Eleven biosynthetic genes explain the majority of natural variation in carotenoid levels in maize grain.

Vitamin A deficiency remains prevalent in parts of Asia, Latin America, and sub-Saharan Africa where maize (Zea mays) is a food staple. Extensive natural variation exists for carotenoids in maize grain. Here, to understand its genetic basis, we conducted a joint linkage and genome-wide association study of the US maize nested association mapping panel. Eleven of the 44 detected quantitative trait loci (QTL) were resolved to individual genes. Six of these were correlated expression and effect QTL (ceeQTL), showing strong correlations between RNA-seq expression abundances and QTL allelic effect estimates across six stages of grain development. These six ceeQTL also had the largest percentage of phenotypic variance explained, and in major part comprised the three to five loci capturing the bulk of genetic variation for each trait. Most of these ceeQTL had strongly correlated QTL allelic effect estimates across multiple traits. These findings provide an in-depth genome-level understanding of the genetic and molecular control of carotenoids in plants. In addition, these findings provide a roadmap to accelerate breeding for provitamin A and other priority carotenoid traits in maize grain that should be readily extendable to other cereals.

Regularized multi-trait multi-locus linear mixed models for genome-wide association studies and genomic selection in crops.

BACKGROUND: We consider two key problems in genomics involving multiple traits: multi-trait genome wide association studies (GWAS), where the goal is to detect genetic variants associated with the traits; and multi-trait genomic selection (GS), where the emphasis is on accurately predicting trait values. Multi-trait linear mixed models build on the linear mixed model to jointly model multiple traits. Existing estimation methods, however, are limited to the joint analysis of a small number of genotypes; in fact, most approaches consider one SNP at a time. Estimating multi-dimensional genetic and environment effects also results in considerable computational burden. Efficient approaches that incorporate regularization into multi-trait linear models (no random effects) have been recently proposed to identify genomic loci associated with multiple traits (Yu et al. in Multitask learning using task clustering with applications to predictive modeling and GWAS of plant varieties. arXiv:1710.01788 , 2017; Yu et al in Front Big Data 2:27, 2019), but these ignore population structure and familial relatedness (Yu et al in Nat Genet 38:203-208, 2006). RESULTS: This work addresses this gap by proposing a novel class of regularized multi-trait linear mixed models along with scalable approaches for estimation in the presence of high-dimensional genotypes and a large number of traits. We evaluate the effectiveness of the proposed methods using datasets in maize and sorghum diversity panels, and demonstrate benefits in both achieving high prediction accuracy in GS and in identifying relevant marker-trait associations. CONCLUSIONS: The proposed regularized multivariate linear mixed models are relevant for both GWAS and GS. We hope that they will facilitate agronomy-related research in plant biology and crop breeding endeavors.

Multiomics approach reveals a role of translational machinery in shaping maize kernel amino acid composition.

Maize (Zea mays) seeds are a good source of protein, despite being deficient in several essential amino acids. However, eliminating the highly abundant but poorly balanced seed storage proteins has revealed that the regulation of seed amino acids is complex and does not rely on only a handful of proteins. In this study, we used two complementary omics-based approaches to shed light on the genes and biological processes that underlie the regulation of seed amino acid composition. We first conducted a genome-wide association study to identify candidate genes involved in the natural variation of seed protein-bound amino acids. We then used weighted gene correlation network analysis to associate protein expression with seed amino acid composition dynamics during kernel development and maturation. We found that almost half of the proteome was significantly reduced during kernel development and maturation, including several translational machinery components such as ribosomal proteins, which strongly suggests translational reprogramming. The reduction was significantly associated with a decrease in several amino acids, including lysine and methionine, pointing to their role in shaping the seed amino acid composition. When we compared the candidate gene lists generated from both approaches, we found a nonrandom overlap of 80 genes. A functional analysis of these genes showed a tight interconnected cluster dominated by translational machinery genes, especially ribosomal proteins, further supporting the role of translation dynamics in shaping seed amino acid composition. These findings strongly suggest that seed biofortification strategies that target the translation machinery dynamics should be considered and explored further.

Reductions in leaf area index, pod production, seed size, and harvest index drive yield loss to high temperatures in soybean.

