Genotype×environment interaction QTL mapping in plants: lessons from Arabidopsis

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.

Induced resistance to herbivory and the intelligent plant

Plant induced responses to environmental stressors are increasingly studied in a behavioral ecology context. This is particularly true for plant induced responses to herbivory that mediate direct and indirect defenses, and tolerance. These seemingly adaptive alterations of plant defense phenotypes in the context of other environmental conditions have led to the discussion of such responses as intelligent behavior. Here we consider the concept of plant intelligence and some of its predictions for chemical information transfer in plant interaction with other organisms. Within this framework, the flow, perception, integration, and storage of environmental information are considered tunable dials that allow plants to respond adaptively to attacking herbivores while integrating past experiences and environmental cues that are predictive of future conditions. The predictive value of environmental information and the costs of acting on false information are important drivers of the evolution of plant responses to herbivory. We identify integrative priming of defense responses as a mechanism that allows plants to mitigate potential costs associated with acting on false information. The priming mechanisms provide short- and long-term memory that facilitates the integration of environmental cues without imposing significant costs. Finally, we discuss the ecological and evolutionary prediction of the plant intelligence hypothesis.

Genotype-by-Environment Effects of Cis-Variations in the Atgl Promoter Mediate the Divergent Pattern of Phenotypic Plasticity for Temperature Adaptation in Two Congeneric Oyster Species

Phenotypic plasticity plays an essential role in adaptive evolution. However, the molecular mechanisms of how genotype-by-environment interaction (G × E) effects shape phenotypic plasticity in marine organisms remain poorly understood. The crucial temperature-responsive trait triacylglycerol (TAG) content and its major gene adipose triglyceride lipase (Atgl) expression have divergent plastic patterns in two congeneric oyster species (Crassostrea gigas and Crassostrea angulata) to adapt to relative-cold/northern and relative-warm/southern habitats, respectively. In this study, eight putative loci were identified in the Atgl promoter region (cis-variations) between wild C. gigas and C. angulata that exhibited differential environmental responsiveness (G × E). The G and G × E effects of each locus were further dissected by measuring the Atgl gene expression of different genotypes in response to temperature changes at the cellular and organismal levels. Two transcription factors, non-environmentally responsive non-POU domain-containing octamer-binding protein (Nono) and environmentally responsive heterogeneous nuclear ribonucleoprotein K (Hnrnpk), were screened for binding to g.-1804 (G locus) and g.-1919 (G + G × E locus), respectively. The specificity of Nono binding to the C. angulata allele mediated the G effects of g.-1804, and the lower environmental sensitivity of Hnrnpk in C. angulata mediated the G × E effects of g.-1919, jointly regulating the trade-offs between higher constitutive and lower plastic expression of Atgl gene expression in C. angulata. This study served as an experimental case to reveal how the genetic variations with G and (or) G × E effects propagate into the divergent pattern of plasticity in environmental adaptive traits, which provides new insights into predicting the adaptability of marine organisms to future climate changes.

Unraveling the specialized metabolic pathways in medicinal plant genomes: a review

Medicinal plants are important sources of bioactive specialized metabolites with significant therapeutic potential. Advances in multi-omics have accelerated the understanding of specialized metabolite biosynthesis and regulation. Genomics, transcriptomics, proteomics, and metabolomics have each contributed new insights into biosynthetic gene clusters (BGCs), metabolic pathways, and stress responses. However, single-omics approaches often fail to fully address these complex processes. Integrated multi-omics provides a holistic perspective on key regulatory networks. High-throughput sequencing and emerging technologies like single-cell and spatial omics have deepened our understanding of cell-specific and spatially resolved biosynthetic dynamics. Despite these advancements, challenges remain in managing large datasets, standardizing protocols, accounting for the dynamic nature of specialized metabolism, and effectively applying synthetic biology for sustainable specialized metabolite production. This review highlights recent progress in omics-based research on medicinal plants, discusses available bioinformatics tools, and explores future research trends aimed at leveraging integrated multi-omics to improve the medicinal quality and sustainable utilization of plant resources.

Genotype-by-environment interactions in beef and dairy cattle populations: A review of methodologies and perspectives on research and applications

Modern livestock production systems are characterized by a greater focus on intensification, involving managing larger numbers of animals to achieve higher productive efficiency and animal health and welfare within herds. Therefore, animal breeding programs need to be strategically designed to select animals that can effectively enhance production performance and animal welfare across a range of environmental conditions. Thus, this review summarizes the main methodologies used for assessing the levels of genotype-by-environment interaction (G × E) in cattle populations. In addition, we explored the importance of integrating genomic and phenotypic information to quantify and account for G × E in breeding programs. An overview of the structure of cattle breeding programs is provided to give insights into the potential outcomes and challenges faced when considering G × E to optimize genetic gains in breeding programs. The role of nutrigenomics and its impact on gene expression related to metabolism in cattle are also discussed, along with an examination of current research findings and their potential implications for future research and practical applications. Out of the 116 studies examined, 60 and 56 focused on beef and dairy cattle, respectively. A total of 83.62% of these studies reported genetic correlations across environmental gradients below 0.80, indicating the presence of G × E. For beef cattle, 69.33%, 24%, 2.67%, 2.67%, and 1.33% of the studies evaluated growth, reproduction, carcass and meat quality, survival, and feed efficiency traits, respectively. By contrast, G × E research in dairy cattle populations predominantly focused on milk yield and milk composition (79.36% of the studies), followed by reproduction and fertility (19.05%), and survival (1.59%) traits. The importance of G × E becomes particularly evident when considering complex traits such as heat tolerance, disease resistance, reproductive performance, and feed efficiency, as highlighted in this review. Genomic models provide a valuable avenue for studying these traits in greater depth, allowing for the identification of candidate genes and metabolic pathways associated with animal fitness, adaptation, and environmental efficiency. Nutrigenetics and nutrigenomics are emerging fields that require extensive investigation to maximize our understanding of gene-nutrient interactions. By studying various transcription factors, we can potentially improve animal metabolism, improving performance, health, and quality of products such as meat and milk.

Tradeoffs in Modeling Context Dependency in Complex Trait Genetics

Genetic effects on complex traits may depend on context, such as age, sex, environmental exposures or social settings. However, it is often unclear if the extent of context dependency, or Gene-by-Environment interaction (GxE), merits more involved models than the additive model typically used to analyze data from genome-wide association studies (GWAS). Here, we suggest considering the utility of GxE models in GWAS as a tradeoff between bias and variance parameters. In particular, We derive a decision rule for choosing between competing models for the estimation of allelic effects. The rule weighs the increased estimation noise when context is considered against the potential bias when context dependency is ignored. In the empirical example of GxSex in human physiology, the increased noise of context-specific estimation often outweighs the bias reduction, rendering GxE models less useful when variants are considered independently. However, we argue that for complex traits, the joint consideration of context dependency across many variants mitigates both noise and bias. As a result, polygenic GxE models can improve both estimation and trait prediction. Finally, we exemplify (using GxDiet effects on longevity in fruit flies) how analyses based on independently ascertained "top hits" alone can be misleading, and that considering polygenic patterns of GxE can improve interpretation.

Study of red vine phenotypic plasticity across central-southern Italy sites: an integrated analysis of the transcriptome and weather indices through WGCNA

The grapevine (Vitis spp., family Vitaceae) is characterized by marked phenotypic plasticity. Its ability to withstand specific environmental conditions depends on the activation of highly coordinated responses resulting from interactions among genotypes (G) and environmental factors (E). In this study, the transcriptomes of commercially ripe berries of the Cabernet Sauvignon and Aglianico genotypes grown in open fields at three different sites in central-southern Italy (Campania, Molise and Sicily) were analyzed with RNA sequencing. These transcriptomic data were integrated with a comprehensive set of weather course indices through weighted gene co-expression network analysis (WGCNA). A total of 11,887 differentially expressed genes (DEGs) were retrieved, most of which were associated with the Aglianico genotype. The plants from the Sicilian site presented the greatest number of DEGs for both genotypes. Most of the weather course data (daily maximum air temperature, relative humidity, air pressure, dew point, and hours of sun radiation) were significantly correlated with the "lightcyan1" module, confirming WGCNA as a powerful method for identifying genes of high biological interest. Within this module, the gene encoding the ACA10 cation transporter was highly expressed in plants of both genotypes from Campania, where the lowest anthocyanin content was recorded. The transcriptome was also correlated with quality traits, such as total soluble solids and polyphenol content. This approach could lead to the identification of a transcriptomic profile that may specifically identify a genotype and its growing site and to the discovery of hub genes that might function as markers of wine quality.

