Genome-wide association study of rice (Oryza sativa L.) leaf traits with a high-throughput leaf scorer

Genetic response of a perennial grass to warm and wet environments interacts and is associated with trait means as well as plasticity

The potential for rapid evolution is an important mechanism allowing species to adapt to changing climatic conditions. Although such potential has been largely studied in various short-lived organisms, to what extent we can observe similar patterns in long-lived plant species, which often dominate natural systems, is largely unexplored. We explored the potential for rapid evolution in Festuca rubra, a long-lived grass with extensive clonal growth dominating in alpine grasslands. We used a field sowing experiment simulating expected climate change in our model region. Specifically, we exposed seeds from five independent seed sources to novel climatic conditions by shifting them along a natural climatic grid and explored the genetic profiles of established seedlings after 3 years. Data on genetic profiles of plants selected under different novel conditions indicate that different climate shifts select significantly different pools of genotypes from common seed pools. Increasing soil moisture was more important than increasing temperature or the interaction of the two climatic factors in selecting pressure. This can indicate negative genetic interaction in response to the combined effects or that the effects of different climates are interactive rather than additive. The selected alleles were found in genomic regions, likely affecting the function of specific genes or their expression. Many of these were also linked to morphological traits (mainly to trait plasticity), suggesting these changes may have a consequence on plant performance. Overall, these data indicate that even long-lived plant species may experience strong selection by climate, and their populations thus have the potential to rapidly adapt to these novel conditions.

Genome wide association studies on seven yield-related traits of 183 rice varieties in Bangladesh

Rice genetic diversity is regulated by multiple genes and is largely dependent on various environmental factors. Uncovering the genetic variations associated with the diversity in rice populations is the key to breed stable and high yielding rice varieties. We performed genome wide association studies (GWASs) on seven rice yielding traits (grain length, grain width, grain weight, panicle length, leaf length, leaf width, and leaf angle) based on a population of 183 rice landraces of Bangladesh. Our GWASs reveal various chromosomal regions and candidate genes that are associated with different traits in Bangladeshi rice varieties. Noteworthy was the recurrent implication of chromosome 10 in all three grain-shape-related traits (grain length, grain width, and grain weight), indicating its pivotal role in shaping rice grain morphology. Our study also underscores the involvement of transposon gene families across these three traits. For leaf related traits, chromosome 10 was found to harbor regions that are significantly associated with leaf length and leaf width. The results of these association studies support previous findings as well as provide additional insights into the genetic diversity of rice. This is the first known GWAS study on various yield-related traits in the varieties of Oryza sativa available in Bangladesh-the fourth largest rice-producing country. We believe this study will accelerate rice genetics research and breeding stable high-yielding rice in Bangladesh.

GWAS supported by computer vision identifies large numbers of candidate regulators of in planta regeneration in Populus trichocarpa

Plant regeneration is an important dimension of plant propagation and a key step in the production of transgenic plants. However, regeneration capacity varies widely among genotypes and species, the molecular basis of which is largely unknown. Association mapping methods such as genome-wide association studies (GWAS) have long demonstrated abilities to help uncover the genetic basis of trait variation in plants; however, the performance of these methods depends on the accuracy and scale of phenotyping. To enable a large-scale GWAS of in planta callus and shoot regeneration in the model tree Populus, we developed a phenomics workflow involving semantic segmentation to quantify regenerating plant tissues over time. We found that the resulting statistics were of highly non-normal distributions, and thus employed transformations or permutations to avoid violating assumptions of linear models used in GWAS. We report over 200 statistically supported quantitative trait loci (QTLs), with genes encompassing or near to top QTLs including regulators of cell adhesion, stress signaling, and hormone signaling pathways, as well as other diverse functions. Our results encourage models of hormonal signaling during plant regeneration to consider keystone roles of stress-related signaling (e.g. involving jasmonates and salicylic acid), in addition to the auxin and cytokinin pathways commonly considered. The putative regulatory genes and biological processes we identified provide new insights into the biological complexity of plant regeneration, and may serve as new reagents for improving regeneration and transformation of recalcitrant genotypes and species.

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.

An Overview of High-Throughput Crop Phenotyping: Platform, Image Analysis, Data Mining, and Data Management

In this chapter, we explore the application of high-throughput crop phenotyping facilities for phenotype data acquisition and the extraction of significant information from the collected data through image processing and data mining methods. Additionally, the construction and outlook of crop phenotype databases are introduced and the need for global cooperation and data sharing is emphasized. High-throughput crop phenotyping significantly improves accuracy and efficiency compared to traditional measurements, making significant contributions to overcoming bottlenecks in the phenotyping field and advancing crop genetics.

Crop/Plant Modeling Supports Plant Breeding: II. Guidance of Functional Plant Phenotyping for Trait Discovery

Observable morphological traits are widely employed in plant phenotyping for breeding use, which are often the external phenotypes driven by a chain of functional actions in plants. Identifying and phenotyping inherently functional traits for crop improvement toward high yields or adaptation to harsh environments remains a major challenge. Prediction of whole-plant performance in functional-structural plant models (FSPMs) is driven by plant growth algorithms based on organ scale wrapped up with micro-environments. In particular, the models are flexible for scaling down or up through specific functions at the organ nexus, allowing the prediction of crop system behaviors from the genome to the field. As such, by virtue of FSPMs, model parameters that determine organogenesis, development, biomass production, allocation, and morphogenesis from a molecular to the whole plant level can be profiled systematically and made readily available for phenotyping. FSPMs can provide rich functional traits representing biological regulatory mechanisms at various scales in a dynamic system, e.g., Rubisco carboxylation rate, mesophyll conductance, specific leaf nitrogen, radiation use efficiency, and source-sink ratio apart from morphological traits. High-throughput phenotyping such traits is also discussed, which provides an unprecedented opportunity to evolve FSPMs. This will accelerate the co-evolution of FSPMs and plant phenomics, and thus improving breeding efficiency. To expand the great promise of FSPMs in crop science, FSPMs still need more effort in multiscale, mechanistic, reproductive organ, and root system modeling. In summary, this study demonstrates that FSPMs are invaluable tools in guiding functional trait phenotyping at various scales and can thus provide abundant functional targets for phenotyping toward crop improvement.

Investigating high throughput phenotyping based morpho-physiological and biochemical adaptations of indian pennywort (Centella asiatica L. urban) in response to different irrigation regimes

Indian pennywort (Centella asiatica L. Urban; Apiaceae) is a herbaceous plant used as traditional medicine in several regions worldwide. An adequate supply of fresh water in accordance with crop requirements is an important tool for maintaining the productivity and quality of medicinal plants. The objective of this study was to find a suitable irrigation schedule for improving the morphological and physiological characteristics, and crop productivity of Indian pennywort using high-throughput phenotyping. Four treatments were considered based on irrigation schedules (100, 75, 50, and 25% of field capacity denoted by I100 [control], I75, I50, and I25, respectively). The number of leaves, plant perimeter, plant volume, and shoot dry weight were sustained in I75 irrigated plants, whereas adverse effects on plant growth parameters were observed when plants were subjected to I25 irrigation for 21 days. Leaf temperature (Tleaf) was also retained in I75 irrigated plants, when compared with control. An increase of 2.0 °C temperature was detected in the Tleaf of plants under I25 irrigation treatment when compared with control. The increase in Tleaf was attributed to a decreased transpiration rate (R2 = 0.93), leading to an elevated crop water stress index. Green reflectance and leaf greenness remained unchanged in plants under I75 irrigation, while significantly decreased under I50 and I25 irrigation. These decreases were attributed to declined leaf osmotic potential, increased non-photochemical quenching, and inhibition of net photosynthetic rate (Pn). The asiatic acid and total centellosides in the leaf tissues, and centellosides yield of plants under I75 irrigation were retained when compared with control, while these parameters were regulated to maximal when exposed to I50 irrigation. Based on the results, I75 irrigation treatment was identified as the optimum irrigation schedule for Indian pennywort in terms of sustained biomass and a stable total centellosides. However, further validation in the field trials at multiple locations and involving different crop rotations is recommended to confirm these findings.

