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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.

As obligate parasites, entomopathogenic nematodes (EPN) rely on insect hosts to complete their development. In insect pest management, EPN infectiousness has varied a lot. A better understanding of their host-finding behavior in the rhizosphere is therefore crucial to enhance EPN potential in biological control. As previously demonstrated, roots can be used as a pathway to insect hosts by EPN, but this interaction and its impact on EPN foraging remain poorly documented. Three artificial model-roots with different degrees of complexity and connectivity were designed to investigate the impact of root architecture on foraging behavior of the EPN Heterorhabditis megidis. Insect baits were placed at the bottom of each model-root that was subsequently buried in moist sand. After injection of the EPN, the number of EPN-infected baits as well as the number of mature nematodes inside each individual carcass was recorded. The influence of insect-induced root volatiles was also evaluated by spiking the baits with a synthetic version of a natural insect-induced root cue. The ecological relevance of the results was tested in soil with two maize genotypes each exhibiting broadly different root architectures. H. megidi performed better in presence of model-roots. Foraging performances of H. megidis declined with the increasing model-root complexity. Adding the synthetic root volatile dramatically changed this pattern and favored the EPN on the most complex model-roots. H. megidis also moved in the vicinity of maize roots to find the insect baits in soil, and natural root architecture also tended to shape H. megidis foraging behavior. This study adds to the scarce body of literature characterizing physical and chemical interactions between EPN and roots. The present data illustrate that root architecture not only modifies plant quality but also shapes upper trophic levels' ecology.

That some plants benefit from being eaten is counterintuitive, yet there is now considerable evidence demonstrating enhanced fitness following herbivory (i.e., plants can overcompensate). Although there is evidence that genetic variation for compensation exists, little is known about the genetic mechanisms leading to enhanced growth and reproduction following herbivory. We took advantage of the compensatory variation in recombinant inbred lines of Arabidopsis thaliana, combined with microarray and QTL analyses to assess the molecular basis of overcompensation. We found three QTL explaining 11.4, 10.1, and 26.7% of the variation in fitness compensation, respectively, and 109 differentially expressed genes between clipped and unclipped plants of the overcompensating ecotype Columbia. From the QTL/microarray screen we uncovered one gene that plays a significant role in overcompensation: glucose-6-phosphate-1-dehydrogenase (G6PDH1). Knockout studies of Transfer-DNA (T-DNA) insertion lines and complementation studies of G6PDH1 verify its role in compensation. G6PDH1 is a key enzyme in the oxidative pentose-phosphate pathway that plays a central role in plant metabolism. We propose that plants capable of overcompensating reprogram their transcriptional activity by up-regulating defensive genes and genes involved in energy metabolism and by increasing DNA content (via endoreduplication) with the increase in DNA content feeding back on pathways involved in defense and metabolism through increased gene expression.

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.

This paper describes the development of high-throughput measurement techniques allowing acquisition of phenotypical data describing maize roots. One of a maize root’s traits is the level of complexity, which was expressed in a Fractal Dimension (FD) calculated from root images. Another important trait is the Root Top Angle (RTA) that was measured using a new machine vision algorithm. The measurement system consisted of a semi-automated imaging box that provided a highly diffuse lighting scene and allowing imaging of up to 700 roots per day. The measurement techniques were evaluated using roots recovered from a set of 200 recombinant inbred lines (RILs) derived from a cross between maize inbreds B73 and CML333. B73 and CML333 are known to have different root characteristics and their progeny are expected to show segregation for root traits. Since standard protocols for the measurement of the two root traits are non-existent, no comparisons could be made. Nevertheless, the data showed that the techniques were capable of confirming significant differences in FD among the two inbred lines and their progeny, as well as measuring variations in RTA that are known for the inbreds and their crosses. In addition, first hypotheses about the inheritance of root complexity (as expressed in the FD) and RTA in maize were derived and tested: initial evidence showed that root complexity is a phenotype probably determined by a multitude of genes with small effects. In contrast, the data indicated that the RTA is additively inherited.

