The statistical analysis of multi-environment data: modeling genotype-by-environment interaction and its genetic basis

Genotype-by-environment interaction (GEI) is an important phenomenon in plant breeding. This paper presents a series of models for describing, exploring, understanding, and predicting GEI. All models depart from a two-way table of genotype by environment means. First, a series of descriptive and explorative models/approaches are presented: Finlay–Wilkinson model, AMMI model, GGE biplot. All of these approaches have in common that they merely try to group genotypes and environments and do not use other information than the two-way table of means. Next, factorial regression is introduced as an approach to explicitly introduce genotypic and environmental covariates for describing and explaining GEI. Finally, QTL modeling is presented as a natural extension of factorial regression, where marker information is translated into genetic predictors. Tests for regression coefficients corresponding to these genetic predictors are tests for main effect QTL expression and QTL by environment interaction (QEI). QTL models for which QEI depends on environmental covariables form an interesting model class for predicting GEI for new genotypes and new environments. For realistic modeling of genotypic differences across multiple environments, sophisticated mixed models are necessary to allow for heterogeneity of genetic variances and correlations across environments. The use and interpretation of all models is illustrated by an example data set from the CIMMYT maize breeding program, containing environments differing in drought and nitrogen stress. To help readers to carry out the statistical analyses, GenStat® programs, 15th Edition and Discovery® version, are presented as “Appendix.

Marker-assisted selection: new tools and strategies

The development of molecular genetics and associated technology has facilitated a quantum leap in our understanding of the underlying genetics of the traits sought through plant breeding. The usefulness of DNA markers for germplasm characterization, and of marker-assisted selection—the manipulation through DNA markers of genomic regions that are involved in the expression of traits of interest—for single-gene transfer, has been well demonstrated. However, when several genomic regions must be manipulated, marker-assisted selection has turned out to be less useful. The efficient and effective application of marker-assisted selection for polygenic trait improvement certainly needs new technology but, more importantly, it requires the development of innovative strategies that bypass the conceptual bottlenecks imposed by current approaches

Use of STSs and SSRs as rapid and reliable preselection tools in a marker-assisted selection-backcross scheme

We describe a new approach for using suitable STS and SSR markers as a powerful molecular tool for screening segregating populations involved in backcross schemes for marker-assisted selection, as a preselection step. Since it can be applied to very large populations, this preselection strategy allows one to increase substantially the pressure of selection at each backcross generation. The technique is fast and reproducible, and can be made even more efficient and costeffective by simultaneous DNA amplification from different primer pairs. In the example illustrated here, three suitable PCR-based markers were used to complete the selection of 300 individuals out of 2300 in less than one month with two people working on the project

Comparative Map and Trait Viewer (CMTV): an integrated bioinformatic tool to construct consensus maps and compare QTL and functional genomics data across genomes and experiments

In the past few decades, a wealth of genomic data has been produced in a wide variety of species using a diverse array of functional and molecular marker approaches. In order to unlock the full potential of the information contained in these independent experiments, researchers need efficient and intuitive means to identify common genomic regions and genes involved in the expression of target phenotypic traits across diverse conditions. To address this need, we have developed a Comparative Map and Trait Viewer (CMTV) tool that can be used to construct dynamic aggregations of a variety of types of genomic datasets. By algorithmically determining correspondences between sets of objects on multiple genomic maps, the CMTV can display syntenic regions across taxa, combine maps from separate experiments into a consensus map, or project data from different maps into a common coordinate framework using dynamic coordinate translations between source and target maps. We present a case study that illustrates the utility of the tool for managing large and varied datasets by integrating data collected by CIMMYT in maize drought tolerance research with data from public sources. This example will focus on one of the visualization features for Quantitative Trait Locus (QTL) data, using likelihood ratio (LR) files produced by generic QTL analysis software and displaying the data in a unique visual manner across different combinations of traits, environments and crosses. Once a genomic region of interest has been identified, the CMTV can search and display additional QTLs meeting a particular threshold for that region, or other functional data such as sets of differentially expressed genes located in the region; it thus provides an easily used means for organizing and manipulating data sets that have been dynamically integrated under the focus of the researcher’s specific hypothesis

