Phenotypic plasticity in plant height shaped by interaction between genetic loci and diurnal temperature range

A MYB transcription factor underlying plant height in sorghum qHT7.1 and maize Brachytic 1 loci

Manipulating plant height is an essential component of crop improvement. Plant height was generally reduced through breeding in wheat, rice, and sorghum to resist lodging and increase grain yield but kept high for bioenergy crops. Here, we positionally cloned a plant height quantitative trait locus (QTL) qHT7.1 as a MYB transcription factor controlling internode elongation, cell proliferation, and cell morphology in sorghum. A 740 bp transposable element insertion in the intronic region caused a partial mis-splicing event, generating a novel transcript that included an additional exon and a premature stop codon, leading to short plant height. The dominant allele had an overall higher expression than the recessive allele across development and internode position, while both alleles' expressions peaked at 46 days after planting and progressively decreased from the top to lower internodes. The orthologue of qHT7.1 was identified to underlie the brachytic1 (br1) locus in maize. A large insertion in exon 3 and a 160 bp insertion at the promoter region were identified in the br1 mutant, while an 18 bp promoter insertion was found to be associated with reduced plant height in a natural recessive allele. CRISPR/Cas9-induced gene knockout of br1 in two maize inbred lines showed significant plant height reduction. These findings revealed functional connections across natural, mutant, and edited alleles of this MYB transcription factor in sorghum and maize. This enriched our understanding of plant height regulation and enhanced our toolbox for fine-tuning plant height for crop improvement.

Temporally resolved growth patterns reveal novel information about the polygenic nature of complex quantitative traits

Plant height can be an indicator of plant health across environments and used to identify superior genotypes. Typically plant height is measured at a single timepoint when plants reach terminal height. Evaluating plant height using unoccupied aerial vehicles allows for measurements throughout the growing season, facilitating a better understanding of plant-environment interactions and the genetic basis of this complex trait. To assess variation throughout development, plant height data was collected from planting until terminal height at anthesis (14 flights 2018, 27 in 2019, 12 in 2020, and 11 in 2021) for a panel of ~500 diverse maize inbred lines. The percent variance explained in plant height throughout the season was significantly explained by genotype (9-48%), year (4-52%), and genotype-by-year interactions (14-36%) to varying extents throughout development. Genome-wide association studies revealed 717 significant single nucleotide polymorphisms associated with plant height and growth rate at different parts of the growing season specific to certain phases of vegetative growth. When plant height growth curves were compared to growth curves estimated from canopy cover, greater Fréchet distance stability was observed in plant height growth curves than for canopy cover. This indicated canopy cover may be more useful for understanding environmental modulation of overall plant growth and plant height better for understanding genotypic modulation of overall plant growth. This study demonstrated that substantial information can be gained from high temporal resolution data to understand how plants differentially interact with the environment and can enhance our understanding of the genetic basis of complex polygenic traits.

Genetic and Environmental Patterns Underlying Phenotypic Plasticity in Flowering Time and Plant Height in Sorghum

Phenotypic plasticity is the property of a genotype to produce different phenotypes under different environmental conditions. Understanding genetic and environmental factors behind phenotypic plasticity helps answer some longstanding biology questions and improve phenotype prediction. In this study, we investigated the phenotypic plasticity of flowering time and plant height with a set of diverse sorghum lines evaluated across 14 natural field environments. An environmental index was identified to quantitatively connect the environments. Reaction norms were then obtained with the identified indices for genetic dissection of phenotypic plasticity and performance prediction. Genome-wide association studies (GWAS) detected different sets of loci for reaction-norm parameters (intercept and slope), including 10 new genomic regions in addition to known maturity (Ma1) and dwarfing genes (Dw1, Dw2, Dw3, Dw4 and qHT7.1). Cross-validations under multiple scenarios showed promising results in predicting diverse germplasm in dynamic environments. Additional experiments conducted at four new environments, including one from a site outside of the geographical region of the initial environments, further validated the predictions. Our findings indicate that identifying the environmental index enriches our understanding of gene-environmental interplay underlying phenotypic plasticity, and that genomic prediction with the environmental dimension facilitates prediction-guided breeding for future environments.

