Genome-wide association mapping for phenotypic plasticity in rice

Far-red light enrichment affects gene expression and architecture as well as growth and photosynthesis in rice

Plants use light as a resource and signal. Photons within the 400-700 nm waveband are considered photosynthetically active. Far-red photons (FR, 700-800 nm) are used by plants to detect nearby vegetation and elicit the shade avoidance syndrome. In addition, FR photons have also been shown to contribute to photosynthesis, but knowledge about these dual effects remains scarce. Here, we study shoot-architectural and photosynthetic responses to supplemental FR light during the photoperiod in several rice varieties. We observed that FR enrichment only mildly affected the rice transcriptome and shoot architecture as compared to established model species, whereas leaf formation, tillering and biomass accumulation were clearly promoted. Consistent with this growth promotion, we found that CO2-fixation in supplemental FR was strongly enhanced, especially in plants acclimated to FR-enriched conditions as compared to control conditions. This growth promotion dominates the effects of FR photons on shoot development and architecture. When substituting FR enrichment with an end-of-day FR pulse, this prevented photosynthesis-promoting effects and elicited shade avoidance responses. We conclude that FR photons can have a dual role, where effects depend on the environmental context: in addition to being an environmental signal, they are also a potent source of harvestable energy.

Multi-model genome-wide association studies for appearance quality in rice

Improving the quality of the appearance of rice is critical to meet market acceptance. Mining putative quality-related genes has been geared towards the development of effective breeding approaches for rice. In the present study, two SL-GWAS (CMLM and MLM) and three ML-GWAS (FASTmrEMMA, mrMLM, and FASTmrMLM) genome-wide association studies were conducted in a subset of 3K-RGP consisting of 198 rice accessions with 553,831 SNP markers. A total of 594 SNP markers were identified using the mixed linear model method for grain quality traits. Additionally, 70 quantitative trait nucleotides (QTNs) detected by the ML-GWAS models were strongly associated with grain aroma (AR), head rice recovery (HRR, %), and percentage of grains with chalkiness (PGC, %). Finally, 39 QTNs were identified using single- and multi-locus GWAS methods. Among the 39 reliable QTNs, 20 novel QTNs were identified for the above-mentioned three quality-related traits. Based on annotation and previous studies, four functional candidate genes (LOC_Os01g66110, LOC_Os01g66140, LOC_Os07g44910, and LOC_Os02g14120) were found to influence AR, HRR (%), and PGC (%), which could be utilized in rice breeding to improve grain quality traits.

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.

Genetic interrogation of phenotypic plasticity informs genome-enabled breeding in cotton

Phenotypic plasticity, or the ability to adapt to and thrive in changing climates and variable environments, is essential for developmental programs in plants. Despite its importance, the genetic underpinnings of phenotypic plasticity for key agronomic traits remain poorly understood in many crops. In this study, we aim to fill this gap by using genome-wide association studies to identify genetic variations associated with phenotypic plasticity in upland cotton (Gossypium hirsutum L.). We identified 73 additive quantitative trait loci (QTLs), 32 dominant QTLs, and 6799 epistatic QTLs associated with 20 traits. We also identified 117 additive QTLs, 28 dominant QTLs, and 4691 epistatic QTLs associated with phenotypic plasticity in 19 traits. Our findings reveal new genetic factors, including additive, dominant, and epistatic QTLs, that are linked to phenotypic plasticity and agronomic traits. Meanwhile, we find that the genetic factors controlling the mean phenotype and phenotypic plasticity are largely independent in upland cotton, indicating the potential for simultaneous improvement. Additionally, we envision a genomic design strategy by utilizing the identified QTLs to facilitate cotton breeding. Taken together, our study provides new insights into the genetic basis of phenotypic plasticity in cotton, which should be valuable for future breeding.

Triose phosphate utilization in leaves is modulated by whole-plant sink-source ratios and nitrogen budgets in rice

Triose phosphate utilization (TPU) is a biochemical process indicating carbon sink-source (im)balance within leaves. When TPU limits leaf photosynthesis, photorespiration-associated amino acid exports probably provide an additional carbon outlet and increase leaf CO2 uptake. However, whether TPU is modulated by whole-plant sink-source relations and nitrogen (N) budgets remains unclear. We address this question by model analyses of gas-exchange data measured on leaves at three growth stages of rice plants grown at two N levels. Sink-source ratio was manipulated by panicle pruning, by using yellower-leaf variant genotypes, and by measuring photosynthesis on adaxial and abaxial leaf sides. Across all these treatments, higher leaf N content resulted in the occurrence of TPU limitation at lower intercellular CO2 concentrations. Photorespiration-associated amino acid export was greater in high-N leaves, but was smaller in yellower-leaf genotypes, panicle-pruned plants, and for abaxial measurement. The feedback inhibition of panicle pruning on rates of TPU was not always observed, presumably because panicle pruning blocked N remobilization from leaves to grains and the increased leaf N content masked feedback inhibition. The leaf-level TPU limitation was thus modulated by whole-plant sink-source relations and N budgets during rice grain filling, suggesting a close link between within-leaf and whole-plant sink limitations.

