F. A. van Eeuwijk

A Mixed-Model Approach to Association Mapping Using Pedigree Information With an Illustration of Resistance to Phytophthora infestans in Potato

Association or linkage disequilibrium (LD)-based mapping strategies are receiving increased attention for the identification of quantitative trait loci (QTL) in plants as an alternative to more traditional, purely linkage-based approaches. An attractive property of association approaches is that they do not require specially designed crosses between inbred parents, but can be applied to collections of genotypes with arbitrary and often unknown relationships between the genotypes. A less obvious additional attractive property is that association approaches offer possibilities for QTL identification in crops with hard to model segregation patterns. The availability of candidate genes and targeted marker systems facilitates association approaches, as will appropriate methods of analysis. We propose an association mapping approach based on mixed models with attention to the incorporation of the relationships between genotypes, whether induced by pedigree, population substructure, or otherwise. Furthermore, we emphasize the need to pay attention to the environmental features of the data as well, i.e., adequate representation of the relations among multiple observations on the same genotypes. We illustrate our modeling approach using 25 years of Dutch national variety list data on late blight resistance in the genetically complex crop of potato. As markers, we used nucleotide binding-site markers, a specific type of marker that targets resistance or resistance-analog genes. To assess the consistency of QTL identified by our mixed-model approach, a second independent data set was analyzed. Two markers were identified that are potentially useful in selection for late blight resistance in potato.

A comparative study of molecular and morphological methods of describing relationships between perennial ryegrass (Lolium perenne L.) varieties

Abstract A sample set of registered perennial ryegrass varieties was used to compare how morphological characterisation and AFLP® (AFLP® is a registered trademark of Keygene N.V.) and STS molecular markers described variety relationships. All the varieties were confirmed as morphologically distinct, and both the STS and AFLP markers exposed sufficient genetic diversity to differentiate these registered ryegrass varieties. Distances obtained by each of the approaches were compared, with special attention given to the coincidences and divergences between the methods. When correlations between morphological, AFLP and STS distances were calculated and the corresponding scatter-plots constructed, the variety relationships appeared to be rather inconsistent across the methods, especially between morphology and the molecular markers. However, some consistencies were found for closely related material. An implication could be that these molecular-marker techniques, while not yet suited to certain operations in the traditional registration of new varieties, could be suitable methods for investigating disputable distinctness situations or possible EDV (EDV= essentially derived variety. An EDV is a variety being clearly distinct from, but conforming in the expression of the essential characteristics of, an ’initial variety’ (IV) from which it is found to have been predominantly derived) relationships, subject to establishing standardised protocols and statistical techniques. Some suggestions for such a protocol, including a statistical test for distinctness, are given.

Linkage Disequilibrium Mapping of Morphological, Resistance, and Other Agronomically Relevant Traits in Modern Spring Barley Cultivars

A set of 148 modern spring barley cultivars was explored for the extent of linkage disequilibrium (LD) between genes governing traits and nearby marker alleles. Associations of agronomically relevant traits (days to heading, plant height), resistance traits (leaf rust, barley yellow dwarf virus (BYD)), and morphological traits (rachilla hair length, lodicule size) with AFLP markers and SSR markers were found. Known major genes and QTLs were confirmed, but also new putative QTLs were found. The LD mapping clearly indicated the common occurrence of Rph3, a gene for hypersensitivity resistance against Puccinia hordei, and also confirmed the QTL Rphq2 for prolonging latency period of P. hordei in seedlings. We also found strong indication for a hitherto not reported gene for resistance or tolerance to BYD on chromosome 2, linked to SSR marker HVM054. Our conclusion is that LD mapping is a valuable additional tool in the search for applicable marker associations with major genes and QTLs.

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

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.

A multi-trait multi-environment QTL mixed model with an application to drought and nitrogen stress trials in maize (Zea mays L.)

Despite QTL mapping being a routine procedure in plant breeding, approaches that fully exploit data from multi-trait multi-environment (MTME) trials are limited. Mixed models have been proposed both for multi-trait QTL analysis and multi-environment QTL analysis, but these approaches break down when the number of traits and environments increases. We present models for an efficient QTL analysis of MTME data with mixed models by reducing the dimensionality of the genetic variance–covariance matrix by structuring this matrix using direct products of relatively simple matrices representing variation in the trait and environmental dimension. In the context of MTME data, we address how to model QTL by environment interactions and the genetic basis of heterogeneity of variance and correlations between traits and environments. We illustrate our approach with an example including five traits across eight stress trials in CIMMYT maize. We detected 36 QTLs affecting yield, anthesis-silking interval, male flowering, ear number, and plant height in maize. Our approach does not require specialised software as it can be implemented in any statistical package with mixed model facilities.

Mixed models including environmental covariables for studying QTL by environment interaction

The study of the phenotypic responses of a set of genotypes in their dependence on the environment has always been an important area of research in plant breeding. Non-parallelism of those responses is called genotype by environment interaction (GEI). GEI especially affects plant breeding strategies, when the phenotypic superiority of genotypes changes in relation to the environment. The study of the genetic basis of GEI involves the modelling of quantitative trait locus (QTL) expression in its dependence on environmental factors. We present a modelling framework for studying the interaction between QTL and environment, using regression models in a mixed model context. We integrate regression models for QTL main effect expression with factorial regression models for genotype by environment interaction, and, in addition, take care to model adequately the residual genetic variation. Factorial regression models describe GEI as differential genotypic sensitivity to one or more environmental covariables. We show how factorial regression models can be generalized to make also QTL expression dependent on environmental covariables. As an illustrative example, we reanalyzed yield data from the North American Barley Genome Project. QTL by environment interaction for yield, as identified at the 2H chromosome could be described as QTL expression in relation to the magnitude of the temperature range during heading.

Linear and bilinear models for the analysis of multi-environment trials: I. An inventory of models

SummaryThe 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.

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

SummaryIn 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.

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

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.

Characterisation of Cannabis accessions with regard to cannabinoid content in relation to other plant characters

SummaryNinety seven Cannabis accessions were evaluated for cannabinoid content and non-chemical plant characters. Variation within populations for cannabinoid content, and consistency of chemical characters at the population level were investigated. The relationship between chemical and other plant characters was very limited. Leaflet width and phenological data can be used for a rough prediction of the chemical phenotype on a population level. Various combinations of cannabinoid content and other economic plant characters were observed, thus a breeding programme will not be hampered by strict linkage. For a selection programme a direct analysis of cannabinoids will be inevitable.