Predictions of heading date in bread wheat (Triticum aestivum L.) using QTL-based parameters of an ecophysiological model

From Neglecting to Including Cultivar-Specific Per Se Temperature Responses: Extending the Concept of Thermal Time in Field Crops

Predicting plant development, a longstanding goal in plant physiology, involves 2 interwoven components: continuous growth and the progression of growth stages (phenology). Current models for winter wheat and soybean assume species-level growth responses to temperature. We challenge this assumption, suggesting that cultivar-specific temperature responses substantially affect phenology. To investigate, we collected field-based growth and phenology data in winter wheat and soybean over multiple years. We used diverse models, from linear to neural networks, to assess growth responses to temperature at various trait and covariate levels. Cultivar-specific nonlinear models best explained phenology-related cultivar-environment interactions. With cultivar-specific models, additional relations to other stressors than temperature were found. The availability of the presented field phenotyping tools allows incorporating cultivar-specific temperature response functions in future plant physiology studies, which will deepen our understanding of key factors that influence plant development. Consequently, this work has implications for crop breeding and cultivation under adverse climatic conditions.

The complex relationship between disease resistance and yield in crops

In plants, growth and defence are controlled by many molecular pathways that are antagonistic to one another. This results in a 'growth-defence trade-off', where plants temporarily reduce growth in response to pests or diseases. Due to this antagonism, genetic variants that improve resistance often reduce growth and vice versa. Therefore, in natural populations, the most disease resistant individuals are often the slowest growing. In crops, slow growth may translate into a yield penalty, but resistance is essential for protecting yield in the presence of disease. Therefore, plant breeders must balance these traits to ensure optimal yield potential and yield stability. In crops, both qualitative and quantitative disease resistance are often linked with genetic variants that cause yield penalties, but this is not always the case. Furthermore, both crop yield and disease resistance are complex traits influenced by many aspects of the plant's physiology, morphology and environment, and the relationship between the molecular growth-defence trade-off and disease resistance-yield antagonism is not well-understood. In this article, we highlight research from the last 2 years on the molecular mechanistic basis of the antagonism between defence and growth. We then discuss the interaction between disease resistance and crop yield from a breeding perspective, outlining the complexity and nuances of this relationship and where research can aid practical methods for simultaneous improvement of yield potential and disease resistance.

Climate change enhances stability of wheat-flowering-date

The stability of winter wheat-flowering-date is crucial for ensuring consistent and robust crop performance across diverse climatic conditions. However, the impact of climate change on wheat-flowering-dates remains uncertain. This study aims to elucidate the influence of climate change on wheat-flowering-dates, predict how projected future climate conditions will affect flowering date stability, and identify the most stable wheat genotypes in the study region. We applied a multi-locus genotype-based (MLG-based) model for simulating wheat-flowering-dates, which we calibrated and evaluated using observed data from the Northern China winter wheat region (NCWWR). This MLG-based model was employed to project flowering dates under different climate scenarios. The simulated flowering dates were then used to assess the stability of flowering dates under varying allelic combinations in projected climatic conditions. Our MLG-based model effectively simulated flowering dates, with a root mean square error (RMSE) of 2.3 days, explaining approximately 88.5 % of the genotypic variation in flowering dates among 100 wheat genotypes. We found that, in comparison to the baseline climate, wheat-flowering-dates are expected to shift earlier within the target sowing window by approximately 11 and 14 days by 2050 under the Representative Concentration Pathways 4.5 (RCP4.5) and RCP8.5 climate scenarios, respectively. Furthermore, our analysis revealed that wheat-flowering-date stability is likely to be further strengthened under projected climate scenarios due to early flowering trends. Ultimately, we demonstrate that the combination of Vrn and Ppd genes, rather than individual Vrn or Ppd genes, plays a critical role in wheat-flowering-date stability. Our results suggest that the combination of Ppd-D1a with winter genotypes carrying the vrn-D1 allele significantly contributes to flowering date stability under current and projected climate scenarios. These findings provide valuable insights for wheat breeders and producers under future climatic conditions.

Integrating biophysical crop growth models and whole genome prediction for their mutual benefit: a case study in wheat phenology

Running crop growth models (CGM) coupled with whole genome prediction (WGP) as a CGM-WGP model introduces environmental information to WGP and genomic relatedness information to the genotype-specific parameters modelled through CGMs. Previous studies have primarily used CGM-WGP to infer prediction accuracy without exploring its potential to enhance CGM and WGP. Here, we implemented a heading and maturity date wheat phenology model within a CGM-WGP framework and compared it with CGM and WGP. The CGM-WGP resulted in more heritable genotype-specific parameters with more biologically realistic correlation structures between genotype-specific parameters and phenology traits compared with CGM-modelled genotype-specific parameters that reflected the correlation of measured phenotypes. Another advantage of CGM-WGP is the ability to infer accurate prediction with much smaller and less diverse reference data compared with that required for CGM. A genome-wide association analysis linked the genotype-specific parameters from the CGM-WGP model to nine significant phenology loci including Vrn-A1 and the three PPD1 genes, which were not detected for CGM-modelled genotype-specific parameters. Selection on genotype-specific parameters could be simpler than on observed phenotypes. For example, thermal time traits are theoretically more independent candidates, compared with the highly correlated heading and maturity dates, which could be used to achieve an environment-specific optimal flowering period. CGM-WGP combines the advantages of CGM and WGP to predict more accurate phenotypes for new genotypes under alternative or future environmental conditions.

