High yield potential,shuttle breeding,genetic diversity,and a new international wheat improvement strategy

The main elements of the international wheat improvement program of the Centro Internacional de Mejoramiento de Maíz y Trigo (CIMMYT), also known as the International Maize and Wheat Improvement Center, have been shuttle breeding at two contrasting locations in Mexico, wide adaptation, durable rust and Septoria resistances, international multisite testing, and the appropriate use of genetic variation to enhance yield gains of subsequently produced lines. Such an approach yielded successes known collectively as the Green Revolution. However, at the beginning of the 21st century, this “cultivar assembly line” approach needs fine tuning to address crop needs under increasingly adopted resource conserving practices, as well as those related to nutritional requirements of the end-users. International wheat improvement will therefore focus on the targeting of traits in respective mega-environments, and the use of participatory methods, especially in marginal environments. The main features of this wheat improvement strategy include the introduction of new and novel sources of genetic variation through wild species, landraces, and, potentially, the use of transgenes for intractable traits. This variation will be combined using international shuttle breeding, and increased breeding efficiency will be achieved through marker-aided methods, more targeted use of crop physiology, plant genetics, biostatistics, and bioinformatics. Likewise, CIMMYT will increase its focus on the needs of end-users by emphasizing regional efforts in participatory research and client-oriented plant breeding.     

Race non-specific resistance to rust diseases in CIMMYT spring wheats

Rust diseases continue to cause significant losses to wheat production worldwide. Although the life of effective race-specific resistance genes can be prolonged by using gene combinations, an alternative approach is to deploy varieties that posses adult plant resistance (APR) based on combinations of minor, slow rusting genes. When present alone, APR genes do not confer adequate resistance especially under high disease pressure; however, combinations of 4?C5 such genes usually result in ??near-immunity?? or a high level of resistance. Although high diversity for APR occurs for all three rusts in improved germplasm, relatively few genes are characterized in detail. Breeding for APR to leaf rust and stripe rust in CIMMYT spring wheats was initiated in the early 1970s by crossing slow rusting parents that lacked effective race-specific resistance genes to prevalent pathogen populations and selecting plants in segregating populations under high disease pressure in field nurseries. Consequently most of the wheat germplasm distributed worldwide now possesses near-immunity or adequate levels of resistance. Some semidwarf wheats such as Kingbird, Pavon 76, Kiritati and Parula show high levels of APR to stem rust race Ug99 and its derivatives based on the Sr2-complex, or a combination of Sr2 with other uncharacterized slow rusting genes. These parents are being utilized in our crossing program and a Mexico-Kenya shuttle breeding scheme is used for selecting resistance to Ug99. High frequencies of lines with near-immunity to moderate levels of resistance are now emerging from these activities. After further yield trials and quality assessments these lines will be distributed internationally through the CIMMYT nursery system.     

Daylength,Temperature and Solar Radiation Effects on the Phenology and Yield Formation of Spring Durum Wheat

Future food security will depend on crop adaptation to changing environments. We studied the limitations imposed by daylength, temperature and solar radiation on wheat yield in eight field experiments conducted at contrasting northern latitudes and involving 42 adapted spring durum wheat genotypes of divergent phenology, and reduced or without photoperiod sensitivity. Air temperatures averaged from sowing to anthesis (SA) increased from northern to southern sites, while daylength and minimum temperatures from anthesis to maturity (grain filling, GF) followed the opposite trend, due to differences in the latitude of sites. The site effect explained 96 % of the variation in the number of days SA, which was much smaller in southern sites. Average minimum daily temperatures above 6.9 °C before anthesis and below 10.8 °C during GF accompanied by photoperiods during GF of less than 14.2 h resulted in less than 14 000 kernels m^?2, which was the threshold below which kernel number limited yield. Radiation during GF lower than 1.8 kJ kernel^?1 day^?1 limited kernel weight, which was then a constraint to the achievement of yield potential.     

Wheat genetic resources enhancement by the International Maize and Wheat Improvement Center (CIMMYT)

The International Maize and Wheat Improvement Center (CIMMYT) acts as a catalyst and leader in a global maize and wheat innovation network that serves the poor in the developing world. Drawing on strong science and effective partnerships, CIMMYT researchers create, share, and use knowledge and technology to increase food security, improve the productivity and profitability of farming systems and sustain natural resources. This people-centered mission does not ignore the fact that CIMMYT’s unique niche is as a genetic resources enhancement center for the developing world, as shown by this review article focusing on wheat. CIMMYT’s value proposition resides therefore in its use of crop genetic diversity: conserving it, studying it, adding value to it, and sharing it in enhanced form with clients worldwide. The main undertakings include: long-term safe conservation of world heritage of both crop resources for future generations, in line with formal agreements under the 2004 International Treaty on Plant Genetic Resources for Food and Agriculture, understanding the rich genetic diversity of two of the most important staples worldwide, exploiting the untapped value of crop genetic resources through discovery of specific, strategically-important traits required for current and future generations of target beneficiaries, and development of strategic germplasm through innovative genetic enhancement. Finally, the Center needs to ensure that its main products reach end-users and improve their livelihoods. In this regard, CIMMYT is the main international, public source of wheat seed-embedded technology to reduce vulnerability and alleviate poverty, helping farmers move from subsistence to income-generating production systems. Beyond a focus on higher grain yields and value-added germplasm, CIMMYT plays an “integrative” role in crop and natural resource management research, promoting the efficient use of water and other inputs, lower production costs, better management of biotic stresses, and enhanced system diversity and resilience.     

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.     

