Complex Agronomic Traits Research Articles

Grapevine pangenome facilitates trait genetics and genomic breeding.

Grapevine breeding is hindered by a limited understanding of the genetic basis of complex agronomic traits. This study constructs a graph-based pangenome reference (Grapepan v.1.0) from 18 newly generated phased telomere-to-telomere assemblies and 11 published assemblies. Using Grapepan v.1.0, we build a variation map with 9,105,787 short variations and 236,449 structural variations (SVs) from the resequencing data of 466 grapevine cultivars. Integrating SVs into a genome-wide association study, we map 148 quantitative trait loci for 29 agronomic traits (50.7% newly identified), with 12 traits significantly contributed by SVs. The estimated heritability improves by 22.78% on average when including SVs. We discovered quantitative trait locus regions under divergent artificial selection in metabolism and berry development between wine and table grapes, respectively. Moreover, significant genetic correlations were detected among the 29 traits. Under a polygenic model, we conducted genomic predictions for each trait. In general, our study facilitates the breeding of superior cultivars via the genomic selection of multiple traits.

Improving complex agronomic and domestication traits in the perennial grain crop intermediate wheatgrass with genetic mapping and genomic prediction.

The perennial grass Thinopyrum intermedium (intermediate wheatgrass [IWG]) is being domesticated as a food crop. With a deep root system and high biomass, IWG can help reduce soil and water erosion and limit nutrient runoff. As a novel grain crop undergoing domestication, IWG lags in yield, seed size, and other agronomic traits compared to annual grains. Better characterization of trait variation and identification of genetic markers associated with loci controlling the traits could help in further improving this crop. The University of Minnesota's Cycle 5 IWG breeding population of 595 spaced plants was evaluated at two locations in 2021 and 2022 for agronomic traits plant height, grain yield, and spike weight, and domestication traits shatter resistance, free grain threshing, and seed size. Pairwise trait correlations were weak to moderate with the highest correlation observed between seed size and height (0.41). Broad-sense trait heritabilities were high (0.68-0.77) except for spike weight (0.49) and yield (0.44). Association mapping using 24,284 genome-wide single nucleotide polymorphism markers identified 30 main quantitative trait loci (QTLs) across all environments and 32 QTL-by-environment interactions (QTE) at each environment. The genomic prediction model significantly improved predictions when parents were used in the training set and significant QTLs and QTEs used as covariates. Seed size was the best predicted trait with model predictive ability (r) of 0.72; yield was predicted moderately well (r=0.45). We expect this discovery of significant genomic loci and mostly high trait predictions from genomic prediction models to help improve future IWG breeding populations.

Small Stomates and Xylem Vessels Associated With Freeze Tolerance in Winter Barley

ABSTRACTFreeze tolerance is a complex agronomic trait that is difficult to evaluate in the field because of year‐to‐year variation in weather. Discovering plant characteristics closely related to freeze tolerance would enable more effective selection for this important trait. To explore possible physiological mechanisms and search for useful characteristics related to freeze tolerance in winter barley, we conducted field and growth chamber experiments with seven freeze‐tolerant and seven freeze‐susceptible genotypes that exhibited contrasting winter survival in preliminary field screenings. In a 2‐year field experiment, malondialdehyde, proline and water‐soluble carbohydrate concentrations were measured during cold acclimation and deacclimation to investigate differences in osmoregulation and membrane stability between freeze‐tolerant and freeze‐sensitive genotypes. All parameters varied by sampling year, and significant differences between freeze tolerance groups were found mainly during cold deacclimation in the spring. In growth chamber experiments, the size of xylem vessels and stomates was measured with and without cold acclimation. Freeze‐tolerant genotypes had smaller xylem vessels and stomates than freeze‐sensitive genotypes with and without cold acclimation, and small stomatal length was associated with a small xylem vessel area. Thus, it may be possible to improve freeze tolerance in winter barley by selecting smaller xylem and stomate cells. This study also validated germplasms of winter barley with differential freeze tolerance for future projects on breeding for improving winter hardiness and on plant physiology and genetics in response to freezing stress.

