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  • 1
    Electronic Resource
    Electronic Resource
    Interpreting genotype × environment interaction in tropical maize using linked molecular markers and environmental covariables (1999)
    Springer
    Theoretical and applied genetics 99 (1999), S. 611-625 
    Details
    ISSN: 1432-2242
    Keywords: Key words Biplot ; Factorial regression ; Genetic marker ; Genotype×environment interaction ; Quantitative trait loci ; Quantitative trait loci × environment interaction ; Partial least squares regression
    Source: Springer Online Journal Archives 1860-2000
    Topics: Biology
    Notes: Abstract  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 F3:4 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.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/s001220051276
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  • 2
    Electronic Resource
    Electronic Resource
    Assessing non-specificity of resistance in wheat to head blight caused by inoculation with European strains of Fusarium culmorum, F. graminearum and F. nivale using a multiplicative model for interaction (1995)
    Springer
    Theoretical and applied genetics 90 (1995), S. 221-228 
    Details
    ISSN: 1432-2242
    Keywords: Head blight ; Resistance breeding ; Genotype-by-environment interaction ; Multiplicative interaction ; Host-specificity
    Source: Springer Online Journal Archives 1860-2000
    Topics: Biology
    Notes: Abstract To determine whether resistance to Fusarium head blight in winter wheat is horizontal and non-species specific, 25 genotypes from five European countries were tested at six locations across Europe in the years 1990, 1991, and 1992. The five genotypes from each country had to cover the range from resistant to susceptible. The locations involved were Wageningen, Vienna, Rennes, Hohenheim, Oberer Lindenhof, and Szeged. In total, 17 local strains of Fusarium culmorum, F. graminearum, and F. nivale were used for experimental inoculation. One strain, F. culmorum IPO 39-01, was used at all locations. Best linear unbiased predictions (BLUPs) for the head blight ratings of the genotypes were formed within each particular location for each combination of year and strain. The BLUPs over all locations were collected in a genotype-by environment table in which the genotypic dimension consisted of the 25 genotypes, while the environmental dimension was made up of 59 year-by-strain-by-location combinations. A multiplicative model was fitted to the genotype by-environment interaction in this table. The inverses of the variances of the genotype-by-environment BLUPs were used as weights. Interactions between genotypes and environments were written as sums of products between genotypic scores and environmental scores. After correction for year-by-location influence very little variation in environmental scores could be ascribed to differences between strains. This provided the basis for the conclusion that the resistance to Fusarium head blight in winter wheat was of the horizontal and non-species specific type. There was no indication for any geographical pattern in virulence genes. Any reasonable aggressive strain, a F. culmorum strain for the cool climates and a F. graminearum strain for the warmer humid areas, should be satisfactory for screening purposes.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00222205
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  • 3
    Electronic Resource
    Electronic Resource
    On the tenability of distinctions: some comments on ‘The statistics of linear models: back to basics’ by John Nelder (1995)
    Springer
    Statistics and computing 5 (1995), S. 93-95 
    Details
    ISSN: 1573-1375
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00143937
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  • 4
    Electronic Resource
    Electronic Resource
    On the use of diagnostic biplots in model screening for genotype by environment tables (1995)
    Springer
    Statistics and computing 5 (1995), S. 141-153 
    Details
    ISSN: 1573-1375
    Keywords: AMMI ; biadditive model ; bilinear model ; concurrence model ; genotype by environment interaction ; multiplicative interaction ; row regression
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract Popular rank-2 and rank-3 models for two-way tables have geometrical properties which can be used as diagnostic keys in screening for an appropriate model. Row and column levels of two-way tables are represented by points in two or three dimensional space, whereupon collinearity and coplanarity of row and column points provide diagnostic keys for informal model choice. Coordinates are obtained from a factorization of the two-way table Y in the matrix product UV T. The rows of U then contain row-point coordinates and the rows of V column-point coordinates. Illustrations of applications of diagnostic biplots in the literature were restricted to data from chemistry and physics with little or no noise. In plant breeding, two-way tables containing substantial amounts of noise regularly arise in the form of genotype by environment tables. To investigate the usefulness of diagnostic biplots for model screening for genotype by environment tables, data tables were generated from a range of two-way models under the addition of various amounts of noise. Chances for correct diagnosis of the generating model depended on the type of model. Diagnostic biplots on their own do not seem to provide a sufficient means for model selection for genotype by environment tables, but in combination with other methods they certainly can provide extra insight into the structure of the data.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00143945
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  • 5
