Identification of QTL for increased fibrous roots in soybean

From phenotyping to genetic mapping: identifying water-stress adaptations in legume root traits

BACKGROUND

Climate change induces perturbation in the global water cycle, profoundly impacting water availability for agriculture and therefore global food security. Water stress encompasses both drought (i.e. water scarcity) that causes the drying of soil and subsequent plant desiccation, and flooding, which results in excess soil water and hypoxia for plant roots. Terrestrial plants have evolved diverse mechanisms to cope with soil water stress, with the root system serving as the first line of defense. The responses of roots to water stress can involve both structural and physiological changes, and their plasticity is a vital feature of these adaptations. Genetic methodologies have been extensively employed to identify numerous genetic loci linked to water stress-responsive root traits. This knowledge is immensely important for developing crops with optimal root systems that enhance yield and guarantee food security under water stress conditions.

RESULTS

This review focused on the latest insights into modifications in the root system architecture and anatomical features of legume roots in response to drought and flooding stresses. Special attention was given to recent breakthroughs in understanding the genetic underpinnings of legume root development under water stress. The review also described various root phenotyping techniques and examples of their applications in different legume species. Finally, the prevailing challenges and prospective research avenues in this dynamic field as well as the potential for using root system architecture as a breeding target are discussed.

CONCLUSIONS

This review integrated the latest knowledge of the genetic components governing the adaptability of legume roots to water stress, providing a reference for using root traits as the new crop breeding targets.

Genetic Control of Tolerance to Drought Stress in Wild Soybean (Glycine soja) at the Vegetative and the Germination Stages

Drought stress, which is becoming more prevalent due to climate change, is a significant abiotic factor that adversely impacts crop production and yield stability. Cultivated soybean (Glycine max), a versatile crop for humans and animals, exhibits sensitivity to drought, resulting in reduced growth and development under drought conditions. However, few genetic studies have assessed wild soybean's (Glycine soja) response to drought stress. In this work, we conducted a genome-wide association study (GWAS) and analysis of wild soybean accessions to identify loci responsible for drought tolerance at the vegetative (n = 187) and the germination stages (n = 135) using the available resequencing data. The GWAS analysis of the leaf wilting score (LWS) identified eight single-nucleotide polymorphisms (SNPs) on chromosomes 10, 11, and 19. Of these, wild soybeans with both SNPs on chromosomes 10 (adenine) and 11 (thymine) produced lower LWS, indicating that these SNPs have an important role in the genetic effect on LWS for drought tolerance at the vegetative stage. At the germination stage, nine SNPs associated with five phenotypic measurements were identified on chromosomes 6, 9, 10, 13, 16, and 17, and the genomic regions identified at the germination stage were different from those identified for the LWS, supporting our previous finding that there may not be a robust correlation between the genes influencing phenotypes at the germination and vegetative stages. This research will benefit marker-assisted breeding programs aimed at enhancing drought tolerance in soybeans.

Identification of Quantitative Trait Loci Controlling Root Morphological Traits in an Interspecific Soybean Population Using 2D Imagery Data

Roots are the hidden and most important part of plants. They serve as stabilizers and channels for uptaking water and nutrients and play a crucial role in the growth and development of plants. Here, two-dimensional image data were used to identify quantitative trait loci (QTL) controlling root traits in an interspecific mapping population derived from a cross between wild soybean 'PI366121' and cultivar 'Williams 82'. A total of 2830 single-nucleotide polymorphisms were used for genotyping, constructing genetic linkage maps, and analyzing QTLs. Forty-two QTLs were identified on twelve chromosomes, twelve of which were identified as major QTLs, with a phenotypic variation range of 36.12% to 39.11% and a logarithm of odds value range of 12.01 to 17.35. Two significant QTL regions for the average diameter, root volume, and link average diameter root traits were detected on chromosomes 3 and 13, and both wild and cultivated soybeans contributed positive alleles. Six candidate genes, Glyma.03G027500 (transketolase/glycoaldehyde transferase), Glyma.03G014500 (dehydrogenases), Glyma.13G341500 (leucine-rich repeat receptor-like protein kinase), Glyma.13G341400 (AGC kinase family protein), Glyma.13G331900 (60S ribosomal protein), and Glyma.13G333100 (aquaporin transporter) showed higher expression in root tissues based on publicly available transcriptome data. These results will help breeders improve soybean genetic components and enhance soybean root morphological traits using desirable alleles from wild soybeans.

