Dynamic effects of interacting genes underlying rice flowering-time phenotypic plasticity and global adaptation

Leveraging Automated Machine Learning for Environmental Data-Driven Genetic Analysis and Genomic Prediction in Maize Hybrids

Genotype, environment, and genotype-by-environment (G×E) interactions play a critical role in shaping crop phenotypes. Here, a large-scale, multi-environment hybrid maize dataset is used to construct and validate an automated machine learning framework that integrates environmental and genomic data for improved accuracy and efficiency in genetic analyses and genomic predictions. Dimensionality-reduced environmental parameters (RD_EPs) aligned with developmental stages are applied to establish linear relationships between RD_EPs and traits to assess the influence of environment on phenotype. Genome-wide association study identifies 539 phenotypic plasticity trait-associated markers (PP-TAMs), 223 environmental stability TAMs (Main-TAMs), and 92 G×E-TAMs, revealing distinct genetic bases for PP and G×E interactions. Training genomic prediction models with both TAMs and RD_EPs increase prediction accuracy by 14.02% to 28.42% over that of genome-wide marker approaches. These results demonstrate the potential of utilizing environmental data for improving genetic analysis and genomic selection, offering a scalable approach for developing climate-adaptive maize varieties.

Modelling and Using Spatial Effects in Nationwide Historical Data Improve Genomic Prediction of Rice Heading Date in Japan

Genomic prediction is a promising strategy for enhancing crop breeding efficiency. Historical data of breeding and cultivation tests from geographically wide regions presumably contain rich information for training genomic prediction models. Therefore, it is essential to explore methodologies to effectively handle such data. To improve the prediction accuracy of models using historical data, we incorporated a spatial model to account for spatial structures among field stations, in addition to conventional genomic prediction models. Targeting the rice heading date from historical data across Japan, we first constructed conventional genomic prediction models using genomic and/or meteorological elements as predictors. Next, we obtain the residual terms. Assuming that the residual terms were partly explained by the spatial effects assigned to each field station, a spatial model was applied to the residual terms and the spatial effects were calculated. Our genomic prediction models performed best when the genome, meteorological elements, and genome-meteorology interactions were included (model 3), and they performed second best when the genome and meteorological elements were included (model 2). For these genomic prediction models, residual terms were spatially biased and corrected for spatial effects. For the best model (model 3), the root mean squared errors (RMSE) of genomic prediction combined with spatial effects were approximately 3.6 days under tenfold cross-validation and approximately 5.1 days under leave-one-line-out cross-validation. The inclusion of the spatial effects improved the RMSEs by approximately 15% and 9% for the former and latter, respectively. Lines with highly improved predictions of the spatial effects were developed, mainly in the northern Tohoku region. The spatial effects were heterogeneous and regional patterns were detected. These findings imply that spatial effects are important not only for improving prediction performance but also for dissecting the model itself to identify the factors contributing to model improvement.

Genetic and transcriptome analyses of the effect of genotype-by-environment interactions on Brassica napus seed oil content

The molecular basis underlying crop traits is complex, with gene-by-environment interactions (GEIs) affecting phenotypes. However, quantitative trait nucleotide (QTN)-by-environment interactions (QEIs) and GEIs for seed oil content (SOC) in oil crops are rare. Here, we detected 11 environmentally specific and 10 stable additive QTNs and 11 QEIs for SOC in rapeseed (Brassica napus) using genome-wide association studies. Weighted gene co-expression network analysis identified 8 Environmental-Developmental Gene co-expression Modules for which the eigengenes correlated with SOC and the environment explained a large proportion of the variance in gene expression. By incorporating information from the multi-omics dataset, 17 candidate genes and 11 candidate GEIs for SOC were predicted. We mined 1 GEI candidate, LIGHT-DEPENDENT SHORT HYPOCOTYLS5 (LSH5), around the environmentally specific QTN qspOC.A02.1 and QEI qeOC.A02.1 detected by climatic indices as covariates. BnaA02.LSH5 was highly expressed in early seed development, and its expression varied significantly across planting sites, with a trend opposite to light-related climatic indices. The BnaA02.lsh5 and BnaC02.lsh5 double mutants had lower SOC, hypocotyl length, photosynthesis, and carbon- and energy-related metabolites compared with wild type. Moreover, BnaA02.LSH5 transcriptionally directly repressed BnaA02.pMDH2 in fatty acid β-oxidation and photosynthetic electron transport. We propose that BnaLSH5 affects seed oil accumulation in response to light intensity. This study provides a basis for creating high-oil germplasm that is adapted to specific environments.

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.

Leveraging Phenotypic Plasticity in Seed Oil Content for Climate-Adapted Breeding and Production

Phenotypic plasticity is the property of an organism to change in response to different environments. Understanding and leveraging crop phenotypic plasticity is crucial for mitigating threats caused by climate change. Here, we assessed phenotypic plasticity in multi-environment trials over 4 years, using 505 inbred lines from a Brassica napus genetic diversity panel. The variation in seed oil content (SOC) plasticity was primarily associated with three environmental indices: precipitation, diurnal temperature range, and ultraviolet B during the flowering or pod-filling stage, alongside five major plasticity genes. Leveraging this information with climate records, we developed a predictive model to estimate SOC for various planting dates in seven major production regions and validated our predictions in new environments. As climate change necessitates new breeding materials with improved genetics, we examined the genetic potentials of existing lines for enhanced SOC in future climates. Using projected environmental data and the identified major plasticity genes, we predicted SOC of genotypes across production regions. We also identified an optimal haplotype, a specific combination of alleles, for each production region to sustainably produce high SOC for future climates. This study offers insights and selection methods that contribute to mitigating the adverse effects of climate change on agriculture.

GEFormer: A genotype-environment interaction-based genomic prediction method that integrates the gating multilayer perceptron and linear attention mechanisms

The integration of genotypic and environmental data can enhance genomic prediction accuracy for crop field traits. Existing genomic prediction methods fail to consider environmental factors and the real growth environments of crops, resulting in low genomic prediction accuracy. In this work, we developed GEFormer, a genotype-environment interaction genomic prediction method that integrates gating multilayer perceptron (gMLP) and linear attention mechanisms. First, GEFormer uses gMLP to extract local and global features among SNPs. Then, Omni-dimensional Dynamic Convolution is used to extract the dynamic and comprehensive features of multiple environmental factors within each day, taking into consideration the real growth pattern of crops. A linear attention mechanism is used to capture the temporal features of environmental changes. Finally, GEFormer uses a gating mechanism to effectively fuse the genomic and environmental features. We examined the accuracy of GEFormer for predicting important agronomic traits of maize, rice, and wheat under three experimental scenarios: untested genotypes in tested environments, tested genotypes in untested environments, and untested genotypes in untested environments. The results showed that GEFormer outperforms six cutting-edge statistical learning methods and four machine learning methods, especially with great advantages under the scenario of untested genotypes in untested environments. In addition, we used GEFormer for three real-world breeding applications: phenotype prediction in unknown environments, hybrid phenotype prediction using an inbred population, and cross-population phenotype prediction. The results showed that GEFormer had better prediction performance in actual breeding scenarios and could be used to assist in crop breeding.