Improving crop yield potential: Underlying biological processes and future prospects

The growing world population and global increases in the standard of living both result in an increasing demand for food, feed and other plant-derived products. In the coming years, plant-based research will be among the major drivers ensuring food security and the expansion of the bio-based economy. Crop productivity is determined by several factors, including the available physical and agricultural resources, crop management, and the resource use efficiency, quality and intrinsic yield potential of the chosen crop. This review focuses on intrinsic yield potential, since understanding its determinants and their biological basis will allow to maximize the plant's potential in food and energy production. Yield potential is determined by a variety of complex traits that integrate strictly regulated processes and their underlying gene regulatory networks. Due to this inherent complexity, numerous potential targets have been identified that could be exploited to increase crop yield. These encompass diverse metabolic and physical processes at the cellular, organ and canopy level. We present an overview of some of the distinct biological processes considered to be crucial for yield determination that could further be exploited to improve future crop productivity.

Dynamic QTL-based ecophysiological models to predict phenotype from genotype and environment data

BACKGROUND

Predicting the phenotype from the genotype is one of the major contemporary challenges in biology. This challenge is greater in plants because their development occurs mostly post-embryonically under diurnal and seasonal environmental fluctuations. Most current crop simulation models are physiology-based models capable of capturing environmental fluctuations but cannot adequately capture genotypic effects because they were not constructed within a genetics framework.

RESULTS

We describe the construction of a mixed-effects dynamic model to predict time-to-flowering in the common bean (Phaseolus vulgaris L.). This prediction model applies the developmental approach used by traditional crop simulation models, uses direct observational data, and captures the Genotype, Environment, and Genotype-by-Environment effects to predict progress towards time-to-flowering in real time. Comparisons to a traditional crop simulation model and to a previously developed static model shows the advantages of the new dynamic model.

CONCLUSIONS

The dynamic model can be applied to other species and to different plant processes. These types of models can, in modular form, gradually replace plant processes in existing crop models as has been implemented in BeanGro, a crop simulation model within the DSSAT Cropping Systems Model. Gene-based dynamic models can accelerate precision breeding of diverse crop species, particularly with the prospects of climate change. Finally, a gene-based simulation model can assist policy decision makers in matters pertaining to prediction of food supplies.

Integration of Crop Growth Models and Genomic Prediction

Crop growth models (CGMs) consist of multiple equations that represent physiological processes of plants and simulate crop growth dynamically given environmental inputs. Because parameters of CGMs are often genotype-specific, gene effects can be related to environmental inputs through CGMs. Thus, CGMs are attractive tools for predicting genotype by environment (G×E) interactions. This chapter reviews CGMs, genetic analyses using these models, and the status of studies that integrate genomic prediction with CGMs. Examples of CGM analyses are also provided.

Adapting to the projected epidemics of Fusarium head blight of wheat in Korea under climate change scenarios

Fusarium head blight (FHB) of wheat, mainly caused by Fusarium graminearum Schwabe, is an emerging threat to wheat production in Korea under a changing climate. The disease occurrence and accumulation of associated trichothecene mycotoxins in wheat kernels strongly coincide with warm and wet environments during flowering. Recently, the International Panel for Climate Change released the 6th Coupled Model Intercomparison Project (CMIP6) climate change scenarios with shared socioeconomic pathways (SSPs). In this study, we adopted GIBSIM, an existing mechanistic model developed in Brazil to estimate the risk infection index of wheat FHB, to simulate the potential FHB epidemics in Korea using the SSP245 and SSP585 scenarios of CMIP6. The GIBSIM model simulates FHB infection risk from airborne inoculum density and infection frequency using temperature, precipitation, and relative humidity during the flowering period. First, wheat heading dates, during which GIBSIM runs, were predicted over suitable areas of winter wheat cultivation using a crop development rate model for wheat phenology and downscaled SSP scenarios. Second, an integrated model combining all results of wheat suitability, heading dates, and FHB infection risks from the SSP scenarios showed a gradual increase in FHB epidemics towards 2100, with different temporal and spatial patterns of varying magnitudes depending on the scenarios. These results indicate that proactive management strategies need to be seriously considered in the near future to minimize the potential impacts of the FHB epidemic under climate change in Korea. Therefore, available wheat cultivars with early or late heading dates were used in the model simulations as a realistic adaptation measure. As a result, wheat cultivars with early heading dates showed significant decreases in FHB epidemics in future periods, emphasizing the importance of effective adaptation measures against the projected increase in FHB epidemics in Korea under climate change.

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.

