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Abstract An early event in plant organogenesis is establishment of a boundary between the stem cell containing meristem and differentiating lateral organ. In maize (Zea mays), evidence suggests a common gene network functions at boundaries of distinct organs and contributes to pleiotropy between leaf angle and tassel branch number, two agronomic traits. To uncover regulatory variation at the nexus of these two traits, we use regulatory network topologies derived from specific developmental contexts to guide multivariate genome-wide association analyses. In addition to defining network plasticity around core pleiotropic loci, we identify new transcription factors that contribute to phenotypic variation in canopy architecture, and structural variation that contributes tocis-regulatory control of pleiotropy between tassel branching and leaf angle across maize diversity. Results demonstrate the power of informing statistical genetics with context-specific developmental networks to pinpoint pleiotropic loci and theircis-regulatory components, which can be used to fine-tune plant architecture for crop improvement. 

Abstract Plant architecture is a major determinant of planting density, which enhances productivity potential for crops per unit area. Genomic prediction is well positioned to expedite genetic gain of plant architectural traits since they are typically highly heritable. Additionally, the adaptation of genomic prediction models to query predictive abilities of markers tagging certain genomic regions could shed light on the genetic architecture of these traits. Here, we leveraged transcriptional networks from a prior study that contextually described developmental progression during tassel and leaf organogenesis in maize (Zea mays) to inform genomic prediction models for architectural traits. Since these developmental processes underlie tassel branching and leaf angle, 2 important agronomic architectural traits, we tested whether genes prioritized from these networks quantitatively contribute to the genetic architecture of these traits. We used genomic prediction models to evaluate the ability of markers in the vicinity of prioritized network genes to predict breeding values of tassel branching and leaf angle traits for 2 diversity panels in maize and diversity panels from sorghum (Sorghum bicolor) and rice (Oryza sativa). Predictive abilities of markers near these prioritized network genes were similar to those using whole-genome marker sets. Notably, markers near highly connected transcription factors from core network motifs in maize yielded predictive abilities that were significantly greater than expected by chance in not only maize but also closely related sorghum. We expect that these highly connected regulators are key drivers of architectural variation that are conserved across closely related cereal crop species. 

Abstract Genomic regions containing loci with effect sizes that interact with environmental factors are desirable targets for selection because of increasingly unpredictable growing seasons. Although selecting upon such gene‐by‐environment (G × E) loci is vital, identifying significantly associated loci is challenging due to the multiple testing correction. Consequently, G × E loci of small‐ to moderate effect sizes may never be identified via traditional genome‐wide association studies (GWAS). Variance GWAS (vGWAS) have been previously shown to identify G × E loci. Combined with its inherent reduction in the severity of multiple testing, we hypothesized that vGWAS could be successfully used to identify genomic regions likely to contain G × E effects. We used publicly available genotypic and phenotypic data in maize (Zea maysL.) to test the ability of two vGWAS approaches to identify G × E loci controlling two flowering traits. We observed high inflation of from both approaches. This suggests that these two vGWAS approaches are not suitable to the task of identifying G × E loci. We advocate that similar future applications of vGWAS use more sophisticated models that can adequately control the inflation of . Otherwise, the application of vGWAS to search for G × E effects that are critical for combating the effects of climate change will not reach its full potential. 

Abstract The ability to accurately quantify the simultaneous effect of multiple genomic loci on multiple traits is now possible due to current and emerging high‐throughput genotyping and phenotyping technologies. To date, most efforts to quantify these genotype‐to‐phenotype relationships have focused on either multi‐trait models that test a single marker at a time or multi‐locus models that quantify associations with a single trait. Therefore, the purpose of this study was to compare the performance of a multi‐trait, multi‐locus stepwise (MSTEP) model selection procedure we developed to (a) a commonly used multi‐trait single‐locus model and (b) a univariate multi‐locus model. We used real marker data in maize (Zea maysL.) and soybean (Glycine maxL.) to simulate multiple traits controlled by various combinations of pleiotropic and nonpleiotropic quantitative trait nucleotides (QTNs). In general, we found that both multi‐trait models outperformed the univariate multi‐locus model, especially when analyzing a trait of low heritability. For traits controlled by either a combination of pleiotropic and nonpleiotropic QTNs or a large number of QTNs (i.e., 50), our MSTEP model often outperformed at least one of the two alternative models. When applied to the analysis of two tocochromanol‐related traits in maize grain, MSTEP identified the same peak‐associated marker that has been reported in a previous study. We therefore conclude that MSTEP is a useful addition to the suite of statistical models that are commonly used to gain insight into the genetic architecture of agronomically important traits. 

Opportunities for research-based learning at the high school level are limited, and with the COVID-19 pandemic, these have been further reduced. Such opportunities are particularly scarce for authentic research experiences (AREs), which allow students to identify as scientists by collecting data that contributes to scientists’ research. In response to the COVID-19 pandemic, we adapted two of our AREs for classroom settings, as remote independent research experiences for students to conduct from home. User guides and protocols from the AREs, Genotype-to-Phenotype Research with Corn and Discover Volvox Development, were adapted to instruct high school students to work on their own with the guidance of scientists and ARE coordinators. These independent authentic research experiences (IAREs) were implemented in the summer of 2020 and have since been available to students. Student responses to reflection questions and the Laboratory Course Assessment Survey indicate that IAREs provide students with significant gains including learning science content and research practices, collaborating with scientists, facing and resolving challenges, and contributing to scientific research. 

cis-Regulatory elements encode the genomic blueprints that ensure the proper spatiotemporal patterning of gene expression necessary for appropriate development and responses to the environment. Accumulating evidence implicates changes to gene expression as a major source of phenotypic novelty in eukaryotes, including acute phenotypes such as disease and cancer in mammals. Moreover, genetic and epigenetic variation affecting cis-regulatory sequences over longer evolutionary timescales has become a recurring theme in studies of morphological divergence and local adaptation. Here, we discuss the functions of and methods used to identify various classes of cis-regulatory elements, as well as their role in plant development and response to the environment. We highlight opportunities to exploit cis-regulatory variants underlying plant development and environmental responses for crop improvement efforts. Although a comprehensive understanding of cis-regulatory mechanisms in plants has lagged behind that in animals, we showcase several breakthrough findings that have profoundly influenced plant biology and shaped the overall understanding of transcriptional regulation in eukaryotes. 

Significance Statement Narrow odd dwarf ( nod ) and Liguleless narrow ( Lgn ) are pleiotropic maize mutants that severely disrupt growth and development. Here, we share our unexpected discovery that the NOD and LGN proteins physically interact at the plasma membrane. Using a combination of genetics, functional genomics and metabolomics, we then show that nod and Lgn impact overlapping stress–response and developmental patterning pathways in maize. 

Grass inflorescence branching involves the integration of competing signals that regulate leaf and meristem growth.