Root complexity is an important factor in the growth and survivability of maize plants under biotic and abiotic stress conditions. To genetically improve root structure in the future, there is a need to identify the genes that govern root complexity. Root complexity itself is ill defined, but indicators derived from images of the root system such as Fractal Dimension can be used as proxies. A disadvantage of using Fractal Dimension as a complexity indicator is that the complexity of the root as seen in the images is captured into a single parameter. This paper describes an alternative method, which translates a root image into a set of parameters. The method consists of computing the intercepts of circles drawn around the centre of the root image with the root branches. This led to characteristic curves from which parameters can be extracted using curve fitting. In addition to the parameters obtained by curve fitting, the density of the root images was included. All parameters were evaluated on their ability to classify the roots among their original genotypes using a method from the realm of Artificial Intelligence, the Support Vector Machine (SVM). The results showed that whilst using merely three parameters originating from the characteristic curves, the SVM algorithm was capable of correctly classifying 99.95% of roots among 235 original genotypes.

Owing to their sessile habits and trophic position within global ecosystems, higher plants display a sundry assortment of adaptations to the threat of predation. Unlike animals, nearly all higher plants can replace reproductive structures lost to predators by activating reserved growing points called axillary meristems. As the first step in a program aimed at defining the genetic architecture of the inflorescence replacement program (IRP) of Arabidopsis thaliana, we describe the results of a quantitative germplasm survey of developmental responses to loss of the primary reproductive axis. Eighty-five diverse accessions were grown in a replicated common garden and assessed for six life history traits and four IRP traits, including the number and lengths of axillary inflorescences present on the day that the first among them re-flowered after basal clipping of the primary inflorescence. Significant natural variation and high heritabilities were observed for all measured characters. Pairwise correlations among the 10 focal traits revealed a multi-dimensional phenotypic space sculpted by ontogenic and plastic allometries as well as apparent constraints and outliers of genetic interest. Cluster analysis of the IRP traits sorted the 85 accessions into 5 associations, a topology that establishes the boundaries within which the evolving Arabidopsis genome extends and restricts the species' IRP repertoire to that observable worldwide.

CIMMYT is the source of improved maize (Zea mays L.) breeding material for a signifi cant portion of the nontemperate maize growing world. Landraces which did not serve as sources for improved maize germplasm may contain untapped allelic variation useful for future breeding progress. Information regarding levels of diversity in different germplasm would help to identify sources for broadening improved breeding pools and in seeking genes and alleles that have not been tapped in modern maize breeding. The objectives of this study were to examine the diversity in maize landraces, modern open pollinated varieties (OPVs), and inbred lines adapted to nontemperate growing areas to fi nd unique sources of allelic diversity that may be used in maize improvement. Twenty-fi ve simple sequence repeat markers were used to characterize 497 individuals from 24 landraces of maize from Mexico, 672 individuals from 23 CIMMYT improved breeding populations, and 261 CIMMYT inbred lines. Number of alleles, gene diversity per locus, unique alleles per locus, and population structure all differ between germplasm groups. The unique alleles found in each germplasm group represent a great reservoir of untapped genetic resources for maize improvement, and implications for hybrid breeding are discussed.

Illinois long-term selection strains of maize (Zea mays L.) have been useful for identifying genomic regions controlling kernel oil, protein, and starch concentrations. To identify kernel trait quantitative trait loci (QTL) in a genetic background more relevant to practical breeding, 150 BC 1 -derived S 1 lines (BC 1 S 1 s) were produced from Illinois High Oil and recurrent parent B73. Oil, protein, and starch were measured in BC 1 S 1 s and in Mo17-topcross hybrids (TCs). Kernel mass of BC 1 S 1 s and grain yield of TCs were also determined. Starch was positively correlated with mass in BC 1 S 1 s (r p = 0.67**, α ≤ 0.01) and with yield in TCs (r p = 0.59**). Oil was negatively correlated with mass in BC 1 S 1 s (r p = -0.29**) and with yield in TCs (r p = -0.30**). Oil was negatively correlated with starch in BC 1 S 1 s (r p = -0.75**) and TCs (r p = -0.66**). A genetic map with length = 1486 cM was created with 110 markers. Multiple regression models with QTL detected by composite interval mapping (CIM) explained 46.9, 45.2, 44.3, and 17.7% of phenotypic variance for oil, protein, starch, and mass, respectively, in BC 1 S 1 s and 17.5, 22.9, 40.1, and 28.7% for oil, protein, starch, and yield, respectively, in TCs. A 22 cM-interval on chromosome 6 in BC 1 S 1 s included oil, protein, and starch QTL, including a QTL explaining 36.7% of the BC 1 S 1 phenotypic variation for oil. No yield QTL were detected in this region. Introgression of this QTL into breeding lines might increase oil while maintaining yield.