Identification of Main-Effect and Environmental Interaction QTL and Their Candidate Genes for Drought Tolerance in a Wheat RIL Population Between Two Elite Spring Cultivars

Understanding the genetics of drought tolerance can expedite the development of drought-tolerant cultivars in wheat. In this study, we dissected the genetics of drought tolerance in spring wheat using a recombinant inbred line (RIL) population derived from a cross between a drought-tolerant cultivar, ‘Reeder’ (PI613586), and a high-yielding but drought-susceptible cultivar, ‘Albany.’ The RIL population was evaluated for grain yield (YLD), grain volume weight (GVW), thousand kernel weight (TKW), plant height (PH), and days to heading (DH) at nine different environments. The Infinium 90 k-based high-density genetic map was generated using 10,657 polymorphic SNP markers representing 2,057 unique loci. Quantitative trait loci (QTL) analysis detected a total of 11 consistent QTL for drought tolerance-related traits. Of these, six QTL were exclusively identified in drought-prone environments, and five were constitutive QTL (identified under both drought and normal conditions). One major QTL on chromosome 7B was identified exclusively under drought environments and explained 13.6% of the phenotypic variation (PV) for YLD. Two other major QTL were detected, one each on chromosomes 7B and 2B under drought-prone environments, and explained 14.86 and 13.94% of phenotypic variation for GVW and YLD, respectively. One novel QTL for drought tolerance was identified on chromosome 2D. In silico expression analysis of candidate genes underlaying the exclusive QTLs associated with drought stress identified the enrichment of ribosomal and chloroplast photosynthesis-associated proteins showing the most expression variability, thus possibly contributing to stress response by modulating the glycosyltransferase (TraesCS6A01G116400) and hexosyltransferase (TraesCS7B01G013300) unique genes present in QTL 21 and 24, respectively. While both parents contributed favorable alleles to these QTL, unexpectedly, the high-yielding and less drought-tolerant parent contributed desirable alleles for drought tolerance at four out of six loci. Regardless of the origin, all QTL with significant drought tolerance could assist significantly in the development of drought-tolerant wheat cultivars, using genomics-assisted breeding approaches.

Marker-based crop model-assisted ideotype design to improve avoidance of abiotic stress in bread wheat

Wheat phenology allows escape from seasonal abiotic stresses including frosts and high temperatures, the latter being forecast to increase with climate change. The use of marker-based crop models to identify ideotypes has been proposed to select genotypes adapted to specific weather and management conditions and anticipate climate change. In this study, a marker-based crop model for wheat phenology was calibrated and tested. Climate analysis of 30 years of historical weather data in 72 locations representing the main wheat production areas in France was performed. We carried out marker-based crop model simulations for 1019 wheat cultivars and three sowing dates, which allowed calculation of genotypic stress avoidance frequencies of frost and heat stress and identification of ideotypes. The phenology marker-based crop model allowed prediction of large genotypic variations for the beginning of stem elongation (GS30) and heading date (GS55). Prediction accuracy was assessed using untested genotypes and environments, and showed median genotype prediction errors of 8.5 and 4.2 days for GS30 and GS55, respectively. Climate analysis allowed the definition of a low risk period for each location based on the distribution of the last frost and first heat days. Clustering of locations showed three groups with contrasting levels of frost and heat risks. Marker-based crop model simulations showed the need to optimize the genotype depending on sowing date, particularly in high risk environments. An empirical validation of the approach showed that it holds good promises to improve frost and heat stress avoidance.

Predicting Rice Heading Date Using an Integrated Approach Combining a Machine Learning Method and a Crop Growth Model

Accurate prediction of heading date under various environmental conditions is expected to facilitate the decision-making process in cultivation management and the breeding process of new cultivars adaptable to the environment. Days to heading (DTH) is a complex trait known to be controlled by multiple genes and genotype-by-environment interactions. Crop growth models (CGMs) have been widely used to predict the phenological development of a plant in an environment; however, they usually require substantial experimental data to calibrate the parameters of the model. The parameters are mostly genotype-specific and are thus usually estimated separately for each cultivar. We propose an integrated approach that links genotype marker data with the developmental genotype-specific parameters of CGMs with a machine learning model, and allows heading date prediction of a new genotype in a new environment. To estimate the parameters, we implemented a Bayesian approach with the advanced Markov chain Monte-Carlo algorithm called the differential evolution adaptive metropolis and conducted the estimation using a large amount of data on heading date and environmental variables. The data comprised sowing and heading dates of 112 cultivars/lines tested at 7 locations for 14 years and the corresponding environmental variables (day length and daily temperature). We compared the predictive accuracy of DTH between the proposed approach, a CGM, and a single machine learning model. The results showed that the extreme learning machine (one of the implemented machine learning models) was superior to the CGM for the prediction of a tested genotype in a tested location. The proposed approach outperformed the machine learning method in the prediction of an untested genotype in an untested location. We also evaluated the potential of the proposed approach in the prediction of the distribution of DTH in 103 F₂ segregation populations derived from crosses between a common parent, Koshihikari, and 103 cultivars/lines. The results showed a high correlation coefficient (ca. 0.8) of the 10, 50, and 90th percentiles of the observed and predicted distribution of DTH. In this study, the integration of a machine learning model and a CGM was better able to predict the heading date of a new rice cultivar in an untested potential environment.

