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Fusarium head blight (FHB) and stem rust are among the most devastating diseases of wheat worldwide.

Genomic selection (GS) models use genome-wide genetic information to predict genetic values of candidates of selection. Originally, these models were developed without considering genotype × environment interaction(G×E). Several authors have proposed extensions of the single-environment GS model that accommodate G×E using either covariance functions or environmental covariates. In this study, we model G×E using a marker × environment interaction (M×E) GS model; the approach is conceptually simple and can be implemented with existing GS software. We discuss how the model can be implemented by using an explicit regression of phenotypes on markers or using co-variance structures (a genomic best linear unbiased prediction-type model). We used the M×E model to analyze three CIMMYT wheat data sets (W1, W2, and W3), where more than 1000 lines were genotyped using genotyping-by-sequencing and evaluated at CIMMYT’s research station in Ciudad Obregon, Mexico, under simulated environmental conditions that covered different irrigation levels, sowing dates and planting systems. We compared the M×E model with a stratified (i.e., within-environment) analysis and with a standard (across-environment) GS model that assumes that effects are constant across environments (i.e., ignoring G×E). The prediction accuracy of the M×E model was substantially greater of that of an across-environment analysis that ignores G×E. Depending on the prediction problem, the M×E model had either similar or greater levels of prediction accuracy than the stratified analyses. The M×E model decomposes marker effects and genomic values into components that are stable across environments (main effects) and others that are environment-specific (interactions). Therefore, in principle, the interaction model could shed light over which variants have effects that are stable across environments and which ones are responsible for G×E. The data set and the scripts required to reproduce the analysis are publicly available as Supporting Information.

Pearson’s correlation coefficient (ρ) is the most commonly reported metric of the success of prediction in genomic selection (GS). However, in real breeding ρ may not be very useful for assessing the quality of the regression in the tails of the distribution, where individuals are chosen for selection. This research used 14 maize and 16 wheat data sets with different trait–environment combinations. Six different models were evaluated by means of a cross-validation scheme (50 random partitions each, with 90% of the individuals in the training set and 10% in the testing set). The predictive accuracy of these algorithms for selecting individuals belonging to the best α=10, 15, 20, 25, 30, 35, 40% of the distribution was estimated using Cohen’s kappa coefficient (κ) and an ad hoc measure, which we call relative efficiency (RE), which indicates the expected genetic gain due to selection when individuals are selected based on GS exclusively. We put special emphasis on the analysis for α=15%, because it is a percentile commonly used in plant breeding programmes (for example, at CIMMYT). We also used ρ as a criterion for overall success. The algorithms used were: Bayesian LASSO (BL), Ridge Regression (RR), Reproducing Kernel Hilbert Spaces (RHKS), Random Forest Regression (RFR), and Support Vector Regression (SVR) with linear (lin) and Gaussian kernels (rbf). The performance of regression methods for selecting the best individuals was compared with that of three supervised classification algorithms: Random Forest Classification (RFC) and Support Vector Classification (SVC) with linear (lin) and Gaussian (rbf) kernels. Classification methods were evaluated using the same cross-validation scheme but with the response vector of the original training sets dichotomised using a given threshold. For α=15%, SVC-lin presented the highest κ coefficients in 13 of the 14 maize data sets, with best values ranging from 0.131 to 0.722 (statistically significant in 9 data sets) and the best RE in the same 13 data sets, with values ranging from 0.393 to 0.948 (statistically significant in 12 data sets). RR produced the best mean for both κ and RE in one data set (0.148 and 0.381, respectively). Regarding the wheat data sets, SVC-lin presented the best κ in 12 of the 16 data sets, with outcomes ranging from 0.280 to 0.580 (statistically significant in 4 data sets) and the best RE in 9 data sets ranging from 0.484 to 0.821 (statistically significant in 5 data sets). SVC-rbf (0.235), RR (0.265) and RHKS (0.422) gave the best κ in one data set each, while RHKS and BL tied for the last one (0.234). Finally, BL presented the best RE in two data sets (0.738 and 0.750), RFR (0.636) and SVC-rbf (0.617) in one and RHKS in the remaining three (0.502, 0.458 and 0.586). The difference between the performance of SVC-lin and that of the rest of the models was not so pronounced at higher percentiles of the distribution. The behaviour of regression and classification algorithms varied markedly when selection was done at different thresholds, that is, κ and RE for each algorithm depended strongly on the selection percentile. Based on the results, we propose classification method as a promising alternative for GS in plant breeding.