Multiplex-genome-editing based rapid directional improvement of complex traits in rice.

Although thousands of genes have been identified or cloned in rice (Oryza sativa) in the last two decades, the majority of them have only been separately characterized in specific varieties or single-gene modified backgrounds, thus limiting their practical application. We developed an optimized multiplex genome editing (MGE) toolbox that can efficiently assemble and stably express up to twelve sgRNA targets in a single plant expression vector. In this study, we established the MGE-based Rapid Directional Improvement (MRDI) strategy for directional improvement of complex agronomic traits in one small-scale rice transformation. This approach provides a rapid and practical procedure, encompassing sgRNA assembly, transgene-free screening and the creation of promising germplasm, by combining the precision of gene editing with phenotype-based field breeding. The MRDI strategy was used to generate the full diversity of twelve main agronomic genes in rice cultivar FXZ for the directional improvement of its growth duration and plant architecture. After applying the MRDI to FXZ, ideal plants with the desired traits of early heading date reduced plant height, and more effective panicles were generated without compromising yield, blast resistance and grain quality. Furthermore, the results of whole-genome sequencing (WGS), including the analysis of structural variations (SVs) and single nucleotide variations (SNVs) in the MGE plants, confirmed the high specificity and low frequency of unwanted mutations associated with this strategy. The MRDI breeding strategy would be a robust approach for exploring and applying crucial agronomic genes, as well as for generating novel elite germplasm in the future.

Detection of quantitative trait nucleotides (QTNs) and QTN-by-environment and QTN-by-QTN interactions for cotton early-maturity traits using the 3VmrMLM method

Early maturity is among the most important and complex agronomic traits of upland cotton (Gossypium hirsutum). Using the recently established 3VmrMLM method, a genome-wide association study was conducted, focusing on three primary early-maturity traits: growth period (GP), the first fruiting branch node (FFBN), and height of the first fruiting branch node (HFFBN). As a result, 45, 37, and 1 quantitative trait nucleotides (QTNs) for GP, 51, 28, and 0 QTNs for FFBN, and 84, 39, and 3 QTNs for HFFBN were detected by the “Single_env”, “Multi_env”, and “Epistasis” modules of IIIVmrMLM software, respectively. Of these QTNs, two for GP, two for FFBN, and three for HFFBN were simultaneously detected in at least three datasets or by at least three modules. The three genes, GH_D08G2175, GH_D08G2201, and GH_D09G2513, associated with the stable QTNs were further verified by qRT-PCR and haplotype analysis. Fourteen QTN-by-environment interactions (QEIs), comprising two for GP, seven for FFBN, and five for HFFBN, were detected, of which nine QEIs exhibited significant phenotypic differences between different genotypes under various environments. Three of the nine QEIs were respectively located within the genes GH_D12G1405, GH_D05G2088, and GH_D11G1027, which are likely to interact with environments and thus affect early maturity. Altogether 21 QTN-by-QTN interactions (QQIs), comprising 5 for GP and 16 for HFFBN, representing interactions between various effects were detected. Ten protein–protein interactions involving 6 QQIs and 10 gene pairs were predicted. The findings offer fresh perspectives on the genes underlying early maturity, advancing the prospects for molecular design breeding in cotton.

Functional genomics of Brassica napus: Progresses, challenges, and perspectives.

Brassica napus, commonly known as rapeseed or canola, is a major oil crop contributing over 13% to the stable supply of edible vegetable oil worldwide. Identification and understanding the gene functions in the B. napus genome is crucial for genomic breeding. A group of genes controlling agronomic traits have been successfully cloned through functional genomics studies in B. napus. In this review, we present an overview of the progress made in the functional genomics of B. napus, including the availability of germplasm resources, omics databases and cloned functional genes. Based on the current progress, we also highlight the main challenges and perspectives in this field. The advances in the functional genomics of B. napus contribute to a better understanding of the genetic basis underlying the complex agronomic traits in B. napus and will expedite the breeding of high quality, high resistance and high yield in B. napus varieties.