Genetic architecture of leaf morphology revealed by integrated trait module in Catalpa bungei

Leaves are crucial for maintaining plant growth and development via photosynthesis, and their function is simultaneously regulated by a suite of phenotypic traits. Although much is known about the genetic architecture of individual leaf traits, unraveling the genetic basis of complex leaf morphology remains a challenge. Based on the functional correlation and coordination of multi-traits, we divided 15 leaf morphological traits into three modules, comprising size (area, length, width, and perimeter), shape (leaf lobes, aspect ratio, circularity, rectangularity, and the relevant ratios), and color (red, green, and blue) for an ornamental tree species, Catalpa bungei. A total of 189 significant single-nucleotide polymorphisms were identified in the leaves of C. bungei: 35, 82, and 76 in the size, shape, and color modules, respectively. Four quantitative trait loci were common between the size and shape modules, which were closely related according to phenotype correlation, genetic mapping, and mRNA analysis. The color module was independent of them. Synergistic changes in the aspect ratio, leaf lobe, and circularity suggest that these traits could be the core indicators of the leaf shape module. The LAS and SRK genes, associated with leaf lobe and circularity, were found to function in plant defense mechanisms and the growth of leaves. The associations between the SRK and CRK2 genes and the leaf lobe and circularity traits were further verified by RT-qPCR. Our findings demonstrate the importance of integrating multi-trait modules to characterize leaf morphology and facilitate a holistic understanding of the genetic architecture of intraspecific leaf morphology diversity.

Revitalization of small millets for nutritional and food security by advanced genetics and genomics approaches

Small millets, also known as nutri-cereals, are smart foods that are expected to dominate food industries and diets to achieve nutritional security. Nutri-cereals are climate resilient and nutritious. Small millet-based foods are becoming popular in markets and are preferred for patients with celiac and diabetes. These crops once ruled as food and fodder but were pushed out of mainstream cultivation with shifts in dietary habits to staple crops during the green revolution. Nevertheless, small millets are rich in micronutrients and essential amino acids for regulatory activities. Hence, international and national organizations have recently aimed to restore these lost crops for their desirable traits. The major goal in reviving these crops is to boost the immune system of the upcoming generations to tackle emerging pandemics and disease infestations in crops. Earlier periods of civilization consumed these crops, which had a greater significance in ethnobotanical values. Along with nutrition, these crops also possess therapeutic traits and have shown vast medicinal use in tribal communities for the treatment of diseases like cancer, cardiovascular disease, and gastrointestinal issues. This review highlights the significance of small millets, their values in cultural heritage, and their prospects. Furthermore, this review dissects the nutritional and therapeutic traits of small millets for developing sustainable diets in near future.

Multi-model genome-wide association studies of leaf anatomical traits and vein architecture in rice

Introduction

The anatomy of rice leaves is closely related to photosynthesis and grain yield. Therefore, exploring insight into the quantitative trait loci (QTLs) and alleles related to rice flag leaf anatomical and vein traits is vital for rice improvement.

Methods

Here, we aimed to explore the genetic architecture of eight flag leaf traits using one single-locus model; mixed-linear model (MLM), and two multi-locus models; fixed and random model circulating probability unification (FarmCPU) and Bayesian information and linkage disequilibrium iteratively nested keyway (BLINK). We performed multi-model GWAS using 329 rice accessions of RDP1 with 700K single-nucleotide polymorphisms (SNPs) markers.

Results

The phenotypic correlation results indicated that rice flag leaf thickness was strongly correlated with leaf mesophyll cells layer (ML) and thickness of both major and minor veins. All three models were able to identify several significant loci associated with the traits. MLM identified three non-synonymous SNPs near NARROW LEAF 1 (NAL1) in association with ML and the distance between minor veins (IVD) traits.

Discussion

Several numbers of significant SNPs associated with known gene function in leaf development and yield traits were detected by multi-model GWAS performed in this study. Our findings indicate that flag leaf traits could be improved via molecular breeding and can be one of the targets in high-yield rice development.

Leaf Count Aided Novel Framework for Rice (Oryza sativa L.) Genotypes Discrimination in Phenomics: Leveraging Computer Vision and Deep Learning Applications

Drought is a detrimental factor to gaining higher yields in rice (Oryza sativa L.), especially amid the rising occurrence of drought across the globe. To combat this situation, it is essential to develop novel drought-resilient varieties. Therefore, screening of drought-adaptive genotypes is required with high precision and high throughput. In contemporary emerging science, high throughput plant phenotyping (HTPP) is a crucial technology that attempts to break the bottleneck of traditional phenotyping. In traditional phenotyping, screening significant genotypes is a tedious task and prone to human error while measuring various plant traits. In contrast, owing to the potential advantage of HTPP over traditional phenotyping, image-based traits, also known as i-traits, were used in our study to discriminate 110 genotypes grown for genome-wide association study experiments under controlled (well-watered), and drought-stress (limited water) conditions, under a phenomics experiment in a controlled environment with RGB images. Our proposed framework non-destructively estimated drought-adaptive plant traits from the images, such as the number of leaves, convex hull, plant-aspect ratio (plant spread), and similarly associated geometrical and morphological traits for analyzing and discriminating genotypes. The results showed that a single trait, the number of leaves, can also be used for discriminating genotypes. This critical drought-adaptive trait was associated with plant size, architecture, and biomass. In this work, the number of leaves and other characteristics were estimated non-destructively from top view images of the rice plant for each genotype. The estimation of the number of leaves for each rice plant was conducted with the deep learning model, YOLO (You Only Look Once). The leaves were counted by detecting corresponding visible leaf tips in the rice plant. The detection accuracy was 86-92% for dense to moderate spread large plants, and 98% for sparse spread small plants. With this framework, the susceptible genotypes (MTU1010, PUSA-1121 and similar genotypes) and drought-resistant genotypes (Heera, Anjali, Dular and similar genotypes) were grouped in the core set with a respective group of drought-susceptible and drought-tolerant genotypes based on the number of leaves, and the leaves' emergence during the peak drought-stress period. Moreover, it was found that the number of leaves was significantly associated with other pertinent morphological, physiological and geometrical traits. Other geometrical traits were measured from the RGB images with the help of computer vision.

Dissecting the Genetic Structure of Maize Leaf Sheaths at Seedling Stage by Image-Based High-Throughput Phenotypic Acquisition and Characterization

The rapid development of high-throughput phenotypic detection techniques makes it possible to obtain a large number of crop phenotypic information quickly, efficiently, and accurately. Among them, image-based phenotypic acquisition method has been widely used in crop phenotypic identification and characteristic research due to its characteristics of automation, non-invasive, non-destructive and high throughput. In this study, we proposed a method to define and analyze the traits related to leaf sheaths including morphology-related, color-related and biomass-related traits at V6 stage. Next, we analyzed the phenotypic variation of leaf sheaths of 418 maize inbred lines based on 87 leaf sheath-related phenotypic traits. In order to further analyze the mechanism of leaf sheath phenotype formation, 25 key traits (2 biomass-related, 19 morphology-related and 4 color-related traits) with heritability greater than 0.3 were analyzed by genome-wide association studies (GWAS). And 1816 candidate genes of 17 whole plant leaf sheath traits and 1,297 candidate genes of 8 sixth leaf sheath traits were obtained, respectively. Among them, 46 genes with clear functional descriptions were annotated by single nucleotide polymorphism (SNPs) that both Top1 and multi-method validated. Functional enrichment analysis results showed that candidate genes of leaf sheath traits were enriched into multiple pathways related to cellular component assembly and organization, cell proliferation and epidermal cell differentiation, and response to hunger, nutrition and extracellular stimulation. The results presented here are helpful to further understand phenotypic traits of maize leaf sheath and provide a reference for revealing the genetic mechanism of maize leaf sheath phenotype formation.

