Quantitative gene-gene and gene-environment mapping for leaf shape variation using tree-based models

Deep learning-based association analysis of root image data and cucumber yield

The root system is important for the absorption of water and nutrients by plants. Cultivating and selecting a root system architecture (RSA) with good adaptability and ultrahigh productivity have become the primary goals of agricultural improvement. Exploring the correlation between the RSA and crop yield is important for cultivating crop varieties with high-stress resistance and productivity. In this study, 277 cucumber varieties were collected for root system image analysis and yield using germination plates and greenhouse cultivation. Deep learning tools were used to train ResNet50 and U-Net models for image classification and segmentation of seedlings and to perform quality inspection and productivity prediction of cucumber seedling root system images. The results showed that U-Net can automatically extract cucumber root systems with high quality (F1_score ≥ 0.95), and the trained ResNet50 can predict cucumber yield grade through seedling root system image, with the highest F1_score reaching 0.86 using 10-day-old seedlings. The root angle had the strongest correlation with yield, and the shallow- and steep-angle frequencies had significant positive and negative correlations with yield, respectively. RSA and nutrient absorption jointly affected the production capacity of cucumber plants. The germination plate planting method and automated root system segmentation model used in this study are convenient for high-throughput phenotypic (HTP) research on root systems. Moreover, using seedling root system images to predict yield grade provides a new method for rapidly breeding high-yield RSA in crops such as cucumbers.

Morphological and Genomic Differences in the Italian Populations of Onopordum tauricum Willd.-A New Source of Vegetable Rennet

Onopordum tauricum Willd., a species distributed in Eastern Europe, has been the subject of various research endeavors aimed at assessing its suitability for extracting vegetable rennet for use in the production of local cheeses as a substitute for animal-derived rennet. In Italy, the species has an extremely fragmented and localized distribution in six locations scattered across the central-northern Apennines and some areas of southern Italy. In this study, both the morphology and genetic diversity of the six known Italian populations were investigated to detect putative ecotypes. To this end, 33 morphological traits were considered for morphometric measurements, while genetic analysis was conducted on the entire genome using the ddRAD-Seq method. Both analyses revealed significant differences among the Apennine populations (SOL, COL, and VIS) and those from southern Italy (ROT, PES, and LEC). Specifically, the southern Italian populations appear to deviate significantly in some characteristics from the typical form of the species. Therefore, its attribution to O. tauricum is currently uncertain, and further genetic and morphological analyses are underway to ascertain its systematic placement within the genus Onopordum.

Species identification through deep learning and geometrical morphology in oaks (Quercus spp.): Pros and cons

Plant phenotypic characteristics, especially leaf morphology of leaves, are an important indicator for species identification. However, leaf shape can be extraordinarily complex in some species, such as oaks. The great variation in leaf morphology and difficulty of species identification in oaks have attracted the attention of scientists since Charles Darwin. Recent advances in discrimination technology have provided opportunities to understand leaf morphology variation in oaks. Here, we aimed to compare the accuracy and efficiency of species identification in two closely related deciduous oaks by geometric morphometric method (GMM) and deep learning using preliminary identification of simple sequence repeats (nSSRs) as a prior. A total of 538 Asian deciduous oak trees, 16 Q. aliena and 23 Q. dentata populations, were firstly assigned by nSSRs Bayesian clustering analysis to one of the two species or admixture and this grouping served as a priori identification of these trees. Then we analyzed the shapes of 2328 leaves from the 538 trees in terms of 13 characters (landmarks) by GMM. Finally, we trained and classified 2221 leaf-scanned images with Xception architecture using deep learning. The two species can be identified by GMM and deep learning using genetic analysis as a priori. Deep learning is the most cost-efficient method in terms of time-consuming, while GMM can confirm the admixture individuals' leaf shape. These various methods provide high classification accuracy, highlight the application in plant classification research, and are ready to be applied to other morphology analysis.

