Analysis of the genes controlling three quantitative traits in three diverse plant species reveals the molecular basis of quantitative traits

Identification of PgRg1-3 Gene for Ginsenoside Rg1 Biosynthesis as Revealed by Combining Genome-Wide Association Study and Gene Co-Expression Network Analysis of Jilin Ginseng Core Collection

Ginseng, an important medicinal plant, is characterized by its main active component, ginsenosides. Among more than 40 ginsenosides, Rg1 is one of the ginsenosides used for measuring the quality of ginseng. Therefore, the identification and characterization of genes for Rg1 biosynthesis are important to elucidate the molecular basis of Rg1 biosynthesis. In this study, we utilized 39,327 SNPs and the corresponding Rg1 content from 344 core ginseng cultivars from Jilin Province. We conducted a genome-wide association study (GWAS) combining weighted gene co-expression network analysis (WGCNA), SNP-Rg1 content association analysis, and gene co-expression network analysis; three candidate Rg1 genes (PgRg1-1, PgRg1-2, and PgRg1-3) and one crucial candidate gene (PgRg1-3) were identified. Functional validation of PgRg1-3 was performed using methyl jasmonate (MeJA) regulation and RNAi, confirming that this gene regulates Rg1 biosynthesis. The spatial-temporal expression patterns of the PgRg1-3 gene and known key enzyme genes involved in ginsenoside biosynthesis differ. Furthermore, variations in their networks have a significant impact on Rg1 biosynthesis. This study established an accurate and efficient method for identifying candidate genes, cloned a novel gene controlling Rg1 biosynthesis, and identified 73 SNPs significantly associated with Rg1 content. This provides genetic resources and effective tools for further exploring the molecular mechanisms of Rg1 biosynthesis and molecular breeding.

Transcriptome-Wide Identification and Integrated Analysis of a UGT Gene Involved in Ginsenoside Ro Biosynthesis in Panax ginseng

Panax ginseng as a traditional medicinal plant with a long history of medicinal use. Ginsenoside Ro is the only oleanane-type ginsenoside in ginseng, and has various pharmacological activities, including anti-inflammatory, detoxification, and antithrombotic activities. UDP-dependent glycosyltransferase (UGT) plays a key role in the synthesis of ginsenoside, and the excavation of UGT genes involved in the biosynthesis of ginsenoside Ro has great significance in enriching ginsenoside genetic resources and further revealing the synthesis mechanism of ginsenoside. In this work, ginsenoside-Ro-synthesis-related genes were mined using the P. ginseng reference-free transcriptome database. Fourteen hub transcripts were identified by differential expression analysis and weighted gene co-expression network analysis. Phylogenetic and synteny block analyses of PgUGAT252645, a UGT transcript among the hub transcripts, showed that PgUGAT252645 belonged to the UGT73 subfamily and was relatively conserved in ginseng plants. Functional analysis showed that PgUGAT252645 encodes a glucuronosyltransferase that catalyzes the glucuronide modification of the C3 position of oleanolic acid using uridine diphosphate glucuronide as the substrate. Furthermore, the mutation at 622 bp of its open reading frame resulted in amino acid substitutions that may significantly affect the catalytic activity of the enzyme, and, as a consequence, affect the biosynthesis of ginsenoside Ro. Results of the in vitro enzyme activity assay of the heterologous expression product in E. coli of PgUGAT252645 verified the above analyses. The function of PgUGAT252645 was further verified by the result that its overexpression in ginseng adventitious roots significantly increased the content of ginsenoside Ro. The present work identified a new UGT gene involved in the biosynthesis of ginsenoside Ro, which not only enriches the functional genes in the ginsenoside synthesis pathway, but also provides the technical basis and theoretical basis for the in-depth excavation of ginsenoside-synthesis-related genes.

