Phenological optimization of late reproductive phase for raising wheat yield potential in irrigated mega-environments.

Increasing grain number through fine-tuning duration of the late reproductive phase (LRP; terminal spikelet to anthesis) without altering anthesis time has been proposed as a genetic strategy to increase yield potential (YP) of wheat. Here we conducted a modelling analysis to evaluate the potential of fine-tuning LRP in raising YP in irrigated mega-environments. Using the known optimal anthesis and sowing date of current elite benchmark genotypes, we applied a gene-based phenology model for long-term simulations of phenological stages and yield-related variables of all potential germplasm with the same duration to anthesis as the benchmark genotypes. These diverse genotypes had the same duration to anthesis but varying LRP duration. Lengthening LRP increased YP and harvest index by increasing grain number to some extent and an excessively long LRP reduced YP due to reduced time for canopy construction for high biomass production of pre-anthesis phase. The current elite genotypes could have their LRP extended for higher YP in most sites. Genotypes with a ratio of the duration of LRP to pre-anthesis phase of about 0.42 ensured high yields (≥95% of YP) with their optimal sowing and anthesis dates. Optimization of intermediate growth stages could be further evaluated in breeding programmes to improve YP.

Genomic variants affecting homoeologous gene expression dosage contribute to agronomic trait variation in allopolyploid wheat.

Allopolyploidy greatly expands the range of possible regulatory interactions among functionally redundant homoeologous genes. However, connection between the emerging regulatory complexity and expression and phenotypic diversity in polyploid crops remains elusive. Here, we use diverse wheat accessions to map expression quantitative trait loci (eQTL) and evaluate their effects on the population-scale variation in homoeolog expression dosage. The relative contribution of cis- and trans-eQTL to homoeolog expression variation is strongly affected by both selection and demographic events. Though trans-acting effects play major role in expression regulation, the expression dosage of homoeologs is largely influenced by cis-acting variants, which appear to be subjected to selection. The frequency and expression of homoeologous gene alleles showing strong expression dosage bias are predictive of variation in yield-related traits, and have likely been impacted by breeding for increased productivity. Our study highlights the importance of genomic variants affecting homoeolog expression dosage in shaping agronomic phenotypes and points at their potential utility for improving yield in polyploid crops.

Effect of Flowering Time-Related Genes on Biomass, Harvest Index, and Grain Yield in CIMMYT Elite Spring Bread Wheat.

Grain yield (YLD) is a function of the total biomass (BM) and of partitioning the biomass by grains, i.e., the harvest index (HI). The most critical developmental stage for their determination is the flowering time, which mainly depends on the vernalization requirement (Vrn) and photoperiod sensitivity genes (Ppd) loci. Allelic variants at the Vrn, Ppd, and earliness per se (Eps) genes of elite spring wheat genotypes included in High Biomass Association Panel (HiBAP) I and II were used to estimate their effects on the phenological stages BM, HI, and YLD. Each panel was grown for two consecutive years in Northwest Mexico. Spring alleles at Vrn-1 had the largest effect on shortening the time to anthesis, and the Ppd-insensitive allele Ppd-D1a had the most significant positive effect on YLD in both panels. In addition, alleles at TaTOE-B1 and TaFT3-B1 promoted between 3.8% and 7.6% higher YLD and 4.2% and 10.2% higher HI in HiBAP I and II, respectively. When the possible effects of the TaTOE-B1 and TaFT3-B1 alleles on the sink and source traits were explored, the favorable allele at TaTOE-B1 showed positive effects on several sink traits mainly related to grain number. The favorable alleles at TaFT3-B1 followed a different pattern, with positive effects on the traits related to grain weight. The results of this study expanded the wheat breeders' toolbox in the quest to breed better-adapted and higher-yielding wheat cultivars.

Using a gene-based phenology model to identify optimal flowering periods of spring wheat in irrigated mega-environments.

To maximize the grain yield of spring wheat, flowering needs to coincide with the optimal flowering period (OFP) by minimizing frost and heat stress on reproductive development. This global study conducted a comprehensive modelling analysis of genotype, environment, and management to identify the OFPs for sites in irrigated mega-environments of spring wheat where the crop matures during a period of increasing temperatures. We used a gene-based phenology model to conduct long-term simulation analysis with parameterized genotypes to identify OFPs and optimal sowing dates for sites in irrigated mega-environments, considering the impacts of frost and heat stress on yield. The validation results showed that the gene-based model accurately predicted wheat heading dates across global wheat environments. The long-term simulations indicated that frost and heat stress significantly advanced or delayed OFPs and shrank the durations of OFPs in irrigated mega-environments when compared with OFPs where the model excluded frost and heat stress impacts. The simulation results (incorporating frost and heat penalties on yield) also showed that earlier flowering generally resulted in higher yields, and early sowing dates and/or early flowering genotypes were suggested to achieve early flowering. These results provided an interpretation of the regulation of wheat flowering to the OFP by the selection of sowing date and cultivar to achieve higher yields in irrigated mega-environments.

