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Understanding how plants adapt to specific environmental changes and identifying genetic markers associated with phenotypic plasticity can help breeders develop plant varieties adapted to a rapidly changing climate. Here, we propose the use of marker effect networks as a novel method to identify markers associated with environmental adaptability. These marker effect networks are built by adapting commonly used software for building gene co-expression networks with marker effects across growth environments as the input data into the networks. To demonstrate the utility of these networks, we built networks from the marker effects of ∼2,000 non-redundant markers from 400 maize hybrids across nine environments. We demonstrate that networks can be generated using this approach, and that the markers that are co-varying are rarely in linkage disequilibrium, thus representing higher biological relevance. Multiple co-varying marker modules associated with different weather factors throughout the growing season were identified within the marker effect networks. Finally, a factorial test of analysis parameters demonstrated marker effect networks are relatively robust to these options, with high overlap in modules associated with the same weather factors across analysis parameters. This novel application of network analysis provides unique insights into phenotypic plasticity, and specific environmental factors that modulate the genome.

Breeders commonly use genetic markers to predict the performance of untested individuals as a way to improve the efficiency of breeding programs. These genomic prediction models have almost exclusively used single nucleotide polymorphisms (SNPs) as their source of genetic information, even though other types of markers exist, such as structural variants (SVs). Given that SVs are associated with environmental adaptation and not all of them are in linkage disequilibrium to SNPs, SVs have the potential to bring additional information to multi-environment prediction models that are not captured by SNPs alone. Here, we evaluated different marker types (SNPs and/or SVs) on prediction accuracy across a range of genetic architectures for simulated traits across multiple environments. Our results show that SVs can improve prediction accuracy by up to 19%, but it is highly dependent on the genetic architecture of the trait. Differences in prediction accuracy across marker types were more pronounced for traits with high heritability, high number of QTLs, and SVs as causative variants. In these scenarios, using SV markers resulted in better prediction accuracies than SNP markers, especially when predicting untested genotypes across environments, likely due to more predictors being in linkage disequilibrium with causative variants. The simulations revealed little impact of different effect sizes between SNPs and SVs as causative variants on prediction accuracy. This study demonstrates the importance of knowing the genetic architecture of a trait in deciding what markers and marker types to use in large scale genomic prediction modeling in a breeding program.Key messageWe demonstrate potential for improved multi-environment genomic prediction accuracy using structural variant markers. However, the degree of observed improvement is highly dependent on the genetic architecture of the trait.

Hydroxycinnamic acids, including ferulic acid and p-coumaric acid, have been tied to multiple positive health and agronomic benefits. However, little work has been done to improve the concentration of hydroxycinnamic acids in maize. We evaluated a set of 12 commercially important maize (Zea mays L.) inbred lines and 66 hybrids derived from their crosses for hydroxycinnamic acid concentration in the grain, grain yield, and test weight. The grain was obtained from replicated field experiments, which were conducted for 3 years. Both ferulic acid and p-coumaric acid were found to be highly heritable, and most of the genetic variation was additive. Grain yield and test weight were not correlated with hydroxycinnamic acid concentration. These findings suggest that breeding maize for improved hydroxycinnamic acid concentration is feasible. Maize hybrids with high hydroxycinnamic acid concentrations in the grain could be useful for the production of dietary supplements or all-natural food additives while imparting enhanced resistance to biotic and abiotic stresses during the growing season and grain storage.

Objectives Advanced tools and resources are needed to efficiently and sustainably produce food for an increasing world population in the context of variable environmental conditions. The maize genomes to fields (G2F) initiative is a multi-institutional initiative effort that seeks to approach this challenge by developing a flexible and distributed infrastructure addressing emerging problems. G2F has generated large-scale phenotypic, genotypic, and environmental datasets using publicly available inbred lines and hybrids evaluated through a network of collaborators that are part of the G2F’s genotype-by-environment (G × E) project. This report covers the public release of datasets for 2014–2017. Data description Datasets include inbred genotypic information; phenotypic, climatic, and soil measurements and metadata information for each testing location across years. For a subset of inbreds in 2014 and 2015, yield component phenotypes were quantified by image analysis. Data released are accompanied by README descriptions. For genotypic and phenotypic data, both raw data and a version without outliers are reported. For climatic data, a version calibrated to the nearest airport weather station and a version without outliers are reported. The 2014 and 2015 datasets are updated versions from the previously released files [1] while 2016 and 2017 datasets are newly available to the public.