Improvements in genetics, technology, and agricultural intensification have increased soybean yields; however, adverse climate conditions may prevent these gains from being fully realized in the future. Higher growing season temperatures reduce soybean yields in key production regions including the US Midwest, and better understanding of the developmental and physiological mechanisms that constrain soybean yield under high temperature conditions is needed. This study tested the response of two soybean cultivars to four elevated temperature treatments (+1.7, +2.6, +3.6, and +4.8 °C) in the field over three growing seasons and identified threshold temperatures for response and linear versus non-linear trait responses to temperature. Yield declined non-linearly to temperature, with decreases apparent when canopy temperature exceeded 20.9 °C for the locally adapted cultivar and 22.7°C for a cultivar adapted to more southern locations. While stem node number increased with increasing temperature, leaf area index decreased substantially. Pod production, seed size, and harvest index significantly decreased with increasing temperature. The seasonal average temperature of even the mildest treatment exceeded the threshold temperatures for yield loss, emphasizing the importance of improving temperature tolerance in soybean germplasm with intensifying climate change.

Importance of genetic architecture in marker selection decisions for genomic prediction.

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.

Exploring the Relationship Between Pattern-Triggered Immunity and Quantitative Resistance to Xanthomonas vasicola pv. vasculorum in Maize.

Bacterial leaf streak (BLS) of maize is an emerging foliar disease of maize in the Americas. It is caused by the gram-negative nonvascular bacterium Xanthomonas vasicola pv. vasculorum. There are no chemical controls available for BLS, and thus, host resistance is crucial for managing X. vasicola pv. vasculorum. The objective of this study was to examine the genetic determinants of resistance to X. vasicola pv. vasculorum in maize, as well as the relationship between other defense-related traits and BLS resistance. Specifically, we examined the correlations among BLS severity, severity for three fungal diseases, flg-22 response, hypersensitive response, and auricle color. We conducted quantitative trait locus (QTL) mapping for X. vasicola pv. vasculorum resistance using the maize recombinant inbred line population Z003 (B73 × CML228). We detected three QTLs for BLS resistance. In addition to the disease resistance QTL, we detected a single QTL for auricle color. We observed significant, yet weak, correlations among BLS severity, levels of pattern-triggered immunity response and leaf flecking. These results will be useful for understanding resistance to X. vasicola pv. vasculorum and mitigating the impact of BLS on maize yields.

Genomic regions influencing aggressive behavior in honey bees are defined by colony allele frequencies.

For social animals, the genotypes of group members affect the social environment, and thus individual behavior, often indirectly. We used genome-wide association studies (GWAS) to determine the influence of individual vs. group genotypes on aggression in honey bees. Aggression in honey bees arises from the coordinated actions of colony members, primarily nonreproductive "soldier" bees, and thus, experiences evolutionary selection at the colony level. Here, we show that individual behavior is influenced by colony environment, which in turn, is shaped by allele frequency within colonies. Using a population with a range of aggression, we sequenced individual whole genomes and looked for genotype-behavior associations within colonies in a common environment. There were no significant correlations between individual aggression and specific alleles. By contrast, we found strong correlations between colony aggression and the frequencies of specific alleles within colonies, despite a small number of colonies. Associations at the colony level were highly significant and were very similar among both soldiers and foragers, but they covaried with one another. One strongly significant association peak, containing an ortholog of the Drosophila sensory gene dpr4 on linkage group (chromosome) 7, showed strong signals of both selection and admixture during the evolution of gentleness in a honey bee population. We thus found links between colony genetics and group behavior and also, molecular evidence for group-level selection, acting at the colony level. We conclude that group genetics dominates individual genetics in determining the fatal decision of honey bees to sting.

A population-level statistic for assessing Mendelian behavior of genotyping-by-sequencing data from highly duplicated genomes.