Combining Crop Growth Modeling With Trait-Assisted Prediction Improved the Prediction of Genotype by Environment Interactions

Plant breeders evaluate their selection candidates in multi-environment trials to estimate their performance in contrasted environments. The number of genotype/environment combinations that can be evaluated is strongly constrained by phenotyping costs and by the necessity to limit the evaluation to a few years. Genomic prediction models taking the genotype by environment interactions (GEI) into account can help breeders identify combination of (possibly unphenotyped) genotypes and target environments optimizing the traits under selection. We propose a new prediction approach in which a secondary trait available on both the calibration and the test sets is introduced as an environment specific covariate in the prediction model (trait-assisted prediction, TAP). The originality of this approach is that the phenotyping of the test set for the secondary trait is replaced by crop-growth model (CGM) predictions. So there is no need to sow and phenotype the test set in each environment which is a clear advantage over the classical trait-assisted prediction models. The interest of this approach, called CGM-TAP, is highest if the secondary trait is easy to predict with CGM and strongly related to the target trait in each environment (and thus capturing GEI). We tested CGM-TAP on bread wheat with heading date as secondary trait and grain yield as target trait. Simple CGM-TAP model with a linear effect of heading date resulted in high predictive abilities in three prediction scenarios (sparse testing, or prediction of new genotypes or of new environments). It increased predictive abilities of all reference GEI models, even those involving sophisticated environmental covariates.

From QTLs to Adaptation Landscapes: Using Genotype-To-Phenotype Models to Characterize G×E Over Time

Genotype by environment interaction (G×E) for the target trait, e.g. yield, is an emerging property of agricultural systems and results from the interplay between a hierarchy of secondary traits involving the capture and allocation of environmental resources during the growing season. This hierarchy of secondary traits ranges from basic traits that correspond to response mechanisms/sensitivities, to intermediate traits that integrate a larger number of processes over time and therefore show a larger amount of G×E. Traits underlying yield differ in their contribution to adaptation across environmental conditions and have different levels of G×E. Here, we provide a framework to study the performance of genotype to phenotype (G2P) modeling approaches. We generate and analyze response surfaces, or adaptation landscapes, for yield and yield related traits, emphasizing the organization of the traits in a hierarchy and their development and interactions over time. We use the crop growth model APSIM-wheat with genotype-dependent parameters as a tool to simulate non-linear trait responses over time with complex trait dependencies and apply it to wheat crops in Australia. For biological realism, APSIM parameters were given a genetic basis of 300 QTLs sampled from a gamma distribution whose shape and rate parameters were estimated from real wheat data. In the simulations, the hierarchical organization of the traits and their interactions over time cause G×E for yield even when underlying traits do not show G×E. Insight into how G×E arises during growth and development helps to improve the accuracy of phenotype predictions within and across environments and to optimize trial networks. We produced a tangible simulated adaptation landscape for yield that we first investigated for its biological credibility by statistical models for G×E that incorporate genotypic and environmental covariables. Subsequently, the simulated trait data were used to evaluate statistical genotype-to-phenotype models for multiple traits and environments and to characterize relationships between traits over time and across environments, as a way to identify traits that could be useful to select for specific adaptation. Designed appropriately, these types of simulated landscapes might also serve as a basis to train other, more deep learning methodologies in order to transfer such network models to real-world situations.

Modelling strategies for assessing and increasing the effectiveness of new phenotyping techniques in plant breeding

New types of phenotyping tools generate large amounts of data on many aspects of plant physiology and morphology with high spatial and temporal resolution. These new phenotyping data are potentially useful to improve understanding and prediction of complex traits, like yield, that are characterized by strong environmental context dependencies, i.e., genotype by environment interactions. For an evaluation of the utility of new phenotyping information, we will look at how this information can be incorporated in different classes of genotype-to-phenotype (G2P) models. G2P models predict phenotypic traits as functions of genotypic and environmental inputs. In the last decade, access to high-density single nucleotide polymorphism markers (SNPs) and sequence information has boosted the development of a class of G2P models called genomic prediction models that predict phenotypes from genome wide marker profiles. The challenge now is to build G2P models that incorporate simultaneously extensive genomic information alongside with new phenotypic information. Beyond the modification of existing G2P models, new G2P paradigms are required. We present candidate G2P models for the integration of genomic and new phenotyping information and illustrate their use in examples. Special attention will be given to the modelling of genotype by environment interactions. The G2P models provide a framework for model based phenotyping and the evaluation of the utility of phenotyping information in the context of breeding programs.

Training set optimization of genomic prediction by means of EthAcc

Genomic prediction is a useful tool for plant and animal breeding programs and is starting to be used to predict human diseases as well. A shortcoming that slows down the genomic selection deployment is that the accuracy of the prediction is not known a priori. We propose EthAcc (Estimated THeoretical ACCuracy) as a method for estimating the accuracy given a training set that is genotyped and phenotyped. EthAcc is based on a causal quantitative trait loci model estimated by a genome-wide association study. This estimated causal model is crucial; therefore, we compared different methods to find the one yielding the best EthAcc. The multilocus mixed model was found to perform the best. We compared EthAcc to accuracy estimators that can be derived via a mixed marker model. We showed that EthAcc is the only approach to correctly estimate the accuracy. Moreover, in case of a structured population, in accordance with the achieved accuracy, EthAcc showed that the biggest training set is not always better than a smaller and closer training set. We then performed training set optimization with EthAcc and compared it to CDmean. EthAcc outperformed CDmean on real datasets from sugar beet, maize, and wheat. Nonetheless, its performance was mainly due to the use of an optimal but inaccessible set as a start of the optimization algorithm. EthAcc's precision and algorithm issues prevent it from reaching a good training set with a random start. Despite this drawback, we demonstrated that a substantial gain in accuracy can be obtained by performing training set optimization.

