Additive main effects and multiplicative interaction (AMMI) analysis of genotype-location interaction in variety trials repeated over years

Genotype-location (GL) interaction effects are of special interest for breeding programmes to identify adaptation targets, adaptive traits and test sites. These effects, generally having relatively low repeatability between years, should be studied on a multiyear basis in annual crops. Their assessment by additive main effects and multiplicative interaction (AMMI) analysis is currently defined for this situation. Two procedures based on cross validations are proposed for testing the GL-interaction principal component axes, exploiting the utilities of the computer programme MATMODEL. The use of Gollob’s F test, F _GH2 test, F _R test and the heuristic criterion based on the signal-to-noise ratio is also envisaged. The consistency of results provided by the testing procedures was verified on four data sets of different cereal crops. Gollob’s test tended to be the most liberal, while the F _GH2 test appeared somewhat more liberal than the F _R test. The signal-to-noise ratio gave results consistent with the F _R test considered at a P?0.01 level of significance. These criteria disagreed in two data sets with the conclusions provided by the two cross-validation procedures which, in turn, also disagreed in one data set. Preference could be given to different testing procedures depending on the number of test years, locations and genotypes.     

Best Linear Unbiased Prediction (BLUP) for regional yield trials: a comparison to additive main effects and multiplicative interaction (AMMI) analysis

Multilocation trials are often used to analyse the adaptability of genotypes in different environments and to find for each environment the genotype that is best adapted; i.e. that is highest yielding in that environment. For this purpose, it is of interest to obtain a reliable estimate of the mean yield of a cultivar in a given environment. This article compares two different statistical estimation procedures for this task: the Additive Main Effects and Multiplicative Interaction (AMMI) analysis and Best Linear Unbiased Prediction (BLUP). A modification of a cross validation procedure commonly used with AMMI is suggested for trials that are laid out as a randomized complete block design. The use of these procedure is exemplified using five faba bean datasets from German registration trails. BLUP was found to outperform AMMI in four of five faba bean datasets.     

Hemiclonal analysis reveals significant genetic, environmental and genotype x environment effects on sperm size in Drosophila melanogaster

Spermatozoa are the most diverse of all animal cells. Variation in size alone is enormous and yet there are still no clear evolutionary explanations that can account for such diversity. The basic genetics of sperm form is also poorly understood, although sperm size is known to have a strong genetic component. Here, using hemiclonal analysis of Drosophila melanogaster, we demonstrate that there is not only a significant additive genetic component contributing to phenotypic variation in sperm length but also a significant environmental effect. Furthermore, the plasticity of sperm size has a significant genetic component to it (a genotype x environment interaction). A genotype x environment interaction could contribute to the maintenance of the substantial genetic variation in this trait and thereby explain the persistent inter-male differences in sperm size seen in numerous taxa. We suggest that the low conditional dependence and high heritability but low evolvability (the coefficient of additive genetic variation) of sperm length is more consistent with a history of stabilizing selection rather than either sexual selection or strong directional selection.     

Challenges and opportunities in genome-wide environmental interaction (GWEI) studies

The interest in performing gene-environment interaction studies has seen a significant increase with the increase of advanced molecular genetics techniques. Practically, it became possible to investigate the role of environmental factors in disease risk and hence to investigate their role as genetic effect modifiers. The understanding that genetics is important in the uptake and metabolism of toxic substances is an example of how genetic profiles can modify important environmental risk factors to disease. Several rationales exist to set up gene-environment interaction studies and the technical challenges related to these studies-when the number of environmental or genetic risk factors is relatively small-has been described before. In the post-genomic era, it is now possible to study thousands of genes and their interaction with the environment. This brings along a whole range of new challenges and opportunities. Despite a continuing effort in developing efficient methods and optimal bioinformatics infrastructures to deal with the available wealth of data, the challenge remains how to best present and analyze genome-wide environmental interaction (GWEI) studies involving multiple genetic and environmental factors. Since GWEIs are performed at the intersection of statistical genetics, bioinformatics and epidemiology, usually similar problems need to be dealt with as for genome-wide association gene-gene interaction studies. However, additional complexities need to be considered which are typical for large-scale epidemiological studies, but are also related to "joining" two heterogeneous types of data in explaining complex disease trait variation or for prediction purposes.     