Identification of quantitative trait loci under drought conditions in tropical maize. 2. Yield components and marker-assisted selection strategies

In most maize-growing areas yield reductions due to drought have been observed. Drought at flowering time is, in some cases, the most damaging. In the experiment reported here, trials with F3 families, derived from a segregating F2 population, were conducted in the field under well-watered conditions (WW) and two other water-stress regimes affecting flowering (intermediate stress, IS, and severe stress, SS). Several yield components were measured on equal numbers of plants per family: grain yield (GY), ear number (ENO), kernel number (KNO), and 100-kernel weight (HKWT). Correlation analysis of these traits showed that they were not independent of each other. Drought resulted in a 60% decrease of GY under SS conditions. By comparing yield under WW and SS conditions, the families that performed best under WW conditions were found to be proportionately more affected by stress, and the yield reductions due to SS conditions were inversely proportional to the performance under drought. Moreover, no positive correlation was observed between a drought-tolerance index (DTI) and yield under WW conditions. The correlation between GY under WW and SS conditions was 0.31. Therefore, in this experiment, selection for yield improvement under WW conditions only, would not be very effective for yield improvement under drought. Quantitative trait loci (QTLs) were identified for GY, ENO and KNO using composite interval mapping (CIM). No major QTLs, expressing more then 13% of the phenotypic variance, were detected for any of these traits, and there were inconsistencies in their genomic positions across water regimes. The use of CIM allowed the evaluation of QTL-by-environment interactions (Q×E) and could thus identify “stable” QTLs CIMMYT, Apartado Postal 6-641, 06600 Mexico D.F., Mexico across drought environments. Two such QTLs for GY, on chromosomes 1 and 10, coincided with two stable QTLs for KNO. Moreover, four genomic regions were identified for the expression of both GY and the anthesis-silking interval (ASI). In three of these, the allelic contributions were for short ASI and GY increase, while for that on chromosome 10 the allelic contribution for short ASI corresponded to a yield reduction. From these results, we hypothesize that to improve yield under drought, marker-assisted selection (MAS) using only the QTLs involved in the expression of yield components appears not to be the best strategy, and neither does MAS using only QTLs involved in the expression of ASI. We would therefore favour a MAS strategy that takes into account a combination of the “best QTLs” for different traits. These QTLs should be stable across target environments, represent the largest percentage possible of the phenotypic variance, and, though not involved directly in the expression of yield, should be involved in the expression of traits significantly correlated with yield, such as ASI

The application of biotechnology to wheat improvement

Today, the world’s population is increasing at the most rapid rate ever. Two hundred people are being added to the planet every minute. It is forecast that by the year 2050, the world’s population will double to nearly 12 billion people. To feed this population, these people will require a staggering increase in food production. In fact, it has been estimated that the world will need to produce more than twice as much food during the next 50 years as was produced since the beginning of agriculture 10 000 years ago. How will researchers continue to develop improved wheat varieties to feed the world in the future? At least for the foreseeable future, plant breeding as it is known today will play a primary role. What will change are the tools that can be employed. This chapter focuses on current approaches for the use of modern molecular-based technologies to develop improved varieties and discusses areas for future applications. Biotechnology can be defined in many different ways, but for the purpose of this chapter, all areas that use molecular approaches to understand and manipulate a plant genome will be considered. However, for the sake of discussion, the techniques are divided between those that make use of molecular markers for studying the genetic material already present within the wheat plant and genetic engineering aimed at the introduction of novel genetic material. It is the latter that often raises concern and that many believe represents ‘modern biotechnology’

role of winter host plants in vineyard colonization and phenology of zygina rhamni hemiptera cicadellidae typhlocybinae