Correlated evolution of dispersal traits and habitat preference in the melicgrasses

PREMISE

Seed dispersal is a critical process impacting individual plants and their communities. Plants have evolved numerous strategies and structures to disperse their seeds, but the evolutionary drivers of this diversity remain poorly understood in most lineages. We tested the hypothesis that the evolution of wind dispersal traits within the melicgrasses (Poaceae: Meliceae Link ex Endl.) was correlated with occupation of open and disturbed habitats.

METHODS

To evaluate wind dispersal potential, we collected seed dispersal structures (diaspores) from 24 melicgrass species and measured falling velocity and estimated dispersal distances. Species' affinity for open and disturbed habitats were recorded using georeferenced occurrence records and land cover maps. To test whether habitat preference and dispersal traits were correlated, we used phylogenetically informed multilevel models.

RESULTS

Melicgrasses display several distinct morphologies associated with wind dispersal, suggesting likely convergence. Open habitat taxa had slower-falling diaspores, consistent with increased wind dispersal potential. However, their shorter stature meant that dispersal distances, at a given wind speed, were not higher than those of their forest-occupying relatives. Species with affinities for disturbed sites had slower-falling diaspores and greater wind dispersal distances, largely explained by lighter diaspores.

CONCLUSIONS

Our results are consistent with the hypothesized evolutionary relationship between habitat preference and dispersal strategy. However, phylogenetic inertia and other plant functions (e.g., water conservation) likely shaped dispersal trait evolution in melicgrasses. It remains unclear if dispersal trait changes were precipitated by or predated changing habitat preferences. Nevertheless, our study provides promising results and a framework for disentangling dispersal strategy evolution.

Strawberry phenotypic plasticity in flowering time is driven by the interaction between genetic loci and temperature

Flowering time (FT), which determines when fruits or seeds can be harvested, is subject to phenotypic plasticity, that is, the ability of a genotype to display different phenotypes in response to environmental variation. Here, we investigated how the environment affects the genetic architecture of FT in cultivated strawberry (Fragaria × ananassa) and modifies its quantitative trait locus (QTL) effects. To this end, we used a bi-parental segregating population grown for 2 years at widely divergent latitudes (five European countries) and combined climatic variables with genomic data (Affymetrix SNP array). Examination, using different phenological models, of the response of FT to photoperiod, temperature, and global radiation indicated that temperature is the main driver of FT in strawberry. We next characterized in the segregating population the phenotypic plasticity of FT by using three statistical approaches that generated plasticity parameters including reaction norm parameters. We detected 25 FT QTLs summarized as 10 unique QTLs. Mean values and plasticity parameter QTLs were co-localized in three of them, including the major 6D_M QTL whose effect is strongly modulated by temperature. The design and validation of a genetic marker for the 6D_M QTL offers great potential for breeding programs, for example selecting early-flowering strawberry varieties well adapted to different environmental conditions.

Linking genetic markers and crop model parameters using neural networks to enhance genomic prediction of integrative traits

Introduction

Predicting the performance (yield or other integrative traits) of cultivated plants is complex because it involves not only estimating the genetic value of the candidates to selection, the interactions between the genotype and the environment (GxE) but also the epistatic interactions between genomic regions for a given trait, and the interactions between the traits contributing to the integrative trait. Classical Genomic Prediction (GP) models mostly account for additive effects and are not suitable to estimate non-additive effects such as epistasis. Therefore, the use of machine learning and deep learning methods has been previously proposed to model those non-linear effects.

Methods

In this study, we propose a type of Artificial Neural Network (ANN) called Convolutional Neural Network (CNN) and compare it to two classical GP regression methods for their ability to predict an integrative trait of sorghum: aboveground fresh weight accumulation. We also suggest that the use of a crop growth model (CGM) can enhance predictions of integrative traits by decomposing them into more heritable intermediate traits.

Results

The results show that CNN outperformed both LASSO and Bayes C methods in accuracy, suggesting that CNN are better suited to predict integrative traits. Furthermore, the predictive ability of the combined CGM-GP approach surpassed that of GP without the CGM integration, irrespective of the regression method used.