High-dimensional and high-throughput genomic, field performance, and environmental data are becoming increasingly available to crop breeding programs, and their integration can facilitate genomic prediction within and across environments and provide insights into the genetic architecture of complex traits and the nature of genotype-by-environment interactions. To partition trait variation into additive and dominance (main effect) genetic and corresponding genetic-by-environment variances, and to identify specific environmental factors that influence genotype-by-environment interactions, we curated and analyzed genotypic and phenotypic data on 1918 maize (Zea mays L.) hybrids and environmental data from 65 testing environments. For grain yield, dominance variance was similar in magnitude to additive variance, and genetic-by-environment variances were more important than genetic main effect variances. Models involving both additive and dominance relationships best fit the data and modeling unique genetic covariances among all environments provided the best characterization of the genotype-by-environment interaction patterns. Similarity of relative hybrid performance among environments was modeled as a function of underlying weather variables, permitting identification of weather covariates driving correlations of genetic effects across environments. The resulting models can be used for genomic prediction of mean hybrid performance across populations of environments tested or for environment-specific predictions. These results can also guide efforts to incorporate high-throughput environmental data into genomic prediction models and predict values in new environments characterized with the same environmental characteristics.

The notion that many nutrients and beneficial phytochemicals in maize are lost due to food product processing is common, but this has not been studied in detail for the phenolic acids. Information regarding changes in phenolic acid content throughout processing is highly valuable because some phenolic acids are chemopreventive agents of aging-related diseases. It is unknown when and why these changes in phenolic acid content might occur during processing, whether some maize genotypes might be more resistant to processing induced changes in phenolic acid content than other genotypes, or if processing affects the bioavailability of phenolic acids in maize-based food products. For this study, a laboratory-scale processing protocol was developed and used to process whole maize kernels into toasted cornflakes. High-throughput microscale wet-lab analyses were applied to determine the concentrations of soluble and insoluble-bound phenolic acids in samples of grain, three intermediate processing stages, and toasted cornflakes obtained from 12 ex-PVP maize inbreds and seven hybrids. In the grain, insoluble-bound ferulic acid was the most common phenolic acid, followed by insoluble-bound p-coumaric acid and soluble cinnamic acid, a precursor to the phenolic acids. Notably, the ferulic acid content was approximately 1950 μg/g, more than ten-times the concentration of many fruits and vegetables. Processing reduced the content of the phenolic acids regardless of the genotype. Most changes occurred during dry milling due to the removal of the bran. The concentration of bioavailable soluble ferulic and p-coumaric acid increased negligibly due to thermal stresses. Therefore, the current dry milling based processing techniques used to manufacture many maize-based foods, including breakfast cereals, are not conducive for increasing the content of bioavailable phenolics in processed maize food products. This suggests that while maize is an excellent source of phenolics, alternative or complementary processing methods must be developed before this nutritional resource can be utilized.

In synchrony with the overall trend toward automation in plant phenotyping, two semi-automatic machine vision methods were devised targeted at counting maize kernels directly on de-husked ears. The ears represented row morphologies ranging from straight to curved, they featured missing kernels, underdeveloped kernels, and broken kernels, but displayed no disease or mould. The first method mimicked a common manual field method of estimating the total ear kernel count, based on counting the number of rows and multiplication by the number of kernels per row. Similarly, in this paper, the operator manually counted the number of rows, and also manually counted the number of kernels in a row image within an (operator determined) quasi-cylindrical mid-section of the ear. The total ear kernel count was then estimated by multiplying the number of rows by the number of kernels per row, yielding full ear extrapolation by multiplication by the ratio between the total ear length and the length of the quasi-cylindrical mid-section. This full ear image based approach achieved a kernel counting error ranging from −7.67% (under-count) to +8.60% (multi-count) among 23 maize ears. The second method only observed a fixed quasi-cylindrical mid-section of the ear. Image frames were acquired of each individual row of kernels located in the quasi-cylindrical mid-section, yielding kernel maps. Among 12 maize ears, the kernel missing error ranged from 0 to 4.24% and the multi-count error ranged from 0 to 1.92%. In total, 41 existing kernels were missed and 25 kernels were multi-counted among 2713 kernels counted.