Historic rewiring of grass flowering time pathways and implications for crop improvement under climate change

Grasses are fundamental to human survival, providing a large percentage of our calories, fuel, and fodder for livestock, and an enormous global carbon sink. A particularly important part of the grass plant is the grain-producing inflorescence that develops in response to both internal and external signals that converge at the shoot tip to influence meristem behavior. Abiotic signals that trigger reproductive development vary across the grass family, mostly due to the unique ecological and phylogenetic histories of each clade. The time it takes a grass to flower has implications for its ability to escape harsh environments, while also indirectly affecting abiotic stress tolerance, inflorescence architecture, and grain yield. Here, we synthesize recent insights into the evolution of grass flowering time in response to past climate change, particularly focusing on genetic convergence in underlying traits. We then discuss how and why the rewiring of a shared ancestral flowering pathway affects grass yields, and outline ways in which researchers are using this and other information to breed higher yielding, climate-proof cereal crops.

Integrating QTL mapping with transcriptome analysis mined candidate genes of growth stages in castor (Ricinus communis L.)

BACKGROUND

The growth stages largely determine the crop yield, while little is known about their genetic mechanisms in castor. In this study, the QTL mapping and candidate gene mining of growth stages were conducted using populations F2 and BC1, combining with differential expression analysis and weighted gene co-expression network analysis (WGCNA). The traits studied included the emergence date (ED), the budding date of primary spike (PSBD), the flowering date of primary spike (PSFD), the maturation date of primary spike (PSMD), and the maturation date of primary branch spike (PBSMD).

RESULTS

A total of 20 QTLs conferring four traits (ED, PSBD, PSFD and PBSMD) were identified in the F2 population, with a phenotypic variation explained (PVE) of single QTL ranged from 0.24 to 25.46%. Five QTLs underlying PSMD and PBSMD were identified in the BC1 population, with a PVE of single QTL ranged from 4.74 to 10.82%. To our surprise, almost all the identified QTLs were clustered within two marker intervals, the RCM1769-RCM1838 on linkage group 6 and RCM950-RCM917 on linkage group 3. Subsequently, 473 open reading frames (ORFs) were searched out within these two clusters and 110 differentially expressed genes (DEGs) were screened out from these ORFs by the comparative transcriptome clean data (a total of 140.86 G) at the budding date, the initial flowering date and the full flowering date between parental racemes. With these DEGs, five distinct gene co-expression modules were generated using WGCNA. Showing significant differential expression between parents, four candidate genes (LOC8261128, LOC8278994, LOC8281165 and LOC8259049) in module MEturquoise, were recognized and were annotated as RcSYN3, RcNTT, RcGG3 and RcSAUR76 respectively. This finding implies their potential role in regulating the growth stages of castor.

CONCLUSION

In this study, numerous QTLs conferring growth stages were detected and four candidate genes were mined, which need to be functionally validated. The results provided a new insight into the genetic structure of ED, PSBD, PSFD, PSMD and PBSMD, offered the candidate genes and molecular markers for their improvement as well in castor.

Embracing plant plasticity or robustness as a means of ensuring food security

The dual challenges of global population explosion and environmental deterioration represent major hurdles for 21st Century agriculture culminating in an unprecedented demand for food security. In this Review, we revisit historical concepts of plasticity and canalization before integrating them with contemporary studies of genotype-environment interactions (G×E) that are currently being carried out at the genome-wide level. In doing so we address both fundamental questions regarding G×E and potential strategies to best secure yields in both current and future climate scenarios.

Genetic interaction network of quantitative trait genes for heading date in rice

Several quantitative trait genes (QTGs) related to rice heading date, a key factor for crop development and yield, have been identified, along with complex interactions among genes. However, a comprehensive genetic interaction network for these QTGs has not yet been established. In this study, we use 18K-rice lines to identify QTGs and their epistatic interactions affecting rice heading date. We identify 264 pairs of interacting QTL and construct a comprehensive genetic network of these QTL. On average, the epistatic effects of QTL pairs are estimated to be approximately 12.5% of additive effects of identified QTL. Importantly, epistasis vary among different alleles of several heading date genes. Additionally, 57 pairs of interacting QTGs are also significant in their epistatic effects on 12 other agronomic traits. The identified QTL genetic interactions are further validated using near-isogenic lines, yeast two-hybrid, and split-luciferase complementation assays. Overall, this study provides a genetic network of rice heading date genes, which plays a crucial role in regulating rice heading date and influencing multiple related agronomic traits. This network serves as a foundation for understanding the genetic mechanisms of rice quantitative traits and for advancing rice molecular breeding.

Redefining the accumulated temperature index for accurate prediction of rice flowering time in diverse environments

Accurate prediction of flowering time across diverse environments is crucial for effective crop management and breeding. While the accumulated temperature index (ATI) is widely used as an indicator for estimating flowering time, its traditional definition lacks systematic evaluation and genetic basis understanding. Here, using data from 422 rice hybrids across 47 locations, we identified the optimal ATI calculation window as 1 day after sowing to 26 days before flowering. Based on this redefined ATI, we developed a single-parameter model that outperforms the state-of-the-art reaction norm index model in both accuracy and stability, especially with limited training data. We identified 10 loci significantly associated with ATI variation, including two near known flowering time genes and four linked to ecotype differentiation. To enhance practical utility, we developed an efficient flowering time prediction kit using 28 functionally relevant markers, complemented by a user-friendly online tool (http://xielab.hzau.edu.cn/ATI). Our approach can be easily applied to other crops, as ATI is commonly used across various agricultural systems.