Genome-Based Prediction of Time to Curd Induction in Cauliflower

The development of cauliflower (Brassica oleracea var. botrytis) is highly dependent on temperature due to vernalization requirements, which often causes delay and unevenness in maturity during months with warm temperatures. Integrating quantitative genetic analyses with phenology modeling was suggested to accelerate breeding strategies toward wide-adaptation cauliflower. The present study aims at establishing a genome-based model simulating the development of doubled haploid (DH) cauliflower lines to predict time to curd induction of DH lines not used for model parameterization and test hybrids derived from the bi-parental cross. Leaf appearance rate and the relation between temperature and thermal time to curd induction were examined in greenhouse trials on 180 DH lines at seven temperatures. Quantitative trait loci (QTL) analyses carried out on model parameters revealed ten QTL for leaf appearance rate (LAR), five for the slope and two for the intercept of linear temperature-response functions. Results of the QTL-based phenology model were compared to a genomic selection (GS) model. Model validation was carried out on data comprising four field trials with 72 independent DH lines, 160 hybrids derived from the parameterization set, and 34 hybrids derived from independent lines of the population. The QTL model resulted in a moderately accurate prediction of time to curd induction (R2 = 0.42-0.51) while the GS model generated slightly better results (R2 = 0.52-0.61). Predictions of time to curd induction of test hybrids from independent DH lines were less precise with R2 = 0.40 for the QTL and R2 = 0.48 for the GS model. Implementation of juvenile-to-adult phase transition is proposed for model improvement.

Optimization of multi-environment trials for genomic selection based on crop models

We propose a statistical criterion to optimize multi-environment trials to predict genotype × environment interactions more efficiently, by combining crop growth models and genomic selection models. Genotype × environment interactions (GEI) are common in plant multi-environment trials (METs). In this context, models developed for genomic selection (GS) that refers to the use of genome-wide information for predicting breeding values of selection candidates need to be adapted. One promising way to increase prediction accuracy in various environments is to combine ecophysiological and genetic modelling thanks to crop growth models (CGM) incorporating genetic parameters. The efficiency of this approach relies on the quality of the parameter estimates, which depends on the environments composing this MET used for calibration. The objective of this study was to determine a method to optimize the set of environments composing the MET for estimating genetic parameters in this context. A criterion called OptiMET was defined to this aim, and was evaluated on simulated and real data, with the example of wheat phenology. The MET defined with OptiMET allowed estimating the genetic parameters with lower error, leading to higher QTL detection power and higher prediction accuracies. MET defined with OptiMET was on average more efficient than random MET composed of twice as many environments, in terms of quality of the parameter estimates. OptiMET is thus a valuable tool to determine optimal experimental conditions to best exploit MET and the phenotyping tools that are currently developed.

Association mapping of leaf rust resistance loci in a spring wheat core collection

We identified 15 potentially novel loci in addition to previously characterized leaf rust resistance genes from 1032 spring wheat accessions. Targeted AM subset panels were instrumental in revealing interesting loci. Leaf rust is a common disease of wheat, consistently reducing yields in many wheat-growing regions of the world. Although fungicides are commonly applied to wheat in the United States (US), genetic resistance can provide less expensive, yet effective control of the disease. Our objectives were to map leaf rust resistance genes in a large core collection of spring wheat accessions selected from the United States Department of Agriculture-Agricultural Research Service National Small Grains Collection (NSGC), determine whether previously characterized race-nonspecific resistance genes could be identified with our panel, and evaluate the use of targeted panels to identify seedling and adult plant resistance (APR) genes. Association mapping (AM) detected five potentially novel leaf rust resistance loci on chromosomes 2BL, 4AS, and 5DL at the seedling stage, and 2DL and 7AS that conditioned both seedling and adult plant resistance. In addition, ten potentially novel race-nonspecific resistance loci conditioned field resistance and lacked seedling resistance. Analyses of targeted subsets of the accessions identified additional loci not associated with resistance in the complete core panel. Using molecular markers, we also confirmed the presence and effectiveness of the race-nonspecific genes Lr34, Lr46, and Lr67 in our panel. Although most of the accessions in this study were susceptible to leaf rust in field and seedling tests, many resistance loci were identified with AM. Through the use of targeted subset panels, more loci were identified than in the larger core panels alone.

Improvement of Predictive Ability by Uniform Coverage of the Target Genetic Space

Genome-enabled prediction provides breeders with the means to increase the number of genotypes that can be evaluated for selection. One of the major challenges in genome-enabled prediction is how to construct a training set of genotypes from a calibration set that represents the target population of genotypes, where the calibration set is composed of a training and validation set. A random sampling protocol of genotypes from the calibration set will lead to low quality coverage of the total genetic space by the training set when the calibration set contains population structure. As a consequence, predictive ability will be affected negatively, because some parts of the genotypic diversity in the target population will be under-represented in the training set, whereas other parts will be over-represented. Therefore, we propose a training set construction method that uniformly samples the genetic space spanned by the target population of genotypes, thereby increasing predictive ability. To evaluate our method, we constructed training sets alongside with the identification of corresponding genomic prediction models for four genotype panels that differed in the amount of population structure they contained (maize Flint, maize Dent, wheat, and rice). Training sets were constructed using uniform sampling, stratified-uniform sampling, stratified sampling and random sampling. We compared these methods with a method that maximizes the generalized coefficient of determination (CD). Several training set sizes were considered. We investigated four genomic prediction models: multi-locus QTL models, GBLUP models, combinations of QTL and GBLUPs, and Reproducing Kernel Hilbert Space (RKHS) models. For the maize and wheat panels, construction of the training set under uniform sampling led to a larger predictive ability than under stratified and random sampling. The results of our methods were similar to those of the CD method. For the rice panel, all training set construction methods led to similar predictive ability, a reflection of the very strong population structure in this panel.