C-terminally encoded peptide-like genes are associated with the development of primary root at qRL16.1 in soybean

Root architecture traits are belowground traits that harness moisture and nutrients from the soil and are equally important to above-ground traits in crop improvement. In soybean, the root length locus qRL16.1 was previously mapped on chromosome 16. The qRL16.1 has been characterized by transcriptome analysis of roots in near-isogenic lines (NILs), gene expression analysis in a pair of lines contrasting with alleles of qRL16.1, and differential gene expression analysis in germplasm accessions contrasting with root length. Two candidate genes, Glyma.16g108500 and Glyma.16g108700, have shown relatively higher expression in longer root accessions than in shorter rooting accessions. The C-terminal domain of Glyma.16g108500 and Glyma.16g108700 is similar to the conserved domain of C-terminally encoded peptides (CEPs) that regulate root length and nutrient response in Arabidopsis. Two polymorphisms upstream of Glyma.16g108500 showed a significant association with primary root length and total root length traits in a germplasm set. Synthetic peptide assay with predicted CEP variants of Glyma.16g108500 and Glyma.16g108700 demonstrated their positive effect on primary root length. The two genes are root-specific in the early stage of soybean growth and showed differential expression only in the primary root. These genes will be useful for improving soybean to develop a deep and robust root system to withstand low moisture and nutrient regimes.

Understandings and future challenges in soybean functional genomics and molecular breeding

Soybean (Glycine max) is a major source of plant protein and oil. Soybean breeding has benefited from advances in functional genomics. In particular, the release of soybean reference genomes has advanced our understanding of soybean adaptation to soil nutrient deficiencies, the molecular mechanism of symbiotic nitrogen (N) fixation, biotic and abiotic stress tolerance, and the roles of flowering time in regional adaptation, plant architecture, and seed yield and quality. Nevertheless, many challenges remain for soybean functional genomics and molecular breeding, mainly related to improving grain yield through high-density planting, maize-soybean intercropping, taking advantage of wild resources, utilization of heterosis, genomic prediction and selection breeding, and precise breeding through genome editing. This review summarizes the current progress in soybean functional genomics and directs future challenges for molecular breeding of soybean.

Genome-Wide Association Studies of Seven Root Traits in Soybean (Glycine max L.) Landraces

Soybean [Glycine max (L.) Merr.], an important oilseed crop, is a low-cost source of protein and oil. In Southeast Asia and Africa, soybeans are widely cultivated for use as traditional food and feed and industrial purposes. Given the ongoing changes in global climate, developing crops that are resistant to climatic extremes and produce viable yields under predicted climatic conditions will be essential in the coming decades. To develop such crops, it will be necessary to gain a thorough understanding of the genetic basis of agronomic and plant root traits. As plant roots generally lie beneath the soil surface, detailed observations and phenotyping throughout plant development present several challenges, and thus the associated traits have tended to be ignored in genomics studies. In this study, we phenotyped 357 soybean landraces at the early vegetative (V2) growth stages and used a 180 K single-nucleotide polymorphism (SNP) soybean array in a genome-wide association study (GWAS) conducted to determine the phenotypic relationships among root traits, elucidate the genetic bases, and identify significant SNPs associated with root trait-controlling genomic regions/loci. A total of 112 significant SNP loci/regions were detected for seven root traits, and we identified 55 putative candidate genes considered to be the most promising. Our findings in this study indicate that a combined approach based on SNP array and GWAS analyses can be applied to unravel the genetic basis of complex root traits in soybean, and may provide an alternative high-resolution marker strategy to traditional bi-parental mapping. In addition, the identified SNPs, candidate genes, and diverse variations in the root traits of soybean landraces will serve as a valuable basis for further application in genetic studies and the breeding of climate-resilient soybeans characterized by improved root traits.