Divergent Flowering Time Responses to Increasing Temperatures Are Associated With Transcriptome Plasticity and Epigenetic Modification Differences at FLC Promoter Region of Arabidopsis thaliana

Understanding the genetic, and transcriptomic changes that drive the phenotypic plasticity of fitness traits is a central question in evolutionary biology. In this study, we utilised 152 natural Swedish Arabidopsis thaliana accessions with re-sequenced genomes, transcriptomes and methylomes and measured flowering times (FTs) under two temperature conditions (10°C and 16°C) to address this question. We revealed that the northern accessions exhibited advanced flowering in response to decreased temperature, whereas the southern accessions delayed their flowering, indicating a divergent flowering response. This contrast in flowering responses was associated with the isothermality of their native ranges, which potentially enables the northern accessions to complete their life cycle more rapidly in years with shorter growth seasons. At the transcriptome level, we observed extensive rewiring of gene co-expression networks, with the expression of 25 core genes being associated with the mean FT and its plastic variation. Notably, variations in FLC expression sensitivity between northern and southern accessions were found to be associated with the divergence FT response. Further analysis suggests that FLC expression sensitivity is associated with differences in CG, CHG and CHH methylation at the promoter region. Overall, our study revealed the association between transcriptome plasticity and flowering time plasticity among different accessions, providing evidence for its relevance in ecological adaptation. These findings offer deeper insights into the genetics of rapid responses to environmental changes and ecological adaptation.

Gene-environment interactions within a precision environmental health framework

Understanding the complex interplay of genetic and environmental factors in disease etiology and the role of gene-environment interactions (GEIs) across human development stages is important. We review the state of GEI research, including challenges in measuring environmental factors and advantages of GEI analysis in understanding disease mechanisms. We discuss the evolution of GEI studies from candidate gene-environment studies to genome-wide interaction studies (GWISs) and the role of multi-omics in mediating GEI effects. We review advancements in GEI analysis methods and the importance of large-scale datasets. We also address the translation of GEI findings into precision environmental health (PEH), showcasing real-world applications in healthcare and disease prevention. Additionally, we highlight societal considerations in GEI research, including environmental justice, the return of results to participants, and data privacy. Overall, we underscore the significance of GEI for disease prediction and prevention and advocate for integrating the exposome into PEH omics studies.

Dual-trait genomic analysis in highly stratified Arabidopsis thaliana populations using genome-wide association summary statistics

Genome-wide association study (GWAS) is a powerful tool to identify genomic loci underlying complex traits. However, the application in natural populations comes with challenges, especially power loss due to population stratification. Here, we introduce a bivariate analysis approach to a GWAS dataset of Arabidopsis thaliana. We demonstrate the efficiency of dual-phenotype analysis to uncover hidden genetic loci masked by population structure via a series of simulations. In real data analysis, a common allele, strongly confounded with population structure, is discovered to be associated with late flowering and slow maturation of the plant. The discovered genetic effect on flowering time is further replicated in independent datasets. Using Mendelian randomization analysis based on summary statistics from our GWAS and expression QTL scans, we predicted and replicated a candidate gene AT1G11560 that potentially causes this association. Further analysis indicates that this locus is co-selected with flowering-time-related genes. The discovered pleiotropic genotype-phenotype map provides new insights into understanding the genetic correlation of complex traits.

Envirotyping within a multi-environment trial allowed identifying genetic determinants of winter oilseed rape yield stability

KEY MESSAGE

A comprehensive environmental characterization allowed identifying stable and interactive QTL for seed yield: QA09 and QC09a were detected across environments; whereas QA07a was specifically detected on the most stressed environments. A main challenge for rapeseed consists in maintaining seed yield while adapting to climate changes and contributing to environmental-friendly cropping systems. Breeding for cultivar adaptation is one of the keys to meet this challenge. Therefore, we propose to identify the genetic determinant of seed yield stability for winter oilseed rape using GWAS coupled with a multi-environmental trial and to interpret them in the light of environmental characteristics. Due to a comprehensive characterization of a multi-environmental trial using 79 indicators, four contrasting envirotypes were defined and used to identify interactive and stable seed yield QTL. A total of four QTLs were detected, among which, QA09 and QC09a, were stable (detected at the multi-environmental trial scale or for different envirotypes and environments); and one, QA07a, was specifically detected into the most stressed envirotype. The analysis of the molecular diversity at QA07a showed a lack of genetic diversity within modern lines compared to older cultivars bred before the selection for low glucosinolate content. The results were discussed in comparison with other studies and methods as well as in the context of breeding programs.

Dissection of QTLs conferring drought tolerance in B. carinata derived B. juncea introgression lines

BACKGROUND

Drought is one of the important abiotic stresses that can significantly reduce crop yields. In India, about 24% of Brassica juncea (Indian mustard) cultivation is taken up under rainfed conditions, leading to low yields due to moisture deficit stress. Hence, there is an urgent need to improve the productivity of mustard under drought conditions. In the present study, a set of 87 B. carinata-derived B. juncea introgression lines (ILs) was developed with the goal of creating drought-tolerant genotypes.

METHOD

The experiment followed the augmented randomized complete block design with four blocks and three checks. ILs were evaluated for seed yield and its contributing traits under both rainfed and irrigated conditions in three different environments created by manipulating locations and years. To identify novel genes and alleles imparting drought tolerance, Quantitative Trait Loci (QTL) analysis was carried out. Genotyping-by-Sequencing (GBS) approach was used to construct the linkage map.

RESULTS

The linkage map consisted of 5,165 SNP markers distributed across 18 chromosomes and spanning a distance of 1,671.87 cM. On average, there was a 3.09 cM gap between adjoining markers. A total of 29 additive QTLs were identified for drought tolerance; among these, 17 (58.6% of total QTLs detected) were contributed by B. carinata (BC 4), suggesting a greater contribution of B. carinata towards improving drought tolerance in the ILs. Out of 17 QTLs, 11 (64.7%) were located on the B genome, indicating more introgression segments on the B genome of B. juncea. Eight QTL hotspots, containing two or more QTLs, governing seed yield contributing traits, water use efficiency, and drought tolerance under moisture deficit stress conditions were identified. Seventeen candidate genes related to biotic and abiotic stresses, viz., SOS2, SOS2 like, NPR1, FAE1-KCS, HOT5, DNAJA1, NIA1, BRI1, RF21, ycf2, WRKY33, PAL, SAMS2, orf147, MAPK3, WRR1 and SUS, were reported in the genomic regions of identified QTLs.

CONCLUSIONS

The significance of B. carinata in improving drought tolerance and WUE by introducing genomic segments in Indian mustard is well demonstrated. The findings also provide valuable insights into the genetic basis of drought tolerance in mustard and pave the way for the development of drought-tolerant varieties.