QTL-seq analysis identified the genomic regions of plant height and days to heading in high-latitude rice.

Introduction: Rice (Oryza sativa L.) is one of the most extensive crops in the world. China's Heilongjiang Province is the northernmost rice-growing region in the world. However, rice cultivars suitable for growth in low-latitude regions may not mature normally due to their distinct climate and short frost-free period. It is necessary to precisely determine the frost-free period for each region to make the best use of the rice growth stage so as to ensure the maturity and yield of different rice cultivars in Heilongjiang Province. The time span of the heading stage is a key parameter for evaluating the adaptability of a rice cultivar to a specific rice-growing region. Given the above facts, it is of high importance to study the associated genes and sites controlling days to heading (DH) and plant height (PH) of rice in Heilongjiang Province. Bulked segregant analysis (BSA) combined with high-throughput sequencing can effectively exclude interferences from background genomic differences, making it suitable for analyzing the associated sites of complex agronomic traits in early generations. Methods: In this study, an F3 segregating population was obtained by crossing two main cultivars that are grown under different temperatures and day-light conditions in Heilongjiang. Two pools of extreme phenotypes were built for the DH and PH of the population. For SNP and InDel variants obtained from whole-genome resequencing in the pools, an association analysis was performed using the Euclidean distance (ED) algorithm and the SNP/InDel index algorithm. Results: The intersection of SNP and InDel regions associated with the phenotypes was considered to obtain the final associated sites. After excluding interferences from the cloned genes on chromosomes 2 and 7, a total length of 6.34Mb on chromosomes 1, 3, and 10 and 3.16Mb on chromosomes 1 and 10 were left associated with PH and DH, respectively. Then, we performed a gene annotation analysis for candidate genes in the remaining regions using multiple genome annotation databases. Our research provides basic data for subsequent gene mapping and cloning. Discussion: By mining more genetic loci associated with the days to heading and plant height of rice, we may provide abundant genetic resources for refined molecular breeding in Heilongjiang Province.

Mapping intron retention events contributing to complex traits using splice quantitative trait locus

BackgroundAlternative splicing (AS) of mRNA plays an important roles in transcriptome diversity, involving regulation of plant growth and stress response. Understanding the variation of AS events underlying GWAS loci in a crop population can provide insight into the molecular mechanisms of complex agronomic traits. To date, genome-wide association studies relating AS events to agronomic traits have rarely been conducted at the population level in crops.ResultsHere, a pipeline was constructed to identify candidate AS events related to complex traits. Firstly, ovule transcriptome data were used to characterize intron retention (IR), the predominant type of AS in plants, on a genome-wide scale. This was done in a natural population consisting of 279 upland cotton lines. Secondly, splice quantitative trait locus (sQTL) analysis was carried out, which yielded a total of 2295 sQTLs involving 1607 genes. Of these, 14.25% (n = 427) were cis-regulatory loci. Integration with expression quantitative trait loci (eQTL) revealed that 53 (21.4%) cis-sGenes were regulated by both cis-sQTLs and cis-eQTLs. Finally, co-localization analysis integrated with GWAS loci in this population showed 32 cis-QTLs to be co-located with genetic regulatory loci related to fiber yield and quality traits, indicating that sQTLs are likely to participate in regulating cotton fiber yield and quality. An in-depth evaluation confirmed that differences in the IR rates of sQTL-regulated candidate genes such as GhLRRK1 and GhGC1 are associated with lint percentage (LP), which has potential in breeding applications.ConclusionThis study provides a clue that AS of mRNA has an impact on crop yield, along with functional sQTLs are new genetic resources for cotton precision breeding.

Identification of genetic loci associated with five agronomic traits in alfalfa using multi-environment trials.