QTL analyses of soybean root system architecture revealed genetic relationships with shoot-related traits

The genetic basis of soybean root system architecture (RSA) and the genetic relationship between shoot and RSA were revealed by integrating data from recombinant inbred population grafting and QTL mapping. Variations in root system architecture (RSA) affect the functions of roots and thus play vital roles in plant adaptations and agricultural productivity. The aim of this study was to unravel the genetic relationship between RSA traits and shoot-related traits in soybean. This study characterized RSA variability at seedling stage in a recombinant inbred population, derived from a cross between cultivated soybean C08 and wild soybean W05, and performed high-resolution quantitative trait locus (QTL) mapping. In total, 34 and 41 QTLs were detected for RSA-related and shoot-related traits, respectively, constituting eight QTL clusters. Significant QTL correspondence was found between shoot biomass and RSA-related traits, consistent with significant correlations between these phenotypes. RSA-related QTLs also overlapped with selection regions in the genome, suggesting the cultivar RSA could be a partial consequence of domestication. Using reciprocal grafting, we confirmed that shoot-derived signals affected root development and the effects were controlled by multiple loci. Meanwhile, RSA-related QTLs were found to co-localize with four soybean flowering-time loci. Consistent with the phenotypes of the parental lines of our RI population, diminishing the function of flowering controlling E1 family through RNA interference (RNAi) led to reduced root growth. This implies that the flowering time-related genes within the RSA-related QTLs are actually contributing to RSA. To conclude, this study identified the QTLs that determine RSA through controlling root growth indirectly via regulating shoot functions, and discovered superior alleles from wild soybean that could be used to improve the root structure in existing soybean cultivars.

A Nuclear Factor Y-B Transcription Factor, GmNFYB17, Regulates Resistance to Drought Stress in Soybean

Soybean is sensitive to drought stress, and increasing tolerance to drought stresses is an important target for improving the performance of soybean in the field. The genetic mechanisms underlying soybean's drought tolerance remain largely unknown. Via a genome-wide association study (GWAS) combined with linkage analysis, we identified 11 single-nucleotide polymorphisms (SNPs) and 22 quantitative trait locus (QTLs) that are significantly associated with soybean drought tolerance. One of these loci, namely qGI10-1, was co-located by GWAS and linkage mapping. The two intervals of qGI10-1 were differentiated between wild and cultivated soybean. A nuclear factor Y transcription factor, GmNFYB17, was located in one of the differentiated regions of qGI10-1 and thus selected as a candidate gene for further analyses. The analysis of 29 homologous genes of GmNFYB17 in soybean showed that most of the genes from this family were involved in drought stress. The over-expression of GmNFYB17 in soybean enhanced drought resistance and yield accumulation. The transgenic plants grew better than control under limited water conditions and showed a lower degree of leaf damage and MDA content but higher RWC, SOD activity and proline content compared with control. Moreover, the transgenic plants showed a fast-growing root system, especially regarding a higher root-top ratio and more branching roots and lateral roots. The better agronomic traits of yield were also found in GmNFYB17 transgenic plants. Thus, the GmNFYB17 gene was proven to positively regulate drought stress resistance and modulate root growth in soybean. These results provide important insights into the molecular mechanisms underlying drought tolerance in soybean.

Shaping the root system architecture in plants for adaptation to drought stress

Root system architecture plays an important role in plant adaptation to drought stress. The root system architecture (RSA) consists of several structural features, which includes number and length of main and lateral roots along with the density and length of root hairs. These features exhibit plasticity under water-limited environments and could be critical to developing crops with efficient root systems for adaptation under drought. Recent advances in the omics approaches have significantly improved our understanding of the regulatory mechanisms of RSA remodeling under drought and the identification of genes and other regulatory elements. Plant response to drought stress at physiological, morphological, biochemical, and molecular levels in root cells is regulated by various phytohormones and their crosstalk. Stress-induced reactive oxygen species play a significant role in regulating root growth and development under drought stress. Several transcription factors responsible for the regulation of RSA under drought have proven to be beneficial for developing drought tolerant crops. Molecular breeding programs for developing drought-tolerant crops have been greatly benefitted by the availability of quantitative trait loci (QTLs) associated with the RSA regulation. In the present review, we have discussed the role of various QTLs, signaling components, transcription factors, microRNAs and crosstalk among various phytohormones in shaping RSA and present future research directions to better understand various factors involved in RSA remodeling for adaptation to drought stress. We believe that the information provided herein may be helpful in devising strategies to develop crops with better RSA for efficient uptake and utilization of water and nutrients under drought conditions.