Discussion

These results are consistent with recent works aiming to develop Genome-to-Phenotype models and advocate for the use of non-linear prediction methods, and the use of combined CGM-GP to enhance the prediction of crop performances.

Physiological Responses of C4 Perennial Bioenergy Grasses to Climate Change: Causes, Consequences, and Constraints

C4 perennial bioenergy grasses are an economically and ecologically important group whose responses to climate change will be important to the future bioeconomy. These grasses are highly productive and frequently possess large geographic ranges and broad environmental tolerances, which may contribute to the evolution of ecotypes that differ in physiological acclimation capacity and the evolution of distinct functional strategies. C4 perennial bioenergy grasses are predicted to thrive under climate change-C4 photosynthesis likely evolved to enhance photosynthetic efficiency under stressful conditions of low [CO2], high temperature, and drought-although few studies have examined how these species will respond to combined stresses or to extremes of temperature and precipitation. Important targets for C4 perennial bioenergy production in a changing world, such as sustainability and resilience, can benefit from combining knowledge of C4 physiology with recent advances in crop improvement, especially genomic selection.

Noninvasive Abiotic Stress Phenotyping of Vascular Plant in Each Vegetative Organ View

The last decades have witnessed a rapid development of noninvasive plant phenotyping, capable of detecting plant stress scale levels from the subcellular to the whole population scale. However, even with such a broad range, most phenotyping objects are often just concerned with leaves. This review offers a unique perspective of noninvasive plant stress phenotyping from a multi-organ view. First, plant sensing and responding to abiotic stress from the diverse vegetative organs (leaves, stems, and roots) and the interplays between these vital components are analyzed. Then, the corresponding noninvasive optical phenotyping techniques are also provided, which can prompt the practical implementation of appropriate noninvasive phenotyping techniques for each organ. Furthermore, we explore methods for analyzing compound stress situations, as field conditions frequently encompass multiple abiotic stressors. Thus, our work goes beyond the conventional approach of focusing solely on individual plant organs. The novel insights of the multi-organ, noninvasive phenotyping study provide a reference for testing hypotheses concerning the intricate dynamics of plant stress responses, as well as the potential interactive effects among various stressors.

Genomic selection in plant breeding: Key factors shaping two decades of progress

Genomic selection, the application of genomic prediction (GP) models to select candidate individuals, has significantly advanced in the past two decades, effectively accelerating genetic gains in plant breeding. This article provides a holistic overview of key factors that have influenced GP in plant breeding during this period. We delved into the pivotal roles of training population size and genetic diversity, and their relationship with the breeding population, in determining GP accuracy. Special emphasis was placed on optimizing training population size. We explored its benefits and the associated diminishing returns beyond an optimum size. This was done while considering the balance between resource allocation and maximizing prediction accuracy through current optimization algorithms. The density and distribution of single-nucleotide polymorphisms, level of linkage disequilibrium, genetic complexity, trait heritability, statistical machine-learning methods, and non-additive effects are the other vital factors. Using wheat, maize, and potato as examples, we summarize the effect of these factors on the accuracy of GP for various traits. The search for high accuracy in GP-theoretically reaching one when using the Pearson's correlation as a metric-is an active research area as yet far from optimal for various traits. We hypothesize that with ultra-high sizes of genotypic and phenotypic datasets, effective training population optimization methods and support from other omics approaches (transcriptomics, metabolomics and proteomics) coupled with deep-learning algorithms could overcome the boundaries of current limitations to achieve the highest possible prediction accuracy, making genomic selection an effective tool in plant breeding.

Importance of phenotypic plasticity in crop resilience

This article comments on:

Guo T, Wei J, Li X, Yu J. 2024. Environmental context of phenotypic plasticity in flowering time in sorghum and rice. Journal of Experimental Botany 75, 1004–1015.