The use of multi-environment trials to test yield-related traits in a diverse alfalfa panel allowed to find multiple molecular markers associated with complex agronomic traits. Yield is one of the most important target traits in alfalfa breeding; however, yield is a complex trait affected by genetic and environmental factors. In this study, we used multi-environment trials to test yield-related traits in a diverse panel composed of 200 alfalfa accessions and varieties. Phenotypic data of maturity stage measured as mean stage by count (MSC), dry matter content, plant height (PH), biomass yield (Yi), and fall dormancy (FD) were collected in three locations in Idaho, Oregon, and Washington from 2018 to 2020. Single-trial and stagewise analyses were used to obtain estimated trait means of entries by environment. The plants were genotyped using a genotyping by sequencing approach and obtained a genotypic matrix with 97,345 single nucleotide polymorphisms. Genome-wide association studies identified a total of 84 markers associated with the traits analyzed. Of those, 29 markers were in noncoding regions and 55 markers were in coding regions. Ten significant SNPs at the same locus were associated with FD and they were linked to a gene annotated as a nuclear fusion defective 4-like (NFD4). Additional SNPs associated with MSC, PH, and Yi were annotated as transcription factors such as Cysteine3Histidine (C3H), Hap3/NF-YB family, and serine/threonine-protein phosphatase 7 proteins, respectively. Our results provide insight into the genetic factors that influence alfalfa maturity, yield, and dormancy, which is helpful to speed up the genetic gain toward alfalfa yield improvement.

Global transcriptome and coexpression network analyses reveal cultivar-specific molecular signatures associated with different rooting depth responses to drought stress in potato.

Potato is one of the most important vegetable crops worldwide. Its growth, development and ultimately yield is hindered by drought stress condition. Breeding and selection of deep-rooted and drought-tolerant potato varieties has become a prime approach for improving the yield and quality of potato (Solanum tuberosum L.) in arid and semiarid areas. A comprehensive understanding of root development-related genes has enabled scientists to formulate strategies to incorporate them into breeding to improve complex agronomic traits and provide opportunities for the development of stress tolerant germplasm. Root response to drought stress is an intricate process regulated through complex transcriptional regulatory network. To understand the rooting depth and molecular mechanism, regulating root response to drought stress in potato, transcriptome dynamics of roots at different stages of drought stress were analyzed in deep (C119) and shallow-rooted (C16) cultivars. Stage-specific expression was observed for a significant proportion of genes in each cultivar and it was inferred that as compared to C16 (shallow-rooted), approximately half of the genes were differentially expressed in deep-rooted cultivar (C119). In C16 and C119, 11 and 14 coexpressed gene modules, respectively, were significantly associated with physiological traits under drought stress. In a comparative analysis, some modules were different between the two cultivars and were associated with differential response to specific drought stress stage. Transcriptional regulatory networks were constructed, and key components determining rooting depth were identified. Through the results, we found that rooting depth (shallow vs deep) was largely determined by plant-type, cell wall organization or biogenesis, hemicellulose metabolic process, and polysaccharide metabolic process. In addition, candidate genes responding to drought stress were identified in deep (C119) and shallow (C16) rooted potato varieties. The results of this study will be a valuable source for further investigations on the role of candidate gene(s) that affect rooting depth and drought tolerance mechanisms in potato.

Root-Related Genes in Crops and Their Application under Drought Stress Resistance-A Review.

Crop growth and development are frequently affected by biotic and abiotic stresses. The adaptation of crops to stress is mostly achieved by regulating specific genes. The root system is the primary organ for nutrient and water uptake, and has an important role in drought stress response. The improvement of stress tolerance to increase crop yield potential and yield stability is a traditional goal of breeders in cultivar development using integrated breeding methods. An improved understanding of genes that control root development will enable the formulation of strategies to incorporate stress-tolerant genes into breeding for complex agronomic traits and provide opportunities for developing stress-tolerant germplasm. We screened the genes associated with root growth and development from diverse plants including Arabidopsis, rice, maize, pepper and tomato. This paper provides a theoretical basis for the application of root-related genes in molecular breeding to achieve crop drought tolerance by the improvement of root architecture.