Interpreting genotype × environment interaction in tropical maize using linked molecular markers and environmental covariables

An understanding of the genetic and environmental basis of genotype×environment interaction (GEI) is of fundamental importance in plant breeding. In mapping quantitative trait loci (QTLs), suitable genetic populations are grown in different environments causing QTLs×environment interaction (QEI). The main objective of the present study is to show how Partial Least Squares (PLS) regression and Factorial Regression (FR) models using genetic markers and environmental covariables can be used for studying QEI related to GEI. Biomass data were analyzed from a multi-environment trial consisting of 161 lines from a F_3:4 maize segregating population originally created with the purpose of mapping QTLs loci and investigating adaptation differences between highland and lowland tropical maize. PLS and FR methods detected 30 genetic markers (out of 86) that explained a sizeable proportion of the interaction of maize lines over four contrasting environments involving two low-altitude sites, one intermediate-altitude site, and one high-altitude site for biomass production. Based on a previous study, most of the 30 markers were associated with QTLs for biomass and exhibited significant QEI. It was found that marker loci in lines with positive GEI for the highland environments contained more highland alleles, whereas marker loci in lines with positive GEI for intermediate and lowland environments contained more lowland alleles. In addition, PLS and FR models identified maximum temperature as the most-important environmental covariable for GEI. Using a stepwise variable selection procedure, a FR model was constructed for GEI and QEI that exclusively included cross products between genetic markers and environmental covariables. Higher maximum temperature in low- and intermediate-altitude sites affected the expression of some QTLs, while minimum temperature affected the expression of other QTLs. Received: 10 January 1999 / Accepted: 12 March 1999     

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.     

Inference of haplotype effects in case-control studies using unphased genotype and environmental data

A retrospective likelihood-based approach was proposed to test and estimate the effect of haplotype on disease risk using unphased genotype data with adjustment for environmental covariates. The proposed method was also extended to handle the data in which the haplotype and environmental covariates are not independent. Likelihood ratio tests were constructed to test the effects of haplotype and gene-environment interaction. The model parameters such as haplotype effect size was estimated using an Expectation Conditional-Maximization (ECM) algorithm developed by Meng and Rubin (1993). Model-based variance estimates were derived using the observed information matrix. Simulation studies were conducted for three different genetic effect models, including dominant effect, recessive effect, and additive effect. The results showed that the proposed method generated unbiased parameter estimates, proper type I error, and true beta coverage probabilities. The model performed well with small or large sample sizes, as well as short or long haplotypes.     

Genetic analyses using GGE model and a mixed linear model approach,and stability analyses using AMMI bi-plot for late-maturity alpha-amylase activity in bread wheat genotypes

Low falling number and discounting grain when it is downgraded in class are the consequences of excessive late-maturity α-amylase activity (LMAA) in bread wheat (Triticum aestivum L.). Grain expressing high LMAA produces poorer quality bread products. To effectively breed for low LMAA, it is necessary to understand what genes control it and how they are expressed, particularly when genotypes are grown in different environments. In this study, an International Collection (IC) of 18 spring wheat genotypes and another set of 15 spring wheat cultivars adapted to South Dakota (SD), USA were assessed to characterize the genetic component of LMAA over 5 and 13 environments, respectively. The data were analysed using a GGE model with a mixed linear model approach and stability analysis was presented using an AMMI bi-plot on R software. All estimated variance components and their proportions to the total phenotypic variance were highly significant for both sets of genotypes, which were validated by the AMMI model analysis. Broad-sense heritability for LMAA was higher in SD adapted cultivars (53%) compared to that in IC (49%). Significant genetic effects and stability analyses showed some genotypes, e.g. ‘Lancer’, ‘Chester’ and ‘LoSprout’ from IC, and ‘Alsen’, ‘Traverse’ and ‘Forefront’ from SD cultivars could be used as parents to develop new cultivars expressing low levels of LMAA. Stability analysis using an AMMI bi-plot revealed that ‘Chester’, ‘Lancer’ and ‘Advance’ were the most stable across environments, while in contrast, ‘Kinsman’, ‘Lerma52’ and ‘Traverse’ exhibited the lowest stability for LMAA across environments.     

