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We investigated increasing genetic gain for grain yield using early generation genomic selection (GS). A training set of 1,334 elite wheat breeding lines tested over three field seasons was used to generate Genomic Estimated Breeding Values (GEBVs) for grain yield under irrigated conditions applying markers and three different prediction methods: (1) Genomic Best Linear Unbiased Predictor (GBLUP), (2) GBLUP with the imputation of missing genotypic data by Ridge Regression BLUP (rrGBLUP_imp), and (3) Reproducing Kernel Hilbert Space (RKHS) a.k.a. Gaussian Kernel (GK). F2 GEBVs were generated for 1,924 individuals from 38 biparental cross populations between 21 parents selected from the training set. Results showed that F2 GEBVs from the different methods were not correlated. Experiment 1 consisted of selecting F2s with the highest average GEBVs and advancing them to form genomically selected bulks and make intercross populations aiming to combine favorable alleles for yield. F4:6 lines were derived from genomically selected bulks, intercrosses, and conventional breeding methods with similar numbers from each. Results of field-testing for Experiment 1 did not find any difference in yield with genomic compared to conventional selection. Experiment 2 compared the predictive ability of the different GEBV calculation methods in F2 using a set of single plant-derived F2:4 lines from randomly selected F2 plants. Grain yield results from Experiment 2 showed a significant positive correlation between observed yields of F2:4 lines and predicted yield GEBVs of F2 single plants from GK (the predictive ability of 0.248, P < 0.001) and GBLUP (0.195, P < 0.01) but no correlation with rrGBLUP_imp. Results demonstrate the potential for the application of GS in early generations of wheat breeding and the importance of using the appropriate statistical model for GEBV calculation, which may not be the same as the best model for inbreds.

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Fusarium head blight (FHB) and stem rust are among the most devastating diseases of wheat worldwide. Fhb1 is the most widely utilized and the only isolated gene for FHB resistance, while Sr2 is a durable stem rust resistance gene used in rust-prone areas. The two loci are closely linked on the short arm of chromosome 3B and the two genes are in repulsion phase among cultivars. With climate change and the shift in Fusarium populations, it is imperative to develop wheat cultivars resistant to both diseases. The present study was dedicated to developing wheat germplasm combining Fhb1 and Sr2 resistance alleles in the International Maize and Wheat Improvement Center (CIMMYT)'s elite cultivars' backgrounds. Four recombinant inbred lines (RILs) in Hartog background that have the resistant Fhb1 and Sr2 alleles in coupled phase linkage were crossed with seven CIMMYT bread wheat lines, resulting in 208 lines. Molecular markers for both genes were employed in addition to the use of pseudo-black chaff (PBC) as a phenotypic marker for the selection of Sr2. At various stages of the selection process, progeny lines were assessed for FHB index, Fusarium damaged kernels (FDK), stem rust, and PBC expression as well as other diseases of interest (stripe rust and leaf spotting diseases). The 25 best lines were selected for CIMMYT's wheat breeding program. In addition to expressing resistance to FHB, most of these 25 lines have an acceptable level of resistance to other tested diseases. These lines will be useful for wheat breeding programs worldwide and potentially speed up the resistance breeding efforts against FHB and stem rust.

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To introduce new genetic diversity into the bread wheat gene pool from its progenitor, Aegilops tauschii (Coss.) Schmalh, 33 primary synthetic hexaploid wheat genotypes (SYN) were crossed to 20 spring bread wheat (BW) cultivars at the International Wheat and Maize Improvement Center. Modified single seed descent was used to develop 97 populations with 50 individuals per population using first back-cross, biparental, and three-way crosses. Individuals from each cross were selected for short stature, early heading, flowering and maturity, minimal lodging, and free threshing. Yield trials were conducted under irrigated, drought, and heat-stress conditions from 2011 to 2014 in Ciudad Obregon, Mexico. Genomic estimated breeding values (GEBVs) of parents and synthetic derived lines (SDLs) were estimated using a genomic best linear unbiased prediction (GBLUP) model with markers in each trial. In each environment, there were SDLs that had higher GEBVs than their recurrent BW parent for yield. The GEBVs of BW parents for yield ranged from -0.32 in heat to 1.40 in irrigated trials. The range of the SYN parent GEBVs for yield was from -2.69 in the irrigated to 0.26 in the heat trials and were mostly negative across environments. The contribution of the SYN parents to improved grain yield of the SDLs was highest under heat stress, with an average GEBV for the top 10% of the SDLs of 0.55 while the weighted average GEBV of their corresponding recurrent BW parents was 0.26. Using the pedigree-based model, the accuracy of genomic prediction for yield was 0.42, 0.43, and 0.49 in the drought, heat and irrigated trials, respectively, while for the marker-based model these values were 0.43, 0.44, and 0.55. The SYN parents introduced novel diversity into the wheat gene pool. Higher GEBVs of progenies were due to introgression and retention of some positive alleles from SYN parents.

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Plants in the field are exposed to varying light and moisture. Agronomic improvement requires knowledge of whole-plant phenotypes expressed in response to simultaneous variation in these essential resources. Most phenotypes, however, have been described from experiments where resources are varied singularly. To test the importance of varying shoot and root resources for phenotyping studies, sister pre-breeding lines of wheat were phenotyped in response to independent or simultaneous exposure to two light levels and soil moisture profiles. The distribution and architecture of the root systems depended strongly on the moisture of the deeper soil layer. For one genotype, roots, specifically lateral roots, were stimulated to grow into moist soil when the upper zone was well-watered and were inhibited by drier deep zones. In contrast, the other genotype showed much less plasticity and responsiveness to upper moist soil, but maintained deeper penetration of roots into the dry layer. The sum of shoot and root responses was greater when treated simultaneously to low light and low soil water, compared to each treatment alone, suggesting the value of whole plant phenotyping in response to multiple conditions for agronomic improvement. The results suggest that canopy management for increased irradiation of leaves would encourage root growth into deeper drier soil, and that genetic variation within closely related breeding lines may exist to favour surface root growth in response to irrigation or in-season rainfall.

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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.

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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.

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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.

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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.

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Genes that reduce height without compromising seedling vigour or coleoptile length have great potential for wheat improvement. We therefore investigated the effects of various reduced height (Rht) genes on the early stages of plant development, using a combination of near isogenic, recombinant, mutant and wild type comparisons. Gibberellin (GA) insensitivity caused by Rht-B1b or Rht-D1b was associated with reduced leaf elongation rate and coleoptile length. Similar results were found for two other sources of dwarfing, Rht11 and Rht17. We found one class of Rht genes (e.g. Rht8) which had no effect on coleoptile length, leaf elongation rate or responsiveness to GA, indicating that these dwarfing genes may act later in wheat development to reduce height and increase harvest index, without affecting early growth. A third class of Rht genes was found in three durum backgrounds. These had reduced coleoptile lengths and leaf elongation rates, but had a greater response to GA than the corresponding tall varieties. We discuss these results in relation to the possible mechanisms underlying the reduction in height and the suitability of the different Rht genes for wheat improvement.