Dissecting the Root Phenotypic and Genotypic Variability of the Iowa Mung Bean Diversity Panel

Mung bean [Vigna radiata (L.) Wilczek] is a drought-tolerant, short-duration crop, and a rich source of protein and other valuable minerals, vitamins, and antioxidants. The main objectives of this research were (1) to study the root traits related with the phenotypic and genetic diversity of 375 mung bean genotypes of the Iowa (IA) diversity panel and (2) to conduct genome-wide association studies of root-related traits using the Automated Root Image Analysis (ARIA) software. We collected over 9,000 digital images at three-time points (days 12, 15, and 18 after germination). A broad sense heritability for days 15 (0.22-0.73) and 18 (0.23-0.87) was higher than that for day 12 (0.24-0.51). We also reported root ideotype classification, i.e., PI425425 (India), PI425045 (Philippines), PI425551 (Korea), PI264686 (Philippines), and PI425085 (Sri Lanka) that emerged as the top five in the topsoil foraging category, while PI425594 (unknown origin), PI425599 (Thailand), PI425610 (Afghanistan), PI425485 (India), and AVMU0201 (Taiwan) were top five in the drought-tolerant and nutrient uptake "steep, cheap, and deep" ideotype. We identified promising genotypes that can help diversify the gene pool of mung bean breeding stocks and will be useful for further field testing. Using association studies, we identified markers showing significant associations with the lateral root angle (LRA) on chromosomes 2, 6, 7, and 11, length distribution (LED) on chromosome 8, and total root length-growth rate (TRL_GR), volume (VOL), and total dry weight (TDW) on chromosomes 3 and 5. We discussed genes that are potential candidates from these regions. We reported beta-galactosidase 3 associated with the LRA, which has previously been implicated in the adventitious root development via transcriptomic studies in mung bean. Results from this work on the phenotypic characterization, root-based ideotype categories, and significant molecular markers associated with important traits will be useful for the marker-assisted selection and mung bean improvement through breeding.

Quantitative Trait Locus Mapping for Drought Tolerance in Soybean Recombinant Inbred Line Population

Improving drought stress tolerance of soybean could be an effective way to minimize the yield reduction in the drought prevailing regions. Identification of drought tolerance-related quantitative trait loci (QTLs) is useful to facilitate the development of stress-tolerant varieties. This study aimed to identify the QTLs for drought tolerance in soybean using a recombinant inbred line (RIL) population developed from the cross between a drought-tolerant ‘PI416937’ and a susceptible ‘Cheonsang’ cultivar. Phenotyping was done with a weighted drought coefficient derived from the vegetative and reproductive traits. The genetic map was constructed using 2648 polymorphic SNP markers that distributed on 20 chromosomes with a mean genetic distance of 1.36 cM between markers. A total of 10 QTLs with 3.52–4.7 logarithm of odds value accounting for up to 12.9% phenotypic variance were identified on seven chromosomes. Five chromosomes—2, 7, 10, 14, and 20—contained one QTL each, and chromosomes 1 and 19 harbored two and three QTLs, respectively. The chromosomal locations of seven QTLs overlapped or located close to the related QTLs and/or potential candidate genes reported earlier. The QTLs and closely linked markers could be utilized in maker-assisted selection to accelerate the breeding for drought tolerance in soybean.

Cell Suspension Culture and In Vitro Screening for Drought Tolerance in Soybean Using Poly-Ethylene Glycol

Soybean (Glycine max (L) Merrill) is used in India mostly as a substantial fund of protein and oil, which makes the crop significantly important. Somaclonal variation has been researched as a base of additional variability for drought in soybean. In the present experiment calli/cell clumps/embryoids rose from immature and mature embryonic axis and cotyledons explants were exposed to different concentrations of polyethylene glycol (PEG6000). A discontinuous method proved to be superior as it permitted the calli/embryoids/cell clumps to regain their regeneration competence. A total of 64 (12.21%) plantlets of genotype JS335 and 78 (13.13%) of genotype JS93-05 were regenerated after four consequent subcultures on the selection medium with an effective lethal concentration of 20% PEG6000, and proliferated calli/embryoids/cell clumps were further subcultured on Murashige and Skoog regeneration medium supplemented with 0.5 mgL^-1 each of α-napthalene acetic acid (NAA), 6-benzyladenine (BA) and Kinetin (Kn), 20.0 gL^-1 sucrose and 7.5 gL^-1 agar. Putative drought-tolerant plantlets were acquired from genotype JS93-05 (38) in more numbers compared to genotype JS335 (26). Random decamer primers confirmed the presence of variability between mother plants and regenerated plants from both the genotypes. Since these plantlets recovered from tolerant calli/embryoids/cell clumps selected from the medium supplemented with PEG6000, the possibility exists that these plants may prove to be tolerant against drought stress.