A process-oriented life-cycle assessment (LCA) model for environmental and resource-related technologies (EASETECH)

The International Journal of Life Cycle Assessment - In life-cycle assessment (LCA), environmental technologies are often modelled as “black-box processes”, where inputs and outputs are...     

Line x tester analysis for fixed effect model in cotton (Gossypium hirsutum L.)

Summary The data from an experiment in cotton consisting of three testers and 12 lines selected deliberately have been analysed. The investigation showed higher specific combining ability variance for yield of seed cotton and number of bolls, indicating the predominance of non-additive gene action. Of parental lines, H777 was found to possess high g.c.a. effects for seed cotton yield, number of bolls and number of sympodes. Parent H842 contributed only for boll weight, whereas H655 was good general combiner for number of monopodes. There appeared to be better chances for increasing the yield by exploiting hybrid vigour for the number of bolls and boll weight. The presence of marked non-additive gene effects, in addition to additive gene effects, indicated the need for exploiting both the fixable and non-fixable components of genetic variance for increasing productivity in cotton.     

A model of gene-gene and gene-environment interactions and its implications for targeting environmental interventions by genotype

Background  

The potential public health benefits of targeting environmental interventions by genotype depend on the environmental and genetic contributions to the variance of common diseases, and the magnitude of any gene-environment interaction. In the absence of prior knowledge of all risk factors, twin, family and environmental data may help to define the potential limits of these benefits in a given population. However, a general methodology to analyze twin data is required because of the potential importance of gene-gene interactions (epistasis), gene-environment interactions, and conditions that break the 'equal environments' assumption for monozygotic and dizygotic twins.     

Bifurcation and stability analysis of a temperature-dependent mite predator-prey interaction model incorporating a prey refuge

The non-linear behavior of a differential equations-based predator-prey model, incorporating a spatial refuge protecting a consant proportion of prey and with temperature-dependent parameters chosen appropriately for a mite interaction on fruit trees, is examined using the numerical bifurcation code AUTO 86. The most significant result of this analysis is the existence of a temperature interval in which increasing the amount of refuge dynamically destabilizes the system; and on part of this interval the interaction is less likely to persist in that predator and prey minimum population densities are lower than when no refuge is available. It is also shown that increasing the amount of refuge can lead to population outbreaks due to the presence of multiple stable states. The ecological implications of a refuge are discussed with respect to the biological control of mite pests.     

Modelling root demography in heterogeneous mountain forests and applications for slope stability analysis

Background and aims

Plant roots provide mechanical cohesion (c _r ) to soil on slopes which are prone to shallow landslides. c _r varies in heterogeneous natural forests due to the spatial, inter- and intra-annual dynamics of root demography. Characterizing root initiation density and mortality, as well as how root growth is influenced by abiotic and biotic factors is essential for exploring a root system’s capacity to reinforce soil.

Methods

In this study, root demography data were monitored using field rhizotrons during 1.5 years in two naturally regenerated mixed forests in the French Alps. These forests are composed of trees growing in groups (tree islands) with large gaps between the islands. Three categories of driving variables were measured: (i) spatial factors: altitude (1,400 m, 1,700 m), ecological patch (gap, tree island), soil depth (0.0–1.0 m divided into five layers of 0.2 m); (ii) temporal factors: month (12 months from March 2010 to February 2011), winter (winter of 2009–2010 and 2010–2011); (iii) biological factors: root diameter classes (]0, 1] mm, ]1, 2] mm, ]2, 5] mm (according to the international standard ISO 31–11, ]x, y] denotes a left half-open interval from x (excluded) to y (included)). Two types of two-part models, a Hurdle model (H) and a Zero-inflated model (ZI) were used to fit root data with a high zero population, i.e. if root initiation or mortality was zero during a given time period, or if roots were not present at all points throughout a soil profile.

Results

Root initiation quantity decreased with increasing soil depth, as well as being lower in tree islands. Both soil depth and ecological patch interacted strongly with altitude. Root dynamics were significantly less active with a lower net production and c _r increment in winter and spring than in summer and autumn. Roots which were ]1, 2] mm in diameter contributed the most to c _r compared to other diameter classes, as they had a high production but a low mortality. With regard to model selection, both H and ZI demonstrated similar outcomes and underestimated extreme values of root demography data.