Drought Tolerant Near Isogenic Lines of Pusa 44 Pyramided With qDTY2.1 and qDTY3.1, Show Accelerated Recovery Response in a High Throughput Phenomics Based Phenotyping

Reproductive stage drought stress (RSDS) is a major challenge in rice production worldwide. Cultivar development with drought tolerance has been slow due to the lack of precise high throughput phenotyping tools to quantify drought stress-induced effects. Most of the available techniques are based on destructive sampling and do not assess the progress of the plant's response to drought. In this study, we have used state-of-the-art image-based phenotyping in a phenomics platform that offers a controlled environment, non-invasive phenotyping, high accuracy, speed, and continuity. In rice, several quantitative trait loci (QTLs) which govern grain yield under drought determine RSDS tolerance. Among these, qDTY2.1 and qDTY3.1 were used for marker-assisted breeding. A set of 35 near-isogenic lines (NILs), introgressed with these QTLs in the popular variety, Pusa 44 were used to assess the efficiency of image-based phenotyping for RSDS tolerance. NILs offered the most reliable contrast since they differed from Pusa 44 only for the QTLs. Four traits, namely, the projected shoot area (PSA), water use (WU), transpiration rate (TR), and red-green-blue (RGB) and near-infrared (NIR) values were used. Differential temporal responses could be seen under drought, but not under unstressed conditions. NILs showed significant level of RSDS tolerance as compared to Pusa 44. Among the traits, PSA showed strong association with yield (80%) as well as with two drought tolerances indices, stress susceptibility index (SSI) and tolerance index (TOL), establishing its ability in identifying the best drought tolerant NILs. The results revealed that the introgression of QTLs helped minimize the mean WU per unit of biomass per day, suggesting the potential role of these QTLs in improving WU-efficiency (WUE). We identified 11 NILs based on phenomics traits as well as performance under imposed drought in the field. The study emphasizes the use of phenomics traits as selection criteria for RSDS tolerance at an early stage, and is the first report of using phenomics parameters in RSDS selection in rice.

Advanced high-throughput plant phenotyping techniques for genome-wide association studies: A review

Linking phenotypes and genotypes to identify genetic architectures that regulate important traits is crucial for plant breeding and the development of plant genomics. In recent years, genome-wide association studies (GWASs) have been applied extensively to interpret relationships between genes and traits. Successful GWAS application requires comprehensive genomic and phenotypic data from large populations. Although multiple high-throughput DNA sequencing approaches are available for the generation of genomics data, the capacity to generate high-quality phenotypic data is lagging far behind. Traditional methods for plant phenotyping mostly rely on manual measurements, which are laborious, inaccurate, and time-consuming, greatly impairing the acquisition of phenotypic data from large populations. In contrast, high-throughput phenotyping has unique advantages, facilitating rapid, non-destructive, and high-throughput detection, and, in turn, addressing the shortcomings of traditional methods. Aim of Review: This review summarizes the current status with regard to the integration of high-throughput phenotyping and GWAS in plants, in addition to discussing the inherent challenges and future prospects. Key Scientific Concepts of Review: High-throughput phenotyping, which facilitates non-contact and dynamic measurements, has the potential to offer high-quality trait data for GWAS and, in turn, to enhance the unraveling of genetic structures of complex plant traits. In conclusion, high-throughput phenotyping integration with GWAS could facilitate the revealing of coding information in plant genomes.

PocketMaize: An Android-Smartphone Application for Maize Plant Phenotyping

A low-cost portable wild phenotyping system is useful for breeders to obtain detailed phenotypic characterization to identify promising wild species. However, compared with the larger, faster, and more advanced in-laboratory phenotyping systems developed in recent years, the progress for smaller phenotyping systems, which provide fast deployment and potential for wide usage in rural and wild areas, is quite limited. In this study, we developed a portable whole-plant on-device phenotyping smartphone application running on Android that can measure up to 45 traits, including 15 plant traits, 25 leaf traits and 5 stem traits, based on images. To avoid the influence of outdoor environments, we trained a DeepLabV3+ model for segmentation. In addition, an angle calibration algorithm was also designed to reduce the error introduced by the different imaging angles. The average execution time for the analysis of a 20-million-pixel image is within 2,500 ms. The application is a portable on-device fast phenotyping platform providing methods for real-time trait measurement, which will facilitate maize phenotyping in field and benefit crop breeding in future.

QTL mapping and candidate gene mining of flag leaf size traits in Japonica rice based on linkage mapping and genome-wide association study

BACKGROUND

As one of the most important factors of the japonica rice plant, leaf shape affects the photosynthesis and carbohydrate accumulation directly. Mining and using new leaf shape related genes/QTLs can further enrich the theory of molecular breeding and accelerate the breeding process of japonica rice.

METHODS

In the present study, 2 RILs and a natural population with 295 japonica rice varieties were used to map QTLs for flag leaf length (FL), flag leaf width (FW) and flag leaf area (FLA) by linkage analysis and genome-wide association study (GWAS) throughout 2 years.

RESULTS

A total of 64 QTLs were detected by 2 ways, and pleiotropic QTLs qFL2 (Chr2_33,332,579) and qFL10 (Chr10_10,107,835; Chr10_10,230,100) consisted of overlapping QTLs mapped by linkage analysis and GWAS throughout the 2 years were identified.

CONCLUSIONS

The candidate genes LOC_Os02g54254, LOC_Os02g54550, LOC_Os10g20160, LOC_Os10g20240, LOC_Os10g20260 were obtained, filtered by linkage disequilibrium (LD), and haplotype analysis. LOC_Os10g20160 (SD-RLK-45) showed outstanding characteristics in quantitative real-time PCR (qRT-PCR) analysis in leaf development period, belongs to S-domain receptor-like protein kinases gene and probably to be a main gene regulating flag leaf width of japonica rice. The results of this study provide valuable resources for mining the main genes/QTLs of japonica rice leaf development and molecular breeding of japonica rice ideal leaf shape.

Genome-wide association study reveals the genetic architecture of 27 agronomic traits in tomato

Tomato (Solanum lycopersicum) is a highly valuable fruit crop, and yield is one of the most important agronomic traits. However, the genetic architecture underlying tomato yield-related traits has not been fully addressed. Based on ∼4.4 million single nucleotide polymorphisms obtained from 605 diverse accessions, we performed a comprehensive genome-wide association study for 27 agronomic traits in tomato. A total of 239 significant associations corresponding to 129 loci, harboring many previously reported and additional genes related to vegetative and reproductive development, were identified, and these loci explained an average of ∼8.8% of the phenotypic variance. A total of 51 loci associated with 25 traits have been under selection during tomato domestication and improvement. Furthermore, a candidate gene, Sl-ACTIVATED MALATE TRANSPORTER15, that encodes an aluminum-activated malate transporter was functionally characterized and shown to act as a pivotal regulator of leaf stomata formation, thereby affecting photosynthesis and drought resistance. This study provides valuable information for tomato genetic research and breeding.

Genome-wide association study of cyanogenic glycosides, proline, sugars, and pigments in Eucalyptus cladocalyx after 18 consecutive dry summers

Natural variation of cyanogenic glycosides, soluble sugars, proline, and nondestructive optical sensing of pigments (chlorophyll, flavonols, and anthocyanins) was examined in ex situ natural populations of Eucalyptus cladocalyx F. Muell. grown under dry environmental conditions in the southern Atacama Desert, Chile. After 18 consecutive dry seasons, considerable plant-to-plant phenotypic variation for all the traits was observed in the field. For example, leaf hydrogen cyanide (HCN) concentrations varied from 0 (two acyanogenic individuals) to 1.54 mg cyanide g^-1 DW. Subsequent genome-wide association study revealed associations with several genes with a known function in plants. HCN content was associated robustly with genes encoding Cytochrome P450 proteins, and with genes involved in the detoxification mechanism of HCN in cells (β-cyanoalanine synthase and cyanoalanine nitrilase). Another important finding was that sugars, proline, and pigment content were linked to genes involved in transport, biosynthesis, and/or catabolism. Estimates of genomic heritability (based on haplotypes) ranged between 0.46 and 0.84 (HCN and proline content, respectively). Proline and soluble sugars had the highest predictive ability of genomic prediction models (PA = 0.65 and PA = 0.71, respectively). PA values for HCN content and flavonols were relatively moderate, with estimates ranging from 0.44 to 0.50. These findings provide new understanding on the genetic architecture of cyanogenic capacity, and other key complex traits in cyanogenic E. cladocalyx.