Upcycling rice yield trial data using a weather-driven crop growth model

Efficient plant breeding plays a significant role in increasing crop yields and attaining food security under climate change. Screening new cultivars through yield trials in multi-environments has improved crop yields, but the accumulated data from these trials has not been effectively upcycled. We propose a simple method that quantifies cultivar-specific productivity characteristics using two regression coefficients: yield-ability (β) and yield-plasticity (α). The recorded yields of each cultivar are expressed as a unique linear regression in response to the theoretical potential yield (Y_p) calculated by a weather-driven crop growth model, called as the "YpCGM method". We apply this to 72510 independent datasets from yield trials of rice that used 237 cultivars measured at 110 locations in Japan over 38 years. The YpCGM method can upcycle accumulated yield data for use in genetic-gain analysis and genome-wide-association studies to guide future breeding programs for developing new cultivars suitable for the world's changing climate.

Identification of genomic regions associated with the plasticity of carbon 13 ratio in soybean

Improving water use efficiency (WUE) for soybean [Glycine max (L.) Merr.] through selection for high carbon isotope (C13) ratio may increase drought tolerance, but increased WUE may limit growth in productive environments. An ideal genotype would be plastic for C13 ratio; that is, be able to alter C13 ratio in response to the environment. Our objective was to identify genomic regions associated with C13 ratio plasticity, C13 ratio stability, and overall C13 ratio in two panels of diverse Maturity Group IV soybean accessions. A second objective was to identify accessions that differed in their C13 ratio plasticity. Panel 1 (205 accessions) was evaluated in seven irrigated and four drought environments, and Panel 2 (373 accessions) was evaluated in four environments. Plasticity was quantified as the slope from regressing C13 ratio of individual genotypes against an environmental index calculated based on the mean within and across environments. The regression intercept was considered a measure of C13 ratio over all environments, and the root mean square error was considered a measure of stability. Combined over both panels, genome-wide association mapping (GWAM) identified 19 single nucleotide polymorphisms (SNPs) for plasticity, 39 SNPs for C13 ratio, and 16 SNPs for stability. Among these SNPs, 71 candidate genes had annotations associated with transpiration or water conservation and transport, root development, root hair elongation, and stomatal complex morphogenesis. The genomic regions associated with plasticity and stability identified in the current study will be a useful resource for implementing genomic selection for improving drought tolerance in soybean.

Climate change challenges, plant science solutions

Climate change is a defining challenge of the 21st century, and this decade is a critical time for action to mitigate the worst effects on human populations and ecosystems. Plant science can play an important role in developing crops with enhanced resilience to harsh conditions (e.g. heat, drought, salt stress, flooding, disease outbreaks) and engineering efficient carbon-capturing and carbon-sequestering plants. Here, we present examples of research being conducted in these areas and discuss challenges and open questions as a call to action for the plant science community.

A model-guided holistic review of exploiting natural variation of photosynthesis traits in crop improvement

Breeding for improved leaf photosynthesis is considered as a viable approach to increase crop yield. Whether it should be improved in combination with other traits has not been assessed critically. Based on the quantitative crop model GECROS that interconnects various traits to crop productivity, we review natural variation in relevant traits, from biochemical aspects of leaf photosynthesis to morpho-physiological crop characteristics. While large phenotypic variations (sometimes >2-fold) for leaf photosynthesis and its underlying biochemical parameters were reported, few quantitative trait loci (QTL) were identified, accounting for a small percentage of phenotypic variation. More QTL were reported for sink size (that feeds back on photosynthesis) or morpho-physiological traits (that affect canopy productivity and duration), together explaining a much greater percentage of their phenotypic variation. Traits for both photosynthetic rate and sustaining it during grain filling were strongly related to nitrogen-related traits. Much of the molecular basis of known photosynthesis QTL thus resides in genes controlling photosynthesis indirectly. Simulation using GECROS demonstrated the overwhelming importance of electron transport parameters, compared with the maximum Rubisco activity that largely determines the commonly studied light-saturated photosynthetic rate. Exploiting photosynthetic natural variation might significantly improve crop yield if nitrogen uptake, sink capacity, and other morpho-physiological traits are co-selected synergistically.