Conclusion

All factors contributed towards explaining the variability of root demography and c _r . We suggest taking into consideration the seasonality of root dynamics when studying root reinforcement.     

A unified statistical model for functional mapping of environment-dependent genetic expression and genotype x environment interactions for ontogenetic development

The effects of quantitative trait loci (QTL) on phenotypic development may depend on the environment (QTL x environment interaction), other QTL (genetic epistasis), or both. In this article, we present a new statistical model for characterizing specific QTL that display environment-dependent genetic expressions and genotype x environment interactions for developmental trajectories. Our model was derived within the maximum-likelihood-based mixture model framework, incorporated by biologically meaningful growth equations and environment-dependent genetic effects of QTL, and implemented with the EM algorithm. With this model, we can characterize the dynamic patterns of genetic effects of QTL governing growth curves and estimate the global effect of the underlying QTL during the course of growth and development. In a real example with rice, our model has successfully detected several QTL that produce differences in their genetic expression between two contrasting environments. These detected QTL cause significant genotype x environment interactions for some fundamental aspects of growth trajectories. The model provides the basis for deciphering the genetic architecture of trait expression adjusted to different biotic and abiotic environments and genetic relationships for growth rates and the timing of life-history events for any organism.     

Flow injection analysis and biosensors: applications for biotechnology and environmental control.

Our experience in industrial bioprocess monitoring and environmental control let us develop a concept for biosensor research which distinguishes itself from other, more popular, approaches. Biosensors must improve and/or simplify existing state-of-the-art analysis systems. Only the parallel development of biosensors and their complementary metrology leads to industrially sound solutions. The combination of flow injection analysis with immobilized enzymes in the form of enzyme columns is already used today for the solution of on-line analytical problems in bioprocesses and environmental control.     

Extinction hazards in experimental Daphnia magna populations: effects of genotype diversity and environmental variation

Extinction is ubiquitous in natural systems and the ultimate fate of all biological populations. However, the factors that contribute to population extinction are still poorly understood, particularly genetic diversity and composition. A laboratory experiment was conducted to examine the influences of environmental variation and genotype diversity on persistence in experimental Daphnia magna populations. Populations were initiated in two blocks with one, two, three, or six randomly selected and equally represented genotypes, fed and checked for extinction daily, and censused twice weekly over a period of 170 days. Our results show no evidence for an effect of the number of genotypes in a population on extinction hazard. Environmental variation had a strong effect on hazards in both experimental blocks, but the direction of the effect differed between blocks. In the first block, variable environments hastened extinction, while in the second block, hazards were reduced under variable food input. This occurred despite greater fluctuations in population size in variable environments in the second block of our experiment. Our results conflict with previous studies, where environmental variation consistently increased extinction risk. They are also at odds with previous studies in other systems that documented significant effects of genetic diversity on population persistence. We speculate that the lack of sexual reproduction, or the phenotypic similarity among our experimental lines, might underlie the lack of a significant effect of genotype diversity in our study.     

Use of genetic effects and genotype by environmental interactions for the classification of mexican races of maize

To examine the questions of whether the additive and dominance effects present for morphological characters in racial crosses are of sufficient consistency and magnitude to allow such genetic effects to be used for racial classification, we used a diallel experiment among the 25 well-defined Mexican races of maize, which include the ancestral stocks of most commercial and genetic maize types. With such an experiment, genetic effects and genotype by environmental interactions for one or more characters can be used to measure genetic and adaptational or environmental similarity. We used average parental effects (general combining abilities), specific effects, and genotype by environmental effects of 21 characters from the diallel (grown at three locations) to group the Mexican races of maize. The groupings based upon average genetic effects and upon genotype by environmental interactions are more satisfactory than groupings based upon specific effects. The standard errors for genetic distances based upon specific (largely dominance) effects seem to be too high for practical use. Principal components analyses of the same data suggest a similar conclusion.-The groupings based upon average genetic effects are in general agreement with previous studies, with the exception of Maíz Dulce, which is grouped with the Cónicos, rather than being isolated from the other Mexican races of maize.