Transposable element polymorphisms improve prediction of complex agronomic traits in rice

Key messageTransposon insertion polymorphisms can improve prediction of complex agronomic traits in rice compared to using SNPs only, especially when accessions to be predicted are less related to the training set.Transposon insertion polymorphisms (TIPs) are significant sources of genetic variation. Previous work has shown that TIPs can improve detection of causative loci on agronomic traits in rice. Here, we quantify the fraction of variance explained by single nucleotide polymorphisms (SNPs) compared to TIPs, and we explore whether TIPs can improve prediction of traits when compared to using only SNPs. We used eleven traits of agronomic relevance from by five different rice population groups (Aus, Indica, Aromatic, Japonica, and Admixed), 738 accessions in total. We assess prediction by applying data split validation in two scenarios. In the within-population scenario, we predicted performance of improved Indica varieties using the rest of Indica accessions. In the across population scenario, we predicted all Aromatic and Admixed accessions using the rest of populations. In each scenario, Bayes C and a Bayesian reproducible kernel Hilbert space regression were compared. We find that TIPs can explain an important fraction of total genetic variance and that they also improve genomic prediction. In the across population prediction scenario, TIPs outperformed SNPs in nine out of the eleven traits analyzed. In some traits like leaf senescence or grain width, using TIPs increased predictive correlation by 30–50%. Our results evidence, for the first time, that TIPs genotyping can improve prediction on complex agronomic traits in rice, especially when accessions to be predicted are less related to training accessions.

Rapid and Directional Improvement of Elite Rice Variety via Combination of Genomics and Multiplex Genome Editing.

High yield and superior quality are the main goals pursued by breeders for crop improvement. However, both of them are complex agronomic traits controlled by multiple genes, so the simultaneous improvement of these traits via sexual recombination is time-consuming and direction-uncontrolled. In this study, to solve this dilemma, we introduced the comparative genomic analysis based multiplex genome editing system (CG-MGE), a method for rapid and directional improvement of multiple traits. Application of this method, association analysis between genotypes and phenotypes was carried out to mine excellent alleles; subsequently, the rare excellent alleles of Gn1a, GW2, TGW3, and Chalk5 were simultaneously created by multiplex genome editing and successfully improved the plant architecture, grain yield, and quality of a widely cultivated elite rice variety. Overall, this study provides a method for rapid and directional improvement of crops, and the application of the CG-MGE will be helpful to accelerate rational design breeding.

Transcriptome and Small RNA Sequencing Reveal the Mechanisms Regulating Harvest Index in Brassica napus.

Harvest index (HI), the ratio of harvested seed weight to total aboveground biomass weight, is an economically critical value reflecting the convergence of complex agronomic traits. HI values in rapeseed (Brassica napus) remain much lower than in other major crops, and the underlying regulatory network is largely unknown. In this study, we performed mRNA and small RNA sequencing to reveal the mechanisms shaping HI in B. napus during the seed-filling stage. A total of 8,410 differentially expressed genes (DEGs) between high-HI and low-HI accessions in four tissues (silique pericarp, seed, leaves, and stem) were identified. Combining with co-expression network, 72 gene modules were identified, and a key gene BnaSTY46 was found to participate in retarded establishment of photosynthetic capacity to influence HI. Further research found that the genes involved in circadian rhythms and response to stimulus may play important roles in HI and that their transcript levels were modulated by differentially expressed microRNAs (DEMs), and we identified 903 microRNAs (miRNAs), including 46 known miRNAs and 857 novel miRNAs. Furthermore, transporter activity-related genes were critical to enhancing HI in good cultivation environments. Of 903 miRNAs, we found that the bna-miR396–Bna.A06SRp34a/Bna.A01EMB3119 pair may control the seed development and the accumulation of storage compounds, thus contributing to higher HI. Our findings uncovered the underlying complex regulatory network behind HI and offer potential approaches to rapeseed improvement.

Genome-Wide Association Analyses Reveal Candidate Genes Controlling Harvest Index and Related Agronomic Traits in Brassica napus L.

