Bringing wild relatives back into the family: recovering genetic diversity in CIMMYT improved wheat germplasm

Summary The dangers of a narrow genetic base of the world's major domesticated food crops have become a great global concern in recent decades. The efforts of the International Maize and Wheat Improvement Center (CIMMYT) to breed common wheat cultivars for resource poor farmers in the developing world (known as the Green Revolution wheats) has met with notable success in terms of improved yield, yield stability, increased disease resistance and utilization efficiency of agricultural inputs. However, much of the success was bought at the cost of an overall reduction in genetic diversity in the species; average Modified Roger's distances (MRD) within groups of germplasm fell from 0.64 in the landraces to a low of 0.58 in the improved lines in the 1980s. Recent efforts by CIMMYT breeders to expand the genetic base of common wheat has included the use of landraces, materials from other breeding programs, and synthetic wheats derived from wild species in the pedigrees of new advanced materials. The result, measured using SSR molecular markers, is a highly significant increase in the latent genetic diversity of recently developed CIMMYT breeding lines and cultivars compared to the original Green Revolution wheats (average MRD of the latest materials (0.63) is not significantly different from that of the landraces, as tested using confidence intervals). At the same time, yield and resistance to biotic and abiotic stresses, and end-use quality continue to increase, indicating that the Green Revolution continues to this day.     

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.     

Long-term association of locations for testing spring bread wheat

Summary The International Spring Wheat Yield Nursery (ISWYN) has been distributed annually since 1964 and the results provide a base for investigating relationships among locations. Ordination and clustering of locations was conducted using 26 years of grain yield data. Ordination and clusters based on the discrimination of germplasm were compared with mega-environments, which are groupings of locations defined by CIMMYT on the basis of climatic factors and perceptions of major biotic and abiotic stresses. Discrepancies among mega-environmental groupings, clusters and ordinations may identify locations for which major stresses affecting wheat yield are yet unidentified.Major environmental discriminators were latitude and the presence or absence of stress, although there was little association of locations due to limited moisture availability. We identified two major spring wheat environments, typified as Asian and European, and suggest the mega-environmental classification does not explain all significant associations among locations. Location groupings based on discrimination of germplasm should be considered in parallel to mega-environments on a regular basis and we propose breeding for a base of broadly adapted germplasm to which specific stress tolerances are incorporated.Abbreviations CIMMYT International Maize & Wheat Improvement Centre - ISWYN International Spring Wheat Yield Nursery - WANA West Asia and North Africa     

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.     

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.     

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.     

A restricted selection index method based on eigenanalysis

Selection indices, used in animal and plant breeding to select the best individuals for the next breeding cycle, are based on phenotypic observations of traits recorded in candidate individuals. The restrictive selection index (RSI) facilitates maximizing the genetic progress of some characters, while leaving others unchanged. Recently a selection index (SI) was proposed based on the eigen analysis method (ESIM), in which the first eigenvector (from the largest eigenvalue) is used as the SI criterion, and its elements determine the proportion of the trait that contributes to the SI. However, the current ESIM, which has two main limitations, is based on the assumption that the vector of coefficients of the index is equal to the genotypic variance-covariance matrix among the traits multiplied by the vector of economic weights, and does not allow one to restrict the number of traits. In this study, we develop a more general ESIM that has two main features, namely, it makes no assumption concerning the coefficients of the index and it can be generalized to a restrictive ESIM (RESIM). We use two datasets to illustrate the theoretical results and practical use of ESIM and RESIM, and to compare them with standard unrestrictive and restrictive selection indices. The main advantages of RESIM over traditional unrestrictive and restrictive SIs are that its statistical sampling properties are known; its selection responses are equal to or greater than those estimated from the traditional restrictive SI; and it does not require economic weights and thus can be used in practical applications when all or some of the traits need to be improved simultaneously (traditional SIs cannot improve several traits simultaneously if economic weights are not available). Finally, we prove that the coefficients of the traditional RSI belong to the space generated by the eigenvectors of RESIM.     

Combining ability and heterosis under pest epidemics in a broad-based global wheat-breeding population

Wheat breeders rarely apply population improvement schemes or select parental sources according to combining ability and heterotic patterns. They rely on pedigree selection methods for breeding new cultivars. This experiment was undertaken to assess the advantages of using diallel crosses to define combining ability and understand heterosis in a broad‐based wheat‐breeding population across different environments affected by yellow rust. Sixty‐four genotypes derived from a full diallel mating scheme were assessed for grain yield in two contrasting growing seasons at two locations for two consecutive years. Parental genotypes showed significant combining ability for grain yield that was affected by yellow rust and genotype‐by‐environment (GE) interactions, both of which affected heterosis for grain yield. Significant GE interactions suggested that decentralized selection for specific environments could maximize the use of this wheat germplasm. Cultivar effects and specific heterosis were the most important factors influencing grain yield. Some crosses capitalized on additive genetic variation for grain yield. This research shows the power of available quantitative breeding tools to help breeders choose parental sources in a population improvement programme.     

Plant traits related to yield of wheat in early, late, or continuous drought conditions

Bread wheats (Triticum aestivum L.) were evaluated for plant characteristics contributing to grain yield and plant adaptation under various drought patterns. The usefulness of these traits as explicit selection criteria in developing drought tolerant wheat varieties was investigated in three experiments. Cultivars from four germplasm groups, representing the four relevant major and distinct global wheat growing environments, were grown under the respective simulated early, late, continuous and no drought conditions by manipulating irrigation in north western Mexico. Additionally, 560 advanced lines from the CIMMYT breeding program were grown under late drought conditions, and 16 randomly selected advanced genotypes were studied in more detail under late and no drought conditions. In these three studies, the association between yield in drought-stressed environments and yield in non drought-stressed environments was interpreted to reflect genotypic high yield potential, mainly by way of high biomass development. However, yield potential only partly explained the superior performance under drought. For each pattern of drought stress, particular and often different plant traits were identified that further contributed specific adaptation to the distinct drought stress conditions. Knowledge of these traits will be useful for developing CIMMYT germplasm for specific drought-stressed areas. Ultimately, these studies demonstrate that both yield potential and specific adaptation traits are useful criteria in breeding for drought environments, and should be combined to achieve optimum performance and adaptation to drought stress. This revised version was published online in August 2006 with corrections to the Cover Date.