Assessment of root phenotypes in mungbean mini-core collection (MMC) from the World Vegetable Center (AVRDC) Taiwan

Mungbean (Vigna radiata L.) is an important food grain legume, but its production capacity is threatened by global warming, which can intensify plant stress and limit future production. Identifying new variation of key root traits in mungbean will provide the basis for breeding lines with effective root characteristics for improved water uptake to mitigate heat and drought stress. The AVRDC mungbean mini core collection consisting of 296 genotypes was screened under modified semi-hydroponic screening conditions to determine the variation for fourteen root-related traits. The AVRDC mungbean mini core collection displayed wide variations for the primary root length, total surface area, and total root length, and based on agglomerative hierarchical clustering eight homogeneous groups displaying different root traits could be identified. Germplasm with potentially favorable root traits has been identified for further studies to identify the donor genotypes for breeding cultivars with enhanced adaptation to water-deficit stress and other stress conditions.

Mapping and validation of a major QTL for primary root length of soybean seedlings grown in hydroponic conditions

BACKGROUND

The root system provides nutrient absorption and is closely related to abiotic stress tolerance, but it is difficult to study the roots under field conditions. This study was conducted to identify quantitative trait loci (QTL) associated with primary root length (PRL) during soybean seedling growth in hydroponic conditions. A total of 103 F7 recombinant inbred lines (RILs) derived from a cross between K099 (short primary root) and Fendou 16 (long primary root) were used to identify QTL for PRL in soybean. The RIL population was genotyped with 223 simple sequence repeats markers covering 20 chromosomes. Phenotyping for primary root length was performed for 3-weeks plants grown in hydoponic conditions. The identified QTL was validated in near isogenic lines and in a separate RIL population.

RESULTS

QTL analysis using inclusive composite interval mapping method identified a major QTL on Gm16 between SSR markers Sat_165 and Satt621, explaining 30.25 % of the total phenotypic variation. The identified QTL, qRL16.1, was further confirmed in a segregating population derived from a residual heterozygous line (RHLs-98). To validate qRL16.1 in a different genetic background, QTL analysis was performed in another F6 RIL population derived from a cross between Union (medium primary root) and Fendou 16, in which a major QTL was detected again in the same genomic region as qRL16.1, explaining 14 % of the total phenotypic variation for PRL. In addition, the effect of qRL16.1 was confirmed using two pair of near-isogenic lines (NILs). PRL was significantly higher in NILs possessing the qRL16.1 allele from Fendou 16 compared to allele from K099.

CONCLUSIONS

The qRL16.1 is a novel QTL for primary root length in soybean which provides important information on the genetic control of root development. Identification of this major QTL will facilitate positional cloning and DNA marker-assisted selection for root traits in soybean.

Mapping Quantitative Trait Loci for Soybean Seedling Shoot and Root Architecture Traits in an Inter-Specific Genetic Population

Wild soybean species (Glycine soja Siebold & Zucc.) comprise a unique resource to widen the genetic base of cultivated soybean [Glycine max (L.) Merr.] for various agronomic traits. An inter-specific mapping population derived from a cross of cultivar Williams 82 and PI 483460B, a wild soybean accession, was utilized for genetic characterization of root architecture traits. The objectives of this study were to identify and characterize quantitative trait loci (QTL) for seedling shoot and root architecture traits, as well as to determine additive/epistatic interaction effects of identified QTLs. A total of 16,469 single nucleotide polymorphisms (SNPs) developed for the Illumina beadchip genotyping platform were used to construct a high resolution genetic linkage map. Among the 11 putative QTLs identified, two significant QTLs on chromosome 7 were determined to be associated with total root length (RL) and root surface area (RSA) with favorable alleles from the wild soybean parent. These seedling root traits, RL (BARC_020495_04641 ~ BARC_023101_03769) and RSA (SNP02285 ~ SNP18129_Magellan), could be potential targets for introgression into cultivated soybean background to improve both tap and lateral roots. The RL QTL region harbors four candidate genes with higher expression in root tissues: Phosphofructokinase (Glyma.07g126400), Snf7 protein (Glyma.07g127300), unknown functional gene (Glyma.07g127900), and Leucine Rich-Repeat protein (Glyma.07g127100). The novel alleles inherited from the wild soybean accession could be used as molecular markers to improve root system architecture and productivity in elite soybean lines.

