Soybean Nested Association Mapping Research Articles

Genomic predictions of genetic variances and correlations among traits for breeding crosses in soybean

Parental selection is perhaps the most critical decision a breeder makes, establishing the foundation of the entire program for years to come. Cross selection based on predicted mean and genetic variance can be further expanded to multiple-trait improvement by predicting the genetic correlation (rG\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${r}_{G}$$\\end{document}) between pairs of traits. Our objective was to empirically assess the ability to predict the family mean, genetic variance, superior progeny mean and genetic correlation through genomic prediction in a soybean population. Data made available through the Soybean Nested Association Mapping project included phenotypic data on seven traits (days to maturity, lodging, oil, plant height, protein, seed size, and seed yield) for 39 families. Training population composition followed a leave-one-family-out cross-validation scheme, with the validation family genetic parameters predicted using the remaining families as the training set. The predictive abilities for family mean and superior progeny mean were significant for all traits while predictive ability of genetic variance was significant for four traits. We were able to validate significant predictive abilities of rG\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${r}_{G}$$\\end{document} for 18 out of 21 (86%) pairwise trait combinations (P < 0.05). The findings from this study support the use of genome-wide marker effects for predicting rG\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${r}_{G}$$\\end{document} in soybean biparental crosses. If successfully implemented in breeding programs, this methodology could help to increase the rate of genetic gain for multiple correlated traits.

Improving predictive ability in sparse testing designs in soybean populations

The availability of high-dimensional genomic data and advancements in genome-based prediction models (GP) have revolutionized and contributed to accelerated genetic gains in soybean breeding programs. GP-based sparse testing is a promising concept that allows increasing the testing capacity of genotypes in environments, of genotypes or environments at a fixed cost, or a substantial reduction of costs at a fixed testing capacity. This study represents the first attempt to implement GP-based sparse testing in soybeans by evaluating different training set compositions going from non-overlapped RILs until almost the other extreme of having same set of genotypes observed across environments for different training set sizes. A total of 1,755 recombinant inbred lines (RILs) tested in nine environments were used in this study. RILs were derived from 39 bi-parental populations of the Soybean Nested Association Mapping (NAM) project. The predictive abilities of various models and training set sizes and compositions were investigated. Training compositions included a range of ratios of overlapping (O-RILs) and non-overlapping (NO-RILs) RILs across environments, as well as a methodology to maximize or minimize the genetic diversity in a fixed-size sample. Reducing the training set size compromised predictive ability in most training set compositions. Overall, maximizing the genetic diversity within the training set and the inclusion of O-RILs increased prediction accuracy given a fixed training set size; however, the most complex model was less affected by these factors. More testing environments in the early stages of the breeding pipeline can provide a more comprehensive assessment of genotype stability and adaptation which are fundamental for the precise selection of superior genotypes adapted to a wide range of environments.

Detecting directional and non-directional epistasis in bi-parental populations using genomic data.

Epistasis, commonly defined as interaction effects between alleles of different loci, is an important genetic component of the variation of phenotypic traits in natural and breeding populations. In addition to its impact on variance, epistasis can also affect the expected performance of a population and is then referred to as directional epistasis. Before the advent of genomic data, the existence of epistasis (both directional and non-directional) was investigated based on complex and expensive mating schemes involving several generations evaluated for a trait of interest. In this study, we propose a methodology to detect the presence of epistasis based on simple inbred biparental populations, both genotyped and phenotyped, ideally along with their parents. Thanks to genomic data, parental proportions as well as shared parental proportions between inbred individuals can be estimated. They allow the evaluation of epistasis through a test of the expected performance for directional epistasis or the variance of genetic values. This methodology was applied to two large multiparental populations, i.e. the American maize and soybean nested association mapping populations, evaluated for different traits. Results showed significant epistasis, especially for the test of directional epistasis, e.g. the increase in anthesis to silking interval observed in most maize inbred progenies or the decrease in grain yield observed in several soybean inbred progenies. In general, the effects detected suggested that shuffling allelic associations of both elite parents had a detrimental effect on the performance of their progeny. This methodology is implemented in the EpiTest R-package and can be applied to any bi/multiparental inbred population evaluated for a trait of interest.