Harnessing translational research in wheat for climate resilience.

Despite being the world's most widely grown crop, research investments in wheat (Triticum aestivum and Triticum durum) fall behind those in other staple crops. Current yield gains will not meet 2050 needs, and climate stresses compound this challenge. However, there is good evidence that heat and drought resilience can be boosted through translating promising ideas into novel breeding technologies using powerful new tools in genetics and remote sensing, for example. Such technologies can also be applied to identify climate resilience traits from among the vast and largely untapped reserve of wheat genetic resources in collections worldwide. This review describes multi-pronged research opportunities at the focus of the Heat and Drought Wheat Improvement Consortium (coordinated by CIMMYT), which together create a pipeline to boost heat and drought resilience, specifically: improving crop design targets using big data approaches; developing phenomic tools for field-based screening and research; applying genomic technologies to elucidate the bases of climate resilience traits; and applying these outputs in developing next-generation breeding methods. The global impact of these outputs will be validated through the International Wheat Improvement Network, a global germplasm development and testing system that contributes key productivity traits to approximately half of the global wheat-growing area.

An integrated framework reinstating the environmental dimension for GWAS and genomic selection in crops.

Identifying mechanisms and pathways involved in gene-environment interplay and phenotypic plasticity is a long-standing challenge. It is highly desirable to establish an integrated framework with an environmental dimension for complex trait dissection and prediction. A critical step is to identify an environmental index that is both biologically relevant and estimable for new environments. With extensive field-observed complex traits, environmental profiles, and genome-wide single nucleotide polymorphisms for three major crops (maize, wheat, and oat), we demonstrated that identifying such an environmental index (i.e., a combination of environmental parameter and growth window) enables genome-wide association studies and genomic selection of complex traits to be conducted with an explicit environmental dimension. Interestingly, genes identified for two reaction-norm parameters (i.e., intercept and slope) derived from flowering time values along the environmental index were less colocalized for a diverse maize panel than for wheat and oat breeding panels, agreeing with the different diversity levels and genetic constitutions of the panels. In addition, we showcased the usefulness of this framework for systematically forecasting the performance of diverse germplasm panels in new environments. This general framework and the companion CERIS-JGRA analytical package should facilitate biologically informed dissection of complex traits, enhanced performance prediction in breeding for future climates, and coordinated efforts to enrich our understanding of mechanisms underlying phenotypic variation.

Progress and Prospects of Developing Climate Resilient Wheat in South Asia Using Modern Pre-Breeding Methods.

Developing climate-resilient wheat is a priority for South Asia since the effect of climate change will be pronounced on the major crops that are staple to the region. South Asia must produce >400 million metric tons (MMT) of wheat by 2050 to meet the demand. However, the current average yield <3 t/ha is not sufficient to meet the requirement. In this review, we are addressing how pre-breeding methods in wheat can address the gap in grain yield as well as reduce the bottleneck of genetic diversity. Physiological pre-breeding which incorporates screening of diverse germplasm from gene banks for physiological and agronomic traits, the strategic crossing of complementary traits, high throughput phenotyping, molecular markers-based generation advancement, genomic prediction, and validation of high-value heat and drought tolerant lines to South Asia can help to alleviate the drastic effect of climate change on wheat production. There are several gene banks, if utilized well, can play a major role in breeding for climate-resilient wheat. CIMMYT's wheat physiological pre-breeding has delivered several hundred lines via the Stress Adapted Trait Yield Nursery (SATYN) to the NARS in many South Asian countries; India, Pakistan, Nepal, Bangladesh, Afghanistan, and Iran. Some of these improved germplasms have resulted in varieties for farmer's field. We conclude the review by pointing out the importance of collaborative interdisciplinary translational research to alleviate the effects of climate change on wheat production in South Asia.

Genetic Dissection of Adult Plant Resistance to Sharp Eyespot Using an Updated Genetic Map of Niavt14 × Xuzhou25 Winter Wheat Recombinant Inbred Line Population.