Enhanced genome-wide association reveals the role of YABBY11-NGATHA-LIKE1 in leaf serration development of Populus

Leaf margins are complex plant morphological features that contribute to leaf shape diversity, which affects plant structure, yield, and adaptation. Although several leaf margin regulators have been identified to date, the genetic basis of their natural variation has not been fully elucidated. In this study, we profiled two distinct leaf morphology types (serrated and smooth) using the persistent homology mathematical framework (PHMF) in two poplar species (Populus tomentosa and Populus simonii, respectively). A combined genome-wide association study (GWAS) and expression quantitative trait nucleotide (eQTN) mapping were applied to create a leaf morphology control module using data from P. tomentosa and P. simonii populations. Natural variation in leaf margins was associated with YABBY11 (YAB11) transcript abundance in poplar. In P. tomentosa, PtoYAB11 carries a premature stop codon (PtoYAB11PSC), resulting in the loss of its positive regulation of NGATHA-LIKE1 (PtoNGAL-1) and RIBULOSE BISPHOSPHATE CARBOXYLASE LARGE SUBUNIT (PtoRBCL). Overexpression of PtoYAB11PSC promoted serrated leaf margins, enlarged leaves, enhanced photosynthesis, and increased biomass. Overexpression of PsiYAB11 in P. tomentosa promoted smooth leaf margins, higher stomatal density, and greater light damage repair ability. In poplar, YAB11-NGAL1 is sensitive to environmental conditions, acts as a positive regulator of leaf margin serration, and may also link environmental signaling to leaf morphological plasticity.

Functional random forests for curve response

The rapid advancement of functional data in various application fields has increased the demand for advanced statistical approaches that can incorporate complex structures and nonlinear associations. In this article, we propose a novel functional random forests (FunFor) approach to model the functional data response that is densely and regularly measured, as an extension of the landmark work of Breiman, who introduced traditional random forests for a univariate response. The FunFor approach is able to predict curve responses for new observations and selects important variables from a large set of scalar predictors. The FunFor approach inherits the efficiency of the traditional random forest approach in detecting complex relationships, including nonlinear and high-order interactions. Additionally, it is a non-parametric approach without the imposition of parametric and distributional assumptions. Eight simulation settings and one real-data analysis consistently demonstrate the excellent performance of the FunFor approach in various scenarios. In particular, FunFor successfully ranks the true predictors as the most important variables, while achieving the most robust variable sections and the smallest prediction errors when comparing it with three other relevant approaches. Although motivated by a biological leaf shape data analysis, the proposed FunFor approach has great potential to be widely applied in various fields due to its minimal requirement on tuning parameters and its distribution-free and model-free nature. An R package named 'FunFor', implementing the FunFor approach, is available at GitHub.

Digital morphometrics: Application of MorphoLeaf in shape visualization and species delimitation, using Cucurbitaceae leaves as a model

PREMISE

Plant leaves are one of the most important organs for plant identification due to their variability across different taxonomic groups. While traditional morphometrics has contributed tremendously to reducing the problems accompanying plant identification and morphology-based species delimitation, image-analysis digital solutions have made it easy to detect more characters to complement existing leaf data sets.

METHODS

Here, we apply MorphoLeaf to generate a morphometric data set from 140 leaf specimens of seven Cucurbitaceae species via landmark extraction, the reparameterization of leaf contours, and data quantification and normalization. A statistical analysis was performed on the resulting data set.

RESULTS

A principal component analysis revealed that leaf blade area, blade perimeter, tooth area, tooth perimeter, the measure of the distance from tooth position to the tip, and the measure of the distance from tooth position to the base are important and informative landmarks that contribute to the variation within the species studied.

DISCUSSION

MorphoLeaf can be applied to quantitatively track leaf diversity, thereby functionally integrating morphometrics and shape visualization into the digital identification of plants. The success of digital morphometrics in leaf outline analyses presents researchers with opportunities to carry out more accurate image-based research in areas such as plant development, evolution, and phenotyping.

