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KEY MESSAGE: We 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. 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, but it is highly dependent on the genetic architecture of the trait and the relative gain in accuracy is minimal. When SVs are the only causative variant type, 70% of the time SV predictors outperform SNP predictors. However, the improvement in accuracy in these instances is only 1.5% on average. Further simulations with predictors in varying degrees of LD with causative variants of different types (e.g., SNPs, SVs, SNPs and SVs) showed that prediction accuracy increased as linkage disequilibrium between causative variants and predictors increased regardless of the marker type. This study demonstrates that knowing the genetic architecture of a trait in deciding what markers to use in large-scale genomic prediction modeling in a breeding program is more important than what types of markers to use.
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Genoma , Modelos Genéticos , Humanos , Simulação por Computador , Genômica/métodos , Fenótipo , Polimorfismo de Nucleotídeo Único , Seleção Genética , GenótipoRESUMO
The development of a system for the operationally simple, scalable conversion of polyhydroxylated biomass into industrially relevant feedstock chemicals is described. This system includes a bimetallic Pd/Re catalyst in combination with hydrogen gas as a terminal reductant and enables the high-yielding reduction of sugar acids. This procedure has been applied to the synthesis of adipate esters, precursors for the production of Nylon-6,6, in excellent yield from biomass-derived sources.
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Adipatos/química , Caprolactama/análogos & derivados , Hidrogênio/química , Polímeros/síntese química , Açúcares Ácidos/química , Adipatos/síntese química , Biomassa , Caprolactama/síntese química , Caprolactama/química , Catálise , Esterificação , Hidrogenação , Hidroxilação , Oxirredução , Paládio/química , Polímeros/química , Rênio/química , Açúcares Ácidos/síntese químicaRESUMO
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.
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Comportamento Alimentar/fisiologia , Raízes de Plantas/microbiologia , Rabditídios/fisiologia , Animais , Controle Biológico de VetoresRESUMO
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 coexpression 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 â¼2,000 nonredundant markers from 400 maize hybrids across 9 environments. We demonstrate that networks can be generated using this approach, and that the markers that are covarying are rarely in linkage disequilibrium, thus representing higher biological relevance. Multiple covarying 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 that 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.
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Genótipo , Fenótipo , Marcadores Genéticos , Desequilíbrio de LigaçãoRESUMO
OBJECTIVES: The Genomes to Fields (G2F) 2022 Maize Genotype by Environment (GxE) Prediction Competition aimed to develop models for predicting grain yield for the 2022 Maize GxE project field trials, leveraging the datasets previously generated by this project and other publicly available data. DATA DESCRIPTION: This resource used data from the Maize GxE project within the G2F Initiative [1]. The dataset included phenotypic and genotypic data of the hybrids evaluated in 45 locations from 2014 to 2022. Also, soil, weather, environmental covariates data and metadata information for all environments (combination of year and location). Competitors also had access to ReadMe files which described all the files provided. The Maize GxE is a collaborative project and all the data generated becomes publicly available [2]. The dataset used in the 2022 Prediction Competition was curated and lightly filtered for quality and to ensure naming uniformity across years.
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Genoma de Planta , Zea mays , Fenótipo , Zea mays/genética , Genótipo , Genoma de Planta/genética , Grão Comestível/genéticaRESUMO
OBJECTIVES: This release note describes the Maize GxE project datasets within the Genomes to Fields (G2F) Initiative. The Maize GxE project aims to understand genotype by environment (GxE) interactions and use the information collected to improve resource allocation efficiency and increase genotype predictability and stability, particularly in scenarios of variable environmental patterns. Hybrids and inbreds are evaluated across multiple environments and phenotypic, genotypic, environmental, and metadata information are made publicly available. DATA DESCRIPTION: The datasets include phenotypic data of the hybrids and inbreds evaluated in 30 locations across the US and one location in Germany in 2020 and 2021, soil and climatic measurements and metadata information for all environments (combination of year and location), ReadMe, and description files for each data type. A set of common hybrids is present in each environment to connect with previous evaluations. Each environment had a collaborator responsible for collecting and submitting the data, the GxE coordination team combined all the collected information and removed obvious erroneous data. Collaborators received the combined data to use, verify and declare that the data generated in their own environments was accurate. Combined data is released to the public with minimal filtering to maintain fidelity to the original data.