Genetic architecture of protein and oil content in soybean seed and meal.

Soybean is grown primarily for the protein and oil extracted from its seed and its value is influenced by these components. The objective of this study was to map marker-trait associations (MTAs) for the concentration of seed protein, oil, and meal protein using the soybean nested association mapping (SoyNAM) population. The composition traits were evaluated on seed harvested from over 5000 inbred lines of the SoyNAM population grown in 10 field locations across 3 years. Estimated heritabilities were at least 0.85 for all three traits. The genotyping of lines with single nucleotide polymorphism markers resulted in the identification of 107 MTAs for the three traits. When MTAs for the three traits that mapped within 5cM intervals were binned together, the MTAs were mapped to 64 intervals on 19 of the 20 soybean chromosomes. The majority of the MTA effects were small and of the 107 MTAs, 37 were for protein content, 39 for meal protein, and 31 for oil content. For cases where a protein and oil MTAs mapped to the same interval, most (94%) significant effects were opposite for the two traits, consistent with the negative correlation between these traits. A coexpression analysis identified candidate genes linked to MTAs and 18 candidate genes were identified. The large number of small effect MTAs for the composition traits suggest that genomic prediction would be more effective in improving these traits than marker-assisted selection.

Screening the Soybean Nested Association Mapping (SoyNAM) Parents for Resistance Toward Isolates of Phytophthora sojae, Fusarium graminearum, and Species of Globisporangium

The Soybean Nested Association Mapping (SoyNAM) populations were developed from 40 parents and have been used to map genes underlying complex traits such as yield and disease resistance. Soilborne pathogens that affect soybean seed and seedlings result in significant losses due to reduced stands and costs associated with replanting. This study compared the response of these 40 SoyNAM parent genotypes to seed- and seedling-rot pathogens Fusarium graminearum, Phytophthora sojae, Globisporangium ultimum var. ultimum, G. ultimum var. sporangiiferum, G. irregulare groups 1 and 2, and G. cryptoirregulare. None of the parental genotypes conferred high levels of resistance to F. graminearum or G. ultimum var. sporangiiferum. Of the 40 parental genotypes, 15 were resistant to P. sojae OH1 (vir 7), indicating that they contain Rps genes, whereas the remaining (including the common parent IA3023) do not have Rps genes. Based on inoculations with known P. sojae pathotypes, Rps1c was the most common, followed by Rps1a, as both Rps genes confer resistance to isolates OH4 (vir 1a, 1c, 7) and OH25 (vir 1a, 1b, 1c, 1k, 7). Eight of the SoyNAM parents had higher levels of partial resistance to P. sojae than Conrad (a cultivar with moderate resistance). There was moderate resistance to G. ultimum var. ultimum among the 40 parents and to G. irregulare subpopulations among the six parents that were evaluated. The SoyNAM parental lines and populations are an excellent resource available for soybean breeders to advance the development of new cultivars with improved resistance to some soilborne pathogens.

Comparing artificial-intelligence techniques with state-of-the-art parametric prediction models for predicting soybean traits.

Soybean [Glycine max (L.) Merr.] is a significant source of protein and oil and is also widely used as animal feed. Thus, developing lines that are superior in terms of yield, protein, and oil content is important to feed the ever-growing population. As opposed to high-cost phenotyping, genotyping is both cost and time efficient for breeders because evaluating new lines in different environments (location-year combinations) can be costly. Several genomic prediction (GP) methods have been developed to use the marker and environment data effectively to predict the yield or other relevant phenotypic traits of crops. Our study compares a conventional GP method (genomic best linear unbiased predictor [GBLUP]), a kernel method (Gaussian kernel [GK]), an artificial-intelligence (AI) method (deep learning [DL]), and a hybrid method that corresponds to the emulation of a DL model using a kernel method (an arc-cosine kernel [AK]) in terms of their prediction accuracies for predicting grain yield, oil, and protein using data from the soybean nested association mapping experiment (1,379 genotypes tested in six environments, all genotypes in all environments). The relative performance of the four methods varied with the response variable and whether the model includes the genotype × environmental interaction (G×E) effects or not. The GBLUP consistently showed better performances, whereas GK and AK followed a similar pattern to GBLUP and DL performed slightly worse than the other three methods in most of the cases; however, this may also be attributed to suboptimal hyperparameters. The DL method performed particularly worse than the other three methods in presence of the G×E effects.