Statistical Learning Methods Applicable to Genome-Wide Association Studies on Unbalanced Case-Control Disease Data

Despite the fact that imbalance between case and control groups is prevalent in genome-wide association studies (GWAS), it is often overlooked. This imbalance is getting more significant and urgent as the rapid growth of biobanks and electronic health records have enabled the collection of thousands of phenotypes from large cohorts, in particular for diseases with low prevalence. The unbalanced binary traits pose serious challenges to traditional statistical methods in terms of both genomic selection and disease prediction. For example, the well-established linear mixed models (LMM) yield inflated type I error rates in the presence of unbalanced case-control ratios. In this article, we review multiple statistical approaches that have been developed to overcome the inaccuracy caused by the unbalanced case-control ratio, with the advantages and limitations of each approach commented. In addition, we also explore the potential for applying several powerful and popular state-of-the-art machine-learning approaches, which have not been applied to the GWAS field yet. This review paves the way for better analysis and understanding of the unbalanced case-control disease data in GWAS.

Quercus species divergence is driven by natural selection on evolutionarily less integrated traits

Functional traits are organismal attributes that can respond to environmental cues, thereby providing important ecological functions. In addition, an organism's potential for adaptation is defined by the patterns of covariation among groups of functionally related traits. Whether an organism is evolutionarily constrained or has the potential for adaptation is based on the phenotypic integration or modularity of these traits. Here, we revisited leaf morphology in two European sympatric white oaks (Quercus petraea (Matt.) Liebl. and Quercus robur L.), sampling 2098 individuals, across much of their geographical distribution ranges. At the phenotypic level, leaf morphology traditionally encompasses discriminant attributes among different oak species. Here, we estimated in situ heritability, genetic correlation, and integration across such attributes. Also, we performed Selection Response Decomposition to test these traits for potential differences in oak species' evolutionary responses. Based on the uncovered functional units of traits (modules) in our study, the morphological module "leaf size gradient" was highlighted among functionally integrated traits. Equally, this module was defined in both oaks as being under "global regulation" in vegetative bud establishment and development. Lamina basal shape and intercalary veins' number were not, or, less integrated within the initially defined leaf functional unit, suggesting more than one module within the leaf traits' ensemble. Since these traits generally show the greatest species discriminatory power, they potentially underwent effective differential response to selection among oaks. Indeed, the selection of these traits could have driven the ecological preferences between the two sympatric oaks growing under different microclimates.

Genetic and Developmental Basis for Increased Leaf Thickness in the Arabidopsis Cvi Ecotype

Leaf thickness is a quantitative trait that is associated with the ability of plants to occupy dry, high irradiance environments. Despite its importance, leaf thickness has been difficult to measure reproducibly, which has impeded progress in understanding its genetic basis, and the associated anatomical mechanisms that pattern it. Here, we used a custom-built dual confocal profilometer device to measure leaf thickness in the Arabidopsis Ler × Cvi recombinant inbred line population and found statistical support for four quantitative trait loci (QTL) associated with this trait. We used publically available data for a suite of traits relating to flowering time and growth responses to light quality and show that three of the four leaf thickness QTL coincide with QTL for at least one of these traits. Using time course photography, we quantified the relative growth rate and the pace of rosette leaf initiation in the Ler and Cvi ecotypes. We found that Cvi rosettes grow slower than Ler, both in terms of the rate of leaf initiation and the overall rate of biomass accumulation. Collectively, these data suggest that leaf thickness is tightly linked with physiological status and may present a tradeoff between the ability to withstand stress and rapid vegetative growth. To understand the anatomical basis of leaf thickness, we compared cross-sections of Cvi and Ler leaves and show that Cvi palisade mesophyll cells elongate anisotropically contributing to leaf thickness. Flow cytometry of whole leaves show that endopolyploidy accompanies thicker leaves in Cvi. Overall, our data suggest that mechanistically, an altered schedule of cellular events affecting endopolyploidy and increasing palisade mesophyll cell length contribute to increase of leaf thickness in Cvi. Ultimately, knowledge of the genetic basis and developmental trajectory leaf thickness will inform the mechanisms by which natural selection acts to produce variation in this adaptive trait.