Molecular, genetic, and genomic basis of seed size and yield characteristics in soybean

Soybean (Glycine max L. Merr.) is a crucial oilseed cash crop grown worldwide and consumed as oil, protein, and food by humans and feed by animals. Comparatively, soybean seed yield is lower than cereal crops, such as maize, rice, and wheat, and the demand for soybean production does not keep up with the increasing consumption level. Therefore, increasing soybean yield per unit area is the most crucial breeding objective and is challenging for the scientific community. Moreover, yield and associated traits are extensively researched in cereal crops, but little is known about soybeans' genetics, genomics, and molecular regulation of yield traits. Soybean seed yield is a complex quantitative trait governed by multiple genes. Understanding the genetic and molecular processes governing closely related attributes to seed yield is crucial to increasing soybean yield. Advances in sequencing technologies have made it possible to conduct functional genomic research to understand yield traits' genetic and molecular underpinnings. Here, we provide an overview of recent progress in the genetic regulation of seed size in soybean, molecular, genetics, and genomic bases of yield, and related key seed yield traits. In addition, phytohormones, such as auxin, gibberellins, cytokinins, and abscisic acid, regulate seed size and yield. Hence, we also highlight the implications of these factors, challenges in soybean yield, and seed trait improvement. The information reviewed in this study will help expand the knowledge base and may provide the way forward for developing high-yielding soybean cultivars for future food demands.

Alternative Splicing Variation: Accessing and Exploiting in Crop Improvement Programs

Alternative splicing (AS) is a gene regulatory mechanism modulating gene expression in multiple ways. AS is prevalent in all eukaryotes including plants. AS generates two or more mRNAs from the precursor mRNA (pre-mRNA) to regulate transcriptome complexity and proteome diversity. Advances in next-generation sequencing, omics technology, bioinformatics tools, and computational methods provide new opportunities to quantify and visualize AS-based quantitative trait variation associated with plant growth, development, reproduction, and stress tolerance. Domestication, polyploidization, and environmental perturbation may evolve novel splicing variants associated with agronomically beneficial traits. To date, pre-mRNAs from many genes are spliced into multiple transcripts that cause phenotypic variation for complex traits, both in model plant Arabidopsis and field crops. Cataloguing and exploiting such variation may provide new paths to enhance climate resilience, resource-use efficiency, productivity, and nutritional quality of staple food crops. This review provides insights into AS variation alongside a gene expression analysis to select for novel phenotypic diversity for use in breeding programs. AS contributes to heterosis, enhances plant symbiosis (mycorrhiza and rhizobium), and provides a mechanistic link between the core clock genes and diverse environmental clues.

Genetic and molecular dissection of ginseng (Panax ginseng Mey.) germplasm using high-density genic SNP markers, secondary metabolites, and gene expressions

Genetic and molecular knowledge of a species is crucial to its gene discovery and enhanced breeding. Here, we report the genetic and molecular dissection of ginseng, an important herb for healthy food and medicine. A mini-core collection consisting of 344 cultivars and landraces was developed for ginseng that represents the genetic variation of ginseng existing in its origin and diversity center. We sequenced the transcriptomes of all 344 cultivars and landraces; identified over 1.5 million genic SNPs, thereby revealing the genic diversity of ginseng; and analyzed them with 26,600 high-quality genic SNPs or a selection of them. Ginseng had a wide molecular diversity and was clustered into three subpopulations. Analysis of 16 ginsenosides, the major bioactive components for healthy food and medicine, showed that ginseng had a wide variation in the contents of all 16 ginsenosides and an extensive correlation of their contents, suggesting that they are synthesized through a single or multiple correlated pathways. Furthermore, we pair-wisely examined the relationships between the cultivars and landraces, revealing their relationships in gene expression, gene variation, and ginsenoside biosynthesis. These results provide new knowledge and new genetic and genic resources for advanced research and breeding of ginseng and related species.