Maize (Zea mays L.) produces high-quality oil valued for oxidative stability and low concentrations of saturated fatty acids. The nutritional value of maize oil could be improved by increasing the concentration of oleic acid, a "heart-friendly" monounsaturated fatty acid. To identify quantitative trait loci (QTL) for the major fatty acids constituting oil from maize kernels, we produced 150 backcross1-derived S 1 (BC 1 S 1 ) lines from donor parent, Illinois High Oil (IHO), and recurrent parent, B73. There was a positive phenotypic correlation between oil and oleic acid (r p = 0.47**, a ≤ 0.01) and negative correlations between oil and linoleic acid (r p = -0.46**), and between oleic and linoleic acids (r p = -0.99**). Multiple regression models with QTL detected by composite interval mapping (CIM) on a genetic map with length = 1486 cM, explained 15.4, 41.6, 51.0, 59.6, and 47.9% of the phenotypic variation for palmitic, stearic, oleic, linoleic, and linolenic acids, respectively. A 6-cM interval on chromosome 6 (bin 6.04) includes QTL for stearic, oleic, linoleic, and linolenic acids, explains 10.9 to 39.6% of the variation for these fatty acids, and is 10 to 16 cM from QTL for oil. Another region on chromosome 6 (bin 6.01) includes QTL for oleic and linoleic acids and was epistatic with the QTL in bin 6.04. One or both of these two QTL regions on chromosome 6 may be responsible for fatty acid variation previously attributed to linoleic acidl.

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.

Maize tassel inflorescence architecture is relevant to efficient production of F(1) seed and yield performance of F(1) hybrids. The objectives of this study were to identify genetic relationships among seven measured tassel inflorescence architecture traits and six calculated traits in a maize backcross population derived from two lines with differing tassel architectures, and identify Quantitative Trait Loci (QTL) involved in the inheritance of those tassel inflorescence architecture traits. A Principal Component (PC) analysis was performed to examine relationships among correlated traits. Traits with high loadings for PC1 were branch number and branch number density, for PC2 were spikelet density on central spike and primary branch, and for PC3 were lengths of tassel and central spike. We detected 45 QTL for individual architecture traits and eight QTL for the three PCs. For control of inflorescence architecture, important QTL were found in bins 7.02 and 9.02. The interval phi034-ramosa1 (ral) in bin 7.02 was associated with six individual architecture trait QTL and explained the largest amount of phenotypic variation (17.3%) for PC1. Interval bnlg344-phi027 in bin 9.02 explained the largest amount of phenotypic variation (14.6%) for PC2. Inflorescence architecture QTL were detected in regions with candidate genes fasciated ear2, thick tassel dwarf1, and ral. However, the vast majority of QTL mapped to regions without known candidate genes, indicating positional cloning efforts will be necessary to identify these genes.

Received: ; accepted:Root architecture is strongly linked to plant survival under abiotic and biotic stressconditions. The objective of this study was to investigate the inheritance of the primaryroot system complexity in maize (Zea mays L.). For a total of 231 recombinant inbred lines(RIL) derived from the IBM (B73×Mo17) population multiple primary root systems wereproduced by applying a replicated alpha lattice experimental design. Digital images ofeach root system were taken at days four and eight after germination. For each root systemimage, the fractal dimension (FD) was computed. Significant differences between RILswere found in the FD calculated after four (FD1) and eight (FD2) days. For FD1 22 QTLs,for FD2 13, and for FD change over time (∆FD) 12 QTLs were found on all ten maizechromosomes explaining between 24.6 and 46.8% of the phenotypic variation. Bothparental inbreds contributed FD-increasing QTL alleles. FD1 and FD2 had fivechromosomal BIN locations in common. Four unique QTLs were identified for thedynamics of root growth between days four and eight. Maize root mutants involved in rootmorphology were located in chromosomal BINs carrying QTLs for FD1 or FD2. Thisstudy demonstrated the usefulness of the IBM population as a maize community resourceto investigate the genetic basis of root complexity in maize.Key words: maize, QTL, primary root system, complexity, fractal dimension