Harvest index (HI) is a complex and vital agronomic trait that is closely related to the economic benefits of rapeseed. In this study, we measured the HI and 13 HI-related agronomic traits of 104 core breeding lines of rapeseed during 3 years and sequenced the populations using the Bnapus50K array. The phenotypic analyses showed the complex connections among HI and other traits. A total of 212 significant SNPs related to the traits and 22 stable SNPs were identified. Four SNPs, A01_1783685 (PH and SYP), C06_26638717 (PH and NSS), C03_4731660 (MIL and MINS), and C09_36899682 (PH and BYP), were identified as potential pleiotropic loci. Compared to previous reports, 49 consensus loci were obtained that were related to PH, TSW, NSP, BAI, NSS, SL, BN, MINS, SYP, and BYP. Twelve stable SNPs were detected as promising novel loci related to BN (A05_19368584 and A05_19764389), SL (A06_23598999, A06_23608274, and C07_38735522), PH (C04_47349279, C04_47585236, and C09_36899680), MINS (C05_6251826), NSS (C06_22559430 and C06_22570315), and HI (C05_6554451). In addition, 39 putative genes were identified in the candidate intervals. This study provides novel insights into the genetic mechanisms of HI and HI-related traits, and lays a foundation for molecular marker development and casual gene cloning to improve the harvest index of rapeseed.

Shaping polyploid wheat for success: Origins, domestication, and the genetic improvement of agronomic traits.

Bread wheat (Triticum aestivum L., AABBDD, 2n = 6x = 42), which accounts for most of the cultivated wheat crop worldwide, is a typical allohexaploid with a genome derived from three diploid wild ancestors. Bread wheat arose and evolved via two sequential allopolyploidization events and was further polished through multiple steps of domestication. Today, cultivated allohexaploid bread wheat has numerous advantageous traits, including adaptive plasticity, favorable yield traits, and extended end-use quality, which have enabled its cultivation well beyond the ranges of its tetraploid and diploid progenitors to become a global staple food crop. In the past decade, rapid advances in wheat genomic research have considerably accelerated our understanding of the bases for the shaping of complex agronomic traits in this polyploid crop. Here, we summarize recent advances in characterizing major genetic factors underlying the origin, evolution, and improvement of polyploid wheats. We end with a brief discussion of the future prospects for the design of gene cloning strategies and modern wheat breeding.

Broadening the horizon of crop research: a decade of advancements in plant molecular genetics to divulge phenotype governing genes.

Advancements in sequencing, genotyping, and computational technologies during the last decade (2011-2020) enabled new forward-genetic approaches, which subdue the impediments of precise gene mapping in varied crops. The modern crop improvement programs rely heavily on two major steps-trait-associated QTL/gene/marker's identification and molecular breeding. Thus, it is vital for basic and translational crop research to identify genomic regions that govern the phenotype of interest. Until the advent of next-generation sequencing, the forward-genetic techniques were laborious and time-consuming. Over the last 10 years, advancements in the area of genome assembly, genotyping, large-scale data analysis, and statistical algorithms have led faster identification of genomic variations regulating the complex agronomic traits and pathogen resistance. In this review, we describe the latest developments in genome sequencing and genotyping along with a comprehensive evaluation of the last 10-year headways in forward-genetic techniques that have shifted the focus of plant research from model plants to diverse crops. We have classified the available molecular genetic methods under bulk-segregant analysis-based (QTL-seq, GradedPool-Seq, QTG-Seq, Exome QTL-seq, and RapMap), target sequence enrichment-based (RenSeq, AgRenSeq, and TACCA), and mutation-based groups (MutMap, NIKS algorithm, MutRenSeq, MutChromSeq), alongside improvements in classical mapping and genome-wide association analyses. Newer methods for outcrossing, heterozygous, and polyploid plant genetics have also been discussed. The use of k-mers has enriched the nature of genetic variants which can be utilized to identify the phenotype-causing genes, independent of reference genomes. We envisage that the recent methods discussed herein will expand the repertoire of useful alleles and help in developing high-yielding and climate-resilient crops.