High-throughput characterization, correlation, and mapping of leaf photosynthetic and functional traits in the soybean (Glycine max) nested association mapping population.

Photosynthesis is a key target to improve crop production in many species including soybean [Glycine max (L.) Merr.]. A challenge is that phenotyping photosynthetic traits by traditional approaches is slow and destructive. There is proof-of-concept for leaf hyperspectral reflectance as a rapid method to model photosynthetic traits. However, the crucial step of demonstrating that hyperspectral approaches can be used to advance understanding of the genetic architecture of photosynthetic traits is untested. To address this challenge, we used full-range (500-2,400 nm) leaf reflectance spectroscopy to build partial least squares regression models to estimate leaf traits, including the rate-limiting processes of photosynthesis, maximum Rubisco carboxylation rate, and maximum electron transport. In total, 11 models were produced from a diverse population of soybean sampled over multiple field seasons to estimate photosynthetic parameters, chlorophyll content, leaf carbon and leaf nitrogen percentage, and specific leaf area (with R2 from 0.56 to 0.96 and root mean square error approximately <10% of the range of calibration data). We explore the utility of these models by applying them to the soybean nested association mapping population, which showed variability in photosynthetic and leaf traits. Genetic mapping provided insights into the underlying genetic architecture of photosynthetic traits and potential improvement in soybean. Notably, the maximum Rubisco carboxylation rate mapped to a region of chromosome 19 containing genes encoding multiple small subunits of Rubisco. We also mapped the maximum electron transport rate to a region of chromosome 10 containing a fructose 1,6-bisphosphatase gene, encoding an important enzyme in the regeneration of ribulose 1,5-bisphosphate and the sucrose biosynthetic pathway. The estimated rate-limiting steps of photosynthesis were low or negatively correlated with yield suggesting that these traits are not influenced by the same genetic mechanisms and are not limiting yield in the soybean NAM population. Leaf carbon percentage, leaf nitrogen percentage, and specific leaf area showed strong correlations with yield and may be of interest in breeding programs as a proxy for yield. This work is among the first to use hyperspectral reflectance to model and map the genetic architecture of the rate-limiting steps of photosynthesis.

Enhancing Genomic Prediction Models for Forecasting Days to Maturity in Soybean Genotypes Using Site-Specific and Cumulative Photoperiod Data

Genomic selection (GS) has revolutionized breeding strategies by predicting the rank performance of post-harvest traits via implementing genomic prediction (GP) models. However, predicting pre-harvest traits in unobserved environments might produce serious biases. In soybean, days to maturity (DTM) represents a crucial stage with a significant impact on yield potential; thus, genotypes must be carefully selected to ensure latitudinal adaptation in this photoperiod-sensitive crop species. This research assessed the use of daylength for predicting DTM in unobserved environments (CV00). A soybean dataset comprising 367 genotypes spanning nine families of the Soybean Nested Association Mapping Panel (SoyNAM) and tested in 11 environments (year-by-location combinations) was considered in this study. The proposed method (CB) returned a root-mean-square error (RMSE) of 5.2 days, a Pearson correlation (PC) of 0.66, and the predicted vs. observed difference in the environmental means (PODEM) ranged from −3.3 to 4.5 days; however, in the absence of daylength data, the conventional GP implementation produced an RMSE of 9 days, a PC of 0.66, and a PODEM range from −14.7 to 7.9 days. These results highlight the importance of dissecting phenotypic variability (G × E) based on photoperiod data and non-predictable environmental stimuli for improving the predictive ability and accuracy of DTM in soybeans.