BACKGROUND: Given the economic and environmental importance of allopolyploids and other species with highly duplicated genomes, there is a need for methods to distinguish paralogs, i.e. duplicate sequences within a genome, from Mendelian loci, i.e. single copy sequences that pair at meiosis. The ratio of observed to expected heterozygosity is an effective tool for filtering loci but requires genotyping to be performed first at a high computational cost, whereas counting the number of sequence tags detected per genotype is computationally quick but very ineffective in inbred or polyploid populations. Therefore, new methods are needed for filtering paralogs. RESULTS: We introduce a novel statistic, Hind/HE, that uses the probability that two reads sampled from a genotype will belong to different alleles, instead of observed heterozygosity. The expected value of Hind/HE is the same across all loci in a dataset, regardless of read depth or allele frequency. In contrast to methods based on observed heterozygosity, it can be estimated and used for filtering loci prior to genotype calling. In addition to filtering paralogs, it can be used to filter loci with null alleles or high overdispersion, and identify individuals with unexpected ploidy and hybrid status. We demonstrate that the statistic is useful at read depths as low as five to 10, well below the depth needed for accurate genotype calling in polyploid and outcrossing species. CONCLUSIONS: Our methodology for estimating Hind/HE across loci and individuals, as well as determining reasonable thresholds for filtering loci, is implemented in polyRAD v1.6, available at https://github.com/lvclark/polyRAD . In large sequencing datasets, we anticipate that the ability to filter markers and identify problematic individuals prior to genotype calling will save researchers considerable computational time.

Assessment of two statistical approaches for variance genome-wide association studies in plants.

Genomic loci that control the variance of agronomically important traits are increasingly important due to the profusion of unpredictable environments arising from climate change. The ability to identify such variance-controlling loci in association studies will be critical for future breeding efforts. Two statistical approaches that have already been used in the variance genome-wide association study (vGWAS) paradigm are the Brown-Forsythe test (BFT) and the double generalized linear model (DGLM). To ensure that these approaches are deployed as effectively as possible, it is critical to study the factors that influence their ability to identify variance-controlling loci. We used genome-wide marker data in maize (Zea mays L.) and Arabidopsis thaliana to simulate traits controlled by epistasis, genotype by environment (GxE) interactions, and variance quantitative trait nucleotides (vQTNs). We then quantified true and false positive detection rates of the BFT and DGLM across all simulated traits. We also conducted a vGWAS using both the BFT and DGLM on plant height in a maize diversity panel. The observed true positive detection rates at the maximum sample size considered (N = 2815) suggest that both of these vGWAS approaches are capable of identifying epistasis and GxE for sufficiently large sample sizes. We also noted that the DGLM decisively outperformed the BFT for simulated traits controlled by vQTNs at sample sizes of N = 500. Although we conclude that there are still certain aspects of vGWAS approaches that need further refinement, this study suggests that the BFT and DGLM are capable of identifying variance-controlling loci in current state-of-the-art plant or agronomic data sets.

HAPPI GWAS: Holistic Analysis with Pre- and Post-Integration GWAS.

MOTIVATION: Advanced publicly available sequencing data from large populations have enabled informative genome-wide association studies (GWAS) that associate SNPs with phenotypic traits of interest. Many publicly available tools able to perform GWAS have been developed in response to increased demand. However, these tools lack a comprehensive pipeline that includes both pre-GWAS analysis, such as outlier removal, data transformation and calculation of Best Linear Unbiased Predictions or Best Linear Unbiased Estimates. In addition, post-GWAS analysis, such as haploblock analysis and candidate gene identification, is lacking. RESULTS: Here, we present Holistic Analysis with Pre- and Post-Integration (HAPPI) GWAS, an open-source GWAS tool able to perform pre-GWAS, GWAS and post-GWAS analysis in an automated pipeline using the command-line interface. AVAILABILITY AND IMPLEMENTATION: HAPPI GWAS is written in R for any Unix-like operating systems and is available on GitHub (https://github.com/Angelovici-Lab/HAPPI.GWAS.git). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

mGWAS Uncovers Gln-Glucosinolate Seed-Specific Interaction and its Role in Metabolic Homeostasis.