Anecdotal data in the past have suggested that the effect of the western corn rootworm, Diabrotica virgifera virgifera LeConte (Coleoptera: Chrysomelidae), on maize yield is greater under drought and the effect of drought is greater under rootworm infestations, but no field experiments have controlled both moisture and rootworm levels. Field studies were conducted in 2012, 2013, and 2014 with treatments in a factorial arrangement of western corn rootworm infestation levels, and maize hybrids (with and without tolerance to drought and rootworm feeding). The experiment was repeated under well-watered and drought conditions in adjacent plots. Leaf water potential and stomatal conductance data suggested significant plant stress was achieved in the drought plots toward the end of the season each year and maize hybrids only played a minor role. In particular, in 2012 and 2013 yield was dramatically lower for the drought experiment than for the well-watered experiment. However, the impacts of rootworm infestation level and maize hybrids on water potential, stomatal conductance, and yield were variable across years and between experiments. In fact, the only year that the main effect of rootworm infestation levels significantly impacted yield was in 2014, when an extremely high infestation level was added and this was only for the well-watered portion of the experiment. Overall, rootworm infestation level played a relatively minor role in maize productivity and it did not appear that soil moisture level influenced that to a large degree.

Remarkable productivity has been achieved in crop species through artificial selection and adaptation to modern agronomic practices. Whether intensive selection has changed the ability of improved cultivars to maintain high productivity across variable environments is unknown. Understanding the genetic control of phenotypic plasticity and genotype by environment (G × E) interaction will enhance crop performance predictions across diverse environments. Here we use data generated from the Genomes to Fields (G2F) Maize G × E project to assess the effect of selection on G × E variation and characterize polymorphisms associated with plasticity. Genomic regions putatively selected during modern temperate maize breeding explain less variability for yield G × E than unselected regions, indicating that improvement by breeding may have reduced G × E of modern temperate cultivars. Trends in genomic position of variants associated with stability reveal fewer genic associations and enrichment of variants 0–5000 base pairs upstream of genes, hypothetically due to control of plasticity by short-range regulatory elements., Breeding has increased crop productivity, but whether it has also changed phenotypic plasticity is unclear. Here, the authors find maize genomic regions selected for high productivity show reduced contribution to genotype by environment variation and provide evidence for regulatory control of phenotypic stability.

Although previous studies have examined the concentration of various nutritional compounds in maize, little focus has been devoted to the study of commercial maize hybrids or their inbred parents. In this study, a genetically and phenotypically diverse set of maize hybrids and inbreds relevant to U.S. commercial maize germplasm was evaluated for its variability in phytochemical content. Total protein, unsaturated fatty acids, tocopherols, soluble phenolics, and insoluble-bound phenolics were evaluated in this study. Of these compounds, only soluble and insoluble-bound phenolic acids exhibited means and variances that were at least as large as the means and variances reported for other sets of germplasm. This suggests that selection for high phenolic acid content is possible in current U.S. commercial germplasm. In contrast, while the total protein, unsaturated fatty acid, or tocopherol content could possibly be improved using current U.S. commercial germplasm, the results of this study indicate that the incorporation of more diverse sources of germplasm would most likely result in quicker genetic gains.