Biotechnological interventions for improving the seed longevity in cereal crops: progress and prospects

Seed longevity is a measure of the viability of seeds during long-term storage and is crucial for germplasm conservation and crop improvement programs. Also, longevity is an important trait for ensuring food and nutritional security. Thus, a better understanding of various factors regulating seed longevity is requisite to improve this trait and to minimize the genetic drift during the regeneration of germplasm. In particular, seed deterioration of cereal crops during storage adversely affects agricultural productivity and food security. The irreversible process of seed deterioration involves a complex interplay between different genes and regulatory pathways leading to: loss of DNA integrity, membrane damage, inactivation of storage enzymes and mitochondrial dysfunction. Identifying the genetic determinants of seed longevity and manipulating them using biotechnological tools hold the key to ensuring prolonged seed storage. Genetics and genomics approaches had identified several genomic regions regulating the longevity trait in major cereals such as: rice, wheat, maize and barley. However, very few studies are available in other Poaceae members, including millets. Deploying omics tools, including genomics, proteomics, metabolomics, and phenomics, and integrating the datasets will pinpoint the precise molecular determinants affecting the survivability of seeds. Given this, the present review enumerates the genetic factors regulating longevity and demonstrates the importance of integrated omics strategies to dissect the molecular machinery underlying seed deterioration. Further, the review provides a roadmap for deploying biotechnological approaches to manipulate the genes and genomic regions to develop improved cultivars with prolonged storage potential.

The Current Developments in Medicinal Plant Genomics Enabled the Diversification of Secondary Metabolites' Biosynthesis

Medicinal plants produce important substrates for their adaptation and defenses against environmental factors and, at the same time, are used for traditional medicine and industrial additives. Plants have relatively little in the way of secondary metabolites via biosynthesis. Recently, the whole-genome sequencing of medicinal plants and the identification of secondary metabolite production were revolutionized by the rapid development and cheap cost of sequencing technology. Advances in functional genomics, such as transcriptomics, proteomics, and metabolomics, pave the way for discoveries in secondary metabolites and related key genes. The multi-omics approaches can offer tremendous insight into the variety, distribution, and development of biosynthetic gene clusters (BGCs). Although many reviews have reported on the plant and medicinal plant genome, chemistry, and pharmacology, there is no review giving a comprehensive report about the medicinal plant genome and multi-omics approaches to study the biosynthesis pathway of secondary metabolites. Here, we introduce the medicinal plant genome and the application of multi-omics tools for identifying genes related to the biosynthesis pathway of secondary metabolites. Moreover, we explore comparative genomics and polyploidy for gene family analysis in medicinal plants. This study promotes medicinal plant genomics, which contributes to the biosynthesis and screening of plant substrates and plant-based drugs and prompts the research efficiency of traditional medicine.

Transcriptome and Phenotype Integrated Analysis Identifies Genes Controlling Ginsenoside Rb1 Biosynthesis and Reveals Their Interactions in the Process in Panax ginseng

Genes are the keys to deciphering the molecular mechanism underlying a biological trait and designing approaches desirable for plant genetic improvement. Ginseng is an important medicinal herb in which ginsenosides have been shown to be the major bioactive component; however, only a few genes involved in ginsenoside biosynthesis have been cloned through orthologue analysis. Here, we report the identification of 21 genes controlling Rb1 biosynthesis by stepwise ginseng transcriptome and Rb1 content integrated analysis. We first identified the candidate genes for Rb1 biosynthesis by integrated analysis of genes with the trait from four aspects, including gene transcript differential expression between highest- and lowest-Rb1 content cultivars, gene transcript expression-Rb1 content correlation, and biological impacts of gene mutations on Rb1 content, followed by the gene transcript co-expression network. Twenty-two candidate genes were identified, of which 21 were functionally validated for Rb1 biosynthesis by gene regulation, genetic transformation, and mutation analysis. These genes were strongly correlated in expression with the previously cloned genes encoding key enzymes for Rb1 biosynthesis. Based on the correlations, a pathway for Rb1 biosynthesis was deduced to indicate the roles of the genes in Rb1 biosynthesis. Moreover, the genes formed a strong co-expression network with the previously cloned Rb1 biosynthesis genes, and the variation in the network was associated with the variation in the Rb1 content. These results indicate that Rb1 biosynthesis is a process of correlative interactions among Rb1 biosynthesis genes. Therefore, this study provides new knowledge, 21 new genes, and 96 biomarkers for Rb1 biosynthesis useful for enhanced research and breeding in ginseng.