Mexican races of maize (Zea mays L.) represent a valuable genetic resource for breeding and genetic surveys. We applied simple sequence repeat (SSR) markers to characterize 25 accessions of races of maize from Mexico. Our objectives were to (1) study the molecular genetic diversity within and among these accessions and (2) examine their relationships as assumed previously on the basis of morphological data. A total of 497 individuals were fingerprinted with 25 SSR markers. We observed a high total number of alleles (7.84 alleles per locus) and total gene diversity (0.61), confirming the broad genetic base of the maize races from Mexico. In addition, the accessions were grouped into distinct racial complexes on the basis of a model-based clustering approach. The principal coordinate analyses of the four Modern Incipient hybrids corroborated the proposed parental races of Chalqueño, Cónico Norteño, Celaya, and Bolita on the basis of the morphological data. Consequently, for some of the accessions, hybridizations provide a clue that can further be used to explain the associations among the Mexican races of maize.

Genetic distance (GD) estimates based on molecular markers are a preferred approach to estimate genetic conformity between putative essentially derived varieties (EDVs) and their initial varieties (IVs). The objectives of our study were to (1) estimate the variation in the marker-estimated parental contribution (p) to the genome of the progeny, (2) investigate the power of AFLP-based GD estimates for discriminating between homozygous lines with F2, BC1, and BC2 relationships, and (3) compare AFLP data with SSR data from a companion study, as well as with theoretical and simulated results. In total, 104 European maize inbred lines comprising 66 triplets were analyzed with 20 AFLP primer combinations and 100 SSR primer pairs. Each triplet consisted of one F2-, or BC1-derived progeny line and both parental lines. For lines with both F2 and BC1 relationship, mean estimates of p based on AFLP (pF2 = 0.46; pBC1 = 0.70) or SSR data (pF2 = 0.45; pBC1 = 0.67) were lower than the respective expected values of 0.50 and 0.75. Considerable differences were detected between AFLP- and SSR-based mean GD estimates for unrelated inbred lines due to the lower degree of polymorphism of AFLPs compared with SSRs. With each marker system, the GD between progeny lines and parents was little affected by the variation in GD between the parents, particularly for lines with BC1 relationship. Substantial differences in Type I (α) and Type II (β) errors were detected between flint and dent germplasm pools with different marker systems and when fixed EDV thresholds were considered. To counterbalance advantages and disadvantages of AFLP and SSRs, a complementary application of the two marker systems is suggested for discrimination between EDVs and independently derived varieties (IDVs).

Maize (Zea mays L.) ear inflorescence architecture is directly relevant to grain yield components, and tassel architecture is relevant to hybrid seed production. The objectives of this study were to (1) determine heritabilities and correlations of a comprehensive set of tassel and ear inflorescence architecture traits in a set of (Illinois Low ProteinxB73) B73 S1 families, (2) identify chromosomal positions of QTL affecting tassel and ear architecture, and (3) identify possible candidate genes associated with these QTL. For tassel traits, the number of detected QTL ranged from one to five, and explained between 6.5 and 35.9% of phenotypic variation. For ear traits, the number of detected QTL ranged from one to nine and phenotypic variation explained by those QTL varied between 7.9 and 53.0%. We detected QTL for tassel architecture traits that required calculation of ratios from measured traits. Some of these calculated traits QTL were detected in regions that did not show QTL for the measured traits, suggesting that calculation of ratios may reveal developmentally relevant patterns of tassel architecture. We detected a QTL on chromosome 7 for tassel branch number near the gene ramosa1 (ra1), which is known to control tassel branch number, making ra1 a candidate gene for tassel branch number. We detected QTL for several traits on chromosomes 6, 8, and 9, where no inflorescence architecture genes have been mapped, thus providing initial information towards new gene discovery for control of inflorescence architecture.

Landraces of maize represent a valuable genetic resource for breeding and genetic studies. Using simple sequence repeat (SSR) markers, we analysed five flint maize populations from Central Europe that had played an important role in the pre-hybrid era in Germany. Our objectives were to (1) investigate the molecular genetic diversity within and among the populations based on the SSR analysis of individuals, (2) compare these results of the SSR analysis based on individuals with those based on bulks, (3) examine genotype frequencies for deviations from Hardy–Weinberg equilibrium (HWE) at individual loci, and (4) test for linkage disequilibrium (LD) between pairs of loci within populations. Thirty individuals and their bulked DNA per population were fingerprinted with 55 SSR markers. Across all populations, 46.7% of the SSR markers deviated significantly from HWE, with an excess of homozygosity in 97% of the cases. This excess of homozygosity can largely be explained by experimental errors during the amplification of SSRs apart from genuine genetic causes. Allele frequencies of the SSR analyses of individuals and bulks were significantly correlated (r=0.85, P< 0.01), suggesting that SSR analysis of bulks is very cost-effective for large-scale molecular characterisation of germplasm collections. No evidence for genome-wide LD among pairs of loci was observed, indicating that the populations are well suited for high resolution association mapping studies.