Genomic selection (GS) has been implemented in animal and plant species, and is regarded as a useful tool for accelerating genetic gains. Varying levels of genomic prediction accuracy have been obtained in plants, depending on the prediction problem assessed and on several other factors, such as trait heritability, the relationship between the individuals to be predicted and those used to train the models for prediction, number of markers, sample size and genotype × environment interaction (GE). The main objective of this article is to describe the results of genomic prediction in International Maize and Wheat Improvement Center's (CIMMYT's) maize and wheat breeding programs, from the initial assessment of the predictive ability of different models using pedigree and marker information to the present, when methods for implementing GS in practical global maize and wheat breeding programs are being studied and investigated. Results show that pedigree (population structure) accounts for a sizeable proportion of the prediction accuracy when a global population is the prediction problem to be assessed. However, when the prediction uses unrelated populations to train the prediction equations, prediction accuracy becomes negligible. When genomic prediction includes modeling GE, an increase in prediction accuracy can be achieved by borrowing information from correlated environments. Several questions on how to incorporate GS into CIMMYT's maize and wheat programs remain unanswered and subject to further investigation, for example, prediction within and between related bi-parental crosses. Further research on the quantification of breeding value components for GS in plant breeding populations is required.Heredity advance online publication, 10 April 2013; doi:10.1038/hdy.2013.16.

Agriculture is the single largest geo-engineering initiative that humans have initiated on planet Earth, largely through the introduction of unprecedented amounts of reactive nitrogen (N) into ecosystems. A major portion of this reactive N applied as fertilizer leaks into the environment in massive amounts, with cascading negative effects on ecosystem health and function. Natural ecosystems utilize many of the multiple pathways in the N cycle to regulate N flow. In contrast, the massive amounts of N currently applied to agricultural systems cycle primarily through the nitrification pathway, a single inefficient route that channels much of this reactive N into the environment. This is largely due to the rapid nitrifying soil environment of present-day agricultural systems.In this Viewpoint paper, the importance of regulating nitrification as a strategy to minimize N leakage and to improve N-use efficiency (NUE) in agricultural systems is highlighted. The ability to suppress soil nitrification by the release of nitrification inhibitors from plant roots is termed 'biological nitrification inhibition' (BNI), an active plant-mediated natural function that can limit the amount of N cycling via the nitrification pathway. The development of a bioassay using luminescent Nitrosomonas to quantify nitrification inhibitory activity from roots has facilitated the characterization of BNI function. Release of BNIs from roots is a tightly regulated physiological process, with extensive genetic variability found in selected crops and pasture grasses. Here, the current status of understanding of the BNI function is reviewed using Brachiaria forage grasses, wheat and sorghum to illustrate how BNI function can be utilized for achieving low-nitrifying agricultural systems. A fundamental shift towards ammonium (NH4(+))-dominated agricultural systems could be achieved by using crops and pastures with high BNI capacities. When viewed from an agricultural and environmental perspective, the BNI function in plants could potentially have a large influence on biogeochemical cycling and closure of the N loop in crop-livestock systems.