Phenotypic Plasticity of Yield and Yield-Related Traits Contributing to the Wheat Yield in a Doubled Haploid Population

Phenotypic plasticity is the ability of an individual genotype to express phenotype variably in different environments. This study investigated the plasticity of yield-related traits of bread wheat by utilising 225 doubled haploid (DH) lines developed from cv. Westonia and cv. Kauz, through two field trials in Western Australia. Plasticity was quantified via two previously published methods: responsiveness to varying ecological conditions and slopes of reaction norms. The spikelets/spike was the most plastic trait, with an overall plasticity of 1.62. The least plastic trait was grain protein content, with an overall plasticity of 0.79. The trait hierarchy based on phenotypic plasticity was spikelets/spike > thousand kernel weight > seed number > seed length > grain yield > grain protein content. An increase in yield plasticity of 0.1 was associated with an increase in maximum yield of 4.45 kg ha-1. The plasticity of seed number and grain protein content were significantly associated with yield plasticity. The maximal yield was positively associated with spikelets/spike and grain yield, whereas it negatively associated with grain protein content. In contrast, the minimal yield was found to be negatively related to the plasticity of spikelets/spike and the plasticity of grain yield, whereas it was not related to grain protein content plasticity. Seed number and seed length exhibited plastic responses at the higher fertilisation state while remaining relatively stable at the lower fertilisation state for the wheat DH population. The finding of the current study will play a key role in wheat improvement under the changing climate. Seed length and seed number should be the breeding target for achieving stable yield in adverse environmental conditions.

Combined BSA-Seq and RNA-Seq Reveal Genes Associated with the Visual Stay-Green of Maize (Zea mays L.)

Maize has become one of the most widely grown grains in the world, and the stay-green mutant allows these plants to maintain their green leaves and photosynthetic potential for longer following anthesis than in non-mutated plants. As a result, stay-green plants have a higher production rate than non-stay-green varieties due to their prolonged grain-filling period. In this study, the candidate genes related to the visual stay-green at the maturation stage of maize were investigated. The F2 population was derived from the T01 (stay-green) and the Xin3 (non-stay-green) cross. Two bulked segregant analysis pools were constructed. According to the method of combining ED (Euclidean distance), Ridit (relative to an identified distribution unit), SmoothG, and SNP algorithms, a region containing 778 genes on chromosome 9 was recognized as the candidate region associated with the visual stay-green in maize. A total of eight modules were identified using WGCNA (weighted correlation network analysis), of which green, brown, pink, and salmon modules were significantly correlated with visual stay-green. BSA, combined with the annotation function, discovered 7 potential candidate genes, while WGCNA discovered 11 stay-green potential candidate genes. The candidate range was further reduced due through association analysis of BSA-seq and RNA-seq. We identified Zm00001eb378880, Zm00001eb383680, and Zm00001eb384100 to be the most likely candidate genes. Our results provide valuable insights into this new germplasm resource with reference to increasing the yield for maize.

Genome-wide mapping of regulatory variants for temperature- and salinity-adaptive genes reveals genetic basis of genotype-by-environment interaction in Crassostrea ariakensis

Regulatory variants in gene expression serve as bridges linking genetic variation and phenotypic plasticity. Environmental conditions typically influence the effects of regulatory variants on phenotypic plasticity; however, such genotype-by-environment interactions (G × E) are poorly understood. This study aimed to investigate the genetic basis of G × E in estuarine oyster (Crassostrea ariakensis), which is an important model animal for studying environmental adaption owing to its high plasticity and large intraspecific divergence. Genome-wide mapping of expression quantitative trait loci (eQTLs) for 23 environmental adaptive genes was performed for 256 estuarine oysters. We identified 1194 eQTL single nucleotide polymorphisms (eSNPs), including 433 cis-eSNPs in four genes and 722 trans-eSNPs in eight genes. The expression variation explanation of cis-eSNPs (9.95%) was significantly higher than that of trans-eSNPs (9.15%). We specifically showed cis- and trans-eSNPs with high linkage disequilibrium (LD) for Traf7, Slc6a5, Ggt, and Dap3. For example, we identified a cis-regulatory LD block containing 68 cis-eSNP and a trans-regulatory LD block, including 20 trans-eSNPs in Traf7. A high proportion (85%) of 40 vital eSNPs exhibited significant G × E effects. We identified crossing and nonparallel interactions of G × E, with the tag cis-eSNPs of Baat and Slc6a5 as representatives. Our results indicated that cis-eQTLs are highly conserved. This study provides insights into the understanding of adaptive evolutionary mechanisms and phenotypic response prediction to variable environments, as well as the genetic improvement for superior adaptive traits for genetic resource conservation and aquaculture.

Genetic mapping of quantitative trait loci associated with drought tolerance in chickpea (Cicer arietinum L.)

Elucidation of the genetic basis of drought tolerance is vital for genomics-assisted breeding of drought tolerant crop varieties. Here, we used genotyping-by-sequencing (GBS) to identify single nucleotide polymorphisms (SNPs) in recombinant inbred lines (RILs) derived from a cross between a drought tolerant chickpea variety, Pusa 362 and a drought sensitive variety, SBD 377. The GBS identified a total of 35,502 SNPs and subsequent filtering of these resulted in 3237 high-quality SNPs included in the eight linkage groups. Fifty-one percent of these SNPs were located in the genic regions distributed throughout the genome. The high density linkage map has total map length of 1069 cm with an average marker interval of 0.33 cm. The linkage map was used to identify 9 robust and consistent QTLs for four drought related traits viz. membrane stability index, relative water content, seed weight and yield under drought, with percent variance explained within the range of 6.29%-90.68% and LOD scores of 2.64 to 6.38, which were located on five of the eight linkage groups. A genomic region on LG 7 harbors quantitative trait loci (QTLs) explaining > 90% phenotypic variance for membrane stability index, and > 10% PVE for yield. This study also provides the first report of major QTLs for physiological traits such as membrane stability index and relative water content for drought stress in chickpea. A total of 369 putative candidate genes were identified in the 6.6 Mb genomic region spanning these QTLs. In-silico expression profiling based on the available transcriptome data revealed that 326 of these genes were differentially expressed under drought stress. KEGG analysis resulted in reduction of candidate genes from 369 to 99, revealing enrichment in various signaling pathways. Haplotype analysis confirmed 5 QTLs among the initially identified 9 QTLs. Two QTLs, qRWC1.1 and qYLD7.1, were chosen based on high SNP density. Candidate gene-based analysis revealed distinct haplotypes in qYLD7.1 associated with significant phenotypic differences, potentially linked to pathways for secondary metabolite biosynthesis. These identified candidate genes bolster defenses through flavonoids and phenylalanine-derived compounds, aiding UV protection, pathogen resistance, and plant structure.The study provides novel genomic regions and candidate genes which can be utilized in genomics-assisted breeding of superior drought tolerant chickpea cultivars.

Comparative genomics, microsynteny, ancestral state reconstruction and selection pressure analysis across distinctive genomes and sub-genomes of Brassicaceae for analysis of evolutionary history of VQ gene family

Any unfavorable condition that affects the metabolism, growth, or development of plants is considered plant stress. The molecular response of plants towards abiotic stresses involves signaling to cellular components, repressing transcription factors, and subsequently induced metabolic changes. Most valine-glutamine (VQ) motif-containing genes in plants encode regulatory proteins that interact with transcription factors and modulate their activity as transcription regulators. Several VQ proteins regulate plant development and stress responses. In spite of the functional importance of VQs, there is relatively little information about their evolutionary history in Brassicaceae or beyond. Brassicaceae is characterized by paleoploidy, mesopolyploidy, and neopolyploidy, offering a resource for studying evolution and diversification. In current study we performed phylogeny of the VQ gene family along with comparative genomics, microsynteny and evolutionary rates analysis across seven species of Brassicaceae. Our findings revealed the following; (1) a large segmental duplication in the shared common ancestor of the family Brassicaceae, resulted in paralogies of VQ1-VQ10, VQ15-VQ24, VQ16-VQ23, VQ17-VQ25, VQ18-VQ26, VQ22-VQ27; (2) chromosomal mapping revealed diverse distributions of the gene family; (3) duplicated segments undergo varying degrees of retention and loss; and (4) Out of the 12 paralogous members, most of the genes are under purifying selection. However, VQ23 in Brassicaceae stands out as it is under positive selection, indicating the need for further investigation. Overall, our results clearly establish that the ancestral VQ1/VQ10, VQ15/VQ24, VQ16/VQ23, VQ17/VQ25, VQ18/VQ26, VQ22/VQ27 genes duplicated in shared common ancestor of Brassicaceae.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12298-023-01347-z.