Genome-wide identification of genes enabling accurate prediction of hybrid performance from parents across environments and populations for gene-based breeding in maize

Accurate prediction of hybrid offspring complex trait phenotype from parents is paramount to enhanced plant breeding, animal breeding, and human medicine. Here we report genome-wide identification of genes enabling accurate prediction of hybrid offspring complex traits from parents using maize grain yield as the target trait. We identified 181 ZmF1GY genes enabling prediction of maize (Zea mays L.) F₁ hybrid grain yield from parents and tested their utility and efficiency for predicting F₁ hybrid grain yields from parents using their expressions, genic SNPs, and number of favorable alleles (NFAs), respectively. The ZmF1GY genes predicted hybrid grain yields from parents at an accuracy of 0.86, presented by correlation coefficient between predicted and observed phenotypes, within an environment, 0.74 across environments, and 0.64 across populations, outperforming genomic prediction by 27-406%, 23%, and 40%, respectively. Furthermore, we identified nine of the ZmF1GY genes containing SNPs or InDels in parents that increased or decreased hybrid grain yields by 14-46%. When the NFAs of these nine ZmF1GY genes were used for hybrid grain yield prediction from parents, they predicted hybrid grain yields at an accuracy of 0.79, outperforming genomic prediction by 21% that was based on up to tens of thousands of genome-wide SNPs. These results demonstrate the feasibility of developing a gene toolkit for a species enabling gene-based breeding across environments and populations that is much more powerful and efficient than current breeding, thereby helping secure the world's food production. The methodology is applicable to all crops, livestock, and humans.

Analysis of the genes controlling cotton fiber length reveals the molecular basis of plant breeding and the genetic potential of current cultivars for continued improvement

Stagnated crop improvement has raised questions of whether and how current crop cultivars can be further improved. Genes are the core determinants of performance of all cultivars. Here, we report the molecular basis of plant breeding and address these questions by analyzing 226 GFL genes controlling and accurately predicting fiber length, an important breeding objective trait, in cotton (Gossypium sp.). We first identified the favorable allele and the number of favorable alleles (NFAs) of each GFL gene, calculated the total NFAs of the 226 GFL genes accumulated in 198 advanced breeding lines, and analyzed them against fiber lengths. Fiber lengths of the breeding lines were strongly correlated with the total NFAs of the GFL genes (r = 0.85, P < 0.0001), suggesting that accumulation of the favorable alleles of the genes controlling objective traits is the molecular basis of cotton breeding. Surprisingly, a breeding line with a fiber length of present cultivars having the longest fibers contained only about 51% of the total NFAs of the 226 GFL genes. The genetic potentials of current cultivars were then predicted using linear and non-linear models, respectively, revealing that a breeding line or cultivar with a fiber length of 33.8 mm could be further improved in fiber length by up to 118%. Finally, we showed that the genetic potential of such a breeding line can be realized through gene-based breeding. Therefore, these findings shed light on continued crop improvement in general and provide 740 genic biomarkers desirable for enhanced cotton fiber breeding.

