheat genotypes, representing a wide range of Fusarium head blight resistance, was inoculated with four strains of Fusarium culmorum. Fusarium head blight ratings were analyzed. The interaction between genotypes, strains, and years was described using a Finlay-Wilkinson model and an Additive Main effects and Multiplicative Interaction effects (AMMI) model. The interaction consisted primarily of a divergence of genotypical responses with increasing disease pressure, modified by genotype specific reactions in certain years. The divergence was mainly caused by one very pathogenic strain. The Fusarium head blight resistance in this study can be described as horizontal resistance in terms of Vanderplank, with the exception of three genotypes selected from one particular cross that showed a strain-year combination dependent resistance which was ineffective in 1 year.     

Genotype x environment interaction of crossover type: detecting its presence and estimating the crossover point

Genotype-environment interaction (GEI) introduces inconsistency in the relative rating of genotypes across environments and plays a key role in formulating strategies for crop improvement. GEI can be either qualitative (i.e., crossover type) or only quantitative (i.e., non-crossover type). Since the presence of crossover-type interaction has a strong implication for breeding for specific adaptation, it is important to assess the frequency of crossover interactions. This paper presents a test for detecting the presence of crossover-type interaction using the response-environment relationship and enumerates the frequency of crossovers and estimation of the crossover point (CP) on the environment axis, which serves as a cut-off point for the two environments groups where different/specific selections can be made. Sixty-four barley lines with various selection histories were grown in northern Syria and Lebanon giving a total of 21 environments (location-year combinations). Linear regression of the genotypic response on the environmental index represented a satisfactory model, and heterogeneity among regressions was significant. At a 5% level of significance, 38% and 19% of the pairs showed crossover interactions when the error variances were considered heterogeneous and homogeneous, respectively, implying that an appreciable number of crossovers took place in the case of barley lines responding to their environments. The CP of 1.64 t/ha, obtained as the CP of regression lines between the genotype numbers 19 and 31, provided maximum genotype x environment-group interaction. Across all environments, genotype nos. 59 and 12 stood first and second for high yield, respectively. The changes in the ranks of genotypes under the groups of environments can be used for selecting specifically adapted genotypes. Received: 25 January 1999 / Accepted: 16 March 1999     

Genotype by environment interactions in winter survival in red deer

1. The extent to which environmental heterogeneity interacts with genetic hetero geneity to affect individual fitness within populations has the potential to affect the dynamics of natural populations and the amount of genetic variation maintained in natural populations, yet is a relatively poorly investigated topic in either ecology or evolutionary biology.
2. Many individual-based studies are precluded from such investigations by the practical problems of measuring heritability of traits affecting fitness and the difficulties of experimental manipulation of the study population. One way of demonstrating how commonly genotype by environmental interactions affect fitness, though not their overall importance in determining fitness, is to investigate fitness in a population subdivided by genotype at one or more marker loci.
3. We analyse data on calf winter survival from a population of red deer from the Isle of Rum, Scotland. Data on individual survival, environmental fluctuations and genotype at 13 loci were collected from 1982 to 1994.
4. We found associations between survival over the first winter of life and calf genotype at two out of three allozyme loci and five out of 10 microsatellite loci. All of the results remained significant under randomization tests. Other genotypes that initially appeared to have an association with survival were rejected when bootstrapped, usually due to insufficient data or anomalies in the data.
5. Our results suggest that associations between fitness and genotype are common. Five out of the seven associations found involved interactions with environmental variables. Four of these showed density-dependent selection with different genotypes showing high survival at high population size compared to low population size and one interacted with autumn rainfall. In a sixth case, genotype interacted with sex.     

Development of phosphogluco-isomerase isozymes in perennial ryegrass (Lolium perenne)

The development of isozymes of phosphogluco-isomerase (PGI; D-glucose-6-phosphate ketol isomerase EC 5.3.1.9.) in perennial ryegrasses was followed from dry seed through to leaf senescence using starch gel electrophoretic separations. Root isozymes were also examined. Two forms of the enzyme were found, one (PGI/2) being present in all tissues and at all stages of the life cycle. The other (PGI/1) had two zones of activity, one of which was detected only in light-exposed tissue. Normal development of this form could be inhibited by growing seedlings on distilled water. Some alleles of PGI/2 not previously reported for ryegrasses are also described.     