Variation in kernel composition across maize ( L.) germplasm is affected by a combination of the plant’s genotype, the environment in which it is grown, and the interaction between these two elements. Adapting exotic germplasm to the US Corn Belt is highly dependent on the plant’s genotype, the environment where it is grown, and the interaction between these components. Phenotypic plasticity is ill-defined when specific exotic germplasm is moved over large latitudinal distances and for the adapted variants being created. Reduced plasticity (or stability) is desired for the adapted variants, as it allows for a more rapid implementation into breeding programs throughout the Corn Belt. Here, doubled haploid lines derived from exotic maize and adapted through backcrossing exotic germplasm to elite adapted lines were used in conjunction with genome-wide association studies to explore stability in four kernel composition traits. Genotypes demonstrated a response to environments that paralleled the mean response of all genotypes used across all traits, with protein content and kernel density exhibiting the highest levels of Type II stability. Genes such as , , and were identified as potential candidates within quantitative trait locus regions. The findings within this study aid in validating previously identified genomic regions and identified novel genomic regions affecting kernel quality traits.

The logistic mixed model (LMM) is well-suited for the genome-wide association study (GWAS) of binary agronomic traits because it can include fixed and random effects that account for spurious associations. The recent implementation of a computationally efficient model fitting and testing approach now makes it practical to use the LMM to search for markers associated with such binary traits on a genome-wide scale. Therefore, the purpose of this work was to assess the applicability of the LMM for GWAS in crop diversity panels. We dichotomized three publicly available quantitative traits in a maize diversity panel and two quantitative traits in a sorghum diversity panel, and them performed a GWAS using both the LMM and the unified mixed linear model (MLM) on these dichotomized traits. Our results suggest that the LMM is capable of identifying statistically significant marker-trait associations in the same genomic regions highlighted in previous studies, and this ability is consistent across both diversity panels. We also show how subpopulation structure in the maize diversity panel can underscore the LMM’s superior control for spurious associations compared to the unified MLM. These results suggest that the LMM is a viable model to use for the GWAS of binary traits in crop diversity panels and we therefore encourage its broader implementation in the agronomic research community.

Maize is an important grain crop in the United States and worldwide. However, maize grain must be processed prior to human consumption. Furthermore, whole grain composition and processing characteristics vary among maize hybrids and can impact the quality of the final processed product. Therefore, in order to produce healthier processed food products from maize, it is necessary to know how to optimize processing parameters for particular sets of germplasm to account for these differences in grain composition and processing characteristics. This includes a better understanding of how current processing techniques impact the nutritional quality of the final processed food product. Here, we describe a microscale protocol that both simulates the processing pipeline to produce cornflakes from large flaking grits and allows for the processing of multiple grain samples simultaneously. The flaking grits, the intermediate processed products, or final processed product, as well as the corn grain itself, can be analyzed for nutritional content as part of a high-throughput analytical pipeline. This procedure was developed specifically for incorporation into a maize breeding research program, and it can be modified for other grain crops. We provide an example of the analysis of insoluble-bound ferulic acid and p-coumaric acid content in maize. Samples were taken at five different processing stages. We demonstrate that sampling can take place at multiple stages during microscale processing, that the processing technique can be utilized in the context of a specialized maize breeding program, and that, in our example, most of the nutritional content was lost during food product processing.

Objectives Crop improvement relies on analysis of phenotypic, genotypic, and environmental data. Given large, well-integrated, multi-year datasets, diverse queries can be made: Which lines perform best in hot, dry environments? Which alleles of specific genes are required for optimal performance in each environment? Such datasets also can be leveraged to predict cultivar performance, even in uncharacterized environments. The maize Genomes to Fields (G2F) Initiative is a multi-institutional organization of scientists working to generate and analyze such datasets from existing, publicly available inbred lines and hybrids. G2F’s genotype by environment project has released 2014 and 2015 datasets to the public, with 2016 and 2017 collected and soon to be made available. Data description Datasets include DNA sequences; traditional phenotype descriptions, as well as detailed ear, cob, and kernel phenotypes quantified by image analysis; weather station measurements; and soil characterizations by site. Data are released as comma separated value spreadsheets accompanied by extensive README text descriptions. For genotypic and phenotypic data, both raw data and a version with outliers removed are reported. For weather data, two versions are reported: a full dataset calibrated against nearby National Weather Service sites and a second calibrated set with outliers and apparent artifacts removed.

