Plant phenotyping is central to understand causal effects of genotypes and environments on trait expression and is a critical factor in expediting plant breeding. Previously, plant phenotypic traits were quantified using invasive, time-consuming, labor-intensive, costinefficient, and often destructive manual sampling methods that were also prone to observer error. In recent years, photogrammetry and image processing techniques have been introduced to plant phenotyping, but cost efficiency issues remain when combining these two techniques within large-scale plant phenotyping studies. Using these high-throughput techniques in basic plant biology research and agriculture are still in the developmental stages but show great promise for rapid phenotyping, which will materially aid both science and crop improvement efforts. In this study, we introduce an automated high-throughput phenotyping pipeline using affordable imaging systems and image processing algorithms to build 2D mosaicked orthophotos. Chamber-based and ground-level field implementations are used to measure phenotypic traits such as leaf length and rosette area in 2D images. Our automated pipeline has cross-platform capabilities and a degree of instrument independence, making it suitable for various situations.

Chromosomal inversions play an important role in local adaptation. Inversions can capture multiple locally adaptive functional variants in a linked block by repressing recombination. However, this recombination suppression makes it difficult to identify the genetic mechanisms that underlie an inversion's role in adaption.In this study, we explore how large-scale transcriptomic data can be used to dissect the functional importance of a 13 Mb inversion locus (Inv4m) found almost exclusively in highland populations of maize (Zea mays ssp. mays). Inv4m introgressed into highland maize from the wild relative Zea mays ssp.mexicana, also present in the highlands of Mexico, and is thought to be important for the adaptation of these populations to cultivation in highland environments. First, using a large publicly available association mapping panel, we confirmed that Inv4m is associated with locally adaptive agronomic phenotypes, but only in highland fields. Second, we created two families segregating for standard and inverted haplotypess of Inv4m in a isogenic B73 background, and measured gene expression variation association with Inv4m across 9 tissues in two experimental conditions. With these data, we quantified both the global transcriptomic effects of the highland Inv4m haplotype, and the local cis-regulatory variation present within the locus. We 1/33 found diverse physiological effects of Inv4m, and speculate that the genetic basis of its effects on adaptive traits is distributed across many separate functional variants. Author SummaryChromosomal inversions are an important type of genomic structural variant. However, mapping causal alleles within their boundaries is difficult because inversions suppress recombination between homologous chromosomes. This means that inversions, regardless of their size, are inherited as a unit. We leveraged the high-dimensional phenotype of gene expression as a tool to study the genetics of a large chromosomal inversion found in highland maize populations in Mexico -Inv4m. We grew plants carrying multiple versions of Inv4m in a common genetic background, and quantified the transcriptional reprogramming induced by alternative alleles at the locus. Inv4m has been shown in previous studies to have a large effect on flowering, but we show that the functional variation within Inv4m affects many developmental and physiological processes.Chromosomal inversions are structural rearrangements that form when a portion of a chromosome breaks in 2 two places and reinserts in the opposite orientation. The reversed order of loci prevent recombination with 3 the non-inverted homologous chromosome, as crossover products are imbalanced and often non-viable [1]. 4This spontaneous, long-distance genetic linkage is important for speciation and local adaptation because it 5 can capture multiple adaptive and potentially interacting loci in a single haplotype [2][3][4]. Inversions are 6 common across taxa [1], often pre-date speciation events, and can spread through admixture [5,6]. They 7 have been link...

Chromosomal inversions play an important role in local adaptation. Inversions can capture multiple locally adaptive functional variants in a linked block by repressing recombination. However, this recombination suppression makes it difficult to identify the genetic mechanisms underlying an inversion’s role in adaptation. In this study, we used large-scale transcriptomic data to dissect the functional importance of a 13 Mb inversion locus (Inv4m) found almost exclusively in highland populations of maize (Zea mays ssp. mays). Inv4m was introgressed into highland maize from the wild relative Zea mays ssp. mexicana, also present in the highlands of Mexico, and is thought to be important for the adaptation of these populations to cultivation in highland environments. However, the specific genetic variants and traits that underlie this adaptation are not known. We created two families segregating for the standard and inverted haplotypes of Inv4m in a common genetic background and measured gene expression effects associated with the inversion across 9 tissues in two experimental conditions. With these data, we quantified both the global transcriptomic effects of the highland Inv4m haplotype, and the local cis-regulatory variation present within the locus. We found diverse physiological effects of Inv4m across the 9 tissues, including a strong effect on the expression of genes involved in photosynthesis and chloroplast physiology. Although we could not confidently identify the causal alleles within Inv4m, this research accelerates progress towards understanding this inversion and will guide future research on these important genomic features.

Conventional phenotyping methods impose a significant bottleneck to the characterization of genotypic and environmental effects on trait expression in plants. In particular, invasive and destructive sampling methods along with manual measurements widely used in conventional studies are labor-intensive, time-consuming, costly, and can lack consistency. These experimental features impede large-scale genetic studies of both crops and wild plant species. Here, we present a high-throughput phenotyping pipeline using photogrammetry and 3D modeling techniques in the model species, Arabidopsis thaliana. We develop novel photogrammetry and computer vision algorithms to quantify 2D and 3D leaf areas for a mapping population of 1050 Arabidopsis thaliana lines, and use 2D areas to analyze plant nastic movements and diurnal cycles. Compared to the 2D leaf areas, 3D leaf areas show an uncorrupted growth trend regardless of plant nastic movement. With optimized algorithms, our pipeline throughput is very computationally efficient for screening a large number of plants. The pipeline not only supports measurement of organ-level growth and development over time, but also enables analysis of whole-plant phenotypes and, thus, identification of genotype-specific performance. Further, the accuracy results evaluating the relationship between physical dimensions and 3D measurements indicate an R 2 = 0.99, and the average 3D area processing time per plant is 0.02 s. Our algorithms provide both high accuracy and throughput in plant phenotyping, thereby, enabling progress in plant genotypic modeling.