Abstract Zygina rhamni Ferrari (Hemiptera: Cicadellidae: Typhlocybinae) is a mesophyll-feeding leafhopper that infests grapevine, Vitis vinifera L. This leafhopper's overwintering strategy and the dynamics of its vineyard colonization, relative to the distance between the grapevines and the winter host plants and different grapevine vegetative densities, are described herein. This species shows a facultative heteroecious life cycle. In autumn, it migrates from vineyards to bramble, Rubus gr. fruticosus, and rose, Rosa chinensis Jacquin. On these host plants, females lay eggs that start to hatch at the end of March. Adults that developed on bramble colonize grapevines beginning in May; the leaf fall of rose, which takes place during the first half of April, probably causes the death of unhatched eggs and nymphs. We found that overwintering females can move back to vineyards and lay eggs until early June. The dynamics of vineyard colonization by the leafhopper involved initial concentration in areas close t..

Identification of several small main-effect QTLs and a large number of epistatic QTLs for drought tolerance related traits in groundnut (Arachishypogaea L.)

Cultivated groundnut or peanut (Arachis hypogaea L.), an allotetraploid (2n = 4x = 40), is a self pollinated and widely grown crop in the semi-arid regions of the world. Improvement of drought tolerance is an important area of research for groundnut breeding programmes. Therefore, for the identification of candidate QTLs for drought tolerance, a comprehensive and refined genetic map containing 191 SSR loci based on a single mapping population (TAG 24 × ICGV 86031), segregating for drought and surrogate traits was developed. Genotyping data and phenotyping data collected for more than ten drought related traits in 2–3 seasons were analyzed in detail for identification of main effect QTLs (M-QTLs) and epistatic QTLs (E-QTLs) using QTL Cartographer, QTLNetwork and Genotype Matrix Mapping (GMM) programmes. A total of 105 M-QTLs with 3.48–33.36% phenotypic variation explained (PVE) were identified using QTL Cartographer, while only 65 M-QTLs with 1.3–15.01% PVE were identified using QTLNetwork. A total of 53 M-QTLs were such which were identified using both programmes. On the other hand, GMM identified 186 (8.54–44.72% PVE) and 63 (7.11–21.13% PVE), three and two loci interactions, whereas only 8 E-QTL interactions with 1.7–8.34% PVE were identified through QTLNetwork. Interestingly a number of co-localized QTLs controlling 2–9 traits were also identified. The identification of few major, many minor M-QTLs and QTL × QTL interactions during the present study confirmed the complex and quantitative nature of drought tolerance in groundnut. This study suggests deployment of modern approaches like marker-assisted recurrent selection or genomic selection instead of marker-assisted backcrossing approach for breeding for drought tolerance in groundnut

Phosphoenolpyruvate carboxylase dentified as a key enzyme in erythrocytic Plasmodium falciparum carbon metabolism

Phospoenolpyruvate carboxylase (PEPC) is absent from humans but encoded in thePlasmodium falciparum genome, suggesting that PEPC has a parasite-specific function. To investigate its importance in P. falciparum, we generated a pepc null mutant (D10Δpepc), which was only achievable when malate, a reduction product of oxaloacetate, was added to the growth medium. D10Δpepc had a severe growth defect in vitro, which was partially reversed by addition of malate or fumarate, suggesting that pepc may be essential in vivo. Targeted metabolomics using 13C-U-D-glucose and 13C-bicarbonate showed that the conversion of glycolytically-derived PEP into malate, fumarate, aspartate and citrate was abolished in D10Δpepc and that pentose phosphate pathway metabolites and glycerol 3-phosphate were present at increased levels. In contrast, metabolism of the carbon skeleton of 13C,15N-U-glutamine was similar in both parasite lines, although the flux was lower in D10Δpepc; it also confirmed the operation of a complete forward TCA cycle in the wild type parasite. Overall, these data confirm the CO2 fixing activity of PEPC and suggest that it provides metabolites essential for TCA cycle anaplerosis and the maintenance of cytosolic and mitochondrial redox balance. Moreover, these findings imply that PEPC may be an exploitable target for future drug discovery