The analysis of the genetic loci affecting phenotypic plasticity of soybean isoflavone content by dQTG.seq model

KEY MESSAGE

The dQTG.seq model was utilized to investigate the genetic underpinnings of phenotypic plasticity in soybean isoflavone content, leading to the identification of 100 marker sites associated with phenotypic plasticity, including 27 transcription factors. Overexpression of Glyma.18G091600 (GmERF7) hairy roots under low temperature, salt, and drought stress confirmed the regulatory role of GmERF7 in the phenotypic plasticity of soybean isoflavone content. Phenotypic plasticity is characteristic of organisms that undergo phenotypic changes in response to environmental fluctuations. This phenomenon is pivotal in evolutionary processes and the emergence of new traits. Isoflavones, significant secondary metabolites found in soybeans, have garnered considerable attention owing to their beneficial physiological effects on human health. The variation in isoflavone content among different soybean varieties is influenced by diverse environmental factors, thereby influencing the evaluation of high and low isoflavone varieties. In this study, we measured the phenotypic plasticity of isoflavone content in recombinant inbred lines Hefeng 25 and L-28 in three different environments over two years. Utilizing the dQTG.seq model, 100 statistically significant markers were identified, and 101 potential genes, including 27 transcription factors, were screened. Through qRT-PCR analysis, elevated expression levels of Glyma.18G091600, Glyma.09G196200, and Glyma.05G229500 were observed in various parts of soybean plants. Under low temperature, drought or salt stress conditions, the related enzymes involved in the isoflavone synthesis pathway were notably upregulated in Glyma.18G091600 (GmERF7) overexpressed hairy roots compared to wild-type controls, resulting to higher phenotypic plasticity values for DZ, GC, GT, and TI. These results suggest that GmERF7 influences the phenotypic plasticity of soybean isoflavone content, enhancing adaptation to adverse environments, while also promoting the synthesis and accumulation of soybean isoflavones.

Comprehensive identification of genomic and environmental determinants of phenotypic plasticity in maize

Maize phenotypes are plastic, determined by the complex interplay of genetics and environmental variables. Uncovering the genes responsible and understanding how their effects change across a large geographic region are challenging. In this study, we conducted systematic analysis to identify environmental indices that strongly influence 19 traits (including flowering time, plant architecture, and yield component traits) measured in the maize nested association mapping (NAM) population grown in 11 environments. Identified environmental indices based on day length, temperature, moisture, and combinations of these are biologically meaningful. Next, we leveraged a total of more than 20 million SNP and SV markers derived from recent de novo sequencing of the NAM founders for trait prediction and dissection. When combined with identified environmental indices, genomic prediction enables accurate performance predictions. Genome-wide association studies (GWASs) detected genetic loci associated with the plastic response to the identified environmental indices for all examined traits. By systematically uncovering the major environmental and genomic factors underlying phenotypic plasticity in a wide variety of traits and depositing our results as a track on the MaizeGDB genome browser, we provide a community resource as well as a comprehensive analytical framework to facilitate continuing complex trait dissection and prediction in maize and other crops. Our findings also provide a conceptual framework for the genetic architecture of phenotypic plasticity by accommodating two alternative models, regulatory gene model and allelic sensitivity model, as special cases of a continuum.

Refining flowering date enhances sesame yield independently of day-length

BACKGROUND

The transition from vegetative to reproductive growth is a key factor in yield maximization. Sesame (Sesamum indicum), an indeterminate short-day oilseed crop, is rapidly being introduced into new cultivation areas. Thus, decoding its flowering mechanism is necessary to facilitate adaptation to environmental conditions. In the current study, we uncover the effect of day-length on flowering and yield components using F2 populations segregating for previously identified quantitative trait loci (Si_DTF QTL) confirming these traits.

RESULTS

Generally, day-length affected all phenotypic traits, with short-day preceding days to flowering and reducing yield components. Interestingly, the average days to flowering required for yield maximization was 50 to 55 days, regardless of day-length. In addition, we found that Si_DTF QTL is more associated with seed-yield and yield components than with days to flowering. A bulk-segregation analysis was applied to identify additional QTL differing in allele frequencies between early and late flowering under both day-length conditions. Candidate genes mining within the identified major QTL intervals revealed two flowering-related genes with different expression levels between the parental lines, indicating their contribution to sesame flowering regulation.

CONCLUSIONS

Our findings demonstrate the essential role of flowering date on yield components and will serve as a basis for future sesame breeding.

Adaptation and the Geographic Spread of Crop Species

Crops are plant species that were domesticated starting about 11,000 years ago from several centers of origin, most prominently the Fertile Crescent, East Asia, and Mesoamerica. From their domestication centers, these crops spread across the globe and had to adapt to differing environments as a result of this dispersal. We discuss broad patterns of crop spread, including the early diffusion of crops associated with the rise and spread of agriculture, the later movement via ancient trading networks, and the exchange between the Old and New Worlds over the last ∼550 years after the European colonization of the Americas. We also examine the various genetic mechanisms associated with the evolutionary adaptation of crops to their new environments after dispersal, most prominently seasonal adaptation associated with movement across latitudes, as well as altitudinal, temperature, and other environmental factors.

Evolutionary patterns of DNA base composition at polymorphic sites highlight the role of the environment in shaping barley and rice genomes

Insights into changes in genome base composition underlying crop domestication can be gained by using comparative genomics. With this approach, previous studies have reported that crop genomes during domestication accumulate more nucleotides adenine (A) and thymine (T) (termed as [AT]-increase) across polymorphic sites. However, the potential influence of the environment or its factors, for example, solar ultraviolet (UV) radiation and temperature, on the [AT]-increase has not been well elucidated. Here, we investigated the [AT]-increase in barley (Hordeum vulgare L.) and rice (Oryza sativa L.) and the association with natural environments, where accessions are distributed. With 12,798,376 and 2,861,535 single-nucleotide polymorphisms from 368 barley and 1375 rice accessions, respectively, we discovered that [AT] increases from wild accessions to improved cultivars, and genomic regions with larger [AT]-increase tend to have higher UV-related motif frequencies, suggesting solar UV radiation as a potential factor in driving genome variation. To link [AT] change with the geographic distribution, we gathered georeferenced accessions and examined their local environments. Interestingly, negative correlations between [AT] and environmental factors were observed (r = -0.39 ∼ -0.75) and modern accessions with higher [AT] values, as compared with wild relatives, are from the environments with lower solar UV radiation or lower temperature. With [AT] and environmental factors as phenotypes, genome-wide association mapping identified three candidate genes that have the potential to contribute to [AT] variation under the effect of environmental conditions. Our findings provide genomic and environmental insights into evolutionary pattern of DNA base composition and underlying mechanisms.

Oryza CLIMtools: A genome-environment association resource reveals adaptive roles for heterotrimeric G proteins in the regulation of rice agronomic traits

Modern crop varieties display a degree of mismatch between their current distributions and the suitability of the local climate for their productivity. To address this issue, we present Oryza CLIMtools (https://gramene.org/CLIMtools/oryza_v1.0/), the first resource for pan-genome prediction of climate-associated genetic variants in a crop species. Oryza CLIMtools consists of interactive web-based databases that enable the user to (1) explore the local environments of traditional rice varieties (landraces) in South-East Asia and (2) investigate the environment by genome associations for 658 Indica and 283 Japonica rice landrace accessions collected from georeferenced local environments and included in the 3K Rice Genomes Project. We demonstrate the value of these resources by identifying an interplay between flowering time and temperature in the local environment that is facilitated by adaptive natural variation in OsHD2 and disrupted by a natural variant in OsSOC1. Prior quantitative trait locus analysis has suggested the importance of heterotrimeric G proteins in the control of agronomic traits. Accordingly, we analyzed the climate associations of natural variants in the different heterotrimeric G protein subunits. We identified a coordinated role of G proteins in adaptation to the prevailing potential evapotranspiration gradient and revealed their regulation of key agronomic traits, including plant height and seed and panicle length. We conclude by highlighting the prospect of targeting heterotrimeric G proteins to produce climate-resilient crops.