Modulation of Wheat Yield Components in Response to Management Intensification to Reduce Yield Gaps

Appropriate genotype selection and management can impact wheat (Triticum aestivum L.) yield in dryland environments, but their impact on yield components and their role in yield modulation are not well understood. Our objectives were to evaluate the yield response of commercial winter wheat genotypes to different management practices reflecting a stepwise increase in management intensity (including a reduction in crop density under high input), and to quantify how the different yield components modulate wheat yield. A factorial experiment evaluated six management (M) intensities ["farmer practice" (FP), "enhanced fertility" (EF), "ecological intensification" (EI), "increased foliar protection" (IFP), "water-limited yield" (Yw), and "increased plant productivity" (IPP)] and four winter wheat genotypes (G) in four Kansas environments (E). Average grain yield was 4.9 Mg ha-1 and ranged from 2.0 to 7.4 Mg ha-1, with significant two-way interactions (E × M and E × G). The EF usually maximized yields in dry environments, while EI, which consisted of EF plus one fungicide application, maximized yields in environments with greater water availability. Across all sources of variation, kernels m-2 and aboveground biomass were the strongest modulators of yield as compared to kernel weight and harvest index, while spikes m-2 and kernels spike-1 modulated yields at a similar magnitude. Foliar fungicides improved yield through increased green canopy cover duration and greater radiation intercepted during grain filling. When crop density was reduced from 2.7 to 1.1 million plants per hectare in an otherwise high-input system, plants produced more productive tillers (with genotype-specific response); however, reduced green canopy cover at anthesis and reduced cumulative solar radiation intercepted during grain filling limited wheat yield-although large differences in canopy cover or intercepted radiation were needed to cause modest changes in yield. Treatments more intensive than EI were not warranted as EF or EI maximized yields at all environments, and practices that promote biomass and kernels m-2 are to be targeted for future increases in wheat yield.

Carbon dioxide responsiveness mitigates rice yield loss under high night temperature

Increasing night-time temperatures are a major threat to sustaining global rice (Oryza sativa L.) production. A simultaneous increase in [CO2] will lead to an inevitable interaction between elevated [CO2] (e[CO2]) and high night temperature (HNT) under current and future climates. Here, we conducted field experiments to identify [CO2] responsiveness from a diverse indica panel comprising 194 genotypes under different planting geometries in 2016. Twenty-three different genotypes were tested under different planting geometries and e[CO2] using a free-air [CO2] enrichment facility in 2017. The most promising genotypes and positive and negative controls were tested under HNT and e[CO2] + HNT in 2018. [CO2] responsiveness, measured as a composite response index on different yield components, grain yield, and photosynthesis, revealed a strong relationship (R2 = 0.71) between low planting density and e[CO2]. The most promising genotypes revealed significantly lower (P < 0.001) impact of HNT in high [CO2] responsive (HCR) genotypes compared to the least [CO2] responsive genotype. [CO2] responsiveness was the major driver determining grain yield and related components in HCR genotypes with a negligible yield loss under HNT. A systematic investigation highlighted that active selection and breeding for [CO2] responsiveness can lead to maintained carbon balance and compensate for HNT-induced yield losses in rice and potentially other C3 crops under current and future warmer climates.

Genetic Bases of Complex Traits: From Quantitative Trait Loci to Prediction

Conceived as a general introduction to the book, this chapter is a reminder of the core concepts of genetic mapping and molecular marker-based prediction. It provides an overview of the principles and the evolution of methods for mapping the variation of complex traits, and methods for QTL-based prediction of human disease risk and animal and plant breeding value. The principles of linkage-based and linkage disequilibrium-based QTL mapping methods are described in the context of the simplest, single-marker, methods. Methodological evolutions are analysed in relation with their ability to account for the complexity of the genotype-phenotype relations. Main characteristics of the genetic architecture of complex traits, drawn from QTL mapping works using large populations of unrelated individuals, are presented. Methods combining marker-QTL association data into polygenic risk score that captures part of an individual's susceptibility to complex diseases are reviewed. Principles of best linear mixed model-based prediction of breeding value in animal- and plant-breeding programs using phenotypic and pedigree data, are summarized and methods for moving from BLUP to marker-QTL BLUP are presented. Factors influencing the additional genetic progress achieved by using molecular data and rules for their optimization are discussed.

Nutritional improvement of cereal crops to combat hidden hunger during COVID-19 pandemic: Progress and prospects

COVID-19 has posed a severe challenge on food security by limiting access to food for the marginally placed population. While access to food is a challenge, access to nutritional food is a greater challenge to the population. The present-day foods are not sufficient to meet the nutritional requirements of the human body. In a pandemic condition, providing nutritious food to the population is imperative to ensure the health and well-being of humankind. Exploiting the existing biodiversity of crop species and deploying classical and modern tools to improve the nutritional potential of these species holds the key to addressing the above challenge. Breeding has been a classical tool of crop improvement that relied predominantly on genetic diversity. Collecting and conserving diverse germplasms and characterizing their diversity using molecular markers is essential to preserve diversity and use them in genetic improvement programs. These markers are also valuable for association mapping analyses to identify the genetic determinants of traits-of-interest in crop species. Association mapping identifies the quantitative trait loci (QTL) underlying the trait-of-interest by exploring marker-trait associations, and these QTLs can further be exploited for the genetic improvement of cultivated species through genomics-assisted breeding. Conventional breeding and genomics approaches are also being applied to develop biofortified cereal crops to reduce nutritional deficiencies in consumers. In this context, chapter explains the prerequisites for association mapping, population structure, genetic diversity, different approaches of performing association mapping to dissect nutritional traits, use the information for genomics-assisted breeding for nutrient-rich cereal crops, and application of genomics strategies in crop biofortification. These approaches will ensure food and nutrition security for all amidst the current COVID-19 crisis.