Environmental context of phenotypic plasticity in flowering time in sorghum and rice

Phenotypic plasticity is an important topic in biology and evolution. However, how to generate broadly applicable insights from individual studies remains a challenge. Here, with flowering time observed from a large geographical region for sorghum and rice genetic populations, we examine the consistency of parameter estimation for reaction norms of genotypes across different subsets of environments and searched for potential strategies to inform the study design. Both sample size and environmental mean range of the subset affected the consistency. The subset with either a large range of environmental mean or a large sample size resulted in genetic parameters consistent with the overall pattern. Furthermore, high accuracy through genomic prediction was obtained for reaction norm parameters of untested genotypes using models built from tested genotypes under the subsets of environments with either a large range or a large sample size. With 1428 and 1674 simulated settings, our analyses suggested that the distribution of environmental index values of a site should be considered in designing experiments. Overall, we showed that environmental context was critical, and considerations should be given to better cover the intended range of the environmental variable. Our findings have implications for the genetic architecture of complex traits, plant-environment interaction, and climate adaptation.

Exploration of quality variation and stability of hybrid rice under multi-environments

Improving quality is an essential goal of rice breeding and production. However, rice quality is not solely determined by genotype, but is also influenced by the environment. Phenotype plasticity refers to the ability of a given genotype to produce different phenotypes under different environmental conditions, which can be a representation of the stability of traits. Seven quality traits of 141 hybrid combinations, deriving from the test-crossing of 7 thermosensitive genic male sterile (TGMS) and 25 restorer lines, were evaluated at 5 trial sites with intermittent sowing of three to five in Southern China. In the Yangtze River Basin, it was observed that delaying the sowing time of hybrid rice combinations leads to an improvement in their overall quality. Twelve parents were identified to have lower plasticity general combing ability (GCA) values with increased ability to produce hybrids with a more stable quality. The parents with superior quality tend to exhibit lower GCA values for plasticity. The genome-wide association study (GWAS) identified 13 and 15 quantitative trait loci (QTLs) associated with phenotype plasticity and BLUP measurement, respectively. Notably, seven QTLs simultaneously affected both phenotype plasticity and BLUP measurement. Two cloned rice quality genes, ALK and GL7, may be involved in controlling the plasticity of quality traits in hybrid rice. The direction of the genetic effect of the QTL6 (ALK) on alkali spreading value (ASV) plasticity varies in different cropping environments. This study provides novel insights into the dynamic genetic basis of quality traits in response to different cropping regions, cultivation practices, and changing climates. These findings establish a foundation for precise breeding and production of stable and high-quality rice.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11032-024-01442-3.

Effect of a native bacterial consortium on growth, yield, and grain quality of durum wheat (Triticum turgidum L. subsp. durum) under different nitrogen rates in the Yaqui Valley, Mexico

A field experiment was carried out to quantify the effect of a native bacterial inoculant on the growth, yield, and quality of the wheat crop, under different nitrogen (N) fertilizer rates in two agricultural seasons. Wheat was sown under field conditions at the Experimental Technology Transfer Center (CETT-910), as a representative wheat crop area from the Yaqui Valley, Sonora México. The experiment was conducted using different doses of nitrogen (0, 130, and 250 kg N ha-1) and a bacterial consortium (BC) (Bacillus subtilis TSO9, B. cabrialesii subsp. tritici TSO2T, B. subtilis TSO22, B. paralicheniformis TRQ65, and Priestia megaterium TRQ8). Results showed that the agricultural season affected chlorophyll content, spike size, grains per spike, protein content, and whole meal yellowness. The highest chlorophyll and Normalized Difference Vegetation Index (NDVI) values, as well as lower canopy temperature values, were observed in treatments under the application of 130 and 250 kg N ha-1 (the conventional Nitrogen dose). Wheat quality parameters such as yellow berry, protein content, Sodium dodecyl sulfate (SDS)-Sedimentation, and whole meal yellowness were affected by the N dose. Moreover, the application of the native bacterial consortium, under 130 kg N ha-1, resulted in a higher spike length and grain number per spike, which led to a higher yield (+1.0 ton ha-1 vs. un-inoculated treatment), without compromising the quality of grains. In conclusion, the use of this bacterial consortium has the potential to significantly enhance wheat growth, yield, and quality while reducing the nitrogen fertilizer application, thereby offering a promising agro-biotechnological alternative for improving wheat production.