Leveraging a graft collection to develop metabolome-based trait prediction for the selection of tomato rootstocks with enhanced salt tolerance

Grafting has been demonstrated to significantly enhance the salt tolerance of crops. However, breeding efforts to develop enhanced graft combinations are hindered by knowledge-gaps as to how rootstocks mediate scion-response to salt stress. We grafted the scion of cultivated M82 onto rootstocks of 254 tomato accessions and explored the morphological and metabolic responses of grafts under saline conditions (EC = 20 dS m^-1) as compared to self-grafted M82 (SG-M82). Correlation analysis and Least Absolute Shrinkage and Selection Operator were performed to address the association between morphological diversification and metabolic perturbation. We demonstrate that grafting the same variety onto different rootstocks resulted in scion phenotypic heterogeneity and emphasized the productivity efficiency of M82 irrespective of the rootstock. Spectrophotometric analysis to test lipid oxidation showed largest variability of malondialdehyde (MDA) equivalents across the population, while the least responsive trait was the ratio of fruit fresh weight to total fresh weight (FFW/TFW). Generally, grafts showed greater values for the traits measured than SG-M82, except for branch number and wild race-originated rootstocks; the latter were associated with smaller scion growth parameters. Highly responsive and correlated metabolites were identified across the graft collection including malate, citrate, and aspartate, and their variance was partly related to rootstock origin. A group of six metabolites that consistently characterized exceptional graft response was observed, consisting of sorbose, galactose, sucrose, fructose, myo-inositol, and proline. The correlation analysis and predictive modelling, integrating phenotype- and leaf metabolite data, suggest a potential predictive relation between a set of leaf metabolites and yield-related traits.

Accurate prediction of complex traits for individuals and offspring from parents using a simple, rapid, and efficient method for gene-based breeding in cotton and maize

Accurate, simple, rapid, and inexpensive prediction of complex traits controlled by numerous genes is paramount to enhanced plant breeding, animal breeding, and human medicine. Here we report a novel method that enables accurate, simple, and rapid prediction of complex traits of individuals or offspring from parents based on the number of favorable alleles (NFAs) of the genes controlling the objective traits. The NFAs of 226 cotton fiber length (GFL) genes and nine maize hybrid grain yield related (ZmF1GY) genes were directly used to predict cotton fiber lengths of individual plants and maize grain yields of F₁ hybrids from parents, respectively, using prediction model-based methods as controls. The NFAs of the 226 GFL genes predicted cotton fiber lengths at an accuracy of 0.85, as the model methods and outperforming genomic prediction by 82 % - 170 %. The NFAs of the nine ZmF1GY genes predicted grain yields of maize hybrids from parents at an accuracy of 0.80, outperforming genomic prediction by 67 %. Moreover, the prediction accuracies of these traits were consistent across years, environments, and eco-agricultural systems. Importantly, the accurate prediction of these traits directly using the NFAs of the genes allows breeding to be performed in greenhouse, phytotron, or off-season, without the need of the model training and validation steps essential and costly for model-based genomic or genic prediction. Therefore, this new method dramatically outperforms the current model-based genomic methods used for phenotype prediction and streamlines the process of breeding, thus promising to substantially enhance current plant and animal breeding.

Design and Analysis of Native Photorespiration Gene Motifs of Promoter Untranslated Region Combinations Under Short Term Abiotic Stress Conditions

Quantitative traits are rarely controlled by a single gene, thereby making multi-gene transformation an indispensable component of modern synthetic biology approaches. However, the shortage of unique gene regulatory elements (GREs) for the robust simultaneous expression of multiple nuclear transgenes is a major bottleneck that impedes the engineering of complex pathways in plants. In this study, we compared the transcriptional efficacies of a comprehensive list of well-documented promoter and untranslated region (UTR) sequences side by side. The strength of GREs was examined by a dual-luciferase assay in conjunction with transient expression in tobacco. In addition, we created suites of new GREs with higher transcriptional efficacies by combining the best performing promoter-UTR sequences. We also tested the impact of elevated temperature and high irradiance on the effectiveness of these GREs. While constitutive promoters ensure robust expression of transgenes, they lack spatiotemporal regulations exhibited by native promoters. Here, we present a proof-of-principle study on the characterization of synthetic promoters based on cis-regulatory elements of three key photorespiratory genes. This conserved biochemical process normally increases under elevated temperature, low CO2, and high irradiance stress conditions and results in ∼25% loss in fixed CO2. To select stress-responsive cis-regulatory elements involved in photorespiration, we analyzed promoters of two chloroplast transporters (AtPLGG1 and AtBASS6) and a key plastidial enzyme, AtPGLP using PlantPAN3.0 and AthaMap. Our results suggest that these motifs play a critical role for PLGG1, BASS6, and PGLP in mediating response to elevated temperature and high-intensity light stress. These findings will not only enable the advancement of metabolic and genetic engineering of photorespiration but will also be instrumental in related synthetic biology approaches.