Genotype imputation for soybean nested association mapping population to improve precision of QTL detection

Key messageSoftware for high imputation accuracy in soybean was identified. Imputed dataset could significantly reduce the interval of genomic regions controlling traits, thus greatly improve the efficiency of candidate gene identification.Genotype imputation is a strategy to increase marker density of existing datasets without additional genotyping. We compared imputation performance of software BEAGLE 5.0, IMPUTE 5 and AlphaPlantImpute and tested software parameters that may help to improve imputation accuracy in soybean populations. Several factors including marker density, extent of linkage disequilibrium (LD), minor allele frequency (MAF), etc., were examined for their effects on imputation accuracy across different software. Our results showed that AlphaPlantImpute had a higher imputation accuracy than BEAGLE 5.0 or IMPUTE 5 tested in each soybean family, especially if the study progeny were genotyped with an extremely low number of markers. LD extent, MAF and reference panel size were positively correlated with imputation accuracy, a minimum number of 50 markers per chromosome and MAF of SNPs > 0.2 in soybean line were required to avoid a significant loss of imputation accuracy. Using the software, we imputed 5176 soybean lines in the soybean nested mapping population (NAM) with high-density markers of the 40 parents. The dataset containing 423,419 markers for 5176 lines and 40 parents was deposited at the Soybase. The imputed NAM dataset was further examined for the improvement of mapping quantitative trait loci (QTL) controlling soybean seed protein content. Most of the QTL identified were at identical or at similar position based on initial and imputed datasets; however, QTL intervals were greatly narrowed. The resulting genotypic dataset of NAM population will facilitate QTL mapping of traits and downstream applications. The information will also help to improve genotyping imputation accuracy in self-pollinated crops.

Identification and molecular mapping of a major quantitative trait locus underlying branch angle in soybean.

A major quantitative trait locus (QTL) modulating soybean (Glycine max) branch angle was identified by linkage analysis using two bi-parental mapping populations with and without pedigree from wild soybean (Glycine soja). Soybean branch angle is a critical architectural trait that affects many other traits of agronomic importance associated with the plant's productivity and grain yield and is thus a vital consideration in soybean breeding. However, the genetic basis for modulating this important trait in soybean and many other crops remain unknown. Previously, we developed a recombinant inbred line (RIL) population derived from a cross between a domesticated soybean (Glycine max) variety, Williams 82, and a wild soybean (Glycine soja) accession, PI 479,752, and observed drastic variation in plant architecture including branch angle among individual RILs. In this study, one of the RILs possessing extremely wide branch angle (WBA) was crossed with an elite soybean cultivar (LD00-3309) possessing narrow branch angle (NBA) to produce an F2 population composed of 147 plants and F2-derived F3 families for inheritance analysis and QTL mapping. We found that branch angle is controlled by a major QTL located on chromosome 19, designated qGmBa1 and that WBA-derived from the wild soybean accession-is dominant over NBA. This locus was also detected as a major one underlying branch angle by QTL mapping using a subset of the soybean nested association mapping (SoyNAM) population composed of 140 RILs, which were derived from a cross between a landrace, PI 437169B, possessing WBA and an elite variety, IA3023, possessing NBA. Molecular markers located in the QTL region defined by both mapping populations can be used for marker-assisted selection of branch angle in soybean breeding.

Development of a Genomic Prediction Pipeline for Maintaining Comparable Sample Sizes in Training and Testing Sets across Prediction Schemes Accounting for the Genotype-by-Environment Interaction

The global growing population is experiencing challenges to satisfy the food chain supply in a world that faces rapid changes in environmental conditions complicating the development of stable cultivars. Emergent methodologies aided by molecular marker information such as marker assisted selection (MAS) and genomic selection (GS) have been widely adopted to assist the development of improved genotypes. In general, the implementation of GS is not straightforward, and it usually requires cross-validation studies to find the optimum set of factors (training set sizes, number of markers, quality control, etc.) to use in real breeding applications. In most cases, these different scenarios (combination of several factors) vary just in the levels of a single factor keeping fixed the levels of the other factors allowing the use of previously developed routines (code reuse). In this study, we present a set of structured modules that are easily to assemble for constructing complex genomic prediction pipelines from scratch. Also, we proposed a novel method for selecting training-testing sets of sizes across different cross-validation schemes (CV2, predicting tested genotypes in observed environments; CV1, predicting untested genotypes in observed environments; CV0, predicting tested genotypes in novel environments; and CV00, predicting untested genotypes in novel environments). To show how our implementation works, we considered two real data sets. These correspond to selected samples of the USDA soybean collection (D1: 324 genotypes observed in 6 environments scored for 9 traits) and of the Soybean Nested Association Mapping (SoyNAM) experiment (D2: 324 genotypes observed in 6 environments scored for 6 traits). In addition, three prediction models which consider the effect of environments and lines (M1: E + L), environments, lines and main effect of markers (M2: E + L + G), and also the inclusion of the interaction between makers and environments (M3: E + L + G + G×E) were considered. The results confirm that under CV2 and CV1 schemes, moderate improvements in predictive ability can be obtained with the inclusion of the interaction component, while for CV0 mixed results were observed, and for CV00 no improvements were shown. However, for this last scenario, the inclusion of weather and soil data potentially could enhance the results of the interaction model.