Development of a Brazilian maize core collection

The aim of this study was to evaluate methods for developing a Brazilian maize core collection. For an initial survey of the active collection, passport information, as well as characterization and evaluation of accessions, were taken into consideration, these then being divided according to geographic region and kernel-type. Multiple sampling methods were evaluated. The strategy of constant sampling generated extensive alterations in extract accession frequency. The multivariate strategy with dispersion graphs and principal components associated with the Tocher method was considered efficient for identifying the most divergent genotypes. The multivariate strategy generated greater alterations in the variance of traits. The average number of traits revealed few modifications with the various sampling strategies used. Therefore, the active collection could be considered as possessing a satisfactory amount of information for most of its accessions. Moreover, the multivariate strategy generated modifications in the variance of the traits, independent of sampling intensity.     

Genetic change following fire in populations of a seed-banking perennial plant

Disturbances such as fire have the potential to remove genetic variation, but seed banks may counter this loss by restoring alleles through a reservoir effect. We used allozyme analysis to characterize genetic change in two populations of the perennial Hypericum cumulicola, an endemic of the fire-prone Florida scrub. We assessed genetic variation before and 1, 2, and 3 years after fire that killed nearly all aboveground plants. Populations increased in size following fire, with most seedlings likely recruited from a persistent seed bank. Four of five loci were variable. Most alleles were present in low frequencies, but our large sample sizes allowed detection of significant trends. Expected heterozygosity increased, and allele presence and allele frequencies showed marked shifts following fire. The post-fire seedling cohort contained new alleles to the study and one new allele to the species. Population differentiation between the two study sites did not change. Our study is the first to directly documents genetic changes following fire, a dominant ecological disturbance worldwide, and is also one of the few to consider shifts in a naturally recruiting post-disturbance seedling cohort. We demonstrate the potential of seed banks to restore genetic variation lost between disturbances. Our study demonstrates that rapid genetic change can occur with disturbance and that fire can have positive effects on the genetics of rare species.     

Genotype by environment interaction for 450-day weight of Nelore cattle analyzed by reaction norm models

Genotype by environment interactions (GEI) have attracted increasing attention in tropical breeding programs because of the variety of production systems involved. In this work, we assessed GEI in 450-day adjusted weight (W450) Nelore cattle from 366 Brazilian herds by comparing traditional univariate single-environment model analysis (UM) and random regression first order reaction norm models for six environmental variables: standard deviations of herd-year (RRMw) and herd-year-season-management (RRMw-m) groups for mean W450, standard deviations of herd-year (RRMg) and herd-year-season-management (RRMg-m) groups adjusted for 365-450 days weight gain (G450) averages, and two iterative algorithms using herd-year-season-management group solution estimates from a first RRMw-m and RRMg-m analysis (RRMITw-m and RRMITg-m, respectively). The RRM results showed similar tendencies in the variance components and heritability estimates along environmental gradient. Some of the variation among RRM estimates may have been related to the precision of the predictor and to correlations between environmental variables and the likely components of the weight trait. GEI, which was assessed by estimating the genetic correlation surfaces, had values < 0.5 between extreme environments in all models. Regression analyses showed that the correlation between the expected progeny differences for UM and the corresponding differences estimated by RRM was higher in intermediate and favorable environments than in unfavorable environments (p < 0.0001).     

Determining environmental covariates which explain genotype environment interaction in winter wheat through probe genotypes and biadditive factorial regression

Genotype-environment interaction has been analyzed in a winter-wheat breeding network using bi-additive factorial regression models. This family of models generalizes both factorial regression and biadditive (or AMMI) models; it fits especially well when abundant external information is available on genotypes and/or environments. Our approach, focused on environmental characterization, was performed with two kinds of covariates: (1) deviations of yield components measured on four probe genotypes and (2) usual indicators of yield-limiting factors. The first step was based on the analysis of a crop diagnosis on four probe genotypes. Difference of kernel number to a threshold number (DKN) and reduction of thousand-kernel weight from a potential value (RTKW) were used to characterize the grain-number formation and the grain-filling periods, respectively. Grain yield was analyzed according to a biadditive factorial regression model using eight environmental covariates (DKN and RTKW measured on each of four probe genotypes). In the second step, the usual indicators of yield-limiting factors were too numerous for the analysis of grain yield. Thus a selection of a subset of environmental covariates was performed on the analysis of DKN and RTKW for the four probe genotypes. Biadditive factorial regression models involved environmental covariates related to each deviation and included environmental main effect, sum of water deficits, an indicator of nitrogen stress, sum of daily radiation, high temperature, pressure of powdery mildew and lodging. The correlations of each environmental covariate to the synthetic variates helped to discard those poorly involved in interaction (with | correlation | <0.3). The grain yield of 12 genotypes was interpreted with the retained covariates using biadditive factorial regression. The models explained about 75% of the interaction sums of squares. In addition, the biadditive factorial regression biplot gave relevant information about the interaction of the genotypes (interaction pattern and sensitivities to environmental covariates) with respect to the environmental covariates and proved to be interesting for such an approach. Received: 8 March 1999 / Accepted: 29 July 1999     