Gln is a key player in plant metabolism. It is one of the major free amino acids that is transported into the developing seed and is central for nitrogen metabolism. However, Gln natural variation and its regulation and interaction with other metabolic processes in seeds remain poorly understood. To investigate the latter, we performed a metabolic genome-wide association study (mGWAS) of Gln-related traits measured from the dry seeds of the Arabidopsis (Arabidopsis thaliana) diversity panel using all potential ratios between Gln and the other members of the Glu family as traits. This semicombinatorial approach yielded multiple candidate genes that, upon further analysis, revealed an unexpected association between the aliphatic glucosinolates (GLS) and the Gln-related traits. This finding was confirmed by an independent quantitative trait loci mapping and statistical analysis of the relationships between the Gln-related traits and the presence of specific GLS in seeds. Moreover, an analysis of Arabidopsis mutants lacking GLS showed an extensive seed-specific impact on Gln levels and composition that manifested early in seed development. The elimination of GLS in seeds was associated with a large effect on seed nitrogen and sulfur homeostasis, which conceivably led to the Gln response. This finding indicates that both Gln and GLS play key roles in shaping the seed metabolic homeostasis. It also implies that select secondary metabolites might have key functions in primary seed metabolism. Finally, our study shows that an mGWAS performed on dry seeds can uncover key metabolic interactions that occur early in seed development.

Diversifying maize genomic selection models.

Genomic selection (GS) is one of the most powerful tools available for maize breeding. Its use of genome-wide marker data to estimate breeding values translates to increased genetic gains with fewer breeding cycles. In this review, we cover the history of GS and highlight particular milestones during its adaptation to maize breeding. We discuss how GS can be applied to developing superior maize inbreds and hybrids. Additionally, we characterize refinements in GS models that could enable the encapsulation of non-additive genetic effects, genotype by environment interactions, and multiple levels of the biological hierarchy, all of which could ultimately result in more accurate predictions of breeding values. Finally, we suggest the stages in a maize breeding program where it would be beneficial to apply GS. Given the current sophistication of high-throughput phenotypic, genotypic, and other -omic level data currently available to the maize community, now is the time to explore the implications of their incorporation into GS models and thus ensure that genetic gains are being achieved as quickly and efficiently as possible.

simplePHENOTYPES: SIMulation of pleiotropic, linked and epistatic phenotypes.

BACKGROUND: Advances in genotyping and phenotyping techniques have enabled the acquisition of a great amount of data. Consequently, there is an interest in multivariate statistical analyses that identify genomic regions likely to contain causal mutations affecting multiple traits (i.e., pleiotropy). As the demand for multivariate analyses increases, it is imperative that optimal tools are available to assess their performance. To facilitate the testing and validation of these multivariate approaches, we developed simplePHENOTYPES, an R/CRAN package that simulates pleiotropy, partial pleiotropy, and spurious pleiotropy in a wide range of genetic architectures, including additive, dominance and epistatic models. RESULTS: We illustrate simplePHENOTYPES' ability to simulate thousands of phenotypes in less than one minute. We then provide two vignettes illustrating how to simulate sets of correlated traits in simplePHENOTYPES. Finally, we demonstrate the use of results from simplePHENOTYPES in a standard GWAS software, as well as the equivalence of simulated phenotypes from simplePHENOTYPES and other packages with similar capabilities. CONCLUSIONS: simplePHENOTYPES is a R/CRAN package that makes it possible to simulate multiple traits controlled by loci with varying degrees of pleiotropy. Its ability to interface with both commonly-used marker data formats and downstream quantitative genetics software and packages should facilitate a rigorous assessment of both existing and emerging statistical GWAS and GS approaches. simplePHENOTYPES is also available at https://github.com/samuelbfernandes/simplePHENOTYPES .

Multi-Trait Genome-Wide Association Studies Reveal Loci Associated with Maize Inflorescence and Leaf Architecture.

Maize inflorescence is a complex phenotype that involves the physical and developmental interplay of multiple traits. Given the evidence that genes could pleiotropically contribute to several of these traits, we used publicly available maize data to assess the ability of multivariate genome-wide association study (GWAS) approaches to identify pleiotropic quantitative trait loci (pQTL). Our analysis of 23 publicly available inflorescence and leaf-related traits in a diversity panel of n = 281 maize lines genotyped with 376,336 markers revealed that the two multivariate GWAS approaches we tested were capable of identifying pQTL in genomic regions coinciding with similar associations found in previous studies. We then conducted a parallel simulation study on the same individuals, where it was shown that multivariate GWAS approaches yielded a higher true-positive quantitative trait nucleotide (QTN) detection rate than comparable univariate approaches for all evaluated simulation settings except for when the correlated simulated traits had a heritability of 0.9. We therefore conclude that the implementation of state-of-the-art multivariate GWAS approaches is a useful tool for dissecting pleiotropy and their more widespread implementation could facilitate the discovery of genes and other biological mechanisms underlying maize inflorescence.