An Assessment of the Factors Influencing the Prediction Accuracy of Genomic Prediction Models Across Multiple Environments.

The effects of climate change create formidable challenges for breeders striving to produce sufficient food quantities in rapidly changing environments. It is therefore critical to investigate the ability of multi-environment genomic prediction (GP) models to predict genomic estimated breeding values (GEBVs) in extreme environments. Exploration of the impact of training set composition on the accuracy of such GEBVs is also essential. Accordingly, we examined the influence of the number of training environments and the use of environmental covariates (ECs) in GS models on four subsets of n = 500 lines of the soybean nested association mapping (SoyNAM) panel grown in nine environments in the US-North Central Region. The ensuing analyses provided insights into the influence of both of these factors for predicting grain yield in the most and the least extreme of these environments. We found that only a subset of the available environments was needed to obtain the highest observed prediction accuracies. The inclusion of ECs in the GP model did not substantially increase prediction accuracies relative to competing models, and instead more often resulted in negative prediction accuracies. Combined with the overall low prediction accuracies for grain yield in the most extreme environment, our findings highlight weaknesses in current GP approaches for prediction in extreme environments, and point to specific areas on which to focus future research efforts.

G2PDeep: a web-based deep-learning framework for quantitative phenotype prediction and discovery of genomic markers.

G2PDeep is an open-access web server, which provides a deep-learning framework for quantitative phenotype prediction and discovery of genomics markers. It uses zygosity or single nucleotide polymorphism (SNP) information from plants and animals as the input to predict quantitative phenotype of interest and genomic markers associated with phenotype. It provides a one-stop-shop platform for researchers to create deep-learning models through an interactive web interface and train these models with uploaded data, using high-performance computing resources plugged at the backend. G2PDeep also provides a series of informative interfaces to monitor the training process and compare the performance among the trained models. The trained models can then be deployed automatically. The quantitative phenotype and genomic markers are predicted using a user-selected trained model and the results are visualized. Our state-of-the-art model has been benchmarked and demonstrated competitive performance in quantitative phenotype predictions by other researchers. In addition, the server integrates the soybean nested association mapping (SoyNAM) dataset with five phenotypes, including grain yield, height, moisture, oil, and protein. A publicly available dataset for seed protein and oil content has also been integrated into the server. The G2PDeep server is publicly available at http://g2pdeep.org. The Python-based deep-learning model is available at https://github.com/shuaizengMU/G2PDeep_model.

Genomic prediction using training population design in interspecific soybean populations.

The online version contains supplementary material available at 10.1007/s11032-021-01203-6.

Quantitative characterization of proximate sensing canopy traits in the SoyNAM population

AbstractThe average increase of soybean [Glycine max (L.) Merr.] grain yield per year is not enough to meet the estimated need by 2050. Contributing factors may include narrowing genetic diversity in cultivated soybean germplasm arising from direct selection for grain yield and insufficient knowledge of the genetic influences on physiological traits associated with grain yield. Canopy development plays a critical role in the light intercepted by soybean crops throughout the growing season. Rapid canopy closure facilitates complete light interception and optimizes growth dynamics, ultimately increasing biomass accumulation and grain yield. This study sought to use ground‐based digital imagery as a phenotyping tool for quantitative characterization of seasonal canopy coverage and light interception in the genetically diverse Soybean Nested Association Mapping (SoyNAM) population. We took weekly measurements of canopy coverage from early vegetative to mid‐reproductive growth for 5,600 recombinant inbred lines during the 2013 and 2014 seasons. An asymptotic logistic growth curve fit to the coverage data allowed us to estimate daily changes in canopy coverage in the intervals between the sampling dates, enabling calculation of canopy dynamic parameters. These parameters included average canopy coverage, cumulative intercepted photosynthetically active radiation for the total sampling period, vegetative growth period, reproductive growth period, and the number of days required to reach 30, 50, and 70% canopy coverage. Variance component estimation showed that genetic differences among families significantly influenced variation in phenotypic expression for all canopy parameters. The narrow‐sense heritabilities for all canopy parameters were high (h2 = .81–.90), suggesting opportunities for genetic gains in canopy development through selection of superior genotypes.