A high-resolution genome-wide association study of the grain ionome and agronomic traits in rice Oryza sativa subsp. indica

This study presents a comprehensive study of the genetic bases controlling variation in the rice ionome employing genome-wide association studies (GWAS) with a diverse panel of indica accessions, each genotyped with 5.2 million markers. GWAS was performed for twelve elements including B, Ca, Co, Cu, Fe, K, Mg, Mn, Mo, Na, P, and Zn and four agronomic traits including days to 50% flowering, grain yield, plant height and thousand grain weight. GWAS identified 128 loci associated with the grain elements and 57 associated with the agronomic traits. There were sixteen co-localization regions containing QTL for two or more traits. Fourteen grain element quantitative trait loci were stable across growing environments, which can be strong candidates to be used in marker-assisted selection to improve the concentrations of nutritive elements in rice grain. Potential candidate genes were revealed including OsNAS3 linked to the locus that controls the variation of Zn and Co concentrations. The effects of starch synthesis and grain filling on multiple grain elements were elucidated through the likely involvement of OsSUS1 and OsGSSB1 genes. Overall, our study provides crucial insights into the genetic basis of ionomic variations in rice and will facilitate improvement in breeding for trace mineral content.

Inferring multilayer interactome networks shaping phenotypic plasticity and evolution

Phenotypic plasticity represents a capacity by which the organism changes its phenotypes in response to environmental stimuli. Despite its pivotal role in adaptive evolution, how phenotypic plasticity is genetically controlled remains elusive. Here, we develop a unified framework for coalescing all single nucleotide polymorphisms (SNPs) from a genome-wide association study (GWAS) into a quantitative graph. This framework integrates functional genetic mapping, evolutionary game theory, and predator-prey theory to decompose the net genetic effect of each SNP into its independent and dependent components. The independent effect arises from the intrinsic capacity of a SNP, only expressed when it is in isolation, whereas the dependent effect results from the extrinsic influence of other SNPs. The dependent effect is conceptually beyond the traditional definition of epistasis by not only characterizing the strength of epistasis but also capturing the bi-causality of epistasis and the sign of the causality. We implement functional clustering and variable selection to infer multilayer, sparse, and multiplex interactome networks from any dimension of genetic data. We design and conduct two GWAS experiments using Staphylococcus aureus, aimed to test the genetic mechanisms underlying the phenotypic plasticity of this species to vancomycin exposure and Escherichia coli coexistence. We reconstruct the two most comprehensive genetic networks for abiotic and biotic phenotypic plasticity. Pathway analysis shows that SNP-SNP epistasis for phenotypic plasticity can be annotated to protein-protein interactions through coding genes. Our model can unveil the regulatory mechanisms of significant loci and excavate missing heritability from some insignificant loci. Our multilayer genetic networks provide a systems tool for dissecting environment-induced evolution.

Assessment of agro-morphological, physiological and yield traits diversity among tropical rice

Rice (Oryza sativa L.) is an essential staple food crop, but the per acre average rice yield is less than its substantial potential in many countries. Rice breeders and growers would benefit from a robust genotypes with better morpho-physiological and yield-related traits. Here, seventy-four new rice genotypes were phenotyped over two years for their gas exchange and yield potential-related traits under Mississippi rice-growing conditions. A wide range of variability was observed among genotypes for all measured traits. Detailed phenotyping of rice genotypes revealed two key relationships that function together to contribute to yield potential under the southern US climate. The first one, grain yield, grain number, and spikelet fertility, showed considerable correlation (r = 0.45 to 0.79, p < 0.001) to harvest index. Conversely, days to anthesis had a high and negative correlation with harvest index (r = −0.79, p < 0.001), which suggests that selection for short duration genotypes with efficient partitioning could improve the yields under southern US climatic conditions. Additive response index revealed a higher positive association with yield traits (R² = 0.59) than physiological (R² = 0.28) and morphological traits (R² = 0.21). Compared with the commercial genotype Rex, 21.6% and 47.3% of the rice genotypes had a higher gas exchange and yield response scores. IR08A172, IR07K142 and IR07F287 were ranked as high performers in physiological and yield response indices. Our study highlights that selection for short-duration yield-related traits with efficient sink capacity traits is desirable for future breeding programs.

