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

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

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

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

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

More genomic resources for less-studied crops

Many of the crop species considered to be minor on a global scale, yet are important locally for food security in the developing world, have remained less-studied crops. Recent years have witnessed the development of large-scale genomic and genetic resources, including simple sequence repeat, single nucleotide polymorphism and diversity array technology markers, expressed sequence tags or transcript reads, bacterial artificial chromosome libraries, genetic and physical maps, and genetic stocks with rich genetic diversity, such as core reference sets and introgression lines in these crops. These resources have the potential to accelerate gene discovery and initiate molecular breeding in these crops, thereby enhancing crop productivity to ensure food security in developing countries

Mapping QTLs and QTL x environment interaction for CIMMYT maize drought stress program using factorial regression and partial least squares methods

The study of QTL x environment interaction (QEI) is important for understanding genotype x environment interaction (GEI) in many quantitative traits. For modeling GEI and QEI, factorial regression (FR) models form a powerful class of models. In FR models, covariables (contrasts) defined on the levels of the genotypic and/or environmental factor(s) are used to describe main effects and interactions. In FR models for QTL expression, considerable numbers of genotypic covariables can occur as for each putative QTL an additional covariable needs to be introduced. For large numbers of genotypic and/or environmental covariables, least square estimation breaks down and partial least squares (PLS) estimation procedures become an attractive alternative. In this paper we develop methodology for analyzing QEI by FR for estimating effects and locations of QTLs and QEI and interpreting QEI in terms of environmental variables. A randomization test for the main effects of QTLs and QEI is presented. A population of F-2 derived F-3 families was evaluated in eight environments differing in drought stress and soil nitrogen content and the traits yield and anthesis silking interval (ASI) were measured. For grain yield, chromosomes 1 and 10 showed significant QEI, whereas in chromosomes 3 and 8 only main effect QTLs were observed. For ASI, QTL main effects were observed on chromosomes 1, 2, 6, 8, and 10, whereas QEI was observed only on chromosome 8. The assessment of the QEI at chromosome 1 for grain yield showed that the QTL main effect explained 35.8% of the QTL + QEI variability, while QEI explained 64.2%. Minimum temperature during flowering time explained 77.6% of the QEI. The QEI analysis at chromosome 10 showed that the QTL main effect explained 59.8% of the QTL + QEI variability, while QEI explained 40.2%. Maximum temperature during flowering time explained 23.8% of the QEI. Results of this study show the possibilities of using FR for mapping QTL and for dissecting QEI in terms of environmental variables. PLS regression is efficient in accounting for background noise produced by other QTLs

Simulation Experiments on Efficiencies of Gene Introgression by Backcrossing

Designing a highly efficient backcross (BC) marker-assisted selection (MAS) experiment is not a straightforward exercise, efficiency being defined here as the ratio between the resources that need to be invested at each generation and the number of generations required to achieve the selection. This paper presents results of simulations conducted for different strategies, using the maize genome as a model, to compare allelic introgression with DNA markers through BCs. Simulation results indicate that the selection response in the BC1 could be increased significantly when the selectable population size (N sl) is 100. Selectable population size is defined as the number of individuals with favorable alleles at the target loci from which selection with markers can be carried out on the rest of the genome at nontarget loci, simulations considered the allelic introgression at one to five target loci, with different population sizes, changes in the recombination frequency between target loci and flanking markers, and different numbers of genotypes selected at each generation. For an introgression at one target locus in a partial line conversion, and using MAS at nontarget loci only at one generation, a selection at BC3 would be more efficient than a selection at BC1 or BC2, due to the increase over generations of the ratio of the standard deviation to the mean of the donor genome contribution. With selection only for the presence of a donor allele at one locus in BC1 and BC2, and MAS at BC3, lines with <5% of the donor genome can be obtained with a N sl of 10 in BC1 and BC2, and 100 in BC3 These results are critical in the application of molecular markers to introgress elite alleles as part of plant improvement program

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’

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