Seed germination and vigor: ensuring crop sustainability in a changing climate.

In the coming decades, maintaining a steady food supply for the increasing world population will require high-yielding crop plants which can be productive under increasingly variable conditions. Maintaining high yields will require the successful and uniform establishment of plants in the field under altered environmental conditions. Seed vigor, a complex agronomic trait that includes seed longevity, germination speed, seedling growth, and early stress tolerance, determines the duration and success of this establishment period. Elevated temperature during early seed development can decrease seed size, number, and fertility, delay germination and reduce seed vigor in crops such as cereals, legumes, and vegetable crops. Heat stress in mature seeds can reduce seed vigor in crops such as lettuce, oat, and chickpea. Warming trends and increasing temperature variability can increase seed dormancy and reduce germination rates, especially in crops that require lower temperatures for germination and seedling establishment. To improve seed germination speed and success, much research has focused on selecting quality seeds for replanting, priming seeds before sowing, and breeding varieties with improved seed performance. Recent strides in understanding the genetic basis of variation in seed vigor have used genomics and transcriptomics to identify candidate genes for improving germination, and several studies have explored the potential impact of climate change on the percentage and timing of germination. In this review, we discuss these recent advances in the genetic underpinnings of seed performance as well as how climate change is expected to affect vigor in current varieties of staple, vegetable, and other crops.

GEPSdb: The Gene Expression Database of Poplar under Stress.

As a model tree species, poplar (Populus L.) has important economic and ecological value. Here, we constructed the GEPSdb (Gene Expression Database of Poplar under Stress; http://gepsdb.ahau-edu.cn/), which is an integrated database of poplar gene expression profiles derived from RNA-seq and microarray library data. This database provides a comprehensive collection of gene expression data from poplar exposed to 14 types of environmental stress from 11 high-quality RNA-seq experiments and 51 microarray libraries. The GEPSdb includes 56 genes from previous literature that have been examined in poplar and functionally verified. By incorporating data from numerous expression analyses, GEPSdb provides a user-friendly web interface for querying, browsing, and visualizing the expression profiles of related genes. Consequently, GEPSdb can be used to link transcription data with phenotypes and can enhance our understanding of important biological processes and mechanisms underlying complex agronomic traits in poplar.

Genome-Wide Association Studies of Soybean Yield-Related Hyperspectral Reflectance Bands Using Machine Learning-Mediated Data Integration Methods.

In conjunction with big data analysis methods, plant omics technologies have provided scientists with cost-effective and promising tools for discovering genetic architectures of complex agronomic traits using large breeding populations. In recent years, there has been significant progress in plant phenomics and genomics approaches for generating reliable large datasets. However, selecting an appropriate data integration and analysis method to improve the efficiency of phenome-phenome and phenome-genome association studies is still a bottleneck. This study proposes a hyperspectral wide association study (HypWAS) approach as a phenome-phenome association analysis through a hierarchical data integration strategy to estimate the prediction power of hyperspectral reflectance bands in predicting soybean seed yield. Using HypWAS, five important hyperspectral reflectance bands in visible, red-edge, and near-infrared regions were identified significantly associated with seed yield. The phenome-genome association analysis of each tested hyperspectral reflectance band was performed using two conventional genome-wide association studies (GWAS) methods and a machine learning mediated GWAS based on the support vector regression (SVR) method. Using SVR-mediated GWAS, more relevant QTL with the physiological background of the tested hyperspectral reflectance bands were detected, supported by the functional annotation of candidate gene analyses. The results of this study have indicated the advantages of using hierarchical data integration strategy and advanced mathematical methods coupled with phenome-phenome and phenome-genome association analyses for a better understanding of the biology and genetic backgrounds of hyperspectral reflectance bands affecting soybean yield formation. The identified yield-related hyperspectral reflectance bands using HypWAS can be used as indirect selection criteria for selecting superior genotypes with improved yield genetic gains in large breeding populations.