Phenotypic Plasticity Contributes to Maize Adaptation and Heterosis

Plant phenotypic plasticity describes altered phenotypic performance of an individual when grown in different environments. Exploring genetic architecture underlying plant plasticity variation may help mitigate the detrimental effects of a rapidly changing climate on agriculture, but little research has been done in this area to date. In the present study, we established a population of 976 maize F₁ hybrids by crossing 488 diverse inbred lines with two elite testers. Genome-wide association study identified hundreds of quantitative trait loci associated with phenotypic plasticity variation across diverse F₁ hybrids, the majority of which contributed very little variance, in accordance with the polygenic nature of these traits. We identified several quantitative trait locus regions that may have been selected during the tropical-temperate adaptation process. We also observed heterosis in terms of phenotypic plasticity, in addition to the traditional genetic value differences measured between hybrid and inbred lines, and the pattern of which was affected by genetic background. Our results demonstrate a landscape of phenotypic plasticity in maize, which will aid in the understanding of its genetic architecture, its contribution to adaptation and heterosis, and how it may be exploited for future maize breeding in a rapidly changing environment.

Alterations in Source-Sink Relations Affect Rice Yield Response to Elevated CO2: A Free-Air CO2 Enrichment Study

To understand the effects of source-sink relationships on rice yield response to elevated CO₂ levels (eCO₂), we conducted a field study using a popular japonica cultivar grown in a free-air CO₂ enrichment environment in 2017-2018. The source-sink ratio of rice was set artificially via source-sink treatments (SSTs) at the heading stage. Five SSTs were performed in 2017 (EXP1): cutting off the flag leaf (LC1) and the top three functional leaves (LC3), removing one branch in every three branches of a panicle (SR1/3) and one branch in every two branches of a panicle (SR1/2), and the control (CK) without any leaf cutting or spikelet removal. The eCO₂ significantly increased grain yield by 15.7% on average over all treatments; it significantly increased grain yield of CK, LC1, LC3, SR1/3, and SR1/2 crops by 13.9, 18.1, 25.3, 12.0, and 10.9%, respectively. The yield response to eCO₂ was associated with a significant increase of panicle number and fully-filled grain percentage (FGP), and the response of crops under different SSTs was significantly positively correlated with FGP and the average grain weight of the seeds. Two SSTs (CK and LC3) were performed in 2018 (EXP2), which confirmed that the yield response of LC3 crops (25.1%) to eCO₂ was significantly higher than that of CK (15.9%). Among the different grain positions, yield response to eCO₂ of grains attached to the lower secondary rachis was greater than that of grains attached to the upper primary rachis. Reducing the source-sink ratio via leaf-cutting enhanced the net photosynthetic rate response of the remaining leaves to eCO₂ and increased the grain filling ability. Conversely, spikelet removal increased the non-structural carbohydrate (NSC) content of the stem, causing feedback inhibition and photosynthetic down-regulation. This study suggests that reducing the source-sink ratio by adopting appropriate management measures can increase the response of rice to eCO₂.

Genome-wide association study and gene network analyses reveal potential candidate genes for high night temperature tolerance in rice

High night temperatures (HNT) are shown to significantly reduce rice (Oryza sativa L.) yield and quality. A better understanding of the genetic architecture of HNT tolerance will help rice breeders to develop varieties adapted to future warmer climates. In this study, a diverse indica rice panel displayed a wide range of phenotypic variability in yield and quality traits under control night (24 °C) and higher night (29 °C) temperatures. Genome-wide association analysis revealed 38 genetic loci associated across treatments (18 for control and 20 for HNT). Nineteen loci were detected with the relative changes in the traits between control and HNT. Positive phenotypic correlations and co-located genetic loci with previously cloned grain size genes revealed common genetic regulation between control and HNT, particularly grain size. Network-based predictive models prioritized 20 causal genes at the genetic loci based on known gene/s expression under HNT in rice. Our study provides important insights for future candidate gene validation and molecular marker development to enhance HNT tolerance in rice. Integrated physiological, genomic, and gene network-informed approaches indicate that the candidate genes for stay-green trait may be relevant to minimizing HNT-induced yield and quality losses during grain filling in rice by optimizing source-sink relationships.

The case for improving crop carbon sink strength or plasticity for a CO2-rich future