Mapping QTL controlling soybean seed sucrose and oligosaccharides in a single family of soybean nested association mapping (SoyNAM) population

AbstractThe meal value of Soybean for monogastric animals is determined partly by sucrose, raffinose and stachyose. Of these, sucrose is desirable, while raffinose and stachyose are indigestible, causing flatulence and abdominal discomfort. The objective of this study was to identify quantitative trait loci (QTL) controlling seed sucrose, raffinose, and stachyose in a set of 140 SoyNAM (Nested Association Mapping) recombinant inbred lines (RILs), developed from the cross between lines IA3023 and LD02‐4485. A total of 3,038 SNP markers from the Illumina SoyNAM BeadChip SNP were used to map the QTLs for sucrose and the RFOs, raffinose, and stachyose. Significant genotypic differences (p &lt; .001) among RILs were observed for sucrose, raffinose and stachyose contents across years. A 3038 Illumina SoyNAM BeadChip SNPs identified three QTLs for sucrose, one on chromosome 1, explaining 10% variance and two on chromosome 3 each explaining 22%. Raffinose QTL was detected on chromosome 6, explaining 6% variance. The mapped QTLs were novel and spanned regions harbouring candidate genes with roles in plant growth including seed development.

Use of family structure information in interaction with environments for leveraging genomic prediction models

The characterization of genomes with great detail offered by the modern genotyping platforms have opened a venue for accurately predicting the genotype-by-environment interaction (GE) effects of untested genotypes in different environmental conditions. Already developed statistical models have shown the advantages of including the GE interaction component in the prediction context using molecular markers, pedigree, or both. In order to leverage the family information of highly structured populations when pedigree data is not available, we developed a model that uses the family membership instead. The proposed model extends the reaction norm model by including the interaction between families and environments (FE). A representative fraction of a soybean Nested Association Mapping population (16,187 grain yield records) comprising 38 bi-parental families (1358 genotypes) observed in 18 environments (2011, 2012, and 2013) was used to contrast the proposed model with three conventional prediction models. Two cross-validation scenarios (prediction of tested [CV2] and untested [CV1] genotypes) with a twofold design (50% for training and testing sets) were used for mimicking prediction situations that breeders face in fields. Results showed that the family factor in interaction with environments explains a sizable amount of the phenotypic variability. This helped to improve the predictive ability with respect to the main effects model (GBLUP) around 41% (CV2) and 49% (CV1), and about 17% with respect to the conventional reaction norm model. The inclusion of the FE term not only improved the global results but also significantly increased the prediction accuracy of those environments where the conventional models showed a very poor performance. These results show the importance of taking into consideration the family structure existing in breeding programs for improving the selection strategies in multi-parental populations.

Prediction Strategies for Leveraging Information of Associated Traits under Single- and Multi-Trait Approaches in Soybeans