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.

MicroRNA397 promotes rice flowering by regulating the photorespiration pathway

The precise timing of flowering plays a pivotal role in ensuring successful plant reproduction and seed production. This process is intricately governed by complex genetic networks that integrate internal and external signals. This study delved into the regulatory function of microRNA397 (miR397) and its target gene LACCASE-15 (OsLAC15) in modulating flowering traits in rice (Oryza sativa). Overexpression of miR397 led to earlier heading dates, decreased number of leaves on the main stem, and accelerated differentiation of the spikelet meristem. Conversely, overexpression of OsLAC15 resulted in delayed flowering and prolonged vegetative growth. Through biochemical and physiological assays, we uncovered that miR397-OsLAC15 had a profound impact on carbohydrate accumulation and photosynthetic assimilation, consequently enhancing the photosynthetic intensity in miR397-overexpressing rice plants. Notably, we identified that OsLAC15 is at least partially localized within the peroxisome organelle, where it regulates the photorespiration pathway. Moreover, we observed that a high CO2 concentration could rescue the late flowering phenotype in OsLAC15-overexpressing plants. These findings shed valuable insights into the regulatory mechanisms of miR397-OsLAC15 in rice flowering and provided potential strategies for developing crop varieties with early flowering and high-yield traits through genetic 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.

QTL epistasis plays a role of homeostasis on heading date in rice

If there was no gene interaction, the gene aggregation effect would increase infinitely with the increase of gene number. Epistasis avoids the endless accumulation of gene effects, playing a role of homeostasis. To confirm the role, QTL epistases were analyzed by four single-segment substitution lines with heading date QTLs in this paper. We found that QTLs of three positive effects and one negative effect generated 62.5% negative dual QTL epistatic effects and 57.7% positive triple QTL epistatic effects, forming the relationship "positive QTLs-negative one order interactions-positive two order interactions". In this way, the aggregation effect of QTLs was partially neutralized by the opposite epistatic effect sum. There also were two exceptions, QTL OsMADS50 and gene Hd3a-2 were always with consistent effect directions with their epistases, implying they could be employed in pyramiding breeding with different objectives. This study elucidated the mechanism of epistatic interactions among four QTLs and provided valuable genetic resources for improving heading date in rice.

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.

Environmental control of rice flowering time

Correct measurement of environmental parameters is fundamental for plant fitness and survival, as well as for timing developmental transitions, including the switch from vegetative to reproductive growth. Important parameters that affect flowering time include day length (photoperiod) and temperature. Their response pathways have been best described in Arabidopsis, which currently offers a detailed conceptual framework and serves as a comparison for other species. Rice, the focus of this review, also possesses a photoperiodic flowering pathway, but 150 million years of divergent evolution in very different environments have diversified its molecular architecture. The ambient temperature perception pathway is strongly intertwined with the photoperiod pathway and essentially converges on the same genes to modify flowering time. When observing network topologies, it is evident that the rice flowering network is centered on EARLY HEADING DATE 1, a rice-specific transcriptional regulator. Here, we summarize the most important features of the rice photoperiodic flowering network, with an emphasis on its uniqueness, and discuss its connections with hormonal, temperature perception, and stress pathways.

A genetic tradeoff for tolerance to moderate and severe heat stress in US hybrid maize

Global climate change is increasing both average temperatures and the frequencies of extreme high temperatures. Past studies have documented a strong negative effect of exposures to temperatures >30°C on hybrid maize yields. However, these studies could not disentangle genetic adaptation via artificial selection from changes in agronomic practices. Because most of the earliest maize hybrids are no longer available, side-by-side comparisons with modern hybrids under current field conditions are generally impossible. Here, we report on the collection and curation of 81 years of public yield trial records covering 4,730 maize hybrids, which enabled us to model genetic variation for temperature responses among maize hybrids. We show that selection may have indirectly and inconsistently contributed to the genetic adaptation of maize to moderate heat stress over this time period while preserving genetic variance for continued adaptation. However, our results reveal the existence of a genetic tradeoff for tolerance to moderate and severe heat stress, leading to a decrease in tolerance to severe heat stress over the same time period. Both trends are particularly conspicuous since the mid-1970s. Such a tradeoff poses challenges to the continued adaptation of maize to warming climates due to a projected increase in the frequency of extreme heat events. Nevertheless, given recent advances in phenomics, enviromics, and physiological modeling, our results offer a degree of optimism for the capacity of plant breeders to adapt maize to warming climates, assuming appropriate levels of R&D investment.

Complex genetic architecture underlying the plasticity of maize agronomic traits

Phenotypic plasticity is the ability of a given genotype to produce multiple phenotypes in response to changing environmental conditions. Understanding the genetic basis of phenotypic plasticity and establishing a predictive model is highly relevant to future agriculture under a changing climate. Here we report findings on the genetic basis of phenotypic plasticity for 23 complex traits using a diverse maize population planted at five sites with distinct environmental conditions. We found that latitude-related environmental factors were the main drivers of across-site variation in flowering time traits but not in plant architecture or yield traits. For the 23 traits, we detected 109 quantitative trait loci (QTLs), 29 for mean values, 66 for plasticity, and 14 for both parameters, and 80% of the QTLs interacted with latitude. The effects of several QTLs changed in magnitude or sign, driving variation in phenotypic plasticity. We experimentally validated one plastic gene, ZmTPS14.1, whose effect was likely mediated by the compensation effect of ZmSPL6 from a downstream pathway. By integrating genetic diversity, environmental variation, and their interaction into a joint model, we could provide site-specific predictions with increased accuracy by as much as 9.9%, 2.2%, and 2.6% for days to tassel, plant height, and ear weight, respectively. This study revealed a complex genetic architecture involving multiple alleles, pleiotropy, and genotype-by-environment interaction that underlies variation in the mean and plasticity of maize complex traits. It provides novel insights into the dynamic genetic architecture of agronomic traits in response to changing environments, paving a practical way toward precision agriculture.

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.