Genetic similarities (GS) based on molecular markers have been proposed as a tool for identification of essentially derived varieties (EDVs). Nevertheless, scientifically reliable criteria for discrimination of EDVs and independently derived varieties with GS estimates are scanty, and implementation into practical breeding has not yet taken place. Our objectives were to (1) assess the influence of chromosome number and length, marker density, and distribution, as well as the degree of polymorphism between the parental inbreds on the distribution of GS between parental inbreds and their progenies [GS(P1,O)] derived from F2 and different backcross populations and (2) evaluate these factors with regard to the power for distinguishing F2- versus BC1- and BC1- versus BC2-derived lines with molecular markers. We developed an approach based on statistical test theory for the identification of EDVs with molecular markers. Standard deviations and overlaps of distributions of GS(P1,O) of F2-, BC1-, and BC2-derived lines were smaller with (1) increasing chromosome number and length, (2) increasing marker density, and (3) uniformly instead of randomly distributed markers, approaching a lower boundary determined by the genetic parameters. The degree of polymorphism between the parental inbreds influenced the power only if the remaining number of polymorphic markers was low. Furthermore, suggestions are made for (1) determining the number of markers required to ascertain a given power and (2) EDV identification procedures.

Genetic distances (GDs) based on molecular markers such as simple sequence repeats (SSRs) have been proposed as an appropriate tool to assess the genetic conformity between putative essentially derived varieties (EDVs) and their initial varieties (IVs). However, for maize (Zea mays L.) and other crops, no consensus has been reached regarding GD thresholds for identification of EDVs, mainly because reliable benchmark data are lacking. Our objectives were to (i) determine the variation in the parental contribution (p) to the genome of homozygous progeny lines derived in recycling breeding programs; (ii) investigate the power of SSR-based GD estimates for discriminating between progeny lines derived from F 2 , BC 1 , and BC 2 populations (BC = backcross); (iii) compare theoretical and simulated results of a companion study to our experimental data; and (iv) draw conclusions with regard to various EDV thresholds suggested hitherto. A total of 220 European and U.S. maize inbred lines comprising 163 triplets were genotyped with 100 uniformly distributed SSRs. A triplet consisted of one F 2 -, or BC 1 -derived progeny line and both parental lines. The SSR-based estimates of p varied from 0.25 to 0.74 for F 2 -derived lines with a mean (0.49) close to the expectation (0.50), and ranged from 0.51 to 0.80 for BC 1 -deived lines with a mean (0.66) significantly smaller than the expectation (0.75). Relative to the variation in p, the GD between progeny lines and parents was little influenced by the variation in the GD between the parents, particularly for BC 1 -derived lines. Suggested GD thresholds for EDVs resulted in different Type I (α) and Type II (β) errors, depending on the germplasm pool. Considerable overlaps in the GD frequency distributions of F 2- , BC 1- , and BC 2 -derived lines indicate that the resolution to discriminate these types of progeny is poor unless a much larger number or a set of extremely polymorphic markers is used.

In addition to genetic distances (GDs) based on molecular markers, morphological traits and heterosis have been proposed as possible tools to assess the genetic conformity between putative essentially derived varieties (EDVs) and their initial varieties (IVs). However, for maize (Zea mays L.) and other crops, no consensus has been reached regarding methods and thresholds for identification of EDVs, mainly because reliable benchmark data are lacking. The objectives of this study were to (i) evaluate the power of morphological traits and heterosis to discriminate between homozygous progenies derived from F 2 , BC 1 , and BC 2 populations (BC = backcross), (ii) compare the findings to published data based on simple sequence repeats (SSRs) and amplified fragment length polymorphisms (AFLPs), and (iii) draw conclusions about the usefulness of the various criteria for identification of EDVs. Morphological distances (MDs) based on 25 traits and midparent beterosis (MPH) for 12 traits were observed for a total of 58 European maize inbred lines comprising 38 triplets. A triplet consisted of one homozygous line derived from a F 2 , BC 1 , or BC 2 population and both parental inbreds. In addition, all inbreds were genotyped with 100 uniformly distributed SSR markers and 20 AFLP primer combinations in companion studies for calculation of GDs. Correlations between the coancestry coefficient, GDs, MDs, and MPH were significant and high for the majority of traits. However, thresholds for EDVs to discriminate between F 2 - and BC 1 -derived, or BC 1 - and BC 2 -derived progenies using only MDs or heterosis yielded considerably higher values for Type I (a) and Type II (1 - β) errors than observed with GDs based on SSRs and AFLPs. Consequently, morphological traits and heterosis are less suited for identification of EDVs in maize than molecular markers.