Novel Loci Underlie Natural Variation in Vitamin E Levels in Maize Grain.

Tocopherols, tocotrienols, and plastochromanols (collectively termed tocochromanols) are lipid-soluble antioxidants synthesized by all plants. Their dietary intake, primarily from seed oils, provides vitamin E and other health benefits. Tocochromanol biosynthesis has been dissected in the dicot Arabidopsis thaliana, which has green, photosynthetic seeds, but our understanding of tocochromanol accumulation in major crops, whose seeds are nonphotosynthetic, remains limited. To understand the genetic control of tocochromanols in grain, we conducted a joint linkage and genome-wide association study in the 5000-line U.S. maize (Zea mays) nested association mapping panel. Fifty-two quantitative trait loci for individual and total tocochromanols were identified, and of the 14 resolved to individual genes, six encode novel activities affecting tocochromanols in plants. These include two chlorophyll biosynthetic enzymes that explain the majority of tocopherol variation, which was not predicted given that, like most major cereal crops, maize grain is nonphotosynthetic. This comprehensive assessment of natural variation in vitamin E levels in maize establishes the foundation for improving tocochromanol and vitamin E content in seeds of maize and other major cereal crops.

Time dependent genetic analysis links field and controlled environment phenotypes in the model C4 grass Setaria.

Vertical growth of plants is a dynamic process that is influenced by genetic and environmental factors and has a pronounced effect on overall plant architecture and biomass composition. We have performed six controlled growth trials of an interspecific Setaria italica x Setaria viridis recombinant inbred line population to assess how the genetic architecture of plant height is influenced by developmental queues, water availability and planting density. The non-destructive nature of plant height measurements has enabled us to monitor height throughout the plant life cycle in both field and controlled environments. We find that plant height is reduced under water limitation and high density planting and affected by growth environment (field vs. growth chamber). The results support a model where plant height is a heritable, polygenic trait and that the major genetic loci that influence plant height function independent of growth environment. The identity and contribution of loci that influence height changes dynamically throughout development and the reduction of growth observed in water limited environments is a consequence of delayed progression through the genetic program which establishes plant height in Setaria. In this population, alleles inherited from the weedy S. viridis parent act to increase plant height early, whereas a larger number of small effect alleles inherited from the domesticated S. italica parent collectively act to increase plant height later in development.

Characterization of Biosynthetic Pathways for the Production of the Volatile Homoterpenes DMNT and TMTT in Zea mays.

Plant volatiles not only have multiple defense functions against herbivores, fungi, and bacteria, but also have been implicated in signaling within the plant and toward other organisms. Elucidating the function of individual plant volatiles will require more knowledge of their biosynthesis and regulation in response to external stimuli. By exploiting the variation of herbivore-induced volatiles among 26 maize (Zea mays) inbred lines, we conducted a nested association mapping and genome-wide association study (GWAS) to identify a set of quantitative trait loci (QTLs) for investigating the pathways of volatile terpene production. The most significant identified QTL affects the emission of (E)-nerolidol, linalool, and the two homoterpenes (E)-3,8-dimethyl-1,4,7-nonatriene (DMNT) and (E,E)-4,8,12-trimethyltrideca-1,3,7,11-tetraene (TMTT). GWAS associated a single nucleotide polymorphism in the promoter of the gene encoding the terpene synthase TPS2 with this QTL Biochemical characterization of TPS2 verified that this plastid-localized enzyme forms linalool, (E)-nerolidol, and (E,E)-geranyllinalool. The subsequent conversion of (E)-nerolidol into DMNT maps to a P450 monooxygenase, CYP92C5, which is capable of converting nerolidol into DMNT by oxidative degradation. A QTL influencing TMTT accumulation corresponds to a similar monooxygenase, CYP92C6, which is specific for the conversion of (E,E)-geranyllinalool to TMTT The DMNT biosynthetic pathway and both monooxygenases are distinct from those previously characterized for DMNT and TMTT synthesis in Arabidopsis thaliana, suggesting independent evolution of these enzymatic activities.