Genetic architecture of seed glycerolipids in Asian cultivated rice

Glycerolipids are essential for rice development and grain quality but its genetic regulation remains unknown. Here we report its genetic base using metabolite-based genome-wide association study and metabolite-based quantitative traits locus (QTL) analyses based on lipidomic profiles of seeds from 587 Asian cultivated rice accessions and 103 chromosomal segment substitution lines, respectively. We found that two genes encoding phosphatidylcholine (PC):diacylglycerol cholinephosphotransferase (OsLP1) and granule-bound starch synthase I (Waxy) contribute to variations in saturated triacylglycerol (TAG) and lyso-PC contents, respectively. We demonstrated that allelic variation in OsLP1 sequence between indica and japonica results in different enzymatic preference for substrate PC-16:0/16:0 and different saturated TAG levels. Further evidence demonstrated that OsLP1 also affects heading date, and that co-selection of OsLP1 and a flooding-tolerant QTL in Aus results in the abundance of saturated TAGs associated with flooding tolerance. Moreover, we revealed that the sequence polymorphisms in Waxy has pleiotropic effects on lyso-PC and amylose content. We proposed that rice seed glycerolipids have been unintentionally shaped during natural and artificial selection for adaptive or import seed quality traits. Collectively, our findings provide valuable genetic resources for rice improvement and evolutionary insights into seed glycerolipid variations in rice.

Integrating and optimizing genomic, weather, and secondary trait data for multiclass classification

Modern plant breeding programs collect several data types such as weather, images, and secondary or associated traits besides the main trait (e.g., grain yield). Genomic data is high-dimensional and often over-crowds smaller data types when naively combined to explain the response variable. There is a need to develop methods able to effectively combine different data types of differing sizes to improve predictions. Additionally, in the face of changing climate conditions, there is a need to develop methods able to effectively combine weather information with genotype data to predict the performance of lines better. In this work, we develop a novel three-stage classifier to predict multi-class traits by combining three data types-genomic, weather, and secondary trait. The method addressed various challenges in this problem, such as confounding, differing sizes of data types, and threshold optimization. The method was examined in different settings, including binary and multi-class responses, various penalization schemes, and class balances. Then, our method was compared to standard machine learning methods such as random forests and support vector machines using various classification accuracy metrics and using model size to evaluate the sparsity of the model. The results showed that our method performed similarly to or better than machine learning methods across various settings. More importantly, the classifiers obtained were highly sparse, allowing for a straightforward interpretation of relationships between the response and the selected predictors.

Genome Scan of Rice Landrace Populations Collected Across Time Revealed Climate Changes' Selective Footprints in the Genes Network Regulating Flowering Time

Analyses of the genetic bases of plant adaptation to climate changes, using genome-scan approaches, are often conducted on natural populations, under hypothesis of out-crossing reproductive regime. We report here on a study based on diachronic sampling (1980 and 2011) of the autogamous crop species, Oryza sativa and Oryza glaberrima, in the tropical forest and the Sudanian savannah of West Africa. First, using historical meteorological data we confirmed changes in temperatures (+ 1 °C on average) and rainfall regime (less predictable and reduced amount) in the target areas. Second, phenotyping the populations for phenology, we observed significantly earlier heading time in the 2010 samples. Third, implementing two genome-scan methods (one of which specially developed for selfing species) on genotyping by sequencing genotypic data of the two populations, we detected 31 independent selection footprints. Gene ontology analysis detected significant enrichment of these selection footprints in genes involved in reproductive processes. Some of them bore known heading time QTLs and genes, including OsGI, Hd1 and OsphyB. This rapid adaptive evolution, originated from subtle changes in the standing variation in genetic network regulating heading time, did not translate into predominance of multilocus genotypes, as it is often the case in selfing plants, and into notable selective sweeps. The high adaptive potential observed results from the multiline genetic structure of the rice landraces, and the rather large and imbricated genetic diversity of the rice meta-population at the farm, the village and the region levels, that hosted the adaptive variants in multiple genetic backgrounds before the advent of the environmental selective pressure. Our results illustrate the evolution of in situ diversity through processes of human and natural selection, and provide a model for rice breeding and cultivars deployment strategies aiming resilience to climate changes. It also calls for further development of population genetic models for adaptation of plant populations to environmental changes. To our best knowledge, this is the first study dealing with climate-changes' selective footprint in crops.

Machine learning for predicting phenotype from genotype and environment

Predicting phenotype with genomic and environmental information is critically needed and challenging. Machine learning methods have emerged as powerful tools to make accurate predictions from large and complex biological data. Here, we review the progress of phenotype prediction models enabled or improved by machine learning methods. We categorized the applications into three scenarios: prediction with genotypic information, with environmental information, and with both. In each scenario, we illustrate the practicality of prediction models, the advantages of machine learning, and the challenges of modeling complex relationships. We discuss the promising potential of leveraging machine learning and genetics theories to develop models that can predict phenotype and also interpret the biological consequences of changes in genotype and environment.

Gradual daylength sensing coupled with optimum cropping modes enhances multi-latitude adaptation of rice and maize

To expand crop planting areas, reestablishment of crop latitude adaptation based on genetic variation in photoperiodic genes can be performed, but it is quite time consuming. By contrast, a crop variety that already exhibits multi-latitude adaptation has the potential to increase its planting areas to be more widely and quickly available. However, the importance and potential of multi-latitude adaptation of crop varieties have not been systematically described. Here, combining daylength-sensing data with the cropping system of elite rice and maize varieties, we found that varieties with gradual daylength sensing coupled with optimum cropping modes have an enhanced capacity for multi-latitude adaptation in China. Furthermore, this multi-latitude adaptation expanded their planting areas and indirectly improved China's nationwide rice and maize unit yield. Thus, coupling the daylength-sensing process with optimum cropping modes to enhance latitude adaptability of excellent varieties represents an exciting approach for deploying crop varieties with the potential to expand their planting areas and quickly improve nationwide crop unit yield in developing countries.

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.

Genome-wide detection of genotype environment interactions for flowering time in Brassica napus

Flowering time is strongly related to the environment, while the genotype-by-environment interaction study for flowering time is lacking in Brassica napus. Here, a total of 11,700,689 single nucleotide polymorphisms in 490 B. napus accessions were used to associate with the flowering time and related climatic index in eight environments using a compressed variance-component mixed model, 3VmrMLM. As a result, 19 stable main-effect quantitative trait nucleotides (QTNs) and 32 QTN-by-environment interactions (QEIs) for flowering time were detected. Four windows of daily average temperature and precipitation were found to be climatic factors highly correlated with flowering time. Ten main-effect QTNs were found to be associated with these flowering-time-related climatic indexes. Using differentially expressed gene (DEG) analysis in semi-winter and spring oilseed rapes, 5,850 and 5,511 DEGs were found to be significantly expressed before and after vernalization. Twelve and 14 DEGs, including 7 and 9 known homologs in Arabidopsis, were found to be candidate genes for stable QTNs and QEIs for flowering time, respectively. Five DEGs were found to be candidate genes for main-effect QTNs for flowering-time-related climatic index. These candidate genes, such as BnaFLCs, BnaFTs, BnaA02.VIN3, and BnaC09.PRR7, were further validated by the haplotype, selective sweep, and co-expression networks analysis. The candidate genes identified in this study will be helpful to breed B. napus varieties adapted to particular environments with optimized flowering time.

