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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.

 

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.

 

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. 

Abstract. Calculating solar-sensor zenith and azimuth angles for hyperspectral images collected by UAVs are important in terms of conducting bi-directional reflectance function (BRDF) correction or radiative transfer modeling-based applications in remote sensing. These applications are even more necessary to perform high-throughput phenotyping and precision agriculture tasks. This study demonstrates an automated Python framework that can calculate the solar-sensor zenith and azimuth angles for a push-broom hyperspectral camera equipped in a UAV. First, the hyperspectral images were radiometrically and geometrically corrected. Second, the high-precision Global Navigation Satellite System (GNSS) and Inertial Measurement Unit (IMU) data for the flight path was extracted and corresponding UAV points for each pixel were identified. Finally, the angles were calculated using spherical trigonometry and linear algebra. The results show that the solar zenith angle (SZA) and solar azimuth angle (SAA) calculated by our method provided higher precision angular values compared to other available tools. The viewing zenith angle (VZA) was lower near the flight path and higher near the edge of the images. The viewing azimuth angle (VAA) pattern showed higher values to the left and lower values to the right side of the flight line. The methods described in this study is easily reproducible to other study areas and applications. 

Abstract Inflorescence architecture in cereal crops directly impacts yield potential through regulation of seed number and harvesting ability. Extensive architectural diversity found in inflorescences of grass species is due to spatial and temporal activity and determinacy of meristems, which control the number and arrangement of branches and flowers, and underlie plasticity. Timing of the floral transition is also intimately associated with inflorescence development and architecture, yet little is known about the intersecting pathways and how they are rewired during development. Here, we show that a single mutation in a gene encoding an AP1/FUL-like MADS-box transcription factor significantly delays flowering time and disrupts multiple levels of meristem determinacy in panicles of the C4 model panicoid grass, Setaria viridis. Previous reports of AP1/FUL-like genes in cereals have revealed extensive functional redundancy, and in panicoid grasses, no associated inflorescence phenotypes have been described. In S. viridis, perturbation of SvFul2, both through chemical mutagenesis and gene editing, converted a normally determinate inflorescence habit to an indeterminate one, and also repressed determinacy in axillary branch and floral meristems. Our analysis of gene networks connected to disruption of SvFul2 identified regulatory hubs at the intersection of floral transition and inflorescence determinacy, providing insights into the optimization of cereal crop architecture. 

Plant meristems are self-renewing groups of pluripotent stem cells that produce lateral organs in a stereotypical pattern. Of interest is how the radially symmetrical meristem produces laminar lateral organs. Both the male and female inflorescence meristems of the dominant Fascicled ear ( Fas1 ) mutant fail to grow as a single point and instead show deep branching. Positional cloning of two independent Fas1 alleles identified an ∼160 kb region containing two floral genes, the MADS-box gene, zmm8 , and the YABBY gene, drooping leaf2 ( drl2 ). Both genes are duplicated within the Fas1 locus and spatiotemporally misexpressed in the mutant inflorescence meristems. Increased zmm8 expression alone does not affect inflorescence development; however, combined misexpression of zmm8 , drl2 , and their syntenic paralogs zmm14 and drl1 , perturbs meristem organization. We hypothesize that misexpression of the floral genes in the inflorescence and their potential interaction cause ectopic activation of a laminar program, thereby disrupting signaling necessary for maintenance of radially symmetrical inflorescence meristems. Consistent with this hypothesis, RNA sequencing and in situ analysis reveal altered expression patterns of genes that define distinct zones of the meristem and developing leaf. Our findings highlight the importance of strict spatiotemporal patterns of expression for both zmm8 and drl2 and provide an example of phenotypes arising from tandem gene duplications.