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Alocação de Recursos , Zea mays , Zea mays/genética , Estações do Ano , Genótipo , AlemanhaRESUMO
OBJECTIVES: This report provides information about the public release of the 2018-2019 Maize G X E project of the Genomes to Fields (G2F) Initiative datasets. G2F is an umbrella initiative that evaluates maize hybrids and inbred lines across multiple environments and makes available phenotypic, genotypic, environmental, and metadata information. The initiative understands the necessity to characterize and deploy public sources of genetic diversity to face the challenges for more sustainable agriculture in the context of variable environmental conditions. DATA DESCRIPTION: Datasets include phenotypic, climatic, and soil measurements, metadata information, and inbred genotypic information for each combination of location and year. Collaborators in the G2F initiative collected data for each location and year; members of the group responsible for coordination and data processing combined all the collected information and removed obvious erroneous data. The collaborators received the data before the DOI release to verify and declare that the data generated in their own locations was accurate. ReadMe and description files are available for each dataset. Previous years of evaluation are already publicly available, with common hybrids present to connect across all locations and years evaluated since this project's inception.
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Genoma de Planta , Zea mays , Fenótipo , Zea mays/genética , Estações do Ano , Genótipo , Genoma de Planta/genéticaRESUMO
Recruitment of microorganisms to the rhizosphere varies among plant genotypes, yet an understanding of whether the microbiome can be altered by selection on the host is relatively unknown. Here, we performed a common garden study to characterize recruitment of rhizosphere microbiome, functional groups, for 20 expired Plant Variety Protection Act maize lines spanning a chronosequence of development from 1949 to 1986. This time frame brackets a series of agronomic innovations, namely improvements in breeding and the application of synthetic nitrogenous fertilizers, technologies that define modern industrial agriculture. We assessed the impact of chronological agronomic improvements on recruitment of the rhizosphere microbiome in maize, with emphasis on nitrogen cycling functional groups. In addition, we quantified the microbial genes involved in nitrogen cycling and predicted functional pathways present in the microbiome of each genotype. Both genetic relatednesses of host plant and decade of germplasm development were significant factors in the recruitment of the rhizosphere microbiome. More recently developed germplasm recruited fewer microbial taxa with the genetic capability for sustainable nitrogen provisioning and larger populations of microorganisms that contribute to N losses. This study indicates that the development of high-yielding varieties and agronomic management approaches of industrial agriculture inadvertently modified interactions between maize and its microbiome.
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Microbiota , Rizosfera , Melhoramento Vegetal , Microbiologia do Solo , Zea maysRESUMO
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.
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Interação Gene-Ambiente , Zea mays , Genótipo , Modelos Genéticos , Fenótipo , Melhoramento VegetalRESUMO
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.
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Ácidos Cumáricos/análise , Zea mays/química , Ácidos Cumáricos/metabolismo , Genótipo , Endogamia , Propionatos/análise , Propionatos/metabolismo , Característica Quantitativa Herdável , Sementes/química , Sementes/genética , Sementes/metabolismo , Zea mays/genética , Zea mays/metabolismoRESUMO
Genomic prediction provides an efficient alternative to conventional phenotypic selection for developing improved cultivars with desirable characteristics. New and improved methods to genomic prediction are continually being developed that attempt to deal with the integration of data types beyond genomic information. Modern automated weather systems offer the opportunity to capture continuous data on a range of environmental parameters at specific field locations. In principle, this information could characterize training and target environments and enhance predictive ability by incorporating weather characteristics as part of the genotype-by-environment (G×E) interaction component in prediction models. We assessed the usefulness of including weather data variables in genomic prediction models using a naïve environmental kinship model across 30 environments comprising the Genomes to Fields (G2F) initiative in 2014 and 2015. Specifically four different prediction scenarios were evaluated (i) tested genotypes in observed environments; (ii) untested genotypes in observed environments; (iii) tested genotypes in unobserved environments; and (iv) untested genotypes in unobserved environments. A set of 1,481 unique hybrids were evaluated for grain yield. Evaluations were conducted using five different models including main effect of environments; general combining ability (GCA) effects of the maternal and paternal parents modeled using the genomic relationship matrix; specific combining ability (SCA) effects between maternal and paternal parents; interactions between genetic (GCA and SCA) effects and environmental effects; and finally interactions between the genetics effects and environmental covariates. Incorporation of the genotype-by-environment interaction term improved predictive ability across all scenarios. However, predictive ability was not improved through inclusion of naive environmental covariates in G×E models. More research should be conducted to link the observed weather conditions with important physiological aspects in plant development to improve predictive ability through the inclusion of weather data.
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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.
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Genoma de Planta/genética , Melhoramento Vegetal , Zea mays/genética , Conjuntos de Dados como Assunto , Genótipo , FenótipoRESUMO
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.