An assessment of true and false positive detection rates of stepwise epistatic model selection as a function of sample size and number of markers.

Association studies have been successful at identifying genomic regions associated with important traits, but routinely employ models that only consider the additive contribution of an individual marker. Because quantitative trait variability typically arises from multiple additive and non-additive sources, utilization of statistical approaches that include main and two-way interaction marker effects of several loci in one model could lead to unprecedented characterization of these sources. Here we examine the ability of one such approach, called the Stepwise Procedure for constructing an Additive and Epistatic Multi-Locus model (SPAEML), to detect additive and epistatic signals simulated using maize and human marker data. Our results revealed that SPAEML was capable of detecting quantitative trait nucleotides (QTNs) at sample sizes as low as n = 300 and consistently specifying signals as additive and epistatic for larger sizes. Sample size and minor allele frequency had a major influence on SPAEML's ability to distinguish between additive and epistatic signals, while the number of markers tested did not. We conclude that SPAEML is a useful approach for providing further elucidation of the additive and epistatic sources contributing to trait variability when applied to a small subset of genome-wide markers located within specific genomic regions identified using a priori analyses.

Population Structure Analyses Provide Insight into the Source Populations Underlying Rural Isolated Communities in Illinois.

We have previously hypothesized that relatively small and isolated rural communities may experience founder effects, defined as the genetic ramifications of small population sizes at the time of a community's establishment. To explore this, we used an Illumina Infinium Omni2.5Exome-8 chip to collect data from 157 individuals from four Illinois communities, three rural and one urban. Genetic diversity estimates of 999,259 autosomal markers suggested that the reduction in heterozygosity due to shared ancestry was approximately 0, indicating a randomly mating population. An eigenanalysis, which is similar to a principal component analysis but run on a genetic coancestry matrix, conducted in the SNPRelate R package revealed that most of these individuals formed one cluster, with a few putative outliers obscuring population variation. An additional eigenanalysis on the same markers in a combined data set including the 2,504 individuals in the 1000 Genomes database found that most of the 157 Illinois individuals clustered into one group in close proximity to individuals of European descent. A final eigenanalysis of the Illinois individuals with the 503 individuals of European descent (within the 1000 Genomes Project) revealed two clusters of individuals and likely two source populations; one British and one consisting of multiple European subpopulations. We therefore demonstrate the feasibility of examining genetic relatedness across Illinois populations and assessing the number of source populations using publicly available databases. When assessed, population structure information can contribute to the understanding of genetic history in rural populations.

Selinene Volatiles Are Essential Precursors for Maize Defense Promoting Fungal Pathogen Resistance.

To ensure food security, maize (Zea mays) is a model crop for understanding useful traits underlying stress resistance. In contrast to foliar biochemicals, root defenses limiting the spread of disease remain poorly described. To better understand belowground defenses in the field, we performed root metabolomic profiling and uncovered unexpectedly high levels of the sesquiterpene volatile ß-selinene and the corresponding nonvolatile antibiotic derivative ß-costic acid. The application of metabolite-based quantitative trait locus mapping using biparental populations, genome-wide association studies, and near-isogenic lines enabled the identification of terpene synthase21 (ZmTps21) on chromosome 9 as a ß-costic acid pathway candidate gene. Numerous closely examined ß-costic acid-deficient inbred lines were found to harbor Zmtps21 pseudogenes lacking conserved motifs required for farnesyl diphosphate cyclase activity. For biochemical validation, a full-length ZmTps21 was cloned, heterologously expressed in Escherichia coli, and demonstrated to cyclize farnesyl diphosphate, yielding ß-selinene as the dominant product. Consistent with microbial defense pathways, ZmTps21 transcripts strongly accumulate following fungal elicitation. Challenged field roots containing functional ZmTps21 alleles displayed ß-costic acid levels over 100 µg g-1 fresh weight, greatly exceeding in vitro concentrations required to inhibit the growth of five different fungal pathogens and rootworm larvae (Diabrotica balteata). In vivo disease resistance assays, using ZmTps21 and Zmtps21 near-isogenic lines, further support the endogenous antifungal role of selinene-derived metabolites. Involved in the biosynthesis of nonvolatile antibiotics, ZmTps21 exists as a useful gene for germplasm improvement programs targeting optimized biotic stress resistance.