Smart breeding driven by big data, artificial intelligence, and integrated genomic-enviromic prediction

The first paradigm of plant breeding involves direct selection-based phenotypic observation, followed by predictive breeding using statistical models for quantitative traits constructed based on genetic experimental design and, more recently, by incorporation of molecular marker genotypes. However, plant performance or phenotype (P) is determined by the combined effects of genotype (G), envirotype (E), and genotype by environment interaction (GEI). Phenotypes can be predicted more precisely by training a model using data collected from multiple sources, including spatiotemporal omics (genomics, phenomics, and enviromics across time and space). Integration of 3D information profiles (G-P-E), each with multidimensionality, provides predictive breeding with both tremendous opportunities and great challenges. Here, we first review innovative technologies for predictive breeding. We then evaluate multidimensional information profiles that can be integrated with a predictive breeding strategy, particularly envirotypic data, which have largely been neglected in data collection and are nearly untouched in model construction. We propose a smart breeding scheme, integrated genomic-enviromic prediction (iGEP), as an extension of genomic prediction, using integrated multiomics information, big data technology, and artificial intelligence (mainly focused on machine and deep learning). We discuss how to implement iGEP, including spatiotemporal models, environmental indices, factorial and spatiotemporal structure of plant breeding data, and cross-species prediction. A strategy is then proposed for prediction-based crop redesign at both the macro (individual, population, and species) and micro (gene, metabolism, and network) scales. Finally, we provide perspectives on translating smart breeding into genetic gain through integrative breeding platforms and open-source breeding initiatives. We call for coordinated efforts in smart breeding through iGEP, institutional partnerships, and innovative technological support.

Plant clock modifications for adapting flowering time to local environments

During and after the domestication of crops from ancestral wild plants, humans selected cultivars that could change their flowering time in response to seasonal daylength. Continuous selection of this trait eventually allowed the introduction of crops into higher or lower latitudes and different climates from the original regions where domestication initiated. In the past two decades, numerous studies have found the causal genes or alleles that change flowering time and have assisted in adapting crop species such as barley (Hordeum vulgare), wheat (Triticum aestivum L.), rice (Oryza sativa L.), pea (Pisum sativum L.), maize (Zea mays spp. mays), and soybean (Glycine max (L.) Merr.) to new environments. This updated review summarizes the genes or alleles that contributed to crop adaptation in different climatic areas. Many of these genes are putative orthologs of Arabidopsis (Arabidopsis thaliana) core clock genes. We also discuss how knowledge of the clock's molecular functioning can facilitate molecular breeding in the future.

Maize Leaf Appearance Rates: A Synthesis From the United States Corn Belt

The relationship between collared leaf number and growing degree days (GDD) is crucial for predicting maize phenology. Biophysical crop models convert GDD accumulation to leaf numbers by using a constant parameter termed phyllochron (°C-day leaf^-1) or leaf appearance rate (LAR; leaf ^oC-day^-1). However, such important parameter values are rarely estimated for modern maize hybrids. To fill this gap, we sourced and analyzed experimental datasets from the United States Corn Belt with the objective to (i) determine phyllochron values for two types of models: linear (1-parameter) and bilinear (3-parameters; phase I and II phyllochron, and transition point) and (ii) explore whether environmental factors such as photoperiod and radiation, and physiological variables such as plant growth rate can explain variability in phyllochron and improve predictability of maize phenology. The datasets included different locations (latitudes between 48° N and 41° N), years (2009-2019), hybrids, and management settings. Results indicated that the bilinear model represented the leaf number vs. GDD relationship more accurately than the linear model (R ² = 0.99 vs. 0.95, n = 4,694). Across datasets, first phase phyllochron, transition leaf number, and second phase phyllochron averaged 57.9 ± 7.5°C-day, 9.8 ± 1.2 leaves, and 30.9 ± 5.7°C-day, respectively. Correlation analysis revealed that radiation from the V3 to the V9 developmental stages had a positive relationship with phyllochron (r = 0.69), while photoperiod was positively related to days to flowering or total leaf number (r = 0.89). Additionally, a positive nonlinear relationship between maize LAR and plant growth rate was found. Present findings provide important parameter values for calibration and optimization of maize crop models in the United States Corn Belt, as well as new insights to enhance mechanisms in crop models.

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

Phenotypic plasticity is observed widely in plants and often studied with reaction norms for adult plant or end-of-season traits. Uncovering genetic, environmental and developmental patterns behind the observed phenotypic variation under natural field conditions is needed. Using a sorghum (Sorghum bicolor) genetic population evaluated for plant height in seven natural field conditions, we investigated the major pattern that differentiated these environments. We then examined the physiological relevance of the identified environmental index by investigating the developmental trajectory of the population with multistage height measurements in four additional environments and conducting crop growth modelling. We found that diurnal temperature range (DTR) during the rapid growth period of sorghum development was an effective environmental index. Three genetic loci (Dw1, Dw3 and qHT7.1) were consistently detected for individual environments, reaction-norm parameters across environments and growth-curve parameters through the season. Their genetic effects changed dynamically along the environmental gradient and the developmental stage. A conceptual model with three-dimensional reaction norms was proposed to showcase the interconnecting components: genotype, environment and development. Beyond genomic and environmental analyses, further integration of development and physiology at the whole-plant and molecular levels into complex trait dissection would enhance our understanding of mechanisms underlying phenotypic variation.

A Method for Identifying Environmental Stimuli and Genes Responsible for Genotype-by-Environment Interactions From a Large-Scale Multi-Environment Data Set

It has not been fully understood in real fields what environment stimuli cause the genotype-by-environment (G × E) interactions, when they occur, and what genes react to them. Large-scale multi-environment data sets are attractive data sources for these purposes because they potentially experienced various environmental conditions. Here we developed a data-driven approach termed Environmental Covariate Search Affecting Genetic Correlations (ECGC) to identify environmental stimuli and genes responsible for the G × E interactions from large-scale multi-environment data sets. ECGC was applied to a soybean (Glycine max) data set that consisted of 25,158 records collected at 52 environments. ECGC illustrated what meteorological factors shaped the G × E interactions in six traits including yield, flowering time, and protein content and when these factors were involved in the interactions. For example, it illustrated the relevance of precipitation around sowing dates and hours of sunshine just before maturity to the interactions observed for yield. Moreover, genome-wide association mapping on the sensitivities to the identified stimuli discovered candidate and known genes responsible for the G × E interactions. Our results demonstrate the capability of data-driven approaches to bring novel insights on the G × E interactions observed in fields.