Atmospheric CO₂ concentration [CO₂] has increased from 260 to 280μmolmol^-1 (level during crop domestication up to the industrial revolution) to currently 400 and will reach 550μmolmol^-1 by 2050. C3 crops are expected to benefit from elevated [CO₂] (e-CO₂) thanks to photosynthesis responsiveness to [CO₂] but this may require greater sink capacity. We review recent literature on crop e-CO₂ responses, related source-sink interactions, how abiotic stresses potentially interact, and prospects to improve e-CO₂ response via breeding or genetic engineering. Several lines of evidence suggest that e-CO₂ responsiveness is related either to sink intrinsic capacity or adaptive plasticity, for example, involving enhanced branching. Wild relatives and old cultivars mostly showed lower photosynthetic rates, less downward acclimation of photosynthesis to e-CO₂ and responded strongly to e-CO₂ due to greater phenotypic plasticity. While reverting to such archaic traits would be an inappropriate strategy for breeding, we argue that substantial enhancement of vegetative sink vigor, inflorescence size and/or number and root sinks will be necessary to fully benefit from e-CO₂. Potential ideotype features based on enhanced sinks are discussed. The generic 'feast-famine' sugar signaling pathway may be suited to engineer sink strength tissue-specifically and stage-specifically and help validate ideotype concepts. Finally, we argue that models better accounting for acclimation to e-CO₂ are needed to predict which trait combinations should be targeted by breeders for a CO₂-rich world.

Genomic-based root plasticity to enhance abiotic stress adaptation and edible yield in grain crops

Phenotypic plasticity refers to changes expressed by a genotype across different environments and is one of the major means by which plants cope with environmental variability. Multi-fold differences in phenotypic plasticity have been noted across crops, with wild ancestors and landraces being more plastic than crops when under stress. Plasticity in response to abiotic stress adaptation, plant architecture, physio-reproductive and quality traits are multi-genic (QTL). Plasticity QTL (pQTL) were either collocated with main effect QTL and QEI (QTL × environment interaction) or located independently from the main effect QTL. For example, variations in root plasticity have been successfully introgressed to enhance abiotic stress adaptation in rice. The independence of genetic control of a trait and of its plasticity suggests that breeders may select for high or low plasticity in combination with high or low performance of economically important traits. Trait plasticity in stressful environments may be harnessed through breeding stress-tolerant crops. There exists a genetic cost associated with plasticity, so a better understanding of the trade-offs between plasticity and productivity is warranted prior to undertaking breeding for plasticity traits together with productivity in stress environments.

Genotypic variation in source and sink traits affects the response of photosynthesis and growth to elevated atmospheric CO2

This study aimed to understand the response of photosynthesis and growth to e-CO₂ conditions (800 vs. 400 μmol mol^-1 ) of rice genotypes differing in source-sink relationships. A proxy trait called local C source-sink ratio was defined as the ratio of flag leaf area to the number of spikelets on the corresponding panicle, and five genotypes differing in this ratio were grown in a controlled greenhouse. Differential CO₂ resources were applied either during the 2 weeks following heading (EXP1) or during the whole growth cycle (EXP2). Under e-CO₂ , low source-sink ratio cultivars (LSS) had greater gains in photosynthesis, and they accumulated less nonstructural carbohydrate in the flag leaf than high source-sink ratio cultivars (HSS). In EXP2, grain yield and biomass gain was also greater in LSS probably caused by their strong sink. Photosynthetic capacity response to e-CO₂ was negatively correlated across genotypes with local C source-sink ratio, a trait highly conserved across environments. HSS were sink-limited under e-CO₂ , probably associated with low triose phosphate utilization (TPU) capacity. We suggest that the local C source-sink ratio is a potential target for selecting more CO₂ -responsive cultivars, pending validation for a broader genotypic spectrum and for field conditions.

The Genetic Architecture for Phenotypic Plasticity of the Rice Grain Ionome

The ionome of the rice grain is crucial for the health of populations that consume rice as a staple food. However, the contribution of phenotypic plasticity to the variation of rice grain ionome and the genetic architecture of phenotypic plasticity are poorly understood. In this study, we investigated the rice grain ionome of a rice diversity panel in up to eight environments. A considerable proportion of phenotypic variance can be attributed to phenotypic plasticity. Then, phenotypic plasticity and mean phenotype were quantified using Bayesian Finlay-Wilkinson regression, and a significant correlation between them was observed. However, the genetic architecture of mean phenotype was distinct from that of phenotypic plasticity. Also, the correlation between them was mainly attributed to the phenotypic divergence between rice subspecies. Furthermore, the results of whole-genome regression analysis showed that the genetic loci related to phenotypic plasticity can explain a considerable proportion of the phenotypic variance in some environments, especially for Cd, Cu, Mn, and Zn. Our study not only sheds light on the genetic architecture of phenotypic plasticity of the rice grain ionome but also suggests that the genetic loci which related to phenotypic plasticity are valuable in rice grain ionome improvement breeding.