A General Bayesian Estimation Method of Linear–Bilinear Models Applied to Plant Breeding Trials With Genotype × Environment Interaction

Statistical analyses of two-way tables with interaction arise in many different fields of research. This study proposes the von Mises–Fisher distribution as a prior on the set of orthogonal matrices in a linear–bilinear model for studying and interpreting interaction in a two-way table. Simulated and empirical plant breeding data were used for illustration; the empirical data consist of a multi-environment trial established in two consecutive years. For the simulated data, vague but proper prior distributions were used, and for the real plant breeding data, observations from the first year were used to elicit a prior for parameters of the model for data of the second year trial. Bivariate Highest Posterior Density (HPD) regions for the posterior scores are shown in the biplots, and the significance of the bilinear terms was tested using the Bayes factor. Results of the plant breeding trials show the usefulness of this general Bayesian approach for breeding trials and for detecting groups of genotypes and environments that cause significant genotype × environment interaction. The present Bayes inference methodology is general and may be extended to other linear–bilinear models by fixing certain parameters equal to zero and relaxing some model constraints.     

Strategic use of Iranian bread wheat landrace accessions for genetic improvement: Core set formulation and validation

Iranian wheat landrace accessions (IWAs) were collected from country‐wide farm fields and market places in 1935 by a professor at the University of Tehran and shared with University of California at Davis, California. IWAs were further submitted to the genebank of International Maize and Wheat Improvement Center (CIMMYT), Mexico. 2,403 IWAs from CIMMYT’s genebank were assayed by DArT‐seq technology to assess genetic diversity. No apparent ecogeographic patterns related to genetic diversity were detected, probably due to long‐term transport and frequent interchange of landraces among farmers. A multivariate clustering procedure combining genotypic and phenotypic information was used in selecting a core‐set, which represented 15% of the hexaploid wheat accessions included in this study. This subset captured an estimated 93% of rare (frequency <0.05) alleles. Multisite phenotypic data (India, Mexico) validated the ability of the core‐set in detecting useful variants. Potential donor accessions for multiple traits (disease resistance, zinc concentration) were identified from the core‐set for wheat‐breeding. This report illustrates a breeder friendly robust core‐set formulation strategy for harnessing the useful genetic variation stored in the genebanks.     

Evaluation of early to medium maturing open pollinated maize varieties in SADC region using GGE biplot based on the SREG model

Analysis of multi-environment trials (METs) of genotypes (G) and genotype × environment (GE) interactions for yield performance across environments, and selection of the best genotypes is an important routine in maize breeding programs. Analysis and interpretation of METs data have been limited to analysis of variance and mean comparison among genotypes. This type of analysis has not been effective in exploiting GE interactions encountered in METs data sets. The objectives of this study were to exploit METs data sets from maize regional trails using G plus GE interaction (GGE) biplot based on the site regression (SREG) model. The GGE biplots displays graphically the relationship among test environments, genotypes and GE interactions. Grain yield data of 35 early to medium maturing open pollinated maize varieties (OPVs) from five seasons (1999–2003) across 59 locations in Southern African Development Community (SADC) were analyzed. The GGE biplots based on the SREG model indicated that yield performance of maize OPVs were under major environments and of GE interactions. The construction of GGE biplots based on SREG model analysis showed the ideal test environments that discriminate well performing maize OPVs from poor ones, the performance of each OPV in specific year, the discrimativiness versus representativeness view of the GGE biplot of the test locations across the years, the relation among OPVs relative to grain yield, the stability of OPVs across environments and which OPVs is best for what.     

Identifying QTLs and Epistasis in Structured Plant Populations Using Adaptive Mixed LASSO

Association analysis in important crop species has generated heightened interest for its potential in dissecting complex traits by utilizing diverse mapping populations. However, the mixed linear model approach is currently limited to single marker analysis, which is not suitable for studying multiple QTL effects, epistasis and gene by environment interactions. In this paper, we propose the adaptive mixed LASSO method that can incorporate a large number of predictors (genetic markers, epistatic effects, environmental covariates, and gene by environment interactions) while simultaneously accounting for the population structure. We show that the adaptive mixed LASSO estimator possesses the oracle property of adaptive LASSO. Algorithms are developed to iteratively estimate the regression coefficients and variance components. Our results demonstrate that the adaptive mixed LASSO method is very promising in modeling multiple genetic effects when a large number of markers are available and the population structure cannot be ignored. It is expected to be a powerful tool for studying the architecture of complex traits in important plant species. Supplemental materials for this article are available from the journal website.     

Lessons learnt from forty years of international spring bread wheat trials

Yield potential is the maximum attainable yield within the limits imposed by the production environment. Better understanding of these constraints and the underlying causes of genotype × environment interaction will improve productivity regionally and globally. For 40 years, the International Maize and Wheat Improvement Center (CIMMYT) has distributed wheat yield trials, and collaborators from across the world have provided yield, disease, and agronomic data. Various analyzes of these data have been conducted over the years to assess the effectiveness of CIMMYT’s Mexican based breeding program, and to identify key selection environments and genotypes with broad adaptation. Analysis of these data confirmed the value of shuttle breeding in Mexico. Well-watered and terminal heat stress selection environments generated in Mexico associate well with their global target areas. Germplasm targeting dry areas is selected and screened for drought tolerance in Mexico using limited irrigation. This type of screening correlated well with environments in South Asia, but less so with sites in West Asia and South America. On the basis of these findings, drought screening in Mexico has been modified to better reflect a wider array of drier environments. In contrast, CIMMYT’s high rainfall environment at Toluca, the other arm of the Mexico-based shuttle, was found to be a poor predictor of global high rainfall areas. The analysis of these data led to the identification of key locations in countries outside Mexico. Integrating information from these sites with that obtained in Mexico has helped improve the efficiency of CIMMYT’s global wheat breeding effort.