Artificial selection in the expansion of rice cultivation

Gene distributions and population genomics suggest artificial selection of ghd7 osprr37, for extremely early heading date of rice, in the Tohoku region of Japan. The ranges of cultivated crops expanded into various environmental conditions around the world after their domestication. Hokkaido, Japan, lies at the northern limit of cultivation of rice, which originated in the tropics. Novel genotypes for extremely early heading date in Hokkaido are controlled by loss-of-function of both Grain number, plant height and heading date 7 (Ghd7) and Oryza sativa Pseudo-Response Regulator 37 (OsPRR37). We traced genotypes for extremely early heading date and analyzed the phylogeny of rice varieties grown historically in Japan. The mutations in Ghd7 and OsPRR37 had distinct local distributions. Population genomics revealed that varieties collected from the Tohoku region of northern Japan formed three clusters. Mutant alleles of Ghd7 and OsPRR37 appear to have allowed rice cultivation to spread into Hokkaido. Our results show that the mutations of two genes might be occurred in the process of artificial selection during early rice cultivation in the Tohoku region.

Genome and Environment Based Prediction Models and Methods of Complex Traits Incorporating Genotype × Environment Interaction

Genomic-enabled prediction models are of paramount importance for the successful implementation of genomic selection (GS) based on breeding values. As opposed to animal breeding, plant breeding includes extensive multienvironment and multiyear field trial data. Hence, genomic-enabled prediction models should include genotype × environment (G × E) interaction, which most of the time increases the prediction performance when the response of lines are different from environment to environment. In this chapter, we describe a historical timeline since 2012 related to advances of the GS models that take into account G × E interaction. We describe theoretical and practical aspects of those GS models, including the gains in prediction performance when including G × E structures for both complex continuous and categorical scale traits. Then, we detailed and explained the main G × E genomic prediction models for complex traits measured in continuous and noncontinuous (categorical) scale. Related to G × E interaction models this review also examine the analyses of the information generated with high-throughput phenotype data (phenomic) and the joint analyses of multitrait and multienvironment field trial data that is also employed in the general assessment of multitrait G × E interaction. The inclusion of nongenomic data in increasing the accuracy and biological reliability of the G × E approach is also outlined. We show the recent advances in large-scale envirotyping (enviromics), and how the use of mechanistic computational modeling can derive the crop growth and development aspects useful for predicting phenotypes and explaining G × E.

Footprints of Selection Derived From Temporal Heterozygosity Patterns in a Barley Nested Association Mapping Population

Nowadays, genetic diversity more than ever represents a key driver of adaptation to climate challenges like drought, heat, and salinity. Therefore, there is a need to replenish the limited elite gene pools with favorable exotic alleles from the wild progenitors of our crops. Nested association mapping (NAM) populations represent one step toward exotic allele evaluation and enrichment of the elite gene pool. We investigated an adaptive selection strategy in the wild barley NAM population HEB-25 based on temporal genomic data by studying the fate of 214,979 SNP loci initially heterozygous in individual BC₁S₃ lines after five cycles of selfing and field propagation. We identified several loci exposed to adaptive selection in HEB-25. In total, 48.7% (104,725 SNPs) of initially heterozygous SNP calls in HEB-25 were fixed in BC₁S_3:8 generation, either toward the wild allele (19.9%) or the cultivated allele (28.8%). Most fixed SNP loci turned out to represent gene loci involved in domestication and flowering time as well as plant height, for example, btr1/btr2, thresh-1, Ppd-H1, and sdw1. Interestingly, also unknown loci were found where the exotic allele was fixed, hinting at potentially useful exotic alleles for plant breeding.

Identification of Potential Gene Regulatory Pathways Affecting the Ratio of Four-Seed Pod in Soybean

The number of four-seed pods is one of the most important agronomic traits affected by gene and environment that can potentially improve soybean (Glycine max) yield. However, the gene regulatory network that affects the ratio of four-seed pod (the ratio of the number of four-seed pods to the total number of pods in each individual plant) is yet unclear. Here, we performed bulked segregant RNA sequencing (BSR-seq) on a series of recombinant inbred lines (RILs) derived from hybrid progenies between Heinong 48 (HN48), a cultivar with a high ratio of four-seed pod, and Henong 64 (HN64), a cultivar with a low ratio of four-seed pod. Two tissues, flower bud and young pod, at two different growth stages, R1 and R3, were analyzed under the ratios of four-seed pod at less than 10% and greater than 30%, respectively. To identify the potential gene regulation pathways associated with the ratio of soybean four-seed pod, we performed differentially expressed analysis on the four bulked groups. A differentially expressed gene (DEG) encoding a photosystem II 5-kDa protein had the function of participating in the energy conversion of photosynthesis. In addition, 79 common DEGs were identified at different developmental stages and under different ratios of four-seed pod. Among them, four genes encoding calcium-binding proteins and a WRKY transcription factor were enriched in the plant-pathogen interaction pathway, and they showed a high level of expression in roots. Moreover, 10 DEGs were identified in the reported quantitative trait locus (QTL) interval of four-seed pod, and two of them were significantly enriched in the pentose and glucuronate interconversion pathway. These findings provide basic insights into the understanding of the underlying gene regulatory network affected by specific environment and lay the foundation for identifying the targets that affect the ratio of four-seed pod in soybean.

Optimizing Genomic-Enabled Prediction in Small-Scale Maize Hybrid Breeding Programs: A Roadmap Review

The usefulness of genomic prediction (GP) for many animal and plant breeding programs has been highlighted for many studies in the last 20 years. In maize breeding programs, mostly dedicated to delivering more highly adapted and productive hybrids, this approach has been proved successful for both large- and small-scale breeding programs worldwide. Here, we present some of the strategies developed to improve the accuracy of GP in tropical maize, focusing on its use under low budget and small-scale conditions achieved for most of the hybrid breeding programs in developing countries. We highlight the most important outcomes obtained by the University of São Paulo (USP, Brazil) and how they can improve the accuracy of prediction in tropical maize hybrids. Our roadmap starts with the efforts for germplasm characterization, moving on to the practices for mating design, and the selection of the genotypes that are used to compose the training population in field phenotyping trials. Factors including population structure and the importance of non-additive effects (dominance and epistasis) controlling the desired trait are also outlined. Finally, we explain how the source of the molecular markers, environmental, and the modeling of genotype-environment interaction can affect the accuracy of GP. Results of 7 years of research in a public maize hybrid breeding program under tropical conditions are discussed, and with the great advances that have been made, we find that what is yet to come is exciting. The use of open-source software for the quality control of molecular markers, implementing GP, and envirotyping pipelines may reduce costs in an efficient computational manner. We conclude that exploring new models/tools using high-throughput phenotyping data along with large-scale envirotyping may bring more resolution and realism when predicting genotype performances. Despite the initial costs, mostly for genotyping, the GP platforms in combination with these other data sources can be a cost-effective approach for predicting the performance of maize hybrids for a large set of growing conditions.