Realistic experimental protocols to screen for drought adaptation in controlled conditions are crucial if high throughput phenotyping is to be used for the identification of high performance lines, and is especially important in the evaluation of transgenes where stringent biosecurity measures restrict the frequency of open field trials. Transgenic DREB1A-wheat events were selected under greenhouse conditions by evaluating survival and recovery under severe drought (SURV) as well as for water use efficiency (WUE). Greenhouse experiments confirmed the advantages of transgenic events in recovery after severe water stress. Under field conditions, the group of transgenic lines did not generally outperform the controls in terms of grain yield under water deficit. However, the events selected for WUE were identified as lines that combine an acceptable yield-even higher yield (WUE-11) under well irrigated conditions-and stable performance across the different environments generated by the experimental treatments.

Successful plant establishment is critical to the development of high-yielding crops. Short coleoptiles can reduce seedling emergence particularly when seed is sown deep as occurs when moisture necessary for germination is deep in the subsoil. Detailed molecular maps for a range of wheat doubled-haploid populations (Cranbrook/Halberd, Sunco/Tasman, CD87/Katepwa and Kukri/Janz) were used to identify genomic regions affecting coleoptile characteristics length, cross-sectional area and degree of spiralling across contrasting soil temperatures. Genotypic variation was large and distributions of genotype means were approximately normal with evidence for transgressive segregation. Narrow-sense heritabilities were high for coleoptile length and cross-sectional area indicating a strong genetic basis for differences among progeny. In contrast, heritabilities for coleoptile spiralling were small. Molecular marker analyses identified a number of significant quantitative trait loci (QTL) for coleoptile growth. Many of the coleoptile growth QTL mapped directly to the Rht-B1 or Rht-D1 dwarfing gene loci conferring reduced cell size through insensitivity to endogenous gibberellins. Other QTL for coleoptile growth were identified throughout the genome. Epistatic interactions were small or non-existent, and there was little evidence for any QTL x temperature interaction. Gene effects at significant QTL were approximately one-half to one-quarter the size of effects at the Rht-B1 and Rht-D1 regions. However, selection at these QTL could together alter coleoptile length by up to 50 mm. In addition to Rht-B1b and Rht-D1b, genomic regions on chromosomes 2B, 2D, 4A, 5D and 6B were repeatable across two or more populations suggesting their potential value for use in breeding and marker-aided selection for greater coleoptile length and improved establishment.

Journal of Experimental Botany, 66 (18), ISSN:1460-2431, ISSN:0022-0957

Hexaploid bread wheat evolved from a rare hybridisation, which resulted in a loss of genetic diversity in the wheat D-genome with respect to the ancestral donor, Aegilops tauschii. Novel genetic variation can be introduced into modern wheat by recreating the above hybridisation ; however, the information associated with the Ae. tauschii accessions in germplasm collections is limited, making rational selection of accessions into a re-synthesis programme difficult. We describe methodologies to identify novel diversity from Ae. tauschii accessions that combines Bayesian analysis of genotypic data, sub-species diversity and geographic information that summarises variation in climate and habitat at the collection point for each accession. Comparisons were made between diversity discovered amongst a panel of Ae. tauschii accessions, bread wheat varieties and lines from the CIMMYT synthetic hexaploid wheat programme. The selection of Ae. tauschii accessions based on differing approaches had significant effect on diversity within each set. Our results suggest that a strategy that combines several criteria will be most effective in maximising the sampled variation across multiple parameters. The analysis of multiple layers of variation in ex situ Ae. tauschii collections allows for an informed and rational approach to the inclusion of wild relatives into crop breeding programmes.