High-throughput phenotyping analysis of maize at the seedling stage using end-to-end segmentation network

Image processing technologies are available for high-throughput acquisition and analysis of phenotypes for crop populations, which is of great significance for crop growth monitoring, evaluation of seedling condition, and cultivation management. However, existing methods rely on empirical segmentation thresholds, thus can have insufficient accuracy of extracted phenotypes. Taking maize as an example crop, we propose a phenotype extraction approach from top-view images at the seedling stage. An end-to-end segmentation network, named PlantU-net, which uses a small amount of training data, was explored to realize automatic segmentation of top-view images of a maize population at the seedling stage. Morphological and color related phenotypes were automatic extracted, including maize shoot coverage, circumscribed radius, aspect ratio, and plant azimuth plane angle. The results show that the approach can segment the shoots at the seedling stage from top-view images, obtained either from the UAV or tractor-based high-throughput phenotyping platform. The average segmentation accuracy, recall rate, and F1 score are 0.96, 0.98, and 0.97, respectively. The extracted phenotypes, including maize shoot coverage, circumscribed radius, aspect ratio, and plant azimuth plane angle, are highly correlated with manual measurements (R2 = 0.96-0.99). This approach requires less training data and thus has better expansibility. It provides practical means for high-throughput phenotyping analysis of early growth stage crop populations.

Genome-Wide Association Studies of Salt Tolerance at Seed Germination and Seedling Stages in Brassica napus

Most crops are sensitive to salt stress, but their degree of susceptibility varies among species and cultivars. In order to understand the salt stress adaptability of Brassica napus to salt stress, we collected the phenotypic data of 505 B. napus accessions at the germination stage under 150 or 215 mM sodium chloride (NaCl) and at the seedling stage under 215 mM NaCl. Genome-wide association studies (GWAS) of 16 salt tolerance coefficients (STCs) were applied to investigate the genetic basis of salt stress tolerance of B. napus. In this study, we mapped 31 salts stress-related QTLs and identified 177 and 228 candidate genes related to salt stress tolerance were detected at germination and seedling stages, respectively. Overexpression of two candidate genes, BnCKX5 and BnERF3 overexpression, were found to increase the sensitivity to salt and mannitol stresses at the germination stage. This study demonstrated that it is a feasible method to dissect the genetic basis of salt stress tolerance at germination and seedling stages in B. napus by GWAS, which provides valuable loci for improving the salt stress tolerance of B. napus. Moreover, these candidate genes are rich genetic resources for the following exploration of molecular mechanisms in adaptation to salt stress in B. napus.

Applications of Multi-Omics Technologies for Crop Improvement

Multiple "omics" approaches have emerged as successful technologies for plant systems over the last few decades. Advances in next-generation sequencing (NGS) have paved a way for a new generation of different omics, such as genomics, transcriptomics, and proteomics. However, metabolomics, ionomics, and phenomics have also been well-documented in crop science. Multi-omics approaches with high throughput techniques have played an important role in elucidating growth, senescence, yield, and the responses to biotic and abiotic stress in numerous crops. These omics approaches have been implemented in some important crops including wheat (Triticum aestivum L.), soybean (Glycine max), tomato (Solanum lycopersicum), barley (Hordeum vulgare L.), maize (Zea mays L.), millet (Setaria italica L.), cotton (Gossypium hirsutum L.), Medicago truncatula, and rice (Oryza sativa L.). The integration of functional genomics with other omics highlights the relationships between crop genomes and phenotypes under specific physiological and environmental conditions. The purpose of this review is to dissect the role and integration of multi-omics technologies for crop breeding science. We highlight the applications of various omics approaches, such as genomics, transcriptomics, proteomics, metabolomics, phenomics, and ionomics, and the implementation of robust methods to improve crop genetics and breeding science. Potential challenges that confront the integration of multi-omics with regard to the functional analysis of genes and their networks as well as the development of potential traits for crop improvement are discussed. The panomics platform allows for the integration of complex omics to construct models that can be used to predict complex traits. Systems biology integration with multi-omics datasets can enhance our understanding of molecular regulator networks for crop improvement. In this context, we suggest the integration of entire omics by employing the "phenotype to genotype" and "genotype to phenotype" concept. Hence, top-down (phenotype to genotype) and bottom-up (genotype to phenotype) model through integration of multi-omics with systems biology may be beneficial for crop breeding improvement under conditions of environmental stresses.

Dissecting the phenotypic components and genetic architecture of maize stem vascular bundles using high-throughput phenotypic analysis

High-throughput phenotyping is increasingly becoming an important tool for rapid advancement of genetic gain in breeding programmes. Manual phenotyping of vascular bundles is tedious and time-consuming, which lags behind the rapid development of functional genomics in maize. More robust and automated techniques of phenotyping vascular bundles traits at high-throughput are urgently needed for large crop populations. In this study, we developed a standard process for stem micro-CT data acquisition and an automatic CT image process pipeline to obtain vascular bundle traits of stems including geometry-related, morphology-related and distribution-related traits. Next, we analysed the phenotypic variation of stem vascular bundles between natural population subgroup (480 inbred lines) based on 48 comprehensively phenotypic information. Also, the first database for stem micro-phenotypes, MaizeSPD, was established, storing 554 pieces of basic information of maize inbred lines, 523 pieces of experimental information, 1008 pieces of CT scanning images and processed images, and 24 192 pieces of phenotypic data. Combined with genome-wide association studies (GWASs), a total of 1562 significant single nucleotide polymorphism (SNPs) were identified for 30 stem micro-phenotypic traits, and 84 unique genes of 20 traits such as VBNum, VBAvArea and PZVBDensity were detected. Candidate genes identified by GWAS mainly encode enzymes involved in cell wall metabolism, transcription factors, protein kinase and protein related to plant signal transduction and stress response. The results presented here will advance our knowledge about phenotypic trait components of stem vascular bundles and provide useful information for understanding the genetic controls of vascular bundle formation and development.

Phenomics-based GWAS analysis reveals the genetic architecture for drought resistance in cotton

Drought resistance (DR) is a complex trait that is regulated by a variety of genes. Without comprehensive profiling of DR-related traits, the knowledge of the genetic architecture for DR in cotton remains limited. Thus, there is a need to bridge the gap between genomics and phenomics. In this study, an automatic phenotyping platform (APP) was systematically applied to examine 119 image-based digital traits (i-traits) during drought stress at the seedling stage, across a natural population of 200 representative upland cotton accessions. Some novel i-traits, as well as some traditional i-traits, were used to evaluate the DR in cotton. The phenomics data allowed us to identify 390 genetic loci by genome-wide association study (GWAS) using 56 morphological and 63 texture i-traits. DR-related genes, including GhRD2, GhNAC4, GhHAT22 and GhDREB2, were identified as candidate genes by some digital traits. Further analysis of candidate genes showed that Gh_A04G0377 and Gh_A04G0378 functioned as negative regulators for cotton drought response. Based on the combined digital phenotyping, GWAS analysis and transcriptome data, we conclude that the phenomics dataset provides an excellent resource to characterize key genetic loci with an unprecedented resolution which can inform future genome-based breeding for improved DR in cotton.

A genome-wide association study reveals the quantitative trait locus and candidate genes that regulate phosphate efficiency in a Vietnamese rice collection

The crucial role of phosphate (Pi) for plant alongside the expected depletion of non-renewable phosphate rock have created an urgent need for phosphate-efficient rice varieties. In this study, 157 greenhouse-grown Vietnamese rice landraces were treated under Pi-deficient conditions to discover the genotypic variation among biochemical traits, including relative efficiency of phosphorus use (REP), relative root to shoot weight ratio (RRSR), relative physiological phosphate use efficiency (RPPUE), and relative phosphate uptake efficiency (RPUpE). Plants were grown in Yoshida nutrient media with either a full (320 μM) or a low Pi supply (10 μM) over six weeks. This genome-wide association study led to the discovery of 31 significant single nucleotide polymorphisms, 18 quantitative trait loci (QTLs), and 85 candidate genes. A common QTL named qRPUUE9.16 was found among the three investigated traits. Some interesting candidate genes, such as PLASMA MEMBRANE PROTEIN1 (OsPM1), CALMODULIN-RELATED CALCIUM SENSOR PROTEIN 15 (OsCML15), phosphatases 2C (PP2C), STRESS-ACTIVATED PROTEIN KINASE (OsSAPK2), and GLYCEROPHOSPHORYL DIESTER PHOSPHODIESTERASES (GDPD13), were found strongly correlated to the Pi starvation. RNA sequencing transcriptomes revealed that 45 out of 85 candidate genes were significantly regulated under Pi starvation. Furthermore, nearly two-thirds of genotypes did not possess the OsPsTOL1 gene; however, no significant difference was observed in response to Pi deficiency between genotypes with or without this gene, suggesting that other QTLs in rice may resist Pi starvation. These results provide new information on the genetics of nutrient use efficiency in rice and may potentially assist with developing more phosphate-efficient rice plants.