Diversity and genetic architecture of agro-morphological traits in a core collection of European traditional tomato

European traditional tomato varieties have been selected by farmers given their consistent performance and adaptation to local growing conditions. Here we developed a multipurpose core collection, comprising 226 accessions representative of the genotypic, phenotypic, and geographical diversity present in European traditional tomatoes, to investigate the basis of their phenotypic variation, gene×environment interactions, and stability for 33 agro-morphological traits. Comparison of the traditional varieties with a modern reference panel revealed that some traditional varieties displayed excellent agronomic performance and high trait stability, as good as or better than that of their modern counterparts. We conducted genome-wide association and genome-wide environment interaction studies and detected 141 quantitative trait loci (QTLs). Out of those, 47 QTLs were associated with the phenotype mean (meanQTLs), 41 with stability (stbQTLs), and 53 QTL-by-environment interactions (QTIs). Most QTLs displayed additive gene actions, with the exception of stbQTLs, which were mostly recessive and overdominant QTLs. Both common and specific loci controlled the phenotype mean and stability variation in traditional tomato; however, a larger proportion of specific QTLs was observed, indicating that the stability gene regulatory model is the predominant one. Developmental genes tended to map close to meanQTLs, while genes involved in stress response, hormone metabolism, and signalling were found within regions affecting stability. A total of 137 marker-trait associations for phenotypic means and stability were novel, and therefore our study enhances the understanding of the genetic basis of valuable agronomic traits and opens up a new avenue for an exploitation of the allelic diversity available within European traditional tomato germplasm.

Identifying SNP markers associated with distinctness, uniformity, and stability testing in Egyptian fenugreek genotypes

Distinctness, uniformity, and stability (DUS) test is the legal requirement in crop breeding to grant the intellectual property right for new varieties by evaluating their morphological characteristics across environments. On the other hand, molecular markers accurately identify genetic variations and validate the purity of the cultivars. Therefore, genomic DUS can improve the efficiency of traditional DUS testing. In this study, 112 Egyptian fenugreek genotypes were grown in Egypt at two locations: Wadi El-Natrun (Wadi), El-Beheira Governorate, with salty and sandy soil, and Giza, Giza governorate, with loamy clay soil. Twelve traits were measured, of which four showed a high correlation above 0.94 over the two locations. We observed significant genotype-by-location interactions (GxL) for seed yield, as it was superior in Wadi, with few overlapping genotypes with Giza. We attribute this superiority in Wadi to the maternal habitat, as most genotypes grew in governorates with newly reclaimed salty and sandy soil. As a first step toward genomic DUS, we performed an association study, and out of 38,142 SNPs, we identified 39 SNPs demonstrating conditional neutrality and four showing pleiotropic effects. Forty additional SNPs overlapped between both locations, each showing a similar impact on the associated trait. Our findings highlight the importance of GxL in validating the effect of each SNP to make better decisions about its suitability in the marker-assisted breeding program and demonstrate its potential use in registering new plant varieties.

The evolution of plasticity at geographic range edges

Phenotypic plasticity enables rapid responses to environmental change, and could facilitate range shifts in response to climate change. What drives the evolution of plasticity at range edges, and the capacity of range-edge individuals to be plastic, remain unclear. Here, we propose that accurately predicting when plasticity itself evolves or mediates adaptive evolution at expanding range edges requires integrating knowledge on the demography and evolution of edge populations. Our synthesis shows that: (i) the demography of edge populations can amplify or attenuate responses to selection for plasticity through diverse pathways, and (ii) demographic effects on plasticity are modified by the stability of range edges. Our spatially explicit synthesis for plasticity has the potential to improve predictions for range shifts with climate change.

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.

Forecasting trait responses in novel environments to aid seed provenancing under climate change

Revegetation projects face the major challenge of sourcing optimal plant material. This is often done with limited information about plant performance and increasingly requires factoring resilience to climate change. Functional traits can be used as quantitative indices of plant performance and guide seed provenancing, but trait values expected under novel conditions are often unknown. To support climate-resilient provenancing efforts, we develop a trait prediction model that integrates the effect of genetic variation with fine-scale temperature variation. We train our model on multiple field plantings of Arabidopsis thaliana and predict two relevant fitness traits-days-to-bolting and fecundity-across the species' European range. Prediction accuracy was high for days-to-bolting and moderate for fecundity, with the majority of trait variation explained by temperature differences between plantings. Projection under future climate predicted a decline in fecundity, although this response was heterogeneous across the range. In response, we identified novel genotypes that could be introduced to genetically offset the fitness decay. Our study highlights the value of predictive models to aid seed provenancing and improve the success of revegetation projects.

Genotype-by-environment and QTL-by-environment interactions in sweet cherry (Prunus avium L.) for flowering date

In sweet cherry (Prunus avium L.), flowering date is strongly dependent on the environment conditions and, therefore, is a trait of major interest for adaptation to climate change. Such trait can be influenced by genotype-by-environment interaction (G×E), that refers to differences in the response of genotypes to different environments. If not taken into account, G×E can reduce selection accuracy and overall genetic gain. However, little is known about G×E in fruit tree species. Flowering date is a highly heritable and polygenic trait for which many quantitative trait loci (QTLs) have been identified. As for the overall genetic performance, differential expression of QTLs in response to environment (QTL-by-environment interaction, QTL×E) can occur. The present study is based on the analysis of a multi-environment trial (MET) suitable for the study of G×E and QTL×E in sweet cherry. It consists of a sweet cherry F₁ full-sib family (n = 121) derived from the cross between cultivars 'Regina' and 'Lapins' and planted in two copies in five locations across four European countries (France, Italy, Slovenia and Spain) covering a large range of climatic conditions. The aim of this work was to study the effect of the environment on flowering date and estimate G×E, to carry QTL detection in different environments in order to study the QTL stability across environments and to estimate QTL×E. A strong effect of the environment on flowering date and its genetic control was highlighted. Two large-effect and environment-specific QTLs with significant QTL×E were identified on linkage groups (LGs) 1 and 4. This work gives new insights into the effect of the environment on a trait of main importance in one of the most economically important fruit crops in temperate regions. Moreover, molecular markers were developed for flowering date and a strategy consisting in using specific markers for warm or cold regions was proposed to optimize marker-assisted selection (MAS) in sweet cherry breeding programs.

Variations in Growth and Photosynthetic Traits of Polyploid Poplar Hybrids and Clones in Northeast China

To evaluate differences among 19 different ploidy hybrid poplar clones grown in northeast China, 21 traits related to growth traits and photosynthetic characteristics were detected and analyzed. Abundant phenotypic variations exist among and within populations, and these variations are the basis of forest tree genetic improvements. In this research, variance analysis showed that the traits except the net photosynthesis rate among the different ploidies and all the other traits exhibited significant differences among the ploidies or clones (p < 0.01). Estimation of phenotypic coefficients of variation, genotypic coefficients of variation, and repeatability is important for selecting superior materials. The larger the value, the greater the potential for material selection improvement. The repeatability of the different traits ranged from 0.88 to 0.99. The phenotypic and genotypic coefficients of variation of all the investigated traits ranged from 6.88% to 57.40% and from 4.85% to 42.89%, respectively. Correlation analysis showed that there were significant positive correlations between tree height, diameter, and volume. Transpiration rate, intercellular carbon dioxide concentration, and stomatal conductance were significantly positively correlated with each other but negatively correlated with instantaneous water use efficiency. Growth traits were weakly correlated with photosynthetic indexes. The rank correlation coefficient showed that most of the growth indicators reached a significant correlation level among different years (0.40-0.98), except 1-year-old tree height with 4-year-old tree height and 1-year-old ground diameter with 3-year-old tree height, which indicated the potential possibility for early selection of elite clones. Principal analysis results showed that the contribution rate of the first principal component was 46.606%, and 2-year-old tree height, 2-year-old ground diameter, 3-year-old tree height, 3-year-old ground diameter, 3-year-old diameter at breast height, 3-year-old volume, 4-year-old tree height, 4-year-old ground diameter, 4-year-old diameter at breast height, and 4-year-old volume showed higher vector values than other traits. With the method of multiple-trait comprehensive evaluation to evaluate clones, SX3.1, SY3.1, and XY4.2 were selected as elite clones, and the genetic gains of height, basal diameter, diameter at breast height, and volume of selected clones ranged from 12.85% to 64.87% in the fourth growth year. The results showed fundamental information for selecting superior poplar clones, which might provide new materials for the regeneration and improvement of forests in Northeast China.