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Arabidopsis/genética , Arabidopsis/crescimento & desenvolvimento , Flores/genética , Flores/crescimento & desenvolvimento , Variação Genética , FenótipoRESUMO
Seven maize, Zea mays L., genotypes selected for native resistance to western corn rootworm, Diabrotica virgifera virgifera LeConte (Coleoptera: Chrysomelidae), larval feeding damage (SUM2068, SUM2162, CRW3(S1)C6, NSS1 x CRW3(S1)C6, PI583927, CRW2(C5), and AR17056-16) were evaluated along with three control maize genotypes in the field for plant damage, larval recovery, adult emergence, root size, and root regrowth. Larvae recovered were further analyzed for head capsule width and dry weight and adults for dry weight. All factors evaluated with the exception of adult dry weight varied significantly among maize genotypes. Control genotypes included a highly susceptible hybrid, B37 x H84, a transgenic rootworm-resistant hybrid expressing the modified Cry3A protein (MIR604), and the untransformed modern hybrid with the same genetic background as the MIR604 we used (isoline) as a second susceptible control. In general, the genotypes previously selected for resistance to western corn rootworm larval feeding had less damage, fewer larvae recovered, smaller larvae recovered, and fewer adults recovered than the susceptible controls. SUM2162 was significantly less damaged than all other native sources of resistance. Western corn rootworm larvae recovered from SUM2162 and SUM2068 were significantly smaller in terms of head capsule width and average weight than larvae recovered from all other maize genotypes, indicating that antibiosis is a mechanism of resistance for these two hybrids.
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Besouros/crescimento & desenvolvimento , Interações Hospedeiro-Parasita , Zea mays/imunologia , Animais , Comportamento Alimentar , Imunidade Inata , Larva/crescimento & desenvolvimento , Análise Multivariada , Fenótipo , Raízes de Plantas/parasitologia , Zea mays/genética , Zea mays/parasitologiaRESUMO
The western corn rootworm, Diabrotica virgifera virgifera (LeConte), is a major pest of maize, Zea mays L., in the United States and Europe, and it is likely to increase in importance as a trend toward increased nonrotated maize favors larger rootworm populations. Options for rootworm management in nonrotated maize in Europe and in nontransgenic "refuge" areas in countries that permit transgenic maize are limited to insecticides. Development of additional options for growers would be helpful. Screening maize germplasm (e.g., landraces, populations, inbreds) for native resistance to western corn rootworm is labor-intensive and is usually conducted on unfinished germplasm and not on hybrid materials. However, we have recently observed that topcrossed (hybrid) materials tend to have reduced western corn rootworm damage. To formally test whether rootworm damage to inbreds and associated hybrids were correlated, we evaluated 25 diverse inbred lines and their B73 hybrids for western corn rootworm damage in seven environments. Overall, hybrids had significantly less damage than inbreds, but unfortunately, the correlation between inbreds and hybrids was not significant. These findings have important implications regarding screening germplasm for western corn rootworm resistance, namely, that inbred materials and perhaps populations should be topcrossed to form hybrid materials before screening for western corn rootworm damage to ensure that valuable sources of resistance to western corn rootworm are not missed during the screening process.
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Besouros/fisiologia , Meio Ambiente , Raízes de Plantas/parasitologia , Zea mays/parasitologia , Análise de Variância , Animais , Hibridização Genética , Endogamia , Missouri , Análise de Componente Principal , Especificidade da Espécie , Zea mays/genéticaRESUMO
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.
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Zea mays/genética , Grão Comestível/genética , Qualidade dos Alimentos , Genoma de Planta , Estudo de Associação Genômica Ampla , Haploidia , Fenótipo , Melhoramento VegetalRESUMO
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.
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Manipulação de Alimentos/métodos , Valor Nutritivo/fisiologia , Zea mays/química , HumanosRESUMO
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.
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Antibiose , Besouros/fisiologia , Herbivoria , Locos de Características Quantitativas , Zea mays/fisiologia , Animais , Besouros/crescimento & desenvolvimento , Illinois , Larva/fisiologia , Missouri , South Dakota , Zea mays/genéticaRESUMO
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.
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Variação Genética , Estudo de Associação Genômica Ampla , Modelos Estatísticos , Sorghum/genética , Zea mays/genética , Modelos LinearesRESUMO
Flowering and height related traits are extensively studied in maize for three main reasons: 1) easily obtained phenotypic measurements, 2) highly heritable, and 3) importance of these traits to adaptation and grain yield. However, variation in flowering and height traits is extensive and findings from previous studies are genotype specific. Herein, a diverse panel of exotic derived doubled haploid lines, in conjunction with genome-wide association analysis, is used to further explore adaptation related trait variation of exotic germplasm for potential use in adapting exotic germplasm to the U.S. Corn-Belt. Phenotypes for the association panel were obtained from six locations across the central-U.S. and genotyping was performed using the genotyping-by-sequencing method. Nineteen flowering time candidate genes were found for three flowering traits. Eighteen candidate genes were found for four height related traits, with the majority of the candidate genes relating to plant hormones auxin and gibberellin. A single gene was discovered for ear height that also had effects on -like flowering gene expression levels. Findings will be used to inform future research efforts of the USDA Germplasm Enhancement of Maize project and eventually aid in the rapid adaptation of exotic germplasm to temperate U.S. environments.