High-throughput phenotyping platform for analyzing drought tolerance in rice

A new imaging platform was constructed to analyze drought-tolerant traits of rice. Rice was used to quantify drought phenotypes through image-based parameters and analyzing tools. Climate change has increased the frequency and severity of drought, which limits crop production worldwide. Developing new cultivars with increased drought tolerance and short breeding cycles is critical. However, achieving this goal requires phenotyping a large number of breeding populations in a short time and in an accurate manner. Novel cutting-edge technologies such as those based on remote sensors are being applied to solve this problem. In this study, new technologies were applied to obtain and analyze imaging data and establish efficient screening platforms for drought tolerance in rice using the drought-tolerant mutant osphyb. Red-Green-Blue images were used to predict plant area, color, and compactness. Near-infrared imaging was used to determine the water content of rice, infrared was used to assess plant temperature, and fluorescence was used to examine photosynthesis efficiency. DroughtSpotter technology was used to determine water use efficiency, plant water loss rate, and transpiration rate. The results indicate that these methods can detect the difference between tolerant and susceptible plants, suggesting their value as high-throughput phenotyping methods for short breeding cycles as well as for functional genetic studies of tolerance to drought stress.

Grain Yields and Nitrogen Use Efficiencies in Different Types of Stay-Green Maize in Response to Nitrogen Fertilizer

The stay-green leaf phenotype is typically associated with increased yields and improved stress resistance in maize breeding, due to higher nitrogen (N) nutrient levels that prolong greenness. The application of N fertilizer can regulate the N status of plants, and furthermore, impact the photosynthetic rates of leaves at the productive stage; however, N deficiencies and N excesses will reduce maize yields. Consequently, it is necessary to develop N fertilizer management strategies for different types of stay-green maize. For this study, the senescent cultivar Lianchuang 808 (LC808), moderate-stay-green cultivar Zhengdan 958 (ZD958), and over stay-green cultivar Denghai 685 (DH685) were selected as experimental models. Our results revealed that yields of ZD958 were slightly higher than DH685 and notably improved over than LC808. Compared with a non-stay-green cultivar LC808, ZD958 and DH685 still maintained higher chlorophyll contents and cell activities following the silking stage, while efficiently slowing the senescence rate. The supply of N fertilizer significantly prolonged leaf greenness and delayed senescence for ZD958 and DH685; however, the effect was not obvious for LC808. The stem remobilization efficiency of N was higher in the moderate-stay-green cultivar ZD958, in contrast to LC808, while the transfer of leaf N was lower than LC808, which guaranteed high leaf N levels, and that sufficient N was transferred to grains in ZD958. To obtain the highest yields, the optimal N fertilizer rates were 228.1 kg hm⁻² for LC0808, 180 kg hm⁻² for ZD958, and 203.8 kg hm⁻² for DH685. In future, the selection of stay-green type crops might serve as an important agricultural strategy to reduce the quantity of N fertilizer and increase N efficiency.

Crop Phenomics and High-Throughput Phenotyping: Past Decades, Current Challenges, and Future Perspectives

Since whole-genome sequencing of many crops has been achieved, crop functional genomics studies have stepped into the big-data and high-throughput era. However, acquisition of large-scale phenotypic data has become one of the major bottlenecks hindering crop breeding and functional genomics studies. Nevertheless, recent technological advances provide us potential solutions to relieve this bottleneck and to explore advanced methods for large-scale phenotyping data acquisition and processing in the coming years. In this article, we review the major progress on high-throughput phenotyping in controlled environments and field conditions as well as its use for post-harvest yield and quality assessment in the past decades. We then discuss the latest multi-omics research combining high-throughput phenotyping with genetic studies. Finally, we propose some conceptual challenges and provide our perspectives on how to bridge the phenotype-genotype gap. It is no doubt that accurate high-throughput phenotyping will accelerate plant genetic improvements and promote the next green revolution in crop breeding.

Mining of favorable alleles for seed reserve utilization efficiency in Oryza sativa by means of association mapping

Background

Wet direct-seeded rice is a possible alternative to conventional puddled transplanted rice; the former uses less water and reduces labor requirements. Improving seed reserve utilization efficiency (SRUE) is a key factor in facilitating the application of this technology. However, the QTLs controlling this trait are poorly investigated. In this study, a genome-wide association study (GWAS) was conducted using a natural population composed of 542 accessions of rice (Oryza sativa L.) which were genotyped using 266 SSR markers. Large phenotypic variations in SRUE were found in the studied population.

Results

The average SRUE over 542 accessions across two years (2016 and 2017) was 0.52 mg.mg^− 1, ranging from 0.22 mg.mg-¹ to 0.93 mg.mg^− 1, with a coefficient of variation of 22.66%. Overall, 2879 marker alleles were detected in the population by 266 pairs of SSR markers, indicating a large genetic variation existing in the population. Using general linear model method, 13 SSR marker loci associated with SRUE were detected and two (RM7309 and RM434) of the 13 loci, were also detected using mixed linear model analyses, with percentage of phenotypic variation explained (PVE) greater than 5% across two years. The 13 association loci (P < 0.01) were located on all chromosomes except chromosome 11, with PVE ranging from 5.05% (RM5158 on chromosome 5) to 12% (RM297 on chromosome 1). Association loci RM7309 on chromosome 6 and RM434 on chromosome 9 revealed by both models were detected in both years. Twenty-three favorable alleles were identified with phenotypic effect values (PEV) ranging from 0.10 mg.mg^− 1 (RM7309–135 bp on chromosome 9) to 0.45 mg.mg^− 1 (RM297–180 bp on chromosome 2). RM297–180 bp showed the largest phenotypic effect value (0.44 mg.mg^− 1 in 2016 and 0.45 mg.mg^− 1 in 2017) with 6.72% of the accessions carrying this allele and the typical carrier accession was Manyedao, followed by RM297–175 bp (0.43 mg.mg^− 1 in 2016 and 0.44 mg.mg^− 1 in 2017).

Conclusion

Nine novel association loci for SRUE were identified, compared with previous studies. The optimal parental combinations for pyramiding more favorable alleles for SRUE were selected and could be used for breeding rice accessions suitable for wet direct seeding in the future.

Effects of Flag Leaf and Number of Vegetative Ramets on Sexual Reproductive Performance in the Clonal Grass Leymus chinensis

Sexual reproduction is vital for population adaptation in clonal plants. The flag leaf is considered to be the primary contributor to sexual reproduction in cereal crops, and there is no unified conclusion on the effect of the number of vegetative ramets on grain yield. However, what effects of the flag leaf and the number of vegetative ramets on sexual reproductive performance of clonal grasses are largely unknown. To test this, under field natural conditions, we grew the rhizomatous grass Leymus chinensis in a homogeneous environment and conducted studies concerning the growth, reproduction and physiology of reproductive ramets in clonal populations. We measured the growth characteristics of different aged leaves, dynamically measured the net photosynthetic rate of different aged leaves and organ biomass, measured the sexual reproductive characteristics of reproductive ramets that had different numbers of connecting vegetative ramets, and performed isotope (¹⁵N) labeling of ramet pairs at the seed-filling stage. In L. chinensis clonal populations, from the heading stage, the photosynthetic contribution of the functional leaves to seed production was much greater than that of the flag leaf; the photosynthetic capacity of both the functional leaves and the flag leaf all gradually declined. Vegetative ramets translocated their own resources to the connected reproductive ramets, and a large proportion of translocated resources were allocated to the leaf and stem to sustain life activities; increase in the number of connecting vegetative ramets increased floret number, seed number, seed-setting rate, inflorescence biomass, seed biomass, and reproductive allocation of reproductive ramets, and these parameters significantly and positively correlated with the biomass of connecting vegetative ramets. We conclude that the functional leaf rather than the flag leaf of L. chinensis is the primary contributor to seed production. Reproductive ramets adopt a strategy of growth first and reproduction later to allocate the translocated resources between the organs, but vegetative ramets are very advantageous for sexual reproduction under the tillering node connection form in L. chinensis. Overall, our study implies that vegetative ramets not only play an important role in the spatial expansion but also in the sexual reproduction of clonal plant populations.