Multi-environment genomic prediction for soluble solids content in peach (Prunus persica)

Genotype-by-environment interaction (G × E) is a common phenomenon influencing genetic improvement in plants, and a good understanding of this phenomenon is important for breeding and cultivar deployment strategies. However, there is little information on G × E in horticultural tree crops, mostly due to evaluation costs, leading to a focus on the development and deployment of locally adapted germplasm. Using sweetness (measured as soluble solids content, SSC) in peach/nectarine assessed at four trials from three US peach-breeding programs as a case study, we evaluated the hypotheses that (i) complex data from multiple breeding programs can be connected using GBLUP models to improve the knowledge of G × E for breeding and deployment and (ii) accounting for a known large-effect quantitative trait locus (QTL) improves the prediction accuracy. Following a structured strategy using univariate and multivariate models containing additive and dominance genomic effects on SSC, a model that included a previously detected QTL and background genomic effects was a significantly better fit than a genome-wide model with completely anonymous markers. Estimates of an individual's narrow-sense and broad-sense heritability for SSC were high (0.57-0.73 and 0.66-0.80, respectively), with 19-32% of total genomic variance explained by the QTL. Genome-wide dominance effects and QTL effects were stable across environments. Significant G × E was detected for background genome effects, mostly due to the low correlation of these effects across seasons within a particular trial. The expected prediction accuracy, estimated from the linear model, was higher than the realised prediction accuracy estimated by cross-validation, suggesting that these two parameters measure different qualities of the prediction models. While prediction accuracy was improved in some cases by combining data across trials, particularly when phenotypic data for untested individuals were available from other trials, this improvement was not consistent. This study confirms that complex data can be combined into a single analysis using GBLUP methods to improve understanding of G × E and also incorporate known QTL effects. In addition, the study generated baseline information to account for population structure in genomic prediction models in horticultural crop improvement.

Towards smart and sustainable development of modern berry cultivars in Europe

Fresh berries are a popular and important component of the human diet. The demand for high-quality berries and sustainable production methods is increasing globally, challenging breeders to develop modern berry cultivars that fulfill all desired characteristics. Since 1994, research projects have characterized genetic resources, developed modern tools for high-throughput screening, and published data in publicly available repositories. However, the key findings of different disciplines are rarely linked together, and only a limited range of traits and genotypes has been investigated. The Horizon2020 project BreedingValue will address these challenges by studying a broader panel of strawberry, raspberry and blueberry genotypes in detail, in order to recover the lost genetic diversity that has limited the aroma and flavor intensity of recent cultivars. We will combine metabolic analysis with sensory panel tests and surveys to identify the key components of taste, flavor and aroma in berries across Europe, leading to a high-resolution map of quality requirements for future berry cultivars. Traits linked to berry yields and the effect of environmental stress will be investigated using modern image analysis methods and modeling. We will also use genetic analysis to determine the genetic basis of complex traits for the development and optimization of modern breeding technologies, such as molecular marker arrays, genomic selection and genome-wide association studies. Finally, the results, raw data and metadata will be made publicly available on the open platform Germinate in order to meet FAIR data principles and provide the basis for sustainable research in the future.

Multienvironment QTL analysis delineates a major locus associated with homoeologous exchanges for water-use efficiency and seed yield in canola

Canola varieties exhibit variation in drought avoidance and drought escape traits, reflecting adaptation to water-deficit environments. Our understanding of underlying genes and their interaction across environments in improving crop productivity is limited. A doubled haploid population was analysed to identify quantitative trait loci (QTL) associated with water-use efficiency (WUE) related traits. High WUE in the vegetative phase was associated with low seed yield. Based on the resequenced parental genome data, we developed sequence-capture-based markers and validated their linkage with carbon isotope discrimination (Δ¹³ C) in an F₂ population. RNA sequencing was performed to determine the expression of candidate genes underlying Δ¹³ C QTL. QTL contributing to main and QTL × environment interaction effects for Δ¹³ C and yield were identified. One multiple-trait QTL for Δ¹³ C, days to flower, plant height, and seed yield was identified on chromosome A09. Interestingly, this QTL region overlapped with a homoeologous exchange (HE) event, suggesting its association with the multiple traits. Transcriptome analysis revealed 121 significantly differentially expressed genes underlying Δ¹³ C QTL on A09 and C09, including in HE regions. Sorting out the negative relationship between vegetative WUE and seed yield is a priority. Genetic and genomic resources and knowledge so developed could improve canola WUE and yield.

Genome-Wide Association Mapping of Grain Metal Accumulation in Wheat

Increasing wheat grain yield while ignoring grain quality and metal accumulation can result in metal deficiencies, particularly in countries where bread wheat accounts for the majority of daily dietary regimes. When the accumulation level exceeds a certain threshold, it becomes toxic and causes various diseases. Biofortification is an effective method of ensuring nutritional security. We screened 200 spring wheat advanced lines from the wheat association mapping initiative for Mn, Fe, Cu, Zn, Ni, and Cd concentrations. Interestingly, high-yielding genotypes had high essential metals, such as Mn, Fe, Cu, and Zn, but low levels of toxic metals, such as Ni and Cd. Positive correlations were found between all metals except Ni and Cd, where no correlation was found. We identified 142 significant SNPs, 26 of which had possible pleiotropic effects on two or more metals. Several QTLs co-located with previously mapped QTL for the same or other metals, whereas others were new. Our findings contribute to wheat genetic biofortification through marker-assisted selection, ensuring nutritional security in the long run.

Root Pulling Force Across Drought in Maize Reveals Genotype by Environment Interactions and Candidate Genes

High-throughput, field-based characterization of root systems for hundreds of genotypes in thousands of plots is necessary for breeding and identifying loci underlying variation in root traits and their plasticity. We designed a large-scale sampling of root pulling force, the vertical force required to extract the root system from the soil, in a maize diversity panel under differing irrigation levels for two growing seasons. We then characterized the root system architecture of the extracted root crowns. We found consistent patterns of phenotypic plasticity for root pulling force for a subset of genotypes under differential irrigation, suggesting that root plasticity is predictable. Using genome-wide association analysis, we identified 54 SNPs as statistically significant for six independent root pulling force measurements across two irrigation levels and four developmental timepoints. For every significant GWAS SNP for any trait in any treatment and timepoint we conducted post hoc tests for genotype-by-environment interaction, using a mixed model ANOVA. We found that 8 of the 54 SNPs showed significant GxE. Candidate genes underlying variation in root pulling force included those involved in nutrient transport. Although they are often treated separately, variation in the ability of plant roots to sense and respond to variation in environmental resources including water and nutrients may be linked by the genes and pathways underlying this variation. While functional validation of the identified genes is needed, our results expand the current knowledge of root phenotypic plasticity at the whole plant and gene levels, and further elucidate the complex genetic architecture of maize root systems.

A B73×Palomero Toluqueño mapping population reveals local adaptation in Mexican highland maize

Generations of farmer selection in the central Mexican highlands have produced unique maize varieties adapted to the challenges of the local environment. In addition to possessing great agronomic and cultural value, Mexican highland maize represents a good system for the study of local adaptation and acquisition of adaptive phenotypes under cultivation. In this study, we characterize a recombinant inbred line population derived from the B73 reference line and the Mexican highland maize variety Palomero Toluqueño. B73 and Palomero Toluqueño showed classic rank-changing differences in performance between lowland and highland field sites, indicative of local adaptation. Quantitative trait mapping identified genomic regions linked to effects on yield components that were conditionally expressed depending on the environment. For the principal genomic regions associated with ear weight and total kernel number, the Palomero Toluqueño allele conferred an advantage specifically in the highland site, consistent with local adaptation. We identified Palomero Toluqueño alleles associated with expression of characteristic highland traits, including reduced tassel branching, increased sheath pigmentation and the presence of sheath macrohairs. The oligogenic architecture of these three morphological traits supports their role in adaptation, suggesting they have arisen from consistent directional selection acting at distinct points across the genome. We discuss these results in the context of the origin of phenotypic novelty during selection, commenting on the role of de novo mutation and the acquisition of adaptive variation by gene flow from endemic wild relatives.