The Dissection of Nitrogen Response Traits Using Drone Phenotyping and Dynamic Phenotypic Analysis to Explore N Responsiveness and Associated Genetic Loci in Wheat

Inefficient nitrogen (N) utilization in agricultural production has led to many negative impacts such as excessive use of N fertilizers, redundant plant growth, greenhouse gases, long-lasting toxicity in ecosystem, and even effect on human health, indicating the importance to optimize N applications in cropping systems. Here, we present a multiseasonal study that focused on measuring phenotypic changes in wheat plants when they were responding to different N treatments under field conditions. Powered by drone-based aerial phenotyping and the AirMeasurer platform, we first quantified 6 N response-related traits as targets using plot-based morphological, spectral, and textural signals collected from 54 winter wheat varieties. Then, we developed dynamic phenotypic analysis using curve fitting to establish profile curves of the traits during the season, which enabled us to compute static phenotypes at key growth stages and dynamic phenotypes (i.e., phenotypic changes) during N response. After that, we combine 12 yield production and N-utilization indices manually measured to produce N efficiency comprehensive scores (NECS), based on which we classified the varieties into 4 N responsiveness (i.e., N-dependent yield increase) groups. The NECS ranking facilitated us to establish a tailored machine learning model for N responsiveness-related varietal classification just using N-response phenotypes with high accuracies. Finally, we employed the Wheat55K SNP Array to map single-nucleotide polymorphisms using N response-related static and dynamic phenotypes, helping us explore genetic components underlying N responsiveness in wheat. In summary, we believe that our work demonstrates valuable advances in N response-related plant research, which could have major implications for improving N sustainability in wheat breeding and production.

Reversed asymmetric warming of sub-diurnal temperature over land during recent decades

In the latter half of the twentieth century, a significant climate phenomenon "diurnal asymmetric warming" emerged, wherein global land surface temperatures increased more rapidly during the night than during the day. However, recent episodes of global brightening and regional droughts and heatwaves have brought notable alterations to this asymmetric warming trend. Here, we re-evaluate sub-diurnal temperature patterns, revealing a substantial increase in the warming rates of daily maximum temperatures (Tmax), while daily minimum temperatures have remained relatively stable. This shift has resulted in a reversal of the diurnal warming trend, expanding the diurnal temperature range over recent decades. The intensified Tmax warming is attributed to a widespread reduction in cloud cover, which has led to increased solar irradiance at the surface. Our findings underscore the urgent need for enhanced scrutiny of recent temperature trends and their implications for the wider earth system.

Genetic analysis of phenotypic plasticity identifies BBX6 as the candidate gene for maize adaptation to temperate regions

Introduction

Climate changes pose a significant threat to crop adaptation and production. Dissecting the genetic basis of phenotypic plasticity and uncovering the responsiveness of regulatory genes to environmental factors can significantly contribute to the improvement of climate- resilience in crops.

Methods

We established a BC1F3:4 population using the elite inbred lines Zheng58 and PH4CV and evaluated plant height (PH) across four environments characterized by substantial variations in environmental factors. Then, we quantified the correlation between the environmental mean of PH (the mean performance in each environment) and the environmental parameters within a specific growth window. Furthermore, we performed GWAS analysis of phenotypic plasticity, and identified QTLs and candidate gene that respond to key environment index. After that, we constructed the coexpression network involving the candidate gene, and performed selective sweep analysis of the candidate gene.

Results

We found that the environmental parameters demonstrated substantial variation across the environments, and genotype by environment interaction contributed to the variations of PH. Then, we identified PTT(35-48) (PTT is the abbreviation for photothermal units), the mean PTT from 35 to 48 days after planting, as the pivotal environmental index that closely correlated with environmental mean of PH. Leveraging the slopes of the response of PH to both the environmental mean and PTT(35-48), we successfully pinpointed QTLs for phenotypic plasticity on chromosomes 1 and 2. Notably, the PH4CV genotypes at these two QTLs exhibited positive contributions to phenotypic plasticity. Furthermore, our analysis demonstrated a direct correlation between the additive effects of each QTL and PTT(35-48). By analyzing transcriptome data of the parental lines in two environments, we found that the 1009 genes responding to PTT(35-48) were enriched in the biological processes related to environmental sensitivity. BBX6 was the prime candidate gene among the 13 genes in the two QTL regions. The coexpression network of BBX6 contained other genes related to flowering time and photoperiod sensitivity. Our investigation, including selective sweep analysis and genetic differentiation analysis, suggested that BBX6 underwent selection during maize domestication.