Maize is an important grain crop in the United States and worldwide. However, maize grain must be processed prior to human consumption. Furthermore, whole grain composition and processing characteristics vary among maize hybrids and can impact the quality of the final processed product. Therefore, in order to produce healthier processed food products from maize, it is necessary to know how to optimize processing parameters for particular sets of germplasm to account for these differences in grain composition and processing characteristics. This includes a better understanding of how current processing techniques impact the nutritional quality of the final processed food product. Here, we describe a microscale protocol that both simulates the processing pipeline to produce cornflakes from large flaking grits and allows for the processing of multiple grain samples simultaneously. The flaking grits, the intermediate processed products, or final processed product, as well as the corn grain itself, can be analyzed for nutritional content as part of a high-throughput analytical pipeline. This procedure was developed specifically for incorporation into a maize breeding research program, and it can be modified for other grain crops. We provide an example of the analysis of insoluble-bound ferulic acid and p-coumaric acid content in maize. Samples were taken at five different processing stages. We demonstrate that sampling can take place at multiple stages during microscale processing, that the processing technique can be utilized in the context of a specialized maize breeding program, and that, in our example, most of the nutritional content was lost during food product processing.

Over the last 70 yr, more than 12,000 maize accessions have been screened for their level of resistance to western corn rootworm, Diabrotica virgifera virgifera (LeConte; Coleoptera: Chrysomelidae), larval feeding. Less than 1% of this germplasm was selected for initiating recurrent selection or other breeding programs. Selected genotypes were mostly characterized by large root systems and superior root regrowth after root damage caused by western corn rootworm larvae. However, no hybrids claiming native (i.e., host plant) resistance to western corn rootworm larval feeding are currently commercially available. We investigated the genetic basis of western corn rootworm resistance in maize materials with improved levels of resistance using linkage disequilibrium mapping approaches. Two populations of topcrossed doubled haploid maize lines (DHLs) derived from crosses between resistant and susceptible maize lines were evaluated for their level of resistance in three to four different environments. For each DHL topcross an average root damage score was estimated and used for quantitative trait loci (QTL) analysis. We found genomic regions contributing to western corn rootworm resistance on all maize chromosomes, except for chromosome 4. Models fitting all QTL simultaneously explained about 30 to 50% of the genotypic variance for root damage scores in both mapping populations. Our findings confirm the complex genetic structure of host plant resistance against western corn rootworm larval feeding in maize. Interestingly, three of these QTL regions also carry genes involved in ascorbate biosynthesis, a key compound we hypothesize is involved in the expression of western corn rootworm resistance.

Prediction of single-cross hybrid performance has been a major goal of plant breeders since the beginning of hybrid breeding. Genomic prediction has shown to be a promising approach, but only limited studies have examined the accuracy of predicting single cross performance. Most of the studies rather focused on predicting top cross performance using single tester to determine the inbred parent’s worth in hybrid combinations. Moreover, no studies have examined the potential of predicting single crosses made among random progenies derived from a series of biparental families, which resembles the structure of germplasm comprising the initial stages of a hybrid maize breeding pipeline. The main objective of this study was to evaluate the potential of genomic prediction for identifying superior single crosses early in the breeding pipeline and optimize its application. To accomplish these objectives, we designed and analyzed a novel population of single-cross hybrids representing the Iowa Stiff Stalk Synthetic/Non-Stiff Stalk heterotic pattern commonly used in the development of North American commercial maize hybrids. The single cross prediction accuracies estimated using cross-validation ranged from 0.40 to 0.74 for grain yield, 0.68 to 0.91 for plant height and 0.54 to 0.94 for staygreen depending on the number of tested parents of the single crosses. The genomic estimated general and specific combining abilities showed a clear advantage over the use of genomic covariances among single crosses, especially when one or both parents of the single cross were untested in hybrid combinations. Overall, our results suggest that genomic prediction of the performance of single crosses made using random progenies from the early stages of the breeding pipeline holds great potential to re-design hybrid breeding and increase its efficiency.