Wheat sharp eyespot, a disease mainly caused by soilborne fungus Rhizoctonia cerealis, is a threat to world wheat production. Wheat's genetic resistance to sharp eyespot is a potential approach to reducing the application of fungicides and farming practice inputs. To identify the genetic basis of sharp eyespot resistance in Niavt14, a recombinant inbred line population comprising 215 F8 lines from Niavt14 × Xuzhou25, was developed. An earlier linkage map (148 simple sequence repeat markers) was updated with 5,792 polymorphic Affymetrix Axiom 55K single-nucleotide polymorphisms to a new map of 5,684.2 centimorgans with 1,406 nonredundant markers. The new linkage map covered all 21 chromosomes of common wheat and showed a good collinearity with the IWGSC RefSeq v1.0 genome. We conducted quantitative trait locus (QTL) mapping for sharp eyespot resistance using the adult plant response data from the field of five consecutive growing seasons and one greenhouse test. Two stable QTL on chromosomes 2B and 7D that were identified in the previous study were confirmed, and three novel, stable QTL, explaining 4.0 to 17.5% phenotypic variation, were mapped on 1D, 6D, and 7A, which were independent of QTL for phenology and plant height. The QTL on 1D, 2B, 6D, and 7A showed low frequencies in 384 landraces (0 to 10%) and 269 elite cultivars (5 to 23%) from the southern winter wheat region and the Yellow and Huai River Valley facultative wheat region in China, respectively. These identified QTL could be used in wheat breeding programs for improving sharp eyespot resistance through marker-assisted selection.

Breeder friendly phenotyping.

The word phenotyping can nowadays invoke visions of a drone or phenocart moving swiftly across research plots collecting high-resolution data sets on a wide array of traits. This has been made possible by recent advances in sensor technology and data processing. Nonetheless, more comprehensive often destructive phenotyping still has much to offer in breeding as well as research. This review considers the 'breeder friendliness' of phenotyping within three main domains: (i) the 'minimum data set', where being 'handy' or accessible and easy to collect and use is paramount, visual assessment often being preferred; (ii) the high throughput phenotyping (HTP), relatively new for most breeders, and requiring significantly greater investment with technical hurdles for implementation and a steeper learning curve than the minimum data set; (iii) detailed characterization or 'precision' phenotyping, typically customized for a set of traits associated with a target environment and requiring significant time and resources. While having been the subject of debate in the past, extra investment for phenotyping is becoming more accepted to capitalize on recent developments in crop genomics and prediction models, that can be built from the high-throughput and detailed precision phenotypes. This review considers different contexts for phenotyping, including breeding, exploration of genetic resources, parent building and translational research to deliver other new breeding resources, and how the different categories of phenotyping listed above apply to each. Some of the same tools and rules of thumb apply equally well to phenotyping for genetic analysis of complex traits and gene discovery.

Genomic-enabled Prediction Accuracies Increased by Modeling Genotype × Environment Interaction in Durum Wheat.

Genomic prediction studies incorporating genotype × environment (G×E) interaction effects are limited in durum wheat. We tested the genomic-enabled prediction accuracy (PA) of Genomic Best Linear Unbiased Predictor (GBLUP) models-six non-G × E and three G × E models-on three basic cross-validation (CV) schemes- in predicting incomplete field trials (CV2), new lines (CV1), and lines in untested environments (CV0)- in a durum wheat panel grown under yield potential, drought stress, and heat stress conditions. For CV0, three scenarios were considered: (i) leave-one environment out (CV0-Env); (ii) leave one site out (CV0-Site); and (iii) leave 1 yr out (CV0-Year). The reaction norm models with G × E effects showed higher PA than the non-G × E models. Among the CV schemes, CV2 and CV0-Env had higher PA (0.58 each) than the CV1 scheme (0.35). When the average of all the models and CV schemes were considered, among the eight traits- grain yield, thousand grain weight, grain number, days to anthesis, days to maturity, plant height, and normalized difference vegetation index at vegetative (NDVIvg) and grain filling (NDVIllg)-, plant height had the highest PA (0.68) and moderate values were observed for grain yield (0.34). The results indicated that genomic selection models incorporating G × E interaction show great promise for forward prediction and application in durum wheat breeding to increase genetic gains.

Genome-Wide Association Analyses Identify QTL Hotspots for Yield and Component Traits in Durum Wheat Grown under Yield Potential, Drought, and Heat Stress Environments.