Genome-Wide Association Study Reveals Genetic Architecture and Candidate Genes for Yield and Related Traits under Terminal Drought, Combined Heat and Drought in Tropical Maize Germplasm

Maize (Zea mays L.) production is constrained by drought and heat stresses. The combination of these two stresses is likely to be more detrimental. To breed for maize cultivars tolerant of these stresses, 162 tropical maize inbred lines were evaluated under combined heat and drought (CHD) and terminal drought (TD) conditions. The mixed linear model was employed for the genome-wide association study using 7834 SNP markers and several phenotypic data including, days to 50% anthesis (AD) and silking (SD), husk cover (HUSKC), and grain yield (GY). In total, 66, 27, and 24 SNPs were associated with the traits evaluated under CHD, TD, and their combined effects, respectively. Of these, four single nucleotide polymorphism (SNP) markers (SNP_161703060 on Chr01, SNP_196800695 on Chr02, SNP_195454836 on Chr05, and SNP_51772182 on Chr07) had pleiotropic effects on both AD and SD under CHD conditions. Four SNPs (SNP_138825271 (Chr03), SNP_244895453 (Chr04), SNP_168561609 (Chr05), and SNP_62970998 (Chr06)) were associated with AD, SD, and HUSKC under TD. Twelve candidate genes containing phytohormone cis-acting regulating elements were implicated in the regulation of plant responses to multiple stress conditions including heat and drought. The SNPs and candidate genes identified in the study will provide invaluable information for breeding climate smart maize varieties under tropical conditions following validation of the SNP markers.

Integrative transcriptome analysis identifies new oxidosqualene cyclase genes involved in ginsenoside biosynthesis in Jilin ginseng

BACKGROUND

Jilin ginseng, Panax ginseng, is a valuable medicinal herb whose ginsenosides are its major bioactive components. The ginseng oxidosqualene cyclase (PgOSC) gene family is known to play important roles in ginsenoside biosynthesis, but few members of the gene family have been functionally studied.

METHODS

The PgOSC gene family has been studied by an integrated analysis of gene expression-ginsenoside content correlation, gene mutation-ginsenoside content association and gene co-expression network, followed by functional analysis through gene regulation.

RESULTS

We found that five of the genes in the PgOSC gene family, including two published ginsenoside biosynthesis genes and three new genes, were involved in ginsenoside biosynthesis. Not only were the expressions of these genes significantly correlated with ginsenoside contents, but also their nucleotide mutations significantly influenced ginsenoside contents. These results were further verified by regulation analysis of the genes by methyl jasmonate (MeJA) in ginseng hairy roots. Four of these five PgOSC genes were mapped to the ginsenoside biosynthesis pathway. These PgOSC genes expressed differently across tissues, but relatively consistent across developmental stages. These PgOSC genes formed a single co-expression network with those published ginsenoside biosynthesis genes, further confirming their roles in ginsenoside biosynthesis. When the network varied, ginsenoside biosynthesis was significantly influenced, thus revealing the molecular mechanism of ginsenoside biosynthesis.

CONCLUSION

At least five of the PgOSC genes, including the three newly identified and two published PgOSC genes, are involved in ginsenoside biosynthesis. These results provide gene resources and knowledge essential for enhanced research and applications of ginsenoside biosynthesis in ginseng.