Prediction of single-cross performance has been a major goal of plant breeders since the beginning of hybrid breeding. Recently, genomic prediction has shown to be a promising approach, but only limited studies have examined the accuracy of predicting single-cross performance. Moreover, no studies have examined the potential of predicting single crosses among random inbreds derived from a series of biparental families, which resembles the structure of germplasm comprising the initial stages of a hybrid maize breeding pipeline. The main objectives of this study were to evaluate the potential of genomic prediction for identifying superior single crosses early in the hybrid breeding pipeline and optimize its application. To accomplish these objectives, we designed and analyzed a novel population of single crosses representing the Iowa Stiff Stalk synthetic/non-Stiff Stalk heterotic pattern commonly used in the development of North American commercial maize hybrids. The performance of single crosses was predicted using parental combining ability and covariance among single crosses. Prediction accuracies were estimated using cross-validation and ranged from 0.28 to 0.77 for grain yield, 0.53 to 0.91 for plant height, and 0.49 to 0.94 for staygreen, depending on the number of tested parents of the single cross and genomic prediction method used. The genomic estimated general and specific combining abilities showed an advantage over genomic covariances among single crosses when one or both parents of the single cross were untested. Overall, our results suggest that genomic prediction of single crosses in the early stages of a hybrid breeding pipeline holds great potential to redesign hybrid breeding and increase its efficiency.

To evaluate the effects of processing and genetics on resistant starch content in corn flakes, a laboratory scale corn flaking procedure was developed at a batch size of 100 g grits. Cooking (0.1 MPa/121°C, 50 min), drying (100°C, 30 min), tempering (room temperature, 30 min) and toasting (200°C, 60 s) resulted in laboratory flakes that had similar color parameters (L, a and b values) but different RVA parameters (lower peak, trough and breakdown viscosities; higher final and setback viscosities) compared to commercial corn flakes. Seven corn hybrids were flaked with the developed procedure and resistant starch contents were determined at each processing stage for each hybrid. Cooking caused the largest decrease in resistant starch content; it remained at a similar level through subsequent processing stages of drying, flaking and toasting. Differences were observed in resistant starch content among hybrids at each processing stage. Therefore, both genetic background and processing affected corn resistant starch content. Hybrids with high resistant starch content in raw flaking grits resulted in high levels of resistant starch in the final toasted flakes. Since resistant starch content is correlated with amylose content, high amylose corn hybrids could be of use in the breakfast cereal industry for manufacture of corn flakes with higher nutritional quality, provided these hybrids have high grain yields and flaking grit yields for economic feasibility to corn producers and dry millers.

The mature root system is a vital plant organ, which is critical to plant performance. Commercial maize (Zea mays L.) breeding has resulted in a steady increase in plant performance over time, along with noticeable changes in above ground vegetative traits, but the corresponding changes in the root system are not presently known. In this study, roughly 2500 core root systems from field trials of a set of 10 diverse elite inbreds formerly protected by Plant Variety Protection plus B73 and Mo17 and the 66 diallel intercrosses among them were evaluated for root traits using high throughput image-based phenotyping. Overall root architecture was modeled by root angle (RA) and stem diameter (SD), while root complexity, the amount of root branching, was quantified using fractal analysis to obtain values for fractal dimension (FD) and fractal abundance (FA). For each trait, per se line effects were highly significant and the most important contributor to trait performance. Mid-parent heterosis and specific combining ability was also highly significant for FD, FA, and RA, while none of the traits showed significant general combining ability. The interaction between the environment and the additive line effect was also significant for all traits. Within the inbred and hybrid generations, FD and FA were highly correlated (rp ≥ 0.74), SD was moderately correlated to FD and FA (0.69 ≥ rp ≥ 0.48), while the correlation between RA and other traits was low (0.13 ≥ rp ≥ −0.40). Inbreds with contrasting effects on complexity and architecture traits were observed, suggesting that root complexity and architecture traits are inherited independently. A more comprehensive understanding of the maize root system and the way it interacts with the environment will be useful for defining adaptation to nutrient acquisition and tolerance to stress from drought and high plant densities, critical factors in the yield gains of modern hybrids. Electronic supplementary material The online version of this article (doi:10.1186/s40064-015-1187-0) contains supplementary material, which is available to authorized users.