Chemical Composition and Nutritional Value of Three Sonchus Species

Species of unconventional food plants of the genus Sonchus are widely consumed in rural populations living in the Brazilian Atlantic Forest. This study investigated the nutritional composition of S. oleraceus, S. asper, and S. arvensis species. The centesimal composition was investigated according to the norms of the Association of Official Analytical Chemists, the occurrence and concentration of carotenoids and vitamins through High-Performance Liquid Chromatography, and minerals with the aid of atomic emission spectrometry in inductively coupled plasma. There was no significant difference between the water content found in the three species. However, S. asper showed higher concentrations of lipids (1.32 g/100 g), carbohydrates (0.34 g/100 g), total carotenoids (5.58 mg/100 g), and Ca (96.25 mg/100 g), while S. arvensis had the highest concentration of vitamins E (72.98 μg/100 g) and K (604.85 mg/100 g). S. oleraceus showed higher concentrations of Fe (23.74 mg/100 g). Statistically, fibers and ash presented the same proportions in S. asper and S. arvensis, as well as proteins in S. oleraceus and S. asper species. The availabilities of these vegetables together with their high nutritional value are important factors that contribute to ensuring food security for families that have these species in their diet.

A cross-scale approach to unravel the molecular basis of plant phenology in temperate and tropical climates

Plants have evolved to time their leafing, flowering and fruiting in appropriate seasons for growth, reproduction and resting. As a consequence of their adaptation to geographically different environments, there is a rich diversity in plant phenology from temperate and tropical climates. Recent progress in genetic and molecular studies will provide numerous opportunities to study the genetic basis of phenological traits and the history of adaptation of phenological traits to seasonal and aseasonal environments. Integrating molecular data with long-term phenology and climate data into predictive models will be a powerful tool to forecast future phenological changes in the face of global environmental change. Here, we review the cross-scale approach from genes to plant communities from three aspects: the latitudinal gradient of plant phenology at the community level, the environmental and genetic factors underlying the diversity of plant phenology, and an integrated approach to forecast future plant phenology based on genetically informed knowledge. Synthesizing the latest knowledge about plant phenology from molecular, ecological and mathematical perspectives will help us understand how natural selection can lead to the further evolution of the gene regulatory mechanisms in phenological traits in future forest ecosystems.

Advances in optical phenotyping of cereal crops

Optical sensors and sensing-based phenotyping techniques have become mainstream approaches in high-throughput phenotyping for improving trait selection and genetic gains in crops. We review recent progress and contemporary applications of optical sensing-based phenotyping (OSP) techniques in cereal crops and highlight optical sensing principles for spectral response and sensor specifications. Further, we group phenotypic traits determined by OSP into four categories - morphological, biochemical, physiological, and performance traits - and illustrate appropriate sensors for each extraction. In addition to the current status, we discuss the challenges of OSP and provide possible solutions. We propose that optical sensing-based traits need to be explored further, and that standardization of the language of phenotyping and worldwide collaboration between phenotyping researchers and other fields need to be established.

QTL mapping for growth-related traits by constructing the first genetic linkage map in Simao pine

BACKGROUND

Simao pine is one of the primary economic tree species for resin and timber production in southwest China. The exploitation and utilization of Simao pine are constrained by the relatively lacking of genetic information. Construction a fine genetic linkage map and detecting quantitative trait locis (QTLs) for growth-related traits is a prerequisite section of Simao Pine's molecular breeding program.

RESULTS

In our study, a high-resolution Simao pine genetic map employed specific locus amplified fragment sequencing (SLAF-seq) technology and based on an F₁ pseudo-testcross population has been constructed. There were 11,544 SNPs assigned to 12 linkage groups (LGs), and the total length of the map was 2,062.85 cM with a mean distance of 0.37 cM between markers. According to the phenotypic variation analysis for three consecutive years, a total of seventeen QTLs for four traits were detected. Among 17 QTLs, there were six for plant height (Dh.16.1, Dh16.2, Dh17.1, Dh18.1-3), five for basal diameter (Dbd.17.1-5), four for needle length (Dnl17.1-3, Dnl18.1) and two for needle diameter (Dnd17.1 and Dnd18.1) respectively. These QTLs individually explained phenotypic variance from 11.0-16.3%, and the logarithm of odds (LOD) value ranged from 2.52 to 3.87.

CONCLUSIONS

In our study, a fine genetic map of Simao pine applied the technology of SLAF-seq has been constructed for the first time. Based on the map, a total of 17 QTLs for four growth-related traits were identified. It provides helpful information for genomic studies and marker-assisted selection (MAS) in Simao pine.

Variation of phenotypic and physiological traits of Robinia pseudoacacia L. from 20 provenances

To select elite Robinia pseudoacacia L. germplasm resources for production, 13 phenotypes and three physiological indicators of 214 seedlings from 20 provenances were systematically evaluated and analyzed. The leaf phenotypic and physiological coefficients of variation among the genotypes ranged from 3.741% to 19.599% and from 8.260% to 42.363%, respectively. The Kentucky provenance had the largest coefficient of variation (18.541%). The average differentiation coefficients between and within provenances were 34.161% and 38.756%, respectively. These close percentages showed that R. pseudoacacia presented high genetic variation among and within provenances, which can be useful for assisted migration and breeding programs. Furthermore, based on the results of correlations, principal component analysis and cluster analysis, breeding improvements targeting R. pseudoacacia’s ornamental value, food value, and stress resistance of were performed. Forty and 30 excellent individuals, accounting for 18.692% and 14.019%, respectively, of the total resources. They were ultimately screened, after comprehensively taking into considering leaf phenotypic traits including compound leaf length, leaflet number and leaflet area and physiological characteristics including proline and soluble protein contents. These selected individuals could provide a base material for improved variety conservation and selection.

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.

Portuguese Common Bean Natural Variation Helps to Clarify the Genetic Architecture of the Legume's Nutritional Composition and Protein Quality

Common bean is a nutritious food legume widely appreciated by consumers worldwide. It is a staple food in Latin America, and a component of the Mediterranean diet, being an affordable source of protein with high potential as a gourmet food. Breeding for nutritional quality, including both macro and micronutrients, and meeting organoleptic consumers' preferences is a difficult task which is facilitated by uncovering the genetic basis of related traits. This study explored the diversity of 106 Portuguese common bean accessions, under two contrasting environments, to gain insight into the genetic basis of nutritional composition (ash, carbohydrates, fat, fiber, moisture, protein, and resistant starch contents) and protein quality (amino acid contents and trypsin inhibitor activity) traits through a genome-wide association study. Single-nucleotide polymorphism-trait associations were tested using linear mixed models accounting for the accessions' genetic relatedness. Mapping resolution to the gene level was achieved in 56% of the cases, with 102 candidate genes proposed for 136 genomic regions associated with trait variation. Only one marker-trait association was stable across environments, highlighting the associations' environment-specific nature and the importance of genotype × environment interaction for crops' local adaptation and quality. This study provides novel information to better understand the molecular mechanisms regulating the nutritional quality in common bean and promising molecular tools to aid future breeding efforts to answer consumers' concerns.