Genome-wide association mapping of leaf mass traits in a Vietnamese rice landrace panel

Leaf traits are often strongly correlated with yield, which poses a major challenge in rice breeding. In the present study, using a panel of Vietnamese rice landraces genotyped with 21,623 single-nucleotide polymorphism markers, a genome-wide association study (GWAS) was conducted for several leaf traits during the vegetative stage. Vietnamese landraces are often poorly represented in panels used for GWAS, even though they are adapted to contrasting agrosystems and can contain original, valuable genetic determinants. A panel of 180 rice varieties was grown in pots for four weeks with three replicates under nethouse conditions. Different leaf traits were measured on the second fully expanded leaf of the main tiller, which often plays a major role in determining the photosynthetic capacity of the plant. The leaf fresh weight, turgid weight and dry weight were measured; then, from these measurements, the relative tissue weight and leaf dry matter percentage were computed. The leaf dry matter percentage can be considered a proxy for the photosynthetic efficiency per unit leaf area, which contributes to yield. By a GWAS, thirteen QTLs associated with these leaf traits were identified. Eleven QTLs were identified for fresh weight, eleven for turgid weight, one for dry weight, one for relative tissue weight and one for leaf dry matter percentage. Eleven QTLs presented associations with several traits, suggesting that these traits share common genetic determinants, while one QTL was specific to leaf dry matter percentage and one QTL was specific to relative tissue weight. Interestingly, some of these QTLs colocalize with leaf- or yield-related QTLs previously identified using other material. Several genes within these QTLs with a known function in leaf development or physiology are reviewed.

Crop Phenomics: Current Status and Perspectives

Reliable, automatic, multifunctional, and high-throughput phenotypic technologies are increasingly considered important tools for rapid advancement of genetic gain in breeding programs. With the rapid development in high-throughput phenotyping technologies, research in this area is entering a new era called 'phenomics.' The crop phenotyping community not only needs to build a multi-domain, multi-level, and multi-scale crop phenotyping big database, but also to research technical systems for phenotypic traits identification and develop bioinformatics technologies for information extraction from the overwhelming amounts of omics data. Here, we provide an overview of crop phenomics research, focusing on two parts, from phenotypic data collection through various sensors to phenomics analysis. Finally, we discussed the challenges and prospective of crop phenomics in order to provide suggestions to develop new methods of mining genes associated with important agronomic traits, and propose new intelligent solutions for precision breeding.

Using hyperspectral analysis as a potential high throughput phenotyping tool in GWAS for protein content of rice quality

BACKGROUND

The advances of hyperspectral technology provide a new analytic means to decrease the gap of phenomics and genomics caused by the fast development of plant genomics with the next generation sequencing technology. Through hyperspectral technology, it is possible to phenotype the biochemical attributes of rice seeds and use the data for GWAS.

RESULTS

The results of correlation analysis indicated that Normalized Difference Spectral Index (NDSI) had high correlation with protein content (PC) with RNDSI 2 = 0.68. Based on GWAS analysis using all the traits, NDSI was able to identify the same SNP loci as rice protein content that was measured by traditional methods. In total, hyperspectral trait NDSI identified all the 43 genes that were identified by biochemical trait PC. NDSI identified 1 extra SNP marker on chromosome 1, which annotated extra 22 genes that were not identified by PC. Kegg annotation results showed that traits NDSI annotated 3 pathways that are exactly the same as PC. The cysteine and methionine metabolic pathway identified by both NDSI and PC was reported important for biosynthesis and metabolism of some of amino acids/protein in rice seeds.

CONCLUSION

This study combined hyperspectral technology and GWAS analysis to dissect PC of rice seeds, which was high throughput and proven to be able to apply to GWAS as a new phenotyping tool. It provided a new means to phenotype one of the important biochemical traits for the determination of rice quality that could be used for genetic studies.

An Accurate Skeleton Extraction Approach From 3D Point Clouds of Maize Plants

Accurate and high-throughput determination of plant morphological traits is essential for phenotyping studies. Nowadays, there are many approaches to acquire high-quality three-dimensional (3D) point clouds of plants. However, it is difficult to estimate phenotyping parameters accurately of the whole growth stages of maize plants using these 3D point clouds. In this paper, an accurate skeleton extraction approach was proposed to bridge the gap between 3D point cloud and phenotyping traits estimation of maize plants. The algorithm first uses point cloud clustering and color difference denoising to reduce the noise of the input point clouds. Next, the Laplacian contraction algorithm is applied to shrink the points. Then the key points representing the skeleton of the plant are selected through adaptive sampling, and neighboring points are connected to form a plant skeleton composed of semantic organs. Finally, deviation skeleton points to the input point cloud are calibrated by building a step forward local coordinate along the tangent direction of the original points. The proposed approach successfully generates accurately extracted skeleton from 3D point cloud and helps to estimate phenotyping parameters with high precision of maize plants. Experimental verification of the skeleton extraction process, tested using three cultivars and different growth stages maize, demonstrates that the extracted matches the input point cloud well. Compared with 3D digitizing data-derived morphological parameters, the NRMSE of leaf length, leaf inclination angle, leaf top length, leaf azimuthal angle, leaf growth height, and plant height, estimated using the extracted plant skeleton, are 5.27, 8.37, 5.12, 4.42, 1.53, and 0.83%, respectively, which could meet the needs of phenotyping analysis. The time required to process a single maize plant is below 100 s. The proposed approach may play an important role in further maize research and applications, such as genotype-to-phenotype study, geometric reconstruction, functional structural maize modeling, and dynamic growth animation.

Combining high-throughput micro-CT-RGB phenotyping and genome-wide association study to dissect the genetic architecture of tiller growth in rice

Manual phenotyping of rice tillers is time consuming and labor intensive, and lags behind the rapid development of rice functional genomics. Thus, automated, non-destructive methods of phenotyping rice tiller traits at a high spatial resolution and high throughput for large-scale assessment of rice accessions are urgently needed. In this study, we developed a high-throughput micro-CT-RGB imaging system to non-destructively extract 739 traits from 234 rice accessions at nine time points. We could explain 30% of the grain yield variance from two tiller traits assessed in the early growth stages. A total of 402 significantly associated loci were identified by genome-wide association study, and dynamic and static genetic components were found across the nine time points. A major locus associated with tiller angle was detected at time point 9, which contained a major gene, TAC1. Significant variants associated with tiller angle were enriched in the 3'-untranslated region of TAC1. Three haplotypes for the gene were found, and rice accessions containing haplotype H3 displayed much smaller tiller angles. Further, we found two loci containing associations with both vigor-related traits identified by high-throughput micro-CT-RGB imaging and yield. The superior alleles would be beneficial for breeding for high yield and dense planting.

Combining high-throughput micro-CT-RGB phenotyping and genome-wide association study to dissect the genetic architecture of tiller growth in rice

Manual phenotyping of rice tillers is time consuming and labor intensive, and lags behind the rapid development of rice functional genomics. Thus, automated, non-destructive methods of phenotyping rice tiller traits at a high spatial resolution and high throughput for large-scale assessment of rice accessions are urgently needed. In this study, we developed a high-throughput micro-CT-RGB imaging system to non-destructively extract 739 traits from 234 rice accessions at nine time points. We could explain 30% of the grain yield variance from two tiller traits assessed in the early growth stages. A total of 402 significantly associated loci were identified by genome-wide association study, and dynamic and static genetic components were found across the nine time points. A major locus associated with tiller angle was detected at time point 9, which contained a major gene, TAC1. Significant variants associated with tiller angle were enriched in the 3'-untranslated region of TAC1. Three haplotypes for the gene were found, and rice accessions containing haplotype H3 displayed much smaller tiller angles. Further, we found two loci containing associations with both vigor-related traits identified by high-throughput micro-CT-RGB imaging and yield. The superior alleles would be beneficial for breeding for high yield and dense planting.