Pedigree analysis for composing a core collection of modern cultivars,with examples from barley (Hordeum vulgare s. lat.)

A method for analyzing the pedigrees of cultivars is developed that allows for the calculation of the effective number of origin lines (n _OL ). The n _OL is defined as the average number of alleles, not identical by descent, per locus in a set of lines. Its relationship with the commonly used coefficient of parentage is clarified. A related quantity, the effective overlap of origin lines (r _OL ) is defined as the average number of alleles, not identical by descent, per locus common in two sets of individuals. A set of 85 modern barley cultivars is used to illustrate the application of n _OL and r _OL . This set originated from 153 mutually unrelated ancestors. The degree to which each ancestor contributed was quantified, and the result was a n _OL of only 43.1. In the set were 51 spring and 34 winter cultivars, with a n _OL of 25.0 and 21.0, respectively. Consequently, the r _OL of these two groups was 2.9, indicating that the two groups can be considered to be nearly distinct genetically since they have only 2.9 origin lines in common. How the effective number of origin lines can be used to create a core collection of cultivars with known pedigrees by maximizing the n _OL in a set of cultivars of given size is also discussed.     

Relationships between genotype x environment interactions and rank orders for a set of genotypes tested in different environments

Multilocation trials in plant breeding lead to cross-classified data sets with rows=genotypes and columns=environments, where the breeder is particularly interested in the rank orders of the genotypes in the different environments. Non-identical rank orders are the result of genotype x environment interactions. Not every interaction, however, causes rank changes among the genotypes (rank-interaction). From a breeder's point of view, interaction is tolerable only as long as it does not affect the rank orders. Therefore, the question arises of under which circumstances does interaction become rank-interaction. This paper contributes to our understanding of this topic. In our study we emphasized the detection of relationships between the similarity of the rank orders (measured by Kendall's coefficient of concordance W) and the functions of the diverse variance components (genotypes, environments, interaction, error). On the basis of extensive data sets on different agricultural crops (faba bean, fodder beet, sugar beet, oats, winter rape) obtained from registration trials (1985–1989) carried out in the Federal Republic of Germany, we obtained the following as main result: W ₂ ^g /( ₂ ^g + ₂ ^v ) where ₂ ^g =genotypic variance and ₂ ^v = ₂ ^ge + ₂ ^o /L with ₂ ^ge =interaction variance, ₂ ^o =error variance and L=number of replications.     

Application of silicon sources increases silicon accumulation in perennial ryegrass turf on two soil types

This study investigated the ability of perennial ryegrass to accumulate silicon and the factors that may influence plant silicon accumulation. Plants were grown in the greenhouse in two soil types, peat:sand mix and Hagerstown-silt-loam, amended with two commercially available sources of silicon, calcium silicate slag and wollastonite at 0, 0.5, 1, 2, 5 and 10 t/ha. Shoot tissue of nine-week-old perennial ryegrass plants was analyzed for silicon content (%) and found to reach a dry matter concentration of up to 4% in this study. Silicon accumulation in perennial ryegrass was influenced by the soil type and source, and was higher in plants grown in low-silicon peat:sand mix compared to Hagerstown-silt-loam. Silicon content (%) in the plants consistently increased with increasing rates of silicon in all four soil and source combinations. Acetic acid (HAc) extractable silicon and Ca increased in both soil types when amended with either of the silicon sources. Effects of silicon sources on soil pH varied with soil type. This study indicates that soil type, source of silicon, and rate of silicon application are important factors influencing the uptake of silicon by perennial ryegrass which is a widely used turfgrass species in golf courses, sports fields, and residential lawns in the United States.