    Electronic Resource
    Electronic Resource
    Analysing genotype by environment interaction in Dutch potato variety trials using factorial regression (1995)
    Springer
    Euphytica 82 (1995), S. 149-155 
    Details
    ISSN: 1573-5060
    Keywords: AMMI ; biadditive model ; factorial regression ; multiplicative interaction ; potato ; variety trials ; Solanum tuberosum
    Source: Springer Online Journal Archives 1860-2000
    Topics: Agriculture, Forestry, Horticulture, Fishery, Domestic Science, Nutrition
    Notes: Summary Genotype by environment interaction was investigated for yield data from the official Dutch Variety List trials for potato. The data set included 64 genotypes by 26 environments, where environments consisted of year by soil type combinations. Factorial regression models incorporating genotypic and environmental covariates in the interaction were used to analyse the data. The merits of factorial regression models were compared with those of biadditive models. Factorial regression models and biadditive models described comparable amounts of interaction, but factorial regression models provided a better basis for biological interpretation of the interaction.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00027061
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  • 6
    Electronic Resource
    Electronic Resource
    Linear and bilinear models for the analysis of multi-environment trials: I. An inventory of models (1995)
    Springer
    Euphytica 84 (1995), S. 1-7 
    Details
    ISSN: 1573-5060
    Keywords: AMMI ; best linear unbiassed prediction ; factorial regression ; genotype by environment interaction ; multiplicative interaction ; reduced rank regression ; two-way table ; variance components ; variety trials
    Source: Springer Online Journal Archives 1860-2000
    Topics: Agriculture, Forestry, Horticulture, Fishery, Domestic Science, Nutrition
    Notes: Summary The multi-environment trial, in which a number of genotypes is evaluated over a range of environmental conditions, is a standard experiment in plant breeding in general, and variety testing in particular. Useful statistical models for the analysis of multi-environment trials, with emphasis on the analysis of genotype by environment interaction, can be found in the classes of linear and bilinear models. Statistical properties of the most important representatives of these model classes are shortly reviewed. Structural differences between the models stem from: (1) the inclusion of random model terms in addition to fixed model terms; (2) the representation of the interaction by additive or multiplicative parameters; (3) the incorporation of concomitant variables on the levels of the environmental factor. For models with bilinear multiplicative structure for the interaction it is described how the interaction can be visualized by biplots. An illustration of the application of the models and biplots is given in a companion paper.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF01677551
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  • 7
    Electronic Resource
    Electronic Resource
    Analysing genotype by environment interaction in Dutch potato variety trials using factorial regression (1995)
    Springer
    Euphytica 84 (1995), S. 23-29 
    Details
    ISSN: 1573-5060
    Keywords: AMMI ; biadditive model ; factorial regression ; multiplicative interaction ; potato ; variety trials ; Solanum tuberosum
    Source: Springer Online Journal Archives 1860-2000
    Topics: Agriculture, Forestry, Horticulture, Fishery, Domestic Science, Nutrition
    Notes: Summary Genotype by environment interaction was investigated for yield data from the official Dutch Variety List trials for potato. The data set included 64 genotypes by 26 environments, where environments consisted of year by soil type combinations. Factorial regression models incorporating genotypic and environmental covariates in the interaction were used to analyse the data. The merits of factorial regression models were compared with those of biadditive models. Factorial regression models and biadditive models described comparable amounts of interaction, but factorial regression models provided a better basis for biological interpreration of the interaction.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF01677553
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  • 8
    Electronic Resource
    Electronic Resource
    Linear and bilinear models for the analysis of multi-environment trials: II. An application to data from the Dutch Maize Variety Trials (1995)
    Springer
    Euphytica 84 (1995), S. 9-22 
    Details
    ISSN: 1573-5060
    Keywords: AMMI ; best linear unbiassed prediction ; factorial regression ; genotype by environment interaction ; maize ; missing values ; multiplicative interaction ; reduced rank regression ; two-way table
    Source: Springer Online Journal Archives 1860-2000
    Topics: Agriculture, Forestry, Horticulture, Fishery, Domestic Science, Nutrition
    Notes: Summary As the sequel to a paper that dealt with the theoretical aspects of linear and bilinear models for the analysis of genotype by environment interaction in multi-environment trials, this paper presents an illustration of the application of these models to real life data. The data come from maize trials that were conducted within the ongoing evaluation programme for the Dutch Descriptive Variety List of Field Crops. The variable that is analyzed is dry matter content. It is shown how linear and bilinear models can be used supplementary to each other within a general strategy for dealing with genotype by environment interaction.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF01677552
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