Discussion

Th is research substantially advances our understanding of critical environmental factors influencing maize adaptation while simultaneously provides an invaluable gene resource for the development of climate-resilient maize hybrid varieties.

Modeling the influence of phenotypic plasticity on maize hybrid performance

Phenotypic plasticity, the ability of an individual to alter its phenotype in response to changes in the environment, has been proposed as a target for breeding crop varieties with high environmental fitness. Here, we used phenotypic and genotypic data from multiple maize (Zea mays L.) populations to mathematically model phenotypic plasticity in response to the environment (PPRE) in inbred and hybrid lines. PPRE can be simply described by a linear model in which the two main parameters, intercept a and slope b, reflect two classes of genes responsive to endogenous (class A) and exogenous (class B) signals that coordinate plant development. Together, class A and class B genes contribute to the phenotypic plasticity of an individual in response to the environment. We also made connections between phenotypic plasticity and hybrid performance or general combining ability (GCA) of yield using 30 F₁ hybrid populations generated by crossing the same maternal line with 30 paternal lines from different maize heterotic groups. We show that the parameters a and b from two given parental lines must be concordant to reach an ideal GCA of F₁ yield. We hypothesize that coordinated regulation of the two classes of genes in the F₁ hybrid genome is the basis for high GCA. Based on this theory, we built a series of predictive models to evaluate GCA in silico between parental lines of different heterotic groups.

Gene-by-environment interactions in plants: Molecular mechanisms, environmental drivers, and adaptive plasticity

Plants demonstrate a broad range of responses to environmental shifts. One of the most remarkable responses is plasticity, which is the ability of a single plant genotype to produce different phenotypes in response to environmental stimuli. As with all traits, the ability of plasticity to evolve depends on the presence of underlying genetic diversity within a population. A common approach for evaluating the role of genetic variation in driving differences in plasticity has been to study genotype-by-environment interactions (G × E). G × E occurs when genotypes produce different phenotypic trait values in response to different environments. In this review, we highlight progress and promising methods for identifying the key environmental and genetic drivers of G × E. Specifically, methodological advances in using algorithmic and multivariate approaches to understand key environmental drivers combined with new genomic innovations can greatly increase our understanding about molecular responses to environmental stimuli. These developing approaches can be applied to proliferating common garden networks that capture broad natural environmental gradients to unravel the underlying mechanisms of G × E. An increased understanding of G × E can be used to enhance the resilience and productivity of agronomic systems.

Envirome-wide associations enhance multi-year genome-based prediction of historical wheat breeding data

Linking high-throughput environmental data (enviromics) to genomic prediction (GP) is a cost-effective strategy for increasing selection intensity under genotype-by-environment interactions (G × E). This study developed a data-driven approach based on Environment-Phenotype Association (EPA) aimed at recycling important G × E information from historical breeding data. EPA was developed in two applications: (1) scanning a secondary source of genetic variation, weighted from the shared reaction-norms of past-evaluated genotypes and (2) pinpointing weights of the similarity among trial-sites (locations), given the historical impact of each envirotyping data variable for a given site. These results were then used as a dimensionality reduction strategy, integrating historical data to feed multi-environment GP models, which led to the development of four new G × E kernels considering genomics, enviromics, and EPA outcomes. The wheat trial data used included 36 locations, 8 years, and three target populations of environments (TPEs) in India. Four prediction scenarios and six kernel models within/across TPEs were tested. Our results suggest that the conventional GBLUP, without enviromic data or when omitting EPA, is inefficient in predicting the performance of wheat lines in future years. Nevertheless, when EPA was introduced as an intermediary learning step to reduce the dimensionality of the G × E kernels while connecting phenotypic and environmental-wide variation, a significant enhancement of G × E prediction accuracy was evident. EPA revealed that the effect of seasonality makes strategies such as "covariable selection" unfeasible because G × E is year-germplasm specific. We propose that the EPA effectively serves as a "reinforcement learner" algorithm capable of uncovering the effect of seasonality over the reaction-norms, with the benefits of better forecasting the similarities between past and future trialing sites. EPA combines the benefits of dimensionality reduction while reducing the uncertainty of genotype-by-year predictions and increasing the resolution of GP for the genotype-specific level.