The Quantitative Trait Loci Mapping of Rice Plant and the Components of Its Extract Confirmed the Anti-Inflammatory and Platelet Aggregation Effects In Vitro and In Vivo

Unpredictable climate change might cause serious lack of food in the world. Therefore, in the present world, it is urgent to prepare countermeasures to solve problems in terms of human survival. In this research, quantitative trait loci (QTLs) were analyzed when rice attacked by white backed planthopper (WBPH) were analyzed using 120 Cheongcheong/Nagdong double haploid lines. Moreover, from the detected QTLs, WBPH resistance-related genes were screened in large candidate genes. Among them, OsCM, a major gene in the synthesis of Cochlioquinone-9 (cq-9), was screened. OsCM has high homology with the sequence of chorismate mutase, and exists in various functional and structural forms in plants that produce aromatic amino acids. It also induces resistance to biotic stress through the synthesis of secondary metabolites in plants. The WBPH resistance was improved in rice overexpressed through map-based cloning of the WBPH resistance-related gene OsCM, which was finally detected by QTL mapping. In addition, cq-9 increased the survival rate of caecal ligation puncture (CLP)-surgery mice by 60%. Moreover, the aorta of rat treated with cq-9 was effective in vasodilation response and significantly reduced the aggregation of rat platelets induced by collagen treatment. A cq-9, which is strongly associated with resistance to WBPH in rice, is also associated with positive effect of CLP surgery mice survival rate, vasodilation, and significantly reduced rat platelet aggregation induced by collagen treatment. Therefore, cq-9 presents research possibilities as a substance in a new paradigm that can act on both Plant-Insect in response to the present unpredictable future.

QTL mapping for bioenergy traits in sweet sorghum recombinant inbred lines

During the past decade, sweet sorghum (Sorghum bicolor Moench L.) has shown great potential for bioenergy production, especially biofuels. In this study, 223 recombinant inbred lines (RILs) derived from a cross between two sweet sorghum lines (Brandes × Wray) were evaluated in three trials. Single-nucleotide polymorphisms (SNPs) derived from genotyping by sequencing of 272 RILs were used to build a high-density genetic map comprising 3,767 SNPs spanning 1,368.83 cM. Multitrait multiple interval mapping (MT-MIM) was carried out to map quantitative trait loci (QTL) for eight bioenergy traits. A total of 33 QTLs were identified for flowering time, plant height, total soluble solids and sucrose (five QTLs each), fibers (four QTLs), and fresh biomass yield, juice extraction yield, and reducing sugars (three QTLs each). QTL hotspots were found on chromosomes 1, 3, 6, 9, and 10, in addition to other QTLs detected on chromosomes 4 and 8. We observed that 14 out of the 33 mapped QTLs were found in all three trials. Upon further development and validation in other crosses, the results provided by the present study have a great potential to be used in marker-assisted selection in sorghum breeding programs for biofuel production.

QTL Map of Early- and Late-Stage Perennial Regrowth in Zea diploperennis

Numerous climate change threats will necessitate a shift toward more sustainable agricultural practices during the 21st century. Conversion of annual crops to perennials that are capable of regrowing over multiple yearly growth cycles could help to facilitate this transition. Perennials can capture greater amounts of carbon and access more water and soil nutrients compared to annuals. In principle it should be possible to identify genes that confer perenniality from wild relatives and transfer them into existing breeding lines to create novel perennial crops. Two major loci controlling perennial regrowth in the maize relative Zea diploperennis were previously mapped to chromosome 2 (reg1) and chromosome 7 (reg2). Here we extend this work by mapping perennial regrowth in segregating populations involving Z. diploperennis and the maize inbreds P39 and Hp301 using QTL-seq and traditional QTL mapping approaches. The results confirmed the existence of a major perennial regrowth QTL on chromosome 2 (reg1). Although we did not observe the reg2 QTL in these populations, we discovered a third QTL on chromosome 8 which we named regrowth3 (reg3). The reg3 locus exerts its strongest effect late in the regrowth cycle. Neither reg1 nor reg3 overlapped with tiller number QTL scored in the same population, suggesting specific roles in the perennial phenotype. Our data, along with prior work, indicate that perennial regrowth in maize is conferred by relatively few major QTL.

Screening and identification of genes affecting grain quality and spikelet fertility during high-temperature treatment in grain filling stage of rice

BACKGROUND

Recent temperature increases due to rapid climate change have negatively affected rice yield and grain quality. Particularly, high temperatures during right after the flowering stage reduce spikelet fertility, while interfering with sugar energy transport, and cause severe damage to grain quality by forming chalkiness grains. The effect of high-temperature on spikelet fertility and grain quality during grain filling stage was evaluated using a double haploid line derived from another culture of F1 by crossing Cheongcheong and Nagdong cultivars. Quantitative trait locus (QTL) mapping identifies candidate genes significantly associated with spikelet fertility and grain quality at high temperatures.

RESULTS

Our analysis screened OsSFq3 that contributes to spikelet fertility and grain quality at high-temperature. OsSFq3 was fine-mapped in the region RM15749-RM15689 on chromosome 3, wherein four candidate genes related to the synthesis and decomposition of amylose, a starch component, were predicted. Four major candidate genes, including OsSFq3, and 10 different genes involved in the synthesis and decomposition of amylose and amylopectin, which are starch constituents, together with relative expression levels were analyzed. OsSFq3 was highly expressed during the initial stage of high-temperature treatment. It exhibited high homology with FLOURY ENDOSPERM 6 in Gramineae plants and is therefore expected to function similarly.

CONCLUSION

The QTL, major candidate genes, and OsSFq3 identified herein could be effectively used in breeding rice varieties to improve grain quality, while tolerating high temperatures, to cope with climate changes. Furthermore, linked markers can aid in marker-assisted selection of high-quality and -yield rice varieties tolerant to high temperatures.

Genetic determinants of seed protein plasticity in response to the environment in Medicago truncatula

As the frequency of extreme environmental events is expected to increase with climate change, identifying candidate genes for stabilizing the protein composition of legume seeds or optimizing this in a given environment is increasingly important. To elucidate the genetic determinants of seed protein plasticity, major seed proteins from 200 ecotypes of Medicago truncatula grown in four contrasting environments were quantified after one-dimensional electrophoresis. The plasticity index of these proteins was recorded for each genotype as the slope of Finlay and Wilkinson's regression and then used for genome-wide association studies (GWASs), enabling the identification of candidate genes for determining this plasticity. This list was enriched in genes related to transcription, DNA repair and signal transduction, with many of them being stress responsive. Other over-represented genes were related to sulfur and aspartate family pathways leading to the synthesis of the nutritionally essential amino acids methionine and lysine. By placing these genes in metabolic pathways, and using a M. truncatula mutant impaired in regenerating methionine from S-methylmethionine, we discovered that methionine recycling pathways are major contributors to globulin composition establishment and plasticity. These data provide a unique resource of genes that can be targeted to mitigate negative impacts of environmental stresses on seed protein composition.

Modeling genome-wide by environment interactions through omnigenic interactome networks

How genes interact with the environment to shape phenotypic variation and evolution is a fundamental question intriguing to biologists from various fields. Existing linear models built on single genes are inadequate to reveal the complexity of genotype-environment (G-E) interactions. Here, we develop a conceptual model for mechanistically dissecting G-E interplay by integrating previously disconnected theories and methods. Under this integration, evolutionary game theory, developmental modularity theory, and a variable selection method allow us to reconstruct environment-induced, maximally informative, sparse, and casual multilayer genetic networks. We design and conduct two mapping experiments by using a desert-adapted tree species to validate the biological application of the model proposed. The model identifies previously uncharacterized molecular mechanisms that mediate trees' response to saline stress. Our model provides a tool to comprehend the genetic architecture of trait variation and evolution and trace the information flow of each gene toward phenotypes within omnigenic networks.

QTL x environment interactions underlie ionome divergence in switchgrass

Ionomics measures elemental concentrations in biological organisms and provides a snapshot of physiology under different conditions. In this study, we evaluate genetic variation of the ionome in outbred, perennial switchgrass in three environments across the species' native range, and explore patterns of genotype-by-environment interactions. We grew 725 clonally replicated genotypes of a large full sib family from a four-way linkage mapping population, created from deeply diverged upland and lowland switchgrass ecotypes, at three common gardens. Concentrations of 18 mineral elements were determined in whole post-anthesis tillers using ion coupled plasma mass spectrometry (ICP-MS). These measurements were used to identify quantitative trait loci (QTL) with and without QTL-by-environment interactions (QTLxE) using a multi-environment QTL mapping approach. We found that element concentrations varied significantly both within and between switchgrass ecotypes, and GxE was present at both the trait and QTL level. Concentrations of 14 of the 18 elements were under some genetic control, and 77 QTL were detected for these elements. 74% of QTL colocalized multiple elements, half of QTL exhibited significant QTLxE, and roughly equal numbers of QTL had significant differences in magnitude and sign of their effects across environments. The switchgrass ionome is under moderate genetic control and by loci with highly variable effects across environments.