Improvement of Predictive Ability by Uniform Coverage of the Target Genetic Space

Genome-enabled prediction provides breeders with the means to increase the number of genotypes that can be evaluated for selection. One of the major challenges in genome-enabled prediction is how to construct a training set of genotypes from a calibration set that represents the target population of genotypes, where the calibration set is composed of a training and validation set. A random sampling protocol of genotypes from the calibration set will lead to low quality coverage of the total genetic space by the training set when the calibration set contains population structure. As a consequence, predictive ability will be affected negatively, because some parts of the genotypic diversity in the target population will be under-represented in the training set, whereas other parts will be over-represented. Therefore, we propose a training set construction method that uniformly samples the genetic space spanned by the target population of genotypes, thereby increasing predictive ability. To evaluate our method, we constructed training sets alongside with the identification of corresponding genomic prediction models for four genotype panels that differed in the amount of population structure they contained (maize Flint, maize Dent, wheat, and rice). Training sets were constructed using uniform sampling, stratified-uniform sampling, stratified sampling and random sampling. We compared these methods with a method that maximizes the generalized coefficient of determination (CD). Several training set sizes were considered. We investigated four genomic prediction models: multi-locus QTL models, GBLUP models, combinations of QTL and GBLUPs, and Reproducing Kernel Hilbert Space (RKHS) models. For the maize and wheat panels, construction of the training set under uniform sampling led to a larger predictive ability than under stratified and random sampling. The results of our methods were similar to those of the CD method. For the rice panel, all training set construction methods led to similar predictive ability, a reflection of the very strong population structure in this panel.

Population Genomics Related to Adaptation in Elite Oat Germplasm

Six hundred thirty five oat ( L.) lines and 4561 single-nucleotide polymorphism (SNP) loci were used to evaluate population structure, linkage disequilibrium (LD), and genotype-phenotype association with heading date. The first five principal components (PCs) accounted for 25.3% of genetic variation. Neither the eigenvalues of the first 25 PCs nor the cross-validation errors from = 1 to 20 model-based analyses suggested a structured population. However, the PC and = 2 model-based analyses supported clustering of lines on spring oat vs. southern United States origin, accounting for 16% of genetic variation ( < 0.0001). Single-locus -statistic () in the highest 1% of the distribution suggested linkage groups that may be differentiated between the two population subgroups. Population structure and kinship-corrected LD of = 0.10 was observed at an average pairwise distance of 0.44 cM (0.71 and 2.64 cM within spring and southern oat, respectively). On most linkage groups LD decay was slower within southern lines than within the spring lines. A notable exception was found on linkage group Mrg28, where LD decay was substantially slower in the spring subpopulation. It is speculated that this may be caused by a heterogeneous translocation event on this chromosome. Association with heading date was most consistent across location-years on linkage groups Mrg02, Mrg12, Mrg13, and Mrg24.

Toward integration of genomic selection with crop modelling: the development of an integrated approach to predicting rice heading dates

It is suggested that accuracy in predicting plant phenotypes can be improved by integrating genomic prediction with crop modelling in a single hierarchical model. Accurate prediction of phenotypes is important for plant breeding and management. Although genomic prediction/selection aims to predict phenotypes on the basis of whole-genome marker information, it is often difficult to predict phenotypes of complex traits in diverse environments, because plant phenotypes are often influenced by genotype-environment interaction. A possible remedy is to integrate genomic prediction with crop/ecophysiological modelling, which enables us to predict plant phenotypes using environmental and management information. To this end, in the present study, we developed a novel method for integrating genomic prediction with phenological modelling of Asian rice (Oryza sativa, L.), allowing the heading date of untested genotypes in untested environments to be predicted. The method simultaneously infers the phenological model parameters and whole-genome marker effects on the parameters in a Bayesian framework. By cultivating backcross inbred lines of Koshihikari × Kasalath in nine environments, we evaluated the potential of the proposed method in comparison with conventional genomic prediction, phenological modelling, and two-step methods that applied genomic prediction to phenological model parameters inferred from Nelder-Mead or Markov chain Monte Carlo algorithms. In predicting heading dates of untested lines in untested environments, the proposed and two-step methods tended to provide more accurate predictions than the conventional genomic prediction methods, particularly in environments where phenotypes from environments similar to the target environment were unavailable for training genomic prediction. The proposed method showed greater accuracy in prediction than the two-step methods in all cross-validation schemes tested, suggesting the potential of the integrated approach in the prediction of phenotypes of plants.