Understanding the genetic bases of economically important traits is fundamentally important in enhancing genetic gains in durum wheat. In this study, a durum panel of 208 lines (comprised of elite materials and exotics from the International Maize and Wheat Improvement Center gene bank) were subjected to genome wide association study (GWAS) using 6,211 DArTseq single nucleotide polymorphisms (SNPs). The panel was phenotyped under yield potential (YP), drought stress (DT), and heat stress (HT) conditions for 2 years. Mean yield of the panel was reduced by 72% (to 1.64 t/ha) under HT and by 60% (to 2.33 t/ha) under DT, compared to YP (5.79 t/ha). Whereas, the mean yield of the panel under HT was 30% less than under DT. GWAS identified the largest number of significant marker-trait associations on chromosomes 2A and 2B with p-values 10-06 to 10-03 and the markers from the whole study explained 7-25% variation in the traits. Common markers were identified for stress tolerance indices: stress susceptibility index, stress tolerance, and stress tolerance index estimated for the traits under DT (82 cM on 2B) and HT (68 and 83 cM on 3B; 25 cM on 7A). GWAS of irrigated (YP and HT combined), stressed (DT and HT combined), combined analysis of three environments (YP + DT + HT), and its comparison with trait per se and stress indices identified QTL hotspots on chromosomes 2A (54-70 cM) and 2B (75-82 cM). This study enhances our knowledge about the molecular markers associated with grain yield and its components under different stress conditions. It identifies several marker-trait associations for further exploration and validation for marker-assisted breeding.

Correction to: Genetic analysis of multi-environmental spring wheat trials identifies genomic regions for locus-specific trade-offs for grain weight and grain number.

Unfortunately, the Fig. 1 of this original article was incorrectly published. The corrected Fig. 1 is given below.

Correction to: Strategic crossing of biomass and harvest index-source and sink-achieves genetic gains in wheat.

[This corrects the article DOI: 10.1007/s10681-017-2040-z.].

Genetic analysis of multi-environmental spring wheat trials identifies genomic regions for locus-specific trade-offs for grain weight and grain number.

KEY MESSAGE: GWAS on multi-environment data identified genomic regions associated with trade-offs for grain weight and grain number. Grain yield (GY) can be dissected into its components thousand grain weight (TGW) and grain number (GN), but little has been achieved in assessing the trade-off between them in spring wheat. In the present study, the Wheat Association Mapping Initiative (WAMI) panel of 287 elite spring bread wheat lines was phenotyped for GY, GN, and TGW in ten environments across different wheat growing regions in Mexico, South Asia, and North Africa. The panel genotyped with the 90 K Illumina Infinitum SNP array resulted in 26,814 SNPs for genome-wide association study (GWAS). Statistical analysis of the multi-environmental data for GY, GN, and TGW observed repeatability estimates of 0.76, 0.62, and 0.95, respectively. GWAS on BLUPs of combined environment analysis identified 38 loci associated with the traits. Among them four loci-6A (85 cM), 5A (98 cM), 3B (99 cM), and 2B (96 cM)-were associated with multiple traits. The study identified two loci that showed positive association between GY and TGW, with allelic substitution effects of 4% (GY) and 1.7% (TGW) for 6A locus and 0.2% (GY) and 7.2% (TGW) for 2B locus. The locus in chromosome 6A (79-85 cM) harbored a gene TaGW2-6A. We also identified that a combination of markers associated with GY, TGW, and GN together explained higher variation for GY (32%), than the markers associated with GY alone (27%). The marker-trait associations from the present study can be used for marker-assisted selection (MAS) and to discover the underlying genes for these traits in spring wheat.

Genomic Prediction with Pedigree and Genotype × Environment Interaction in Spring Wheat Grown in South and West Asia, North Africa, and Mexico.

Developing genomic selection (GS) models is an important step in applying GS to accelerate the rate of genetic gain in grain yield in plant breeding. In this study, seven genomic prediction models under two cross-validation (CV) scenarios were tested on 287 advanced elite spring wheat lines phenotyped for grain yield (GY), thousand-grain weight (GW), grain number (GN), and thermal time for flowering (TTF) in 18 international environments (year-location combinations) in major wheat-producing countries in 2010 and 2011. Prediction models with genomic and pedigree information included main effects and interaction with environments. Two random CV schemes were applied to predict a subset of lines that were not observed in any of the 18 environments (CV1), and a subset of lines that were not observed in a set of the environments, but were observed in other environments (CV2). Genomic prediction models, including genotype × environment (G×E) interaction, had the highest average prediction ability under the CV1 scenario for GY (0.31), GN (0.32), GW (0.45), and TTF (0.27). For CV2, the average prediction ability of the model including the interaction terms was generally high for GY (0.38), GN (0.43), GW (0.63), and TTF (0.53). Wheat lines in site-year combinations in Mexico and India had relatively high prediction ability for GY and GW. Results indicated that prediction ability of lines not observed in certain environments could be relatively high for genomic selection when predicting G×E interaction in multi-environment trials.