Genetic Dissection of Phosphorus Use Efficiency and Genotype-by-Environment Interaction in Maize

Genotype-by-environment interaction (G-by-E) is a common but potentially problematic phenomenon in plant breeding. In this study, we investigated the genotypic performance and two measures of plasticity on a phenotypic and genetic level by assessing 234 maize doubled haploid lines from six populations for 15 traits in seven macro-environments with a focus on varying soil phosphorus levels. It was found intergenic regions contributed the most to the variation of phenotypic linear plasticity. For 15 traits, 124 and 31 quantitative trait loci (QTL) were identified for genotypic performance and phenotypic plasticity, respectively. Further, some genes associated with phosphorus use efficiency, such as Zm00001eb117170, Zm00001eb258520, and Zm00001eb265410, encode small ubiquitin-like modifier E3 ligase were identified. By significantly testing the main effect and G-by-E effect, 38 main QTL and 17 interaction QTL were identified, respectively, in which MQTL38 contained the gene Zm00001eb374120, and its effect was related to phosphorus concentration in the soil, the lower the concentration, the greater the effect. Differences in the size and sign of the QTL effect in multiple environments could account for G-by-E. At last, the superiority of G-by-E in genomic selection was observed. In summary, our findings will provide theoretical guidance for breeding P-efficient and broadly adaptable varieties.

AirMeasurer: open-source software to quantify static and dynamic traits derived from multiseason aerial phenotyping to empower genetic mapping studies in rice

Low-altitude aerial imaging, an approach that can collect large-scale plant imagery, has grown in popularity recently. Amongst many phenotyping approaches, unmanned aerial vehicles (UAVs) possess unique advantages as a consequence of their mobility, flexibility and affordability. Nevertheless, how to extract biologically relevant information effectively has remained challenging. Here, we present AirMeasurer, an open-source and expandable platform that combines automated image analysis, machine learning and original algorithms to perform trait analysis using 2D/3D aerial imagery acquired by low-cost UAVs in rice (Oryza sativa) trials. We applied the platform to study hundreds of rice landraces and recombinant inbred lines at two sites, from 2019 to 2021. A range of static and dynamic traits were quantified, including crop height, canopy coverage, vegetative indices and their growth rates. After verifying the reliability of AirMeasurer-derived traits, we identified genetic variants associated with selected growth-related traits using genome-wide association study and quantitative trait loci mapping. We found that the AirMeasurer-derived traits had led to reliable loci, some matched with published work, and others helped us to explore new candidate genes. Hence, we believe that our work demonstrates valuable advances in aerial phenotyping and automated 2D/3D trait analysis, providing high-quality phenotypic information to empower genetic mapping for crop improvement.

Genome-Wide Association Study in Bread Wheat Identifies Genomic Regions Associated with Grain Yield and Quality under Contrasting Water Availability

The objectives of this study were to identify genetic loci in the bread wheat genome that would influence yield stability and quality under water stress, and to identify accessions that can be recommended for cultivation in dry and hot regions. We performed a genome-wide association study (GWAS) using a panel of 232 wheat accessions spanning diverse ecogeographic regions. Plants were evaluated in the Israeli Northern Negev, under two environments: water-limited (D; 250 mm) and well-watered (W; 450 mm) conditions; they were genotyped with ~71,500 SNPs derived from exome capture sequencing. Of the 14 phenotypic traits evaluated, 12 had significantly lower values under D compared to W conditions, while the values for two traits were higher under D. High heritability (H2 = 0.5-0.9) was observed for grain yield, spike weight, number of grains per spike, peduncle length, and plant height. Days to heading and grain yield could be partitioned based on accession origins. GWAS identified 154 marker-trait associations (MTAs) for yield and quality-related traits, 82 under D and 72 under W, and identified potential candidate genes. We identified 24 accessions showing high and/or stable yields under D conditions that can be recommended for cultivation in regions under the threat of global climate change.