Characterizing the impact of an exotic soybean line on elite cultivar development

The genetic diversity of North American soybean cultivars has been largely influenced by a small number of ancestors. High yielding breeding lines that possess exotic pedigrees have been developed, but identifying beneficial exotic alleles has been difficult as a result of complex interactions of yield alleles with genetic backgrounds and environments as well as the highly quantitative nature of yield. PI 416937 has been utilized in the development of many high yielding lines that have been entered into the USDA Southern States Uniform Tests over the past ~20 years. The primary goal of this research was to identify genomic regions under breeding selection from PI 416937 and introduce a methodology for identifying and potentially utilizing beneficial diversity from lines prevalent in the ancestry of elite cultivars. Utilizing SoySNP50K Infinium BeadChips, 52 high yielding PI 416937-derived lines as well as their parents were genotyped to identify PI 416937 alleles under breeding selection. Nine genomic regions across three chromosomes and 17 genomic regions across seven chromosomes were identified where PI 416937 alleles were under positive or negative selection. Minimal significant associations between PI 416937 alleles and yield were observed in replicated yield trials of five RIL populations, highlighting the difficulty of consistently detecting yield associations.

Identification of quantitative trait loci for carbon isotope ratio (δ13C) in a recombinant inbred population of soybean

KEY MESSAGE

QTL analysis identified 16 QTLs, grouped in eight loci on seven soybean chromosomes that were associated with carbon isotope ratio (δ13C) in a biparental recombinant inbred population. Drought is a major limitation to soybean yield, and the frequency of drought stress is likely to increase under future climatic scenarios. Water use efficiency (WUE) is associated with drought tolerance, and carbon isotope ratio (δ13C) is positively correlated with WUE. In this study, 196 F6-derived recombinant inbred lines from a cross of PI 416997 (high WUE) × PI 567201D (low WUE) were evaluated in four environments to identify genomic regions associated with δ13C. There were positive correlations of δ13C values between different environments (0.67 ≤ r ≤ 0.78). Genotype, environment, and genotype × environment interactions had significant effects on δ13C. Narrow sense heritability of δ13C was 90% when estimated across environments. There was a total of 16 QTLs on seven chromosomes with individual QTLs explaining between 2.5 and 29.9% of the phenotypic variation and with additive effects ranging from 0.07 to 0.22‰. These 16 QTLs likely identified eight loci based on their overlapping confidence intervals. Of these eight loci, two loci on chromosome 20 (Gm20) were detected in at least three environments and were considered as stable QTLs. Additive QTLs on Gm20 showed epistatic interactions with 10 QTLs present across nine chromosomes. Five QTLs were identified across environments and showed significant QTL × environment interactions. These findings demonstrate that additive QTLs and QTL × QTL interactions play significant roles in genetic control of the δ13C trait. Markers flanking identified QTLs may facilitate marker-assisted selection to accumulate desirable QTLs to improve WUE and drought tolerance in soybean.

Soybean Root System Architecture Trait Study through Genotypic, Phenotypic, and Shape-Based Clusters

We report a root system architecture (RSA) traits examination of a larger scale soybean accession set to study trait genetic diversity. Suffering from the limitation of scale, scope, and susceptibility to measurement variation, RSA traits are tedious to phenotype. Combining 35,448 SNPs with an imaging phenotyping platform, 292 accessions (replications = 14) were studied for RSA traits to decipher the genetic diversity. Based on literature search for root shape and morphology parameters, we used an ideotype-based approach to develop informative root (iRoot) categories using root traits. The RSA traits displayed genetic variability for root shape, length, number, mass, and angle. Soybean accessions clustered into eight genotype- and phenotype-based clusters and displayed similarity. Genotype-based clusters correlated with geographical origins. SNP profiles indicated that much of US origin genotypes lack genetic diversity for RSA traits, while diverse accession could infuse useful genetic variation for these traits. Shape-based clusters were created by integrating convolution neural net and Fourier transformation methods, enabling trait cataloging for breeding and research applications. The combination of genetic and phenotypic analyses in conjunction with machine learning and mathematical models provides opportunities for targeted root trait breeding efforts to maximize the beneficial genetic diversity for future genetic gains.