Association mapping and genetic dissection of drought-induced canopy temperature differences in rice

Drought-stressed plants display reduced stomatal conductance, which results in increased leaf temperature by limiting transpiration. In this study, thermal imaging was used to quantify the differences in canopy temperature under drought in a rice diversity panel consisting of 293 indica accessions. The population was grown under paddy field conditions and drought stress was imposed for 2 weeks at flowering. The canopy temperature of the accessions during stress negatively correlated with grain yield (r= -0.48) and positively with plant height (r=0.56). Temperature values were used to perform a genome-wide association (GWA) analysis using a 45K single nucleotide polynmorphism (SNP) map. A quantitative trait locus (QTL) for canopy temperature under drought was detected on chromosome 3 and fine-mapped using a high-density imputed SNP map. The candidate genes underlying the QTL point towards differences in the regulation of guard cell solute intake for stomatal opening as the possible source of temperature variation. Genetic variation for the significant markers of the QTL was present only within the tall, low-yielding landraces adapted to drought-prone environments. The absence of variation in the shorter genotypes, which showed lower leaf temperature and higher grain yield, suggests that breeding for high grain yield in rice under paddy conditions has reduced genetic variation for stomatal response under drought.

The rice genome revolution: from an ancient grain to Green Super Rice

Rice is a staple crop for half the world's population, which is expected to grow by 3 billion over the next 30 years. It is also a key model for studying the genomics of agroecosystems. This dual role places rice at the centre of an enormous challenge facing agriculture: how to leverage genomics to produce enough food to feed an expanding global population. Scientists worldwide are investigating the genetic variation among domesticated rice species and their wild relatives with the aim of identifying loci that can be exploited to breed a new generation of sustainable crops known as Green Super Rice.

Incorporating genome-wide association into eco-physiological simulation to identify markers for improving rice yields

We explored the use of the eco-physiological crop model GECROS to identify markers for improved rice yield under well-watered (control) and water deficit conditions. Eight model parameters were measured from the control in one season for 267 indica genotypes. The model accounted for 58% of yield variation among genotypes under control and 40% under water deficit conditions. Using 213 randomly selected genotypes as the training set, 90 single nucleotide polymorphism (SNP) loci were identified using a genome-wide association study (GWAS), explaining 42-77% of crop model parameter variation. SNP-based parameter values estimated from the additive loci effects were fed into the model. For the training set, the SNP-based model accounted for 37% (control) and 29% (water deficit) of yield variation, less than the 78% explained by a statistical genomic prediction (GP) model for the control treatment. Both models failed in predicting yields of the 54 testing genotypes. However, compared with the GP model, the SNP-based crop model was advantageous when simulating yields under either control or water stress conditions in an independent season. Crop model sensitivity analysis ranked the SNP loci for their relative importance in accounting for yield variation, and the rank differed greatly between control and water deficit environments. Crop models have the potential to use single-environment information for predicting phenotypes under different environments.

Genome-wide association study for salinity tolerance at the flowering stage in a panel of rice accessions from Thailand

BACKGROUND

Salt stress, a major plant environmental stress, is a critical constraint for rice productivity. Dissecting the genetic loci controlling salt tolerance in rice for improving productivity, especially at the flowering stage, remains challenging. Here, we conducted a genome-wide association study (GWAS) of salt tolerance based on exome sequencing of the Thai rice accessions.

RESULTS

Photosynthetic parameters and cell membrane stability under salt stress at the flowering stage; and yield-related traits of 104 Thai rice (Oryza sativa L.) accessions belonging to the indica subspecies were evaluated. The rice accessions were subjected to exome sequencing, resulting in 112,565 single nucleotide polymorphisms (SNPs) called with a minor allele frequency of at least 5%. LD decay analysis of the panel indicates that the average LD for SNPs at 20 kb distance from each other was 0.34 (r2), which decayed to its half value (~ 0.17) at around 80 kb. By GWAS performed using mixed linear model, two hundred loci containing 448 SNPs on exons were identified based on the salt susceptibility index of the net photosynthetic rate at day 6 after salt stress; and the number of panicles, filled grains and unfilled grains per plant. One hundred and forty six genes, which accounted for 73% of the identified loci, co-localized with the previously reported salt quantitative trait loci (QTLs). The top four regions that contained a high number of significant SNPs were found on chromosome 8, 12, 1 and 2. While many are novel, their annotation is consistent with potential involvement in plant salt tolerance and in related agronomic traits. These significant SNPs greatly help narrow down the region within these QTLs where the likely underlying candidate genes can be identified.

CONCLUSIONS

Insight into the contribution of potential genes controlling salt tolerance from this GWAS provides further understanding of salt tolerance mechanisms of rice at the flowering stage, which can help improve yield productivity under salinity via gene cloning and genomic selection.