Validation of Functional Polymorphisms Affecting Maize Plant Height by Unoccupied Aerial Systems (UAS) Discovers Novel Temporal Phenotypes

Plant height (PHT) in maize (Zea mays L.) has been scrutinized genetically and phenotypically due to relationship with other agronomically valuable traits (e.g. yield). Heritable variation of PHT is determined by many discovered quantitative trait loci (QTLs); however, phenotypic effects of such loci often lack validation across environments and genetic backgrounds, especially in the hybrid state grown by farmers rather than the inbred state more often used by geneticists. A previous genome wide association study using a topcrossed hybrid diversity panel identified two novel quantitative trait variants (QTVs) controlling both PHT and grain yield. Here, heterogeneous inbred families demonstrated that these two loci, characterized by two single nucleotide polymorphisms (SNPs), cause phenotypic variation in inbred lines, but that size of these effects were variable across four different genetic backgrounds, ranging from 1 to 10 cm. Weekly unoccupied aerial system flights demonstrated the two SNPs had larger effects, varying from 10 to 25 cm, in early growth while effects decreased towards the end of the season. These results show that allelic effect sizes of economically valuable loci are both dynamic in temporal growth and dynamic across genetic backgrounds, resulting in informative phenotypic variability overlooked following traditional phenotyping methods. Public genotyping data shows recent favorable allele selection in elite temperate germplasm with little change across tropical backgrounds. As these loci remain rarer in tropical germplasm, with effects most visible early in growth, they are useful for breeding and selection to expand the genetic basis of maize.

Comparing a Mixed Model Approach to Traditional Stability Estimators for Mapping Genotype by Environment Interactions and Yield Stability in Soybean [Glycine max (L.) Merr.]

Identifying genetic loci associated with yield stability has helped plant breeders and geneticists begin to understand the role and influence of genotype by environment (GxE) interactions in soybean [Glycine max (L.) Merr.] productivity, as well as other crops. Quantifying a genotype's range of performance across testing locations has been developed over decades with dozens of methodologies available. This includes directly modeling GxE interactions as part of an overall model for yield, as well as methods which generate overall yield "stability" values from multi-environment trial data. Correspondence between these methods as it pertains to the outcomes of genome wide association studies (GWAS) has not been well defined. In this study, the GWAS results for yield and yield stability were compared in 213 soybean lines across 11 environments to determine their utility and potential intersection. Both univariate and multivariate conventional stability estimates were considered alongside a mixed model for yield that fit marker by environment interactions as a random effect. One-hundred and six total QTL were discovered across all mapping results, however, genetic loci that were significant in the mixed model for grain yield that fit marker by environment interactions were completely distinct from those that were significant when mapping using traditional stability measures as a phenotype. Furthermore, 73.21% of QTL discovered in the mixed model were determined to cause a crossover interaction effect which cause genotype rank changes between environments. Overall, the QTL discovered via explicitly mapping GxE interactions also explained more yield variance that those QTL associated with differences in traditional stability estimates making their theoretical impact on selection greater. A lack of intersecting results between mapping approaches highlights the importance of examining stability in multiple contexts when attempting to manipulate GxE interactions in soybean.

Genome-wide association of rice response to blast fungus identifies loci for robust resistance under high nitrogen

BACKGROUND

Nitrogen fertilization is known to increase disease susceptibility, a phenomenon called Nitrogen-Induced Susceptibility (NIS). In rice, this phenomenon has been observed in infections with the blast fungus Magnaporthe oryzae. A previous classical genetic study revealed a locus (NIS1) that enhances susceptibility to rice blast under high nitrogen fertilization. In order to further address the underlying genetics of plasticity in susceptibility to rice blast after fertilization, we analyzed NIS under greenhouse-controlled conditions in a panel of 139 temperate japonica rice strains. A genome-wide association analysis was conducted to identify loci potentially involved in NIS by comparing susceptibility loci identified under high and low nitrogen conditions, an approach allowing for the identification of loci validated across different nitrogen environments. We also used a novel NIS Index to identify loci potentially contributing to plasticity in susceptibility under different nitrogen fertilization regimes.

RESULTS

A global NIS effect was observed in the population, with the density of lesions increasing by 8%, on average, under high nitrogen fertilization. Three new QTL, other than NIS1, were identified. A rare allele of the RRobN1 locus on chromosome 6 provides robust resistance in high and low nitrogen environments. A frequent allele of the NIS2 locus, on chromosome 5, exacerbates blast susceptibility under the high nitrogen condition. Finally, an allele of NIS3, on chromosome 10, buffers the increase of susceptibility arising from nitrogen fertilization but increases global levels of susceptibility. This allele is almost fixed in temperate japonicas, as a probable consequence of genetic hitchhiking with a locus involved in cold stress adaptation.

CONCLUSIONS

Our results extend to an entire rice subspecies the initial finding that nitrogen increases rice blast susceptibility. We demonstrate the usefulness of estimating plasticity for the identification of novel loci involved in the response of rice to the blast fungus under different nitrogen regimes.

Allelic response of yield component traits to resource availability in spring wheat

Investigation of resource availability on allele effects for four yield component quantitative trait loci provides guidance for the improvement of grain yield in high and low yielding environments. A greater understanding of grain yield (GY) and yield component traits in spring wheat may increase selection efficiency for improved GY in high and low yielding environments. The objective of this study was to determine allelic response of four yield component quantitative trait loci (QTL) to variable resource levels which were manipulated by varying intraspecific plant competition and seeding density. The four QTL investigated in this study had been previously identified as impacting specific yield components. They included QTn.mst-6B for productive tiller number (PTN), WAPO-A1 for spikelet number per spike (SNS), and QGw.mst-3B and TaGW2-A1 for kernel weight (KWT). Near-isogenic lines for each of the four QTL were grown in multiple locations with three competition (border, no-border and space-planted) and two seeding densities (normal 216 seeds m^-2 and low 76 seeds m^-2). Allele response at QTn.mst-6B was driven by changes in resource availability, whereas allele response at WAPO-A1 and TaGW2-A1 was relatively unaffected by resource availability. The QTn.mst-6B.1 allele at QTn.mst-6B conferred PTN plasticity resulting in significant GY increases in high resource environments. The gw2-A1 allele at TaGW2-A1 significantly increased KWT, SNS and GPC offering a source of GY improvement without negatively impacting end-use quality. QGw.mst-3B allelic variation did not significantly impact KWT but did significantly impact SPS. Treatment effects in both experiments often resulted in significant positive impacts on GY and yield component traits when resource availability was increased. Results provide guidance for leveraging yield component QTL to improve GY performance in high- and low-yield environments.

Extracting the GEMs: Genotype, Environment, and Microbiome Interactions Shaping Host Phenotypes

One of the fundamental tenets of biology is that the phenotype of an organism (Y) is determined by its genotype (G), the environment (E), and their interaction (GE). Quantitative phenotypes can then be modeled as Y = G + E + GE + e, where e is the biological variance. This simple and tractable model has long served as the basis for studies investigating the heritability of traits and decomposing the variability in fitness. The importance and contribution of microbe interactions to a given host phenotype is largely unclear, nor how this relates to the traditional GE model. Here we address this fundamental question and propose an expansion of the original model, referred to as GEM, which explicitly incorporates the contribution of the microbiome (M) to the host phenotype, while maintaining the simplicity and tractability of the original GE model. We show that by keeping host, environment, and microbiome as separate but interacting variables, the GEM model can capture the nuanced ecological interactions between these variables. Finally, we demonstrate with an in vitro experiment how the GEM model can be used to statistically disentangle the relative contributions of each component on specific host phenotypes.