Genome-wide association study of soybean seed germination under drought stress

Drought stress, which is increasing with climate change, is a serious threat to agricultural sustainability worldwide. Seed germination is an essential growth phase that ensures the successful establishment and productivity of soybean, which can lose substantial productivity in soils with water deficits. However, only limited genetic information is available about how germinating soybean seeds may exert drought tolerance. In this study, we examined the germinating seed drought-tolerance phenotypes and genotypes of a panel of 259 released Chinese soybean cultivars panel. Based on 4616 Single-Nucleotide Polymorphisms (SNPs), we conducted a mixed-linear model GWAS that identified a total of 15 SNPs associated with at least one drought-tolerance index. Notably, three of these SNPs were commonly associated with two drought-tolerance indices. Two of these SNPs are positioned upstream of genes, and 11 of them are located in or near regions where QTLs have been previously mapped by linkage analysis, five of which are drought-related. The SNPs detected in this study can both drive hypothesis-driven research to deepen our understanding of genetic basis of soybean drought tolerance at the germination stage and provide useful genetic resources that can facilitate the selection of drought stress traits via genomic-assisted selection.

Computer vision and machine learning enabled soybean root phenotyping pipeline

BACKGROUND

Root system architecture (RSA) traits are of interest for breeding selection; however, measurement of these traits is difficult, resource intensive, and results in large variability. The advent of computer vision and machine learning (ML) enabled trait extraction and measurement has renewed interest in utilizing RSA traits for genetic enhancement to develop more robust and resilient crop cultivars. We developed a mobile, low-cost, and high-resolution root phenotyping system composed of an imaging platform with computer vision and ML based segmentation approach to establish a seamless end-to-end pipeline - from obtaining large quantities of root samples through image based trait processing and analysis.

RESULTS

This high throughput phenotyping system, which has the capacity to handle hundreds to thousands of plants, integrates time series image capture coupled with automated image processing that uses optical character recognition (OCR) to identify seedlings via barcode, followed by robust segmentation integrating convolutional auto-encoder (CAE) method prior to feature extraction. The pipeline includes an updated and customized version of the Automatic Root Imaging Analysis (ARIA) root phenotyping software. Using this system, we studied diverse soybean accessions from a wide geographical distribution and report genetic variability for RSA traits, including root shape, length, number, mass, and angle.

CONCLUSIONS

This system provides a high-throughput, cost effective, non-destructive methodology that delivers biologically relevant time-series data on root growth and development for phenomics, genomics, and plant breeding applications. This phenotyping platform is designed to quantify root traits and rank genotypes in a common environment thereby serving as a selection tool for use in plant breeding. Root phenotyping platforms and image based phenotyping are essential to mirror the current focus on shoot phenotyping in breeding efforts.

Computer vision and machine learning enabled soybean root phenotyping pipeline

BACKGROUND

Root system architecture (RSA) traits are of interest for breeding selection; however, measurement of these traits is difficult, resource intensive, and results in large variability. The advent of computer vision and machine learning (ML) enabled trait extraction and measurement has renewed interest in utilizing RSA traits for genetic enhancement to develop more robust and resilient crop cultivars. We developed a mobile, low-cost, and high-resolution root phenotyping system composed of an imaging platform with computer vision and ML based segmentation approach to establish a seamless end-to-end pipeline - from obtaining large quantities of root samples through image based trait processing and analysis.

RESULTS

This high throughput phenotyping system, which has the capacity to handle hundreds to thousands of plants, integrates time series image capture coupled with automated image processing that uses optical character recognition (OCR) to identify seedlings via barcode, followed by robust segmentation integrating convolutional auto-encoder (CAE) method prior to feature extraction. The pipeline includes an updated and customized version of the Automatic Root Imaging Analysis (ARIA) root phenotyping software. Using this system, we studied diverse soybean accessions from a wide geographical distribution and report genetic variability for RSA traits, including root shape, length, number, mass, and angle.