Theoretical considerations suggest that wheat yield potential could be increased by up to 50% through the genetic improvement of radiation use efficiency (RUE). However, to achieve agronomic impacts, structural and reproductive aspects of the crop must be improved in parallel. A Wheat Yield Consortium (WYC) has been convened that fosters linkage between ongoing research platforms in order to develop a cohesive portfolio of activities that will maximize the probability of impact in farmers' fields. Attempts to increase RUE will focus on improving the performance and regulation of Rubisco, introduction of C(4)-like traits such as CO(2)-concentrating mechanisms, improvement of light interception, and improvement of photosynthesis at the spike and whole canopy levels. For extra photo-assimilates to translate into increased grain yield, reproductive aspects of growth must be tailored to a range of agro-ecosystems to ensure that stable expression of a high harvest index (HI) is achieved. Adequate partitioning among plant organs will be critical to achieve favourable expression of HI, and to ensure that plants with heavier grain have strong enough stems and roots to avoid lodging. Trait-based hybridization strategies will aim to achieve their simultaneous expression in elite agronomic backgrounds, and wide crossing will be employed to augment genetic diversity where needed; for example, to introduce traits for improving RUE from wild species or C(4) crops. Genomic selection approaches will be employed, especially for difficult-to-phenotype traits. Genome-wide selection will be evaluated and is likely to complement crossing of complex but complementary traits by identifying favourable allele combinations among progeny. Products will be delivered to national wheat programmes worldwide via well-established international nursery systems and are expected to make a significant contribution to global food security.

Plant breeders simultaneously select for qualitative traits controlled by one or a small number of major genes, as well as for polygenic traits controlled by multiple genes that may be detected as quantitative trait loci (QTL). In this study, we applied computer simulation to investigate simultaneous selection for alleles at both major and minor gene (as QTL) loci in breeding populations of two wheat parental lines, HM14BS and Sunstate. Loci targeted for selection included six major genes affecting plant height, disease resistance, and grain quality, plus 6 known and 11 "unidentified" QTL affecting coleoptile length (CL). Parental line HM14BS contributed the target alleles at two of the major gene loci, while parental line Sunstate contributed target alleles at four loci. The parents have similar plant height, but HM14BS has a longer coleoptile, a desirable attribute for deep sowing in rainfed environments. Including the wild-type allele at the major reduced-height locus Rht-D1, HM14BS was assumed to have 13 QTL for increased CL, and Sunstate four; these assumptions being derived from mapping studies and empirical data from an actual HM14BS/Sunstate population. Simulation indicated that compared to backcross populations, a single biparental F(1) cross produced the highest frequency of target genotypes (six desired alleles at major genes plus desired QTL alleles for long CL). From 1,000 simulation runs, an average of 2.4 individuals with the target genotype were present in unselected F(1)-derived doubled haploid (DH) or recombinant inbred line (RIL) populations of size 200. A selection scheme for the six major genes increased the number of target individuals to 19.1, and additional marker-assisted selection (MAS) for CL increased the number to 23.0. Phenotypic selection (PS) of CL outperformed MAS in this study due to the high heritability of CL, incompletely linked markers for known QTL, and the existence of unidentified QTL. However, a selection scheme combining MAS and PS was equally as efficient as PS and would result in net savings in production and time to delivery of long coleoptile wheats containing the six favorable alleles.

Wheat crops with greater early vigour shade the soil surface more rapidly and reduce water loss. Evaporative losses affect water-use efficiency particularly in drier regions where most of the rainfall occurs early in the growing season before canopy closure. Greater seedling leaf area and longer coleoptiles are major determinants of increased vigour and better crop establishment. A previously developed high vigour breeding line ‘Vigour 18’ was used to establish a large recombinant inbred family and framework map to identify a QTL on chromosome 6A that accounted for up to 8% of the variation for coleoptile length, 14% of seedling leaf width and was associated with increased plant height. The SSR marker NW3106, nearest to the 6A QTL, was also associated with greater leaf width in a breeding population that was also derived from a cross involving the high vigour donor line ‘Vigour18’. The association between the NW3106 marker and coleoptile length was validated in a second breeding population which was developed using an unrelated long coleoptile donor line. The ‘Vigour18’ allele of the QTL on chromosome 6A promoted coleoptile length and leaf width during early plant growth but was also associated with increased plant height at maturity. Markers linked to the QTL are being used to increase the frequency of increased vigour and long coleoptile alleles in early generations of breeding populations.