Exploiting the Repetitive Fraction of the Wheat Genome for High-Throughput Single-Nucleotide Polymorphism Discovery and Genotyping

Transposable elements (TEs) account for more than 80% of the wheat genome. Although they represent a major obstacle for genomic studies, TEs are also a source of polymorphism and consequently of molecular markers such as insertion site-based polymorphism (ISBP) markers. Insertion site-based polymorphisms have been found to be a great source of genome-specific single-nucleotide polymorphism (SNPs) in the hexaploid wheat ( L.) genome. Here, we report on the development of a high-throughput SNP discovery approach based on sequence capture of ISBP markers. By applying this approach to the reference sequence of chromosome 3B from hexaploid wheat, we designed 39,077 SNPs that are evenly distributed along the chromosome. We demonstrate that these SNPs can be efficiently scored with the KASPar (Kompetitive allele-specific polymerase chain reaction) genotyping technology. Finally, through genetic diversity and genome-wide association studies, we also demonstrate that ISBP-derived SNPs can be used in marker-assisted breeding programs.

Velocity of temperature and flowering time in wheat - assisting breeders to keep pace with climate change

By accelerating crop development, warming climates may result in mismatches between key sensitive growth stages and extreme climate events, with severe consequences for crop yield and food security. Using recent estimates of gene responses to vernalization and photoperiod in wheat, we modelled the flowering times of all 'potential' genotypes as influenced by the velocity of climate change across the Australian wheatbelt. In the period 1957-2010, seasonal increases in temperature of 0.012 °C yr(-1) were recorded and changed flowering time of a mid-season wheat genotype by an average -0.074 day yr(-1) , with flowering 'velocity' of up to 0.95 km yr(-1) towards the coastal edges of the wheatbelt; this is an estimate of how quickly the given genotype would have to be 'moved' across the landscape to maintain its original flowering time. By 2030, these national changes are projected to accelerate by up to 3-fold for seasonal temperature and by up to 5-fold for flowering time between now and 2030, with average national shifts in flowering time of 0.33 and 0.41 day yr(-1) between baseline and the worst climate scenario tested for 2030 and 2050, respectively. Without new flowering alleles in commercial germplasm, the life cycle of wheat crops is predicted to shorten by 2 weeks by 2030 across the wheatbelt for the most pessimistic climate scenario. While current cultivars may be otherwise suitable for future conditions, they will flower earlier due to warmer temperatures. To allow earlier sowing to escape frost, heat and terminal drought, and to maintain current growing period of early-sown wheat crops in the future, breeders will need to develop and/or introduce new genetic sources for later flowering, more so in the eastern part of the wheatbelt.

Assessment of the Potential Impacts of Wheat Plant Traits across Environments by Combining Crop Modeling and Global Sensitivity Analysis

A crop can be viewed as a complex system with outputs (e.g. yield) that are affected by inputs of genetic, physiology, pedo-climatic and management information. Application of numerical methods for model exploration assist in evaluating the major most influential inputs, providing the simulation model is a credible description of the biological system. A sensitivity analysis was used to assess the simulated impact on yield of a suite of traits involved in major processes of crop growth and development, and to evaluate how the simulated value of such traits varies across environments and in relation to other traits (which can be interpreted as a virtual change in genetic background). The study focused on wheat in Australia, with an emphasis on adaptation to low rainfall conditions. A large set of traits (90) was evaluated in a wide target population of environments (4 sites × 125 years), management practices (3 sowing dates × 3 nitrogen fertilization levels) and CO2 (2 levels). The Morris sensitivity analysis method was used to sample the parameter space and reduce computational requirements, while maintaining a realistic representation of the targeted trait × environment × management landscape (∼ 82 million individual simulations in total). The patterns of parameter × environment × management interactions were investigated for the most influential parameters, considering a potential genetic range of +/- 20% compared to a reference cultivar. Main (i.e. linear) and interaction (i.e. non-linear and interaction) sensitivity indices calculated for most of APSIM-Wheat parameters allowed the identification of 42 parameters substantially impacting yield in most target environments. Among these, a subset of parameters related to phenology, resource acquisition, resource use efficiency and biomass allocation were identified as potential candidates for crop (and model) improvement.

Genetic Architecture of Flowering Phenology in Cereals and Opportunities for Crop Improvement

Cereal crop species including bread wheat (Triticum aestivum L.), barley (Hordeum vulgare L.), rice (Oryza sativa L.), and maize (Zea mays L.) provide the bulk of human nutrition and agricultural products for industrial use. These four cereals are central to meet future demands of food supply for an increasing world population under a changing climate. A prerequisite for cereal crop production is the transition from vegetative to reproductive and grain-filling phases starting with flower initiation, a key developmental switch tightly regulated in all flowering plants. Although studies in the dicotyledonous model plant Arabidopsis thaliana build the foundations of our current understanding of plant phenology genes and regulation, the availability of genome assemblies with high-confidence sequences for rice, maize, and more recently bread wheat and barley, now allow the identification of phenology-associated gene orthologs in monocots. Together with recent advances in next-generation sequencing technologies, QTL analysis, mutagenesis, complementation analysis, and RNA interference, many phenology genes have been functionally characterized in cereal crops and conserved as well as functionally divergent genes involved in flowering were found. Epigenetic and other molecular regulatory mechanisms that respond to environmental and endogenous triggers create an enormous plasticity in flowering behavior among cereal crops to ensure flowering is only induced under optimal conditions. In this review, we provide a summary of recent discoveries of flowering time regulators with an emphasis on four cereal crop species (bread wheat, barley, rice, and maize), in particular, crop-specific regulatory mechanisms and genes. In addition, pleiotropic effects on agronomically important traits such as grain yield, impact on adaptation to new growing environments and conditions, genetic sequence-based selection and targeted manipulation of phenology genes, as well as crop growth simulation models for predictive crop breeding, are discussed.