Phenotypic and genome-wide association analysis of spike ethylene in diverse wheat genotypes under heat stress.

The gaseous phytohormone ethylene plays an important role in spike development in wheat (Triticum aestivum). However, the genotypic variation and the genomic regions governing spike ethylene (SET) production in wheat under long-term heat stress remain unexplored. We investigated genotypic variation in the production of SET and its relationship with spike dry weight (SDW) in 130 diverse wheat elite lines and landraces under heat-stressed field conditions. We employed an Illumina iSelect 90K single nucleotide polymorphism (SNP) genotyping array to identify the genetic loci for SET and SDW through a genome-wide association study (GWAS) in a subset of the Wheat Association Mapping Initiative (WAMI) panel. The SET and SDW exhibited appreciable genotypic variation among wheat genotypes at the anthesis stage. There was a strong negative correlation between SET and SDW. The GWAS uncovered five and 32 significant SNPs for SET, and 22 and 142 significant SNPs for SDW, in glasshouse and field conditions, respectively. Some of these SNPs closely localized to the SNPs for plant height, suggesting close associations between plant height and spike-related traits. The phenotypic and genetic elucidation of SET and its relationship with SDW supports future efforts toward gene discovery and breeding wheat cultivars with reduced ethylene effects on yield under heat stress.

Genome-wide association study for grain yield and related traits in an elite spring wheat population grown in temperate irrigated environments.

KEY MESSAGE: Through genome-wide association study, loci for grain yield and yield components were identified in chromosomes 5A and 6A in spring wheat (Triticum aestivum). Genome-wide association study (GWAS) was conducted for grain yield (YLD) and yield components on a wheat association mapping initiative (WAMI) population of 287 elite, spring wheat lines grown under temperate irrigated high-yield potential condition in Ciudad Obregón, Mexico, during four crop cycles (from 2009-2010 to 2012-2013). The population was genotyped with high-density Illumina iSelect 90K single nucleotide polymorphisms (SNPs) assay. An analysis of traits across subpopulations indicated that lines with 1B/1R translocation had higher YLD, grain weight, and taller plants than lines without the translocation. GWAS using 18,704 SNPs identified 31 loci that explained 5-14 % of the variation in individual traits. We identified SNPs in chromosome 5A and 6A that were significantly associated with yield and yield components. Four loci were detected for YLD in chromosomes 3B, 5A, 5B, and 6A and the locus in 5A explained 5 % of the variation for grain number/m(2). The locus for YLD in chromosome 6A also explained 6 % of the variation in grain weight. Loci significantly associated with maturity were identified in chromosomes 2B, 3B, 4B, 4D, and 6A and for plant height in 1A and 6A. Loci were also detected for canopy temperature at grain filling (2D, 4D, 6A), chlorophyll index at grain filling (3B and 6A), biomass (3D and 6A) and harvest index (1D, 1B, and 3B) that explained 5-10 % variation. These markers will be further validated.

Population structure and marker-trait association analysis of the US peanut (Arachis hypogaea L.) mini-core collection.

Peanut (Arachis hypogaea L.) is one of the most important oilseed and nutritional crops in the world. To efficiently utilize the germplasm collection, a peanut mini-core containing 112 accessions was established in the United States. To determine the population structure and its impact on marker-trait association, this mini-core collection was assessed by genotyping 94 accessions with 81 SSR markers and two functional SNP markers from fatty acid desaturase 2 (FAD2). Seed quality traits (including oil content, fatty acid composition, flavonoids, and resveratrol) were obtained through nuclear magnetic resonance (NMR), gas chromatography (GC), and high-performance liquid chromatography (HPLC) analysis. Genetic diversity and population structure analysis identified four major subpopulations that are related to four botanical varieties. Model comparison with different levels of population structure and kinship control was conducted for each trait and association analyses with the selected models verified that the functional SNP from the FAD2A gene is significantly associated with oleic acid (C18:1), linoleic acid (C18:2), and oleic-to-linoleic (O/L) ratio across this diverse collection. Even though the allele distribution of FAD2A was structured among the four subpopulations, the effect of FAD2A gene remained significant after controlling population structure and had a likelihood-ratio-based R ( 2 ) (R ( LR ) ( 2 ) ) value of 0.05 (oleic acid), 0.09 (linoleic acid), and 0.07 (O/L ratio) because the FAD2A alleles were not completely fixed within subpopulations. Our genetic analysis demonstrated that this peanut mini-core panel is suitable for association mapping. Phenotypic characterization for seed quality traits and association testing of the functional SNP from FAD2A gene provided information for further breeding and genetic research.