The availability of molecular markers has revolutionized conventional ways to improve genotypes in plant and animal breeding through genome-based predictions. Several models and methods have been developed to leverage the genomic information in the prediction context to allow more efficient ways to screen and select superior genotypes. In plant breeding, usually, grain yield (yield) is the main trait to drive the selection of superior genotypes; however, in many cases, the information of associated traits is also routinely collected and it can potentially be used to enhance the selection. In this research, we considered different prediction strategies to leverage the information of the associated traits ([AT]; full: all traits observed for the same genotype; and partial: some traits observed for the same genotype) under an alternative single-trait model and the multi-trait approach. The alternative single-trait model included the information of the AT for yield prediction via the phenotypic covariances while the multi-trait model jointly analyzed all the traits. The performance of these strategies was assessed using the marker and phenotypic information from the Soybean Nested Association Mapping (SoyNAM) project observed in Nebraska in 2012. The results showed that the alternative single-trait strategy, which combines the marker and the information of the AT, outperforms the multi-trait model by around 12% and the conventional single-trait strategy (baseline) by 25%. When no information on the AT was available for those genotypes in the testing sets, the multi-trait model reduced the baseline results by around 6%. For the cases where genotypes were partially observed (i.e., some traits observed but not others for the same genotype), the multi-trait strategy showed improvements of around 6% for yield and between 2% to 9% for the other traits. Hence, when yield drives the selection of superior genotypes, the single-trait and multi-trait genomic prediction will achieve significant improvements when some genotypes have been fully or partially tested, with the alternative single-trait model delivering the best results. These results provide empirical evidence of the usefulness of the AT for improving the predictive ability of prediction models for breeding applications.

Photosynthesis in the fleeting shadows: an overlooked opportunity for increasing crop productivity?

SummaryPhotosynthesis measurements are traditionally taken under steady‐state conditions; however, leaves in crop fields experience frequent fluctuations in light and take time to respond. This slow response reduces the efficiency of carbon assimilation. Transitions from low to high light require photosynthetic induction, including the activation of Rubisco and the opening of stomata, whereas transitions from high to low light require the relaxation of dissipative energy processes, collectively known as non‐photochemical quenching (NPQ). Previous attempts to assess the impact of these delays on net carbon assimilation have used simplified models of crop canopies, limiting the accuracy of predictions. Here, we use ray tracing to predict the spatial and temporal dynamics of lighting for a rendered mature Glycine max (soybean) canopy to review the relative importance of these delays on net cumulative assimilation over the course of both a sunny and a cloudy summer day. Combined limitations result in a 13% reduction in crop carbon assimilation on both sunny and cloudy days, with induction being more important on cloudy than on sunny days. Genetic variation in NPQ relaxation rates and photosynthetic induction in parental lines of a soybean nested association mapping (NAM) population was assessed. Short‐term NPQ relaxation (<30 min) showed little variation across the NAM lines, but substantial variation was found in the speeds of photosynthetic induction, attributable to Rubisco activation. Over the course of a sunny and an intermittently cloudy day these would translate to substantial differences in total crop carbon assimilation. These findings suggest an unexplored potential for breeding improved photosynthetic potential in our major crops.

Quantitative Disease Resistance Loci towards Phytophthora sojae and Three Species of Pythium in Six Soybean Nested Association Mapping Populations

Comparison of quantitative disease resistance loci (QDRL) towards the diverse array of soilborne pathogens that affect soybean [Glycine max (L.) Merr.] is key to the incorporation of resistance in breeding programs. The water molds Phytophthora sojae (Kauffman &amp; Gerdmann), Pythium irregulare (Buisman), Pythium ultimum var. ultimum (Trow), and Pythium ultimum var. sporangiiferum (Drechsler) contribute to soybean yield losses annually. Six Soybean Nested Association Mapping (SoyNAM) populations were evaluated for resistance to one or more of these pathogens. Four were screened with a tray test to measure lesion length after inoculation with Ph. sojae; cup assays were used to screen three, three, and two populations for resistance towards Py. irregulare, Py. ultimum var. ultimum, and Py. ultimum var. sporangiiferum, respectively. There were two to eight major or minor QDRL identified within each SoyNAM population towards one or more of these water molds for a total of 33 QDRL. The SoyNAM populations evaluated for resistance to two or more water molds had different QDRL towards each pathogen, indicating that within a source of resistance, mechanisms are potentially specific to the pathogen. Only 3 of the 33 QDRL were associated with resistance to more than one pathogen. There was a major QDRL on chromosome 3 associated with resistance to Py. ultimum var. ultimum and Py. ultimum var. sporangiiferum, and QDRL on chromosomes 13 and 17 shared a flanking marker for both Py. irregulare and Py. ultimum var. ultimum. The SoyNAM population can serve as a diverse resource to map QDRL and compare mechanisms across pathogens and isolates.