Crop epigenetics and the molecular hardware of genotype × environment interactions

Crop plants encounter thermal environments which fluctuate on a diurnal and seasonal basis. Future climate resilient cultivars will need to respond to thermal profiles reflecting more variable conditions, and harness plasticity that involves regulation of epigenetic processes and complex genomic regulatory networks. Compartmentalization within plant cells insulates the genomic central processing unit within the interphase nucleus. This review addresses the properties of the chromatin hardware in which the genome is embedded, focusing on the biophysical and thermodynamic properties of DNA, histones and nucleosomes. It explores the consequences of thermal and ionic variation on the biophysical behavior of epigenetic marks such as DNA cytosine methylation (5mC), and histone variants such as H2A.Z, and how these contribute to maintenance of chromatin integrity in the nucleus, while enabling specific subsets of genes to be regulated. Information is drawn from theoretical molecular in vitro studies as well as model and crop plants and incorporates recent insights into the role epigenetic processes play in mediating between environmental signals and genomic regulation. A preliminary speculative framework is outlined, based on the evidence of what appears to be a cohesive set of interactions at molecular, biophysical and electrostatic level between the various components contributing to chromatin conformation and dynamics. It proposes that within plant nuclei, general and localized ionic homeostasis plays an important role in maintaining chromatin conformation, whilst maintaining complex genomic regulation that involves specific patterns of epigenetic marks. More generally, reversible changes in DNA methylation appear to be consistent with the ability of nuclear chromatin to manage variation in external ionic and temperature environment. Whilst tentative, this framework provides scope to develop experimental approaches to understand in greater detail the internal environment of plant nuclei. It is hoped that this will generate a deeper understanding of the molecular mechanisms underlying genotype × environment interactions that may be beneficial for long-term improvement of crop performance in less predictable climates.

Phenotypic and genetic dissection of component traits for early vigour in rice using plant growth modelling, sugar content analyses and association mapping

Early vigour of rice, defined as seedling capacity to accumulate shoot dry weight (SDW) rapidly, is a complex trait. It depends on a genotype propensity to assimilate, store, and/or use non-structural carbohydrates (NSC) for producing large and/or numerous leaves, involving physiological trade-offs in the expression of component traits and, possibly, physiological and genetic linkages. This study explores a plant-model-assisted phenotyping approach to dissect the genetic architecture of rice early vigour, applying the Genome Wide Association Study (GWAS) to morphological and NSC measurements, as well as fitted parameters for the functional-structural plant model, Ecomeristem. Leaf size, number, SDW, and source-leaf NSC concentration were measured on a panel of 123 japonica accessions. The data were used to estimate Ecomeristem genotypic parameters driving organ appearance rate, size, and carbon dynamics. GWAS was performed based on 12 221 single-nucleotide polymorphisms (SNP). Twenty-three associations were detected at P <1×10(-4) and 64 at P <5×10(-4). Associations for NSC and model parameters revealed new regions related to early vigour that had greater significance than morphological traits, providing additional information on the genetic control of early vigour. Plant model parameters were used to characterize physiological and genetic trade-offs among component traits. Twelve associations were related to loci for cloned genes, with nine related to organogenesis, plant height, cell size or cell number. The potential use of these associations as markers for breeding is discussed.

Integrating Crop Growth Models with Whole Genome Prediction through Approximate Bayesian Computation

Genomic selection, enabled by whole genome prediction (WGP) methods, is revolutionizing plant breeding. Existing WGP methods have been shown to deliver accurate predictions in the most common settings, such as prediction of across environment performance for traits with additive gene effects. However, prediction of traits with non-additive gene effects and prediction of genotype by environment interaction (G×E), continues to be challenging. Previous attempts to increase prediction accuracy for these particularly difficult tasks employed prediction methods that are purely statistical in nature. Augmenting the statistical methods with biological knowledge has been largely overlooked thus far. Crop growth models (CGMs) attempt to represent the impact of functional relationships between plant physiology and the environment in the formation of yield and similar output traits of interest. Thus, they can explain the impact of G×E and certain types of non-additive gene effects on the expressed phenotype. Approximate Bayesian computation (ABC), a novel and powerful computational procedure, allows the incorporation of CGMs directly into the estimation of whole genome marker effects in WGP. Here we provide a proof of concept study for this novel approach and demonstrate its use with synthetic data sets. We show that this novel approach can be considerably more accurate than the benchmark WGP method GBLUP in predicting performance in environments represented in the estimation set as well as in previously unobserved environments for traits determined by non-additive gene effects. We conclude that this proof of concept demonstrates that using ABC for incorporating biological knowledge in the form of CGMs into WGP is a very promising and novel approach to improving prediction accuracy for some of the most challenging scenarios in plant breeding and applied genetics.