Genome-wide association reveals novel genomic loci controlling rice grain yield and its component traits under water-deficit stress during the reproductive stage

A diversity panel comprising of 296 indica rice genotypes was phenotyped under non-stress and water-deficit stress conditions during the reproductive stage in the 2013 and 2014 dry seasons (DSs) at IRRI, Philippines. We investigated the genotypic variability for grain yield, yield components, and related traits, and conducted genome-wide association studies (GWAS) using high-density 45K single nucleotide polymorphisms. We detected 38 loci in 2013 and 64 loci in 2014 for non-stress conditions and 69 loci in 2013 and 55 loci in 2014 for water-deficit stress. Desynchronized flowering time confounded grain yield and its components under water-deficit stress in the 2013 experiment. Statistically corrected grain yield and yield component values using days to flowering helped to detect 31 additional genetic loci for grain yield, its components, and the harvest index in 2013. There were few overlaps in the detected loci between years and treatments, and when compared with previous studies using the same panel, indicating the complexity of yield formation under stress. Nevertheless, our analyses provided important insights into the potential links between grain yield with seed set and assimilate partitioning. Our findings demonstrate the complex genetic architecture of yield formation and we propose exploring the genetic basis of less complex component traits as an alternative route for further yield enhancement.

Introgression of Physiological Traits for a Comprehensive Improvement of Drought Adaptation in Crop Plants

Burgeoning population growth, industrial demand, and the predicted global climate change resulting in erratic monsoon rains are expected to severely limit fresh water availability for agriculture both in irrigated and rainfed ecosystems. In order to remain food and nutrient secure, agriculture research needs to focus on devising strategies to save water in irrigated conditions and to develop superior cultivars with improved water productivity to sustain yield under rainfed conditions. Recent opinions accruing in the scientific literature strongly favor the adoption of a "trait based" crop improvement approach for increasing water productivity. Traits associated with maintenance of positive tissue turgor and maintenance of increased carbon assimilation are regarded as most relevant to improve crop growth rates under water limiting conditions and to enhance water productivity. The advent of several water saving agronomic practices notwithstanding, a genetic enhancement strategy of introgressing distinct physiological, morphological, and cellular mechanisms on to a single elite genetic background is essential for achieving a comprehensive improvement in drought adaptation in crop plants. The significant progress made in genomics, though would provide the necessary impetus, a clear understanding of the "traits" to be introgressed is the most essential need of the hour. Water uptake by a better root architecture, water conservation by preventing unproductive transpiration are crucial for maintaining positive tissue water relations. Improved carbon assimilation associated with carboxylation capacity and mesophyll conductance is important in sustaining crop growth rates under water limited conditions. Besides these major traits, we summarize the available information in literature on classifying various drought adaptive traits. We provide evidences that Water-Use Efficiency when introgressed with moderately higher transpiration, would significantly enhance growth rates and water productivity in rice through an improved photosynthetic capacity.

The genetics underlying natural variation of plant-plant interactions, a beloved but forgotten member of the family of biotic interactions

Despite the importance of plant-plant interactions on crop yield and plant community dynamics, our understanding of the genetic and molecular bases underlying natural variation of plant-plant interactions is largely limited in comparison with other types of biotic interactions. By listing 63 quantitative trait loci (QTL) mapping and global gene expression studies based on plants directly challenged by other plants, we explored whether the genetic architecture and the function of the candidate genes underlying natural plant-plant interactions depend on the type of interactions between two plants (competition versus commensalism versus reciprocal helping versus asymmetry). The 16 transcriptomic studies are unevenly distributed between competitive interactions (n = 12) and asymmetric interactions (n = 4, all focusing on response to parasitic plants). By contrast, 17 and 30 QTL studies were identified for competitive interactions and asymmetric interactions (either weed suppressive ability or response to parasitic plants), respectively. Surprisingly, no studies have been carried out on the identification of genetic and molecular bases underlying natural variation in positive interactions. The candidate genes underlying natural plant-plant interactions can be classified into seven categories of plant function that have been identified in artificial environments simulating plant-plant interactions either frequently (photosynthesis, hormones), only recently (cell wall modification and degradation, defense pathways against pathogens) or rarely (ABC transporters, histone modification and meristem identity/life history traits). Finally, we introduce several avenues that need to be explored in the future to obtain a thorough understanding of the genetic and molecular bases underlying plant-plant interactions within the context of realistic community complexity.

Prescreening in large populations as a tool for identifying elevated CO2-responsive genotypes in plants

Elevated atmospheric CO2 concentration (e[CO2]) can stimulate the photosynthesis and productivity of C3 species including food and forest crops. Intraspecific variation in responsiveness to e[CO2] can be exploited to increase productivity under e[CO2]. However, active selection of genotypes to increase productivity under e[CO2] is rarely performed across a wide range of germplasm, because of constraints of space and the cost of CO2 fumigation facilities. If we are to capitalise on recent advances in whole genome sequencing, approaches are required to help overcome these issues of space and cost. Here, we discuss the advantage of applying prescreening as a tool in large genome×e[CO2] experiments, where a surrogate for e[CO2] was used to select cultivars for more detailed analysis under e[CO2] conditions. We discuss why phenotypic prescreening in population-wide screening for e[CO2] responsiveness is necessary, what approaches could be used for prescreening for e[CO2] responsiveness, and how the data can be used to improve genetic selection of high-performing cultivars. We do this within the framework of understanding the strengths and limitations of genotype-phenotype mapping.