Image analysis-based recognition and quantification of grain number per panicle in rice

BACKGROUND

The number grain per panicle of rice is an important phenotypic trait and a significant index for variety screening and cultivation management. The methods that are currently used to count the number of grains per panicle are manually conducted, making them labor intensive and time consuming. Existing image-based grain counting methods had difficulty in separating overlapped grains.

RESULTS

In this study, we aimed to develop an image analysis-based method to quickly quantify the number of rice grains per panicle. We compared the counting accuracy of several methods among different image acquisition devices and multiple panicle shapes on both Indica and Japonica subspecies of rice. The linear regression model developed in this study had a grain counting accuracy greater than 96% and 97% for Japonica and Indica rice, respectively. Moreover, while the deep learning model that we used was more time consuming than the linear regression model, the average counting accuracy was greater than 99%.

CONCLUSIONS

We developed a rice grain counting method that accurately counts the number of grains on a detached panicle, and believe this method can be a huge asset for guiding the development of high throughput methods for counting the grain number per panicle in other crops.

Genome-Wide Association Studies of Image Traits Reveal Genetic Architecture of Drought Resistance in Rice

Understanding how plants respond to drought can benefit drought resistance (DR) breeding. Using a non-destructive phenotyping facility, 51 image-based traits (i-traits) for 507 rice accessions were extracted. These i-traits can be used to monitor drought responses and evaluate DR. High heritability and large variation of these traits was observed under drought stress in the natural population. A genome-wide association study (GWAS) of i-traits and traditional DR traits identified 470 association loci, some containing known DR-related genes. Of these 470 loci, 443 loci (94%) were identified using i-traits, 437 loci (93%) co-localized with previously reported DR-related quantitative trait loci, and 313 loci (66.6%) were reproducibly identified by GWAS in different years. Association networks, established based on GWAS results, revealed hub i-traits and hub loci. This demonstrates the feasibility and necessity of dissecting the complex DR trait into heritable and simple i-traits. As proof of principle, we illustrated the power of this integrated approach to identify previously unreported DR-related genes. OsPP15 was associated with a hub i-trait, and its role in DR was confirmed by genetic transformation experiments. Furthermore, i-traits can be used for DR linkage analyses, and 69 i-trait locus associations were identified by both GWAS and linkage analysis of a recombinant inbred line population. Finally, we confirmed the relevance of i-traits to DR in the field. Our study provides a promising novel approach for the genetic dissection and discovery of causal genes for DR.

Genetic dissection and validation of candidate genes for flag leaf size in rice (Oryza sativa L.)

Two major loci with functional candidate genes were identified and validated affecting flag leaf size, which offer desirable genes to improve leaf architecture and photosynthetic capacity in rice. Leaf size is a major determinant of plant architecture and yield potential in crops. However, the genetic and molecular mechanisms regulating leaf size remain largely elusive. In this study, quantitative trait loci (QTLs) for flag leaf length and flag leaf width in rice were detected with high-density single nucleotide polymorphism genotyping of a chromosomal segment substitution line (CSSL) population, in which each line carries one or a few chromosomal segments from the japonica cultivar Nipponbare in a common background of the indica variety Zhenshan 97. In total, 14 QTLs for flag leaf length and nine QTLs for flag leaf width were identified in the CSSL population. Among them, qFW4-2 for flag leaf width was mapped to a 37-kb interval, with the most likely candidate gene being the previously characterized NAL1. Another major QTL for both flag leaf width and length was delimited by substitution mapping to a small region of 13.5 kb that contains a single gene, Ghd7.1. Mutants of Ghd7.1 generated using CRISPR/CAS9 approach showed reduced leaf size. Allelic variation analyses also validated Ghd7.1 as a functional candidate gene for leaf size, photosynthetic capacity and other yield-related traits. These results provide useful genetic information for the improvement of leaf size and yield in rice breeding programs.

Favorable Marker Alleles for Panicle Exsertion Length in Rice (Oryza sativa L.) Mined by Association Mapping and the RSTEP-LRT Method

The panicle exsertion length (PEL) in rice (Oryza sativa L.) is an important trait for hybrid seed production. We investigated the PEL in a chromosome segment substitution line (CSSL) population consisting of 66 lines and a natural population composed of 540 varieties. In the CSSL population, a total of seven QTLs for PEL were detected across two environments. The percentage of phenotypic variance explained (PVE) ranged from 10.22 to 50.18%, and the additive effect ranged from -1.77 to 6.47 cm. Among the seven QTLs, qPEL10.2 had the largest PVE, 44.05 and 50.18%, with an additive effect of 5.91 and 6.47 cm in 2015 and in 2016, respectively. In the natural population, 13 SSR marker loci were detected that were associated with PEL in all four environments, with the PVE ranging from 1.20 to 6.26%. Among the 13 loci, 7 were novel. The RM5746-170 bp allele had the largest phenotypic effect (5.11 cm), and the typical carrier variety was Qiaobinghuang. An RM5620-RM6100 region harboring the EUI2 locus on chromosome 10 was detected in both populations. The sequencing results showed that the accessions with a shorter PEL contained the A base, while the accessions with a longer PEL contained the G base at the 1,475 bp location of the EUI2 gene.

Description of olive morphological parameters by using open access software

BACKGROUND

The morphological analysis of olive leaves, fruits and endocarps may represent an efficient tool for the characterization and discrimination of cultivars and the establishment of relationships among them. In recent years, much attention has been focused on the application of molecular markers, due to their high diagnostic efficiency and independence from environmental and phenological variables.

RESULTS

In this study, we present a semi-automatic methodology of detecting various morphological parameters. With the aid of computing and image analysis tools, we created semi-automatic algorithms applying intuitive mathematical descriptors that quantify many fruit, leaf and endocarp morphological features. In particular, we examined quantitative and qualitative characters such as size, shape, symmetry, contour roughness and presence of additional structures such as nipple, petiole, endocarp surface roughness, etc..

CONCLUSION

We illustrate the performance and the applicability of our approach on Greek olive cultivars; on sets of images from fruits, leaves and endocarps. In addition, the proposed methodology was also applied for the description of other crop species morphologies such as tomato, grapevine and pear. This allows us to describe crop morphologies efficiently and robustly in a semi-automated way.

Enhancing crop yield by optimizing plant developmental features

A number of plant features and traits, such as overall plant architecture, leaf structure and morphological features, vascular architecture and flowering time are important determinants of photosynthetic efficiency and hence the overall performance of crop plants. The optimization of such developmental traits thus has great potential to increase biomass and crop yield. Here, we provide a comprehensive review of these developmental traits in crop plants, summarizing their genetic regulation and highlighting the potential of manipulating these traits for crop improvement. We also briefly review the effects of domestication on the developmental features of crop plants. Finally, we discuss the potential of functional genomics-based approaches to optimize plant developmental traits to increase yield.

Genetic control of the root system in rice under normal and drought stress conditions by genome-wide association study

A variety of adverse conditions including drought stress severely affect rice production. Root system plays a critical role in drought avoidance, which is one of the major mechanisms of drought resistance. In this study, we adopted genome-wide association study (GWAS) to dissect the genetic basis controlling various root traits by using a natural population consisting of 529 representative rice accessions. A total of 413 suggestive associations, containing 143 significant associations, were identified for 21 root traits, such as maximum root length, root volume, and root dry weight under normal and drought stress conditions at the maturation stage. More than 80 percent of the suggestive loci were located in the region of reported QTLs for root traits, while about 20 percent of suggestive loci were novel loci detected in this study. Besides, 11 reported root-related genes, including DRO1, WOX11, and OsPID, were found to co-locate with the association loci. We further proved that the association results can facilitate the efficient identification of causal genes for root traits by the two case studies of Nal1 and OsJAZ1. These loci and their candidate causal genes provide an important basis for the genetic improvement of root traits and drought resistance.