Characteristics of RNA alternative splicing and its potential roles in ginsenoside biosynthesis in a single plant of ginseng, Panax ginseng C.A. Meyer

RNA alternative splicing (AS) is prevalent in higher organisms and plays a paramount role in biology; therefore, it is crucial to have comprehensive knowledge on AS to understand biology. However, knowledge is limited about how AS activates in a single plant and functions in a biological process. Ginseng is one of the most widely used medicinal herbs that is abundant in a number of medicinal bioactive components, especially ginsenosides. In this study, we sequenced the transcripts of 14 organs from a 4-year-old ginseng plant and quantified their ginsenoside contents. We identified AS genes by analyzing their transcripts with the ginseng genome and verified their AS events by PCR. The plant had a total of 13,863 AS genes subjected to 30,801 AS events with five mechanisms: skipped exon, retained intron, alternative 5'splice site, alternative 3' splice site, and mutually exclusive exon. The genes that were more conserved, had more exons, and/or expressed across organs were more likely to be subjected to AS. AS genes were enriched in over 500 GO terms in the plant even though the number of AS gene-enriched GO terms varied across organs. At least 24 AS genes were found to be involved in ginsenoside biosynthesis. These AS genes were significantly up-enriched and more likely to form a co-expression network, thus suggesting the functions of AS and correlations of the AS genes in the process. This study provides comprehensive insights into the molecular characteristics and biological functions of AS in a single plant; thus, helping better understand biology.

Transcriptome-Wide Analysis for Ginsenoside Rb3 Synthesis-Related Genes and Study on the Expression of Methyl Jasmonate Treatment in Panax ginseng

Panax ginseng C. A. Meyer is a kind of renascent herb that belongs to the genus Panax in the family Araliaceae. It is a traditional Chinese precious herbal medicine with a long history of medicinal use. Ginsenoside Rb3 is one of the important active ingredients in ginseng and has important physiological activity in the treatment of many diseases. In this study, we screened and systematically analyzed the candidate genes related to ginsenoside Rb3 synthesis through bioinformatics methods; discussed the functions, expression patterns, and interactions of the genes related to ginsenoside Rb3 synthesis; and finally, selected seven genes, mainly PgRb3, that directly contribute to the synthesis of ginsenoside Rb3. This study provides a reference for revealing the expression rules of ginsenoside Rb3 synthesis-related genes and elucidating the regulatory mechanism of methyl jasmonate, lays a theoretical foundation for the research of ginsenoside Rb3 synthesis, and provides theoretical and technical support for the factory production of ginsenoside monomer saponins.

Accurate Prediction of a Quantitative Trait Using the Genes Controlling the Trait for Gene-Based Breeding in Cotton

Accurate phenotype prediction of quantitative traits is paramount to enhanced plant research and breeding. Here, we report the accurate prediction of cotton fiber length, a typical quantitative trait, using 474 cotton (Gossypium ssp.) fiber length (GFL) genes and nine prediction models. When the SNPs/InDels contained in 226 of the GFL genes or the expressions of all 474 GFL genes was used for fiber length prediction, a prediction accuracy of r = 0.83 was obtained, approaching the maximally possible prediction accuracy of a quantitative trait. This has improved by 116%, the prediction accuracies of the fiber length thus far achieved for genomic selection using genome-wide random DNA markers. Moreover, analysis of the GFL genes identified 125 of the GFL genes that are key to accurate prediction of fiber length, with which a prediction accuracy similar to that of all 474 GFL genes was obtained. The fiber lengths of the plants predicted with expressions of the 125 key GFL genes were significantly correlated with those predicted with the SNPs/InDels of the above 226 SNP/InDel-containing GFL genes (r = 0.892, P = 0.000). The prediction accuracies of fiber length using both genic datasets were highly consistent across environments or generations. Finally, we found that a training population consisting of 100-120 plants was sufficient to train a model for accurate prediction of a quantitative trait using the genes controlling the trait. Therefore, the genes controlling a quantitative trait are capable of accurately predicting its phenotype, thereby dramatically improving the ability, accuracy, and efficiency of phenotype prediction and promoting gene-based breeding in cotton and other species.