An integrated framework reinstating the environmental dimension for GWAS and genomic selection in crops

Identifying mechanisms and pathways involved in gene-environment interplay and phenotypic plasticity is a long-standing challenge. It is highly desirable to establish an integrated framework with an environmental dimension for complex trait dissection and prediction. A critical step is to identify an environmental index that is both biologically relevant and estimable for new environments. With extensive field-observed complex traits, environmental profiles, and genome-wide single nucleotide polymorphisms for three major crops (maize, wheat, and oat), we demonstrated that identifying such an environmental index (i.e., a combination of environmental parameter and growth window) enables genome-wide association studies and genomic selection of complex traits to be conducted with an explicit environmental dimension. Interestingly, genes identified for two reaction-norm parameters (i.e., intercept and slope) derived from flowering time values along the environmental index were less colocalized for a diverse maize panel than for wheat and oat breeding panels, agreeing with the different diversity levels and genetic constitutions of the panels. In addition, we showcased the usefulness of this framework for systematically forecasting the performance of diverse germplasm panels in new environments. This general framework and the companion CERIS-JGRA analytical package should facilitate biologically informed dissection of complex traits, enhanced performance prediction in breeding for future climates, and coordinated efforts to enrich our understanding of mechanisms underlying phenotypic variation.

Genetic architecture and adaptation of flowering time among environments

The genetic basis of flowering time changes across environments, and pleiotropy may limit adaptive evolution of populations in response to local conditions. However, little information is known about how genetic architecture changes among environments. We used genome-wide association studies (GWAS) in Boechera stricta (Graham) Al-Shehbaz, a relative of Arabidopsis, to examine flowering variation among environments and associations with climate conditions in home environments. Also, we used molecular population genetics to search for evidence of historical natural selection. GWAS found 47 significant quantitative trait loci (QTLs) that influence flowering time in one or more environments, control plastic changes in phenology between experiments, or show associations with climate in sites of origin. Genetic architecture of flowering varied substantially among environments. We found that some pairs of QTLs showed similar patterns of pleiotropy across environments. A large-effect QTL showed molecular signatures of adaptive evolution and is associated with climate in home environments. The derived allele at this locus causes later flowering and predominates in sites with greater water availability. This work shows that GWAS of climate associations and ecologically important traits across diverse environments can be combined with molecular signatures of natural selection to elucidate ecological genetics of adaptive evolution.

Enviromic Assembly Increases Accuracy and Reduces Costs of the Genomic Prediction for Yield Plasticity in Maize

Quantitative genetics states that phenotypic variation is a consequence of the interaction between genetic and environmental factors. Predictive breeding is based on this statement, and because of this, ways of modeling genetic effects are still evolving. At the same time, the same refinement must be used for processing environmental information. Here, we present an "enviromic assembly approach," which includes using ecophysiology knowledge in shaping environmental relatedness into whole-genome predictions (GP) for plant breeding (referred to as enviromic-aided genomic prediction, E-GP). We propose that the quality of an environment is defined by the core of environmental typologies and their frequencies, which describe different zones of plant adaptation. From this, we derived markers of environmental similarity cost-effectively. Combined with the traditional additive and non-additive effects, this approach may better represent the putative phenotypic variation observed across diverse growing conditions (i.e., phenotypic plasticity). Then, we designed optimized multi-environment trials coupling genetic algorithms, enviromic assembly, and genomic kinships capable of providing in-silico realization of the genotype-environment combinations that must be phenotyped in the field. As proof of concept, we highlighted two E-GP applications: (1) managing the lack of phenotypic information in training accurate GP models across diverse environments and (2) guiding an early screening for yield plasticity exerting optimized phenotyping efforts. Our approach was tested using two tropical maize sets, two types of enviromics assembly, six experimental network sizes, and two types of optimized training set across environments. We observed that E-GP outperforms benchmark GP in all scenarios, especially when considering smaller training sets. The representativeness of genotype-environment combinations is more critical than the size of multi-environment trials (METs). The conventional genomic best-unbiased prediction (GBLUP) is inefficient in predicting the quality of a yet-to-be-seen environment, while enviromic assembly enabled it by increasing the accuracy of yield plasticity predictions. Furthermore, we discussed theoretical backgrounds underlying how intrinsic envirotype-phenotype covariances within the phenotypic records can impact the accuracy of GP. The E-GP is an efficient approach to better use environmental databases to deliver climate-smart solutions, reduce field costs, and anticipate future scenarios.

Genomic prediction of maize microphenotypes provides insights for optimizing selection and mining diversity

Effective evaluation of millions of crop genetic stocks is an essential component of exploiting genetic diversity to achieve global food security. By leveraging genomics and data analytics, genomic prediction is a promising strategy to efficiently explore the potential of these gene banks by starting with phenotyping a small designed subset. Reliable genomic predictions have enhanced selection of many macroscopic phenotypes in plants and animals. However, the use of genomicprediction strategies for analysis of microscopic phenotypes is limited. Here, we exploited the power of genomic prediction for eight maize traits related to the shoot apical meristem (SAM), the microscopic stem cell niche that generates all the above-ground organs of the plant. With 435 713 genomewide single-nucleotide polymorphisms (SNPs), we predicted SAM morphology traits for 2687 diverse maize inbreds based on a model trained from 369 inbreds. An empirical validation experiment with 488 inbreds obtained a prediction accuracy of 0.37-0.57 across eight traits. In addition, we show that a significantly higher prediction accuracy was achieved by leveraging the U value (upper bound for reliability) that quantifies the genomic relationships of the validation set with the training set. Our findings suggest that double selection considering both prediction and reliability can be implemented in choosing selection candidates for phenotyping when exploring new diversity is desired. In this case, individuals with less extreme predicted values and moderate reliability values can be considered. Our study expands the turbocharging gene banks via genomic prediction from the macrophenotypes into the microphenotypic space.

Environmental Signal-Dependent Regulation of Flowering Time in Rice

The transition from the vegetative to the reproductive stage of growth is a critical event in the lifecycle of a plant and is required for the plant’s reproductive success. Flowering time is tightly regulated by an internal time-keeping system and external light conditions, including photoperiod, light quality, and light quantity. Other environmental factors, such as drought and temperature, also participate in the regulation of flowering time. Thus, flexibility in flowering time in response to environmental factors is required for the successful adaptation of plants to the environment. In this review, we summarize our current understanding of the molecular mechanisms by which internal and environmental signals are integrated to regulate flowering time in Arabidopsis thaliana and rice (Oryza sativa).