It has been claimed that plant breeding reduces genetic diversity in elite germplasm which could seriously jeopardize the continued ability to improve crops. The main objective of this study was to examine the loss of genetic diversity in spring bread wheat during (1) its domestication, (2) the change from traditional landrace cultivars (LCs) to modern breeding varieties, and (3) 50 years of international breeding. We studied 253 CIMMYT or CIMMYT-related modern wheat cultivars, LCs, and Triticum tauschii accessions, the D-genome donor of wheat, with 90 simple sequence repeat (SSR) markers dispersed across the wheat genome. A loss of genetic diversity was observed from T. tauschii to the LCs, and from the LCs to the elite breeding germplasm. Wheat's genetic diversity was narrowed from 1950 to 1989, but was enhanced from 1990 to 1997. Our results indicate that breeders averted the narrowing of the wheat germplasm base and subsequently increased the genetic diversity through the introgression of novel materials. The LCs and T. tauschii contain numerous unique alleles that were absent in modern spring bread wheat cultivars. Consequently, both the LCs and T. tauschii represent useful sources for broadening the genetic base of elite wheat breeding germplasm.

Heterotic groups and patterns are of fundamental importance in hybrid breeding of maize (Zea mays L.). The major goal of this study was to investigate the relationship between heterosis and genetic distance determined with simple sequence repeat (SSR) markers. The objectives of our research were to (i) compare the genetic diversity within and between seven tropical maize populations, (ii) test alternative hypotheses on the relationship between panmictic midparent heterosis (PMPH) and genetic distances determined with SSR markers, and (iii) evaluate the use of SSR markers for grouping of germplasm and establishing heterotic patterns in hybrid breeding of tropical maize. Published data of a diallel of seven tropical maize populations evaluated for agronomic traits in seven environments were reanalyzed to calculate PMPH in population hybrids. In addition, 48 individuals from each population were sampled and assayed with 85 SSR markers covering the entire maize genome. A total of 532 alleles in the 7 × 48 genotypes assayed were detected. The analysis of molecular variance (AMOVA) revealed that 89.8% of the variation was found within populations and only 10.2% between populations. The correlation between PMPH and the squared modified Roger's distance (MRD) based on SSR markers was significantly positive (P < 0.05) only for grain yield (r = 0.63). With SSR analyses, it was possible to assign Population 29 (Pop29) to the established Heterotic Group A and propose new heterotic groups (Pop25, Pop43). We conclude that SSR markers provide a powerful tool for grouping of germplasm and are a valuable complementation to field trials for identifying groups with satisfactory heterotic response.

Heterotic groups and patterns are of fundamental importance in hybrid breeding. The objectives of our research were to: (1) investigate the relationship of simple sequence repeats (SSR) based genetic distances between populations and panmictic midparent heterosis (PMPH) in a broad range of CIMMYT maize germplasm, (2) evaluate the usefulness of SSR markers for defining heterotic groups and patterns in subtropical germplasm, and (3) examine applications of SSR markers for broadening heterotic groups by systematic introgression of other germplasm. Published data of two diallels and one factorial evaluated for grain yield were re-analyzed to calculate the PMPH in population hybrids. Additionally, 20 pools and populations widely used in CIMMYT's breeding program were assayed with 83 SSR markers covering the entire maize genome. Correlations of squared modified Roger's distance (MRD(2)) and PMPH were mostly positive and significant, but adaption problems caused deviations in some cases. For intermediate- and early-maturity subtropical germplasm, two heterotic groups could be suggested consisting of a flint and dent composite. We concluded that the relationships between the populations obtained by SSR analyses are in excellent agreement with pedigree information. SSR markers are a valuable complementation to field trials for identifying heterotic groups and can be used to introgress exotic germplasm systematically.

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.