An integrated hyperspectral imaging and genome-wide association analysis platform provides spectral and genetic insights into the natural variation in rice

With progress of genetic sequencing technology, plant genomics has experienced rapid development and subsequently triggered the progress of plant phenomics. In this study, a high-throughput hyperspectral imaging system (HHIS) was developed to obtain 1,540 hyperspectral indices at whole-plant level during tillering, heading, and ripening stages. These indices were used to quantify traditional agronomic traits and to explore genetic variation. We performed genome-wide association study (GWAS) of these indices and traditional agronomic traits in a global rice collection of 529 accessions. With the genome-level suggestive P-value threshold, 989 loci were identified. Of the 1,540 indices, we detected 502 significant indices (designated as hyper-traits) that exhibited phenotypic and genetic relationship with traditional agronomic traits and had high heritability. Many hyper-trait-associated loci could not be detected using traditional agronomic traits. For example, we identified a candidate gene controlling chlorophyll content (Chl). This gene, which was not identified based on Chl, was significantly associated with a chlorophyll-related hyper-trait in GWAS and was demonstrated to control Chl. Moreover, our study demonstrates that red edge (680-760 nm) is vital for rice research for phenotypic and genetic insights. Thus, combination of HHIS and GWAS provides a novel platform for dissection of complex traits and for crop breeding.

A high-throughput stereo-imaging system for quantifying rape leaf traits during the seedling stage

BACKGROUND

The fitness of the rape leaf is closely related to its biomass and photosynthesis. The study of leaf traits is significant for improving rape leaf production and optimizing crop management. Canopy structure and individual leaf traits are the major indicators of quality during the rape seedling stage. Differences in canopy structure reflect the influence of environmental factors such as water, sunlight and nutrient supply. The traits of individual rape leaves traits indicate the growth period of the rape as well as its canopy shape.

RESULTS

We established a high-throughput stereo-imaging system for the reconstruction of the three-dimensional canopy structure of rape seedlings from which leaf area and plant height can be extracted. To evaluate the measurement accuracy of leaf area and plant height, 66 rape seedlings were randomly selected for automatic and destructive measurements. Compared with the manual measurements, the mean absolute percentage error of automatic leaf area and plant height measurements was 3.68 and 6.18%, respectively, and the squares of the correlation coefficients (R2) were 0.984 and 0.845, respectively. Compared with the two-dimensional projective imaging method, the leaf area extracted using stereo-imaging was more accurate. In addition, a semi-automatic image analysis pipeline was developed to extract 19 individual leaf shape traits, including 11 scale-invariant traits, 3 inner cavity related traits, and 5 margin-related traits, from the images acquired by the stereo-imaging system. We used these quantified traits to classify rapes according to three different leaf shapes: mosaic-leaf, semi-mosaic-leaf, and round-leaf. Based on testing of 801 seedling rape samples, we found that the leave-one-out cross validation classification accuracy was 94.4, 95.6, and 94.8% for stepwise discriminant analysis, the support vector machine method and the random forest method, respectively.

CONCLUSIONS

In this study, a nondestructive and high-throughput stereo-imaging system was developed to quantify canopy three-dimensional structure and individual leaf shape traits with improved accuracy, with implications for rape phenotyping, functional genomics, and breeding.

Genome-wide Diversity and Association Mapping for Capsaicinoids and Fruit Weight in Capsicum annuum L

Accumulated capsaicinoid content and increased fruit size are traits resulting from Capsicum annuum domestication. In this study, we used a diverse collection of C. annuum to generate 66,960 SNPs using genotyping by sequencing. The study identified 1189 haplotypes containing 3413 SNPs. Length of individual linkage disequilibrium (LD) blocks varied along chromosomes, with regions of high and low LD interspersed with an average LD of 139 kb. Principal component analysis (PCA), Bayesian model based population structure analysis and an Euclidean tree built based on identity by state (IBS) indices revealed that the clustering pattern of diverse accessions are in agreement with capsaicin content (CA) and fruit weight (FW) classifications indicating the importance of these traits in shaping modern pepper genome. PCA and IBS were used in a mixed linear model of capsaicin and dihydrocapsaicin content and fruit weight to reduce spurious associations because of confounding effects of subpopulations in genome-wide association study (GWAS). Our GWAS results showed SNPs in Ankyrin-like protein, IKI3 family protein, ABC transporter G family and pentatricopeptide repeat protein are the major markers for capsaicinoids and of 16 SNPs strongly associated with FW in both years of the study, 7 are located in known fruit weight controlling genes.

Photoperiod-H1 (Ppd-H1) Controls Leaf Size

Leaf size is a major determinant of plant photosynthetic activity and biomass; however, it is poorly understood how leaf size is genetically controlled in cereal crop plants like barley (Hordeum vulgare). We conducted a genome-wide association scan for flowering time, leaf width, and leaf length in a diverse panel of European winter cultivars grown in the field and genotyped with a single-nucleotide polymorphism array. The genome-wide association scan identified PHOTOPERIOD-H1 (Ppd-H1) as a candidate gene underlying the major quantitative trait loci for flowering time and leaf size in the barley population. Microscopic phenotyping of three independent introgression lines confirmed the effect of Ppd-H1 on leaf size. Differences in the duration of leaf growth and consequent variation in leaf cell number were responsible for the leaf size differences between the Ppd-H1 variants. The Ppd-H1-dependent induction of the BARLEY MADS BOX genes BM3 and BM8 in the leaf correlated with reductions in leaf size and leaf number. Our results indicate that leaf size is controlled by the Ppd-H1- and photoperiod-dependent progression of plant development. The coordination of leaf growth with flowering may be part of a reproductive strategy to optimize resource allocation to the developing inflorescences and seeds.

QTL Detection and Elite Alleles Mining for Stigma Traits in Oryza sativa by Association Mapping

Stigma traits are very important for hybrid seed production in Oryza sativa, which is a self-pollinated crop; however, the genetic mechanism controlling the traits is poorly understood. In this study, we investigated the phenotypic data of 227 accessions across 2 years and assessed their genotypic variation with 249 simple sequence repeat (SSR) markers. By combining phenotypic and genotypic data, a genome-wide association (GWA) map was generated. Large phenotypic variations in stigma length (STL), stigma brush-shaped part length (SBPL) and stigma non-brush-shaped part length (SNBPL) were found. Significant positive correlations were identified among stigma traits. In total, 2072 alleles were detected among 227 accessions, with an average of 8.3 alleles per SSR locus. GWA mapping detected 6 quantitative trait loci (QTLs) for the STL, 2 QTLs for the SBPL and 7 QTLs for the SNBPL. Eleven, 5, and 12 elite alleles were found for the STL, SBPL, and SNBPL, respectively. Optimal cross designs were predicted for improving the target traits. The detected genetic variation in stigma traits and QTLs provides helpful information for cloning candidate STL genes and breeding rice cultivars with longer STLs in the future.

Variation in genomic methylation in natural populations of Populus simonii is associated with leaf shape and photosynthetic traits

DNA methylation, one of the best-studied types of chromatin modification, suppresses the expression of transposable elements, pseudogenes, repetitive sequences, and individual genes. However, the extent and variation of genome-wide DNA methylation in natural populations of plants remain relatively unknown. To investigate variation in DNA methylation and whether this variation associates with important plant traits, including leaf shape and photosynthesis, 20 413 DNA methylation sites were examined in a poplar association population (505 individuals) using methylation-sensitive amplification polymorphism (MSAP) technology. Calculation of epi-population structure and kinships assigned individuals into subsets (K=3), revealing that the natural population of P. simonii consists of three subpopulations. Population epigenetic distance and geographic distance showed a significant correlation (r=0.4688, P<0.001), suggesting that environmental factors may affect epigenetics. Single-marker approaches were also used to identify significant marker-trait associations, which found 1087 high-confidence DNA methylation markers associated with different phenotypic traits explaining ~5-15% of the phenotypic variance. Among these loci, 147 differentially methylated fragments were obtained by sequencing, representing 130 candidate genes. Expression analysis of six candidate genes indicated that these genes might play important roles in leaf development and regulation of photosynthesis. This study provides association analysis to study the effects of DNA methylation on plant development and these data indicate that epigenetics bridges environmental and genetic factors in affecting plant growth and development.