CONCLUSIONS

This system provides a high-throughput, cost effective, non-destructive methodology that delivers biologically relevant time-series data on root growth and development for phenomics, genomics, and plant breeding applications. This phenotyping platform is designed to quantify root traits and rank genotypes in a common environment thereby serving as a selection tool for use in plant breeding. Root phenotyping platforms and image based phenotyping are essential to mirror the current focus on shoot phenotyping in breeding efforts.

Genome-Wide Association Study of Topsoil Root System Architecture in Field-Grown Soybean [Glycine max (L.) Merr.]

Water and nutrient acquisition is a critical function of plant root systems. Root system architecture (RSA) traits are often complex and controlled by many genes. This is the first genome-wide association study reporting genetic loci for RSA traits for field-grown soybean (Glycine max). A collection of 289 soybean genotypes was grown in three environments, root crowns were excavated, and 12 RSA traits assessed. The first two components of a principal component analysis of these 12 traits were used as additional aggregate traits for a total of 14 traits. Marker-trait association for RSA traits were identified using 31,807 single-nucleotide polymorphisms (SNPs) by a genome-wide association analysis. In total, 283 (non-unique) SNPs were significantly associated with one or more of the 14 root traits. Of these, 246 were unique SNPs and 215 SNPs were associated with a single root trait, while 26, four, and one SNPs were associated with two, three, and four root traits, respectively. The 246 SNPs marked 67 loci associated with at least one of the 14 root traits. Seventeen loci on 13 chromosomes were identified by SNPs associated with more than one root trait. Several genes with annotation related to processes that could affect root architecture were identified near these 67 loci. Additional follow-up studies will be needed to confirm the markers and candidate genes identified for RSA traits and to examine the importance of the different root characteristics for soybean productivity under a range of soil and environmental conditions.

Comprehensive Genome-Wide Association Analysis Reveals the Genetic Basis of Root System Architecture in Soybean

Increasing the understanding genetic basis of the variability in root system architecture (RSA) is essential to improve resource-use efficiency in agriculture systems and to develop climate-resilient crop cultivars. Roots being underground, their direct observation and detailed characterization are challenging. Here, were characterized twelve RSA-related traits in a panel of 137 early maturing soybean lines (Canadian soybean core collection) using rhizoboxes and two-dimensional imaging. Significant phenotypic variation (P < 0.001) was observed among these lines for different RSA-related traits. This panel was genotyped with 2.18 million genome-wide single-nucleotide polymorphisms (SNPs) using a combination of genotyping-by-sequencing and whole-genome sequencing. A total of 10 quantitative trait locus (QTL) regions were detected for root total length and primary root diameter through a comprehensive genome-wide association study. These QTL regions explained from 15 to 25% of the phenotypic variation and contained two putative candidate genes with homology to genes previously reported to play a role in RSA in other species. These genes can serve to accelerate future efforts aimed to dissect genetic architecture of RSA and breed more resilient varieties.

Research Progress and Perspective on Drought Stress in Legumes: A Review

Climate change, food shortage, water scarcity, and population growth are some of the threatening challenges being faced in today's world. Drought stress (DS) poses a constant challenge for agricultural crops and has been considered a severe constraint for global agricultural productivity; its intensity and severity are predicted to increase in the near future. Legumes demonstrate high sensitivity to DS, especially at vegetative and reproductive stages. They are mostly grown in the dry areas and are moderately drought tolerant, but severe DS leads to remarkable production losses. The most prominent effects of DS are reduced germination, stunted growth, serious damage to the photosynthetic apparatus, decrease in net photosynthesis, and a reduction in nutrient uptake. To curb the catastrophic effect of DS in legumes, it is imperative to understand its effects, mechanisms, and the agronomic and genetic basis of drought for sustainable management. This review highlights the impact of DS on legumes, mechanisms, and proposes appropriate management approaches to alleviate the severity of water stress. In our discussion, we outline the influence of water stress on physiological aspects (such as germination, photosynthesis, water and nutrient uptake), growth parameters and yield. Additionally, mechanisms, various management strategies, for instance, agronomic practices (planting time and geometry, nutrient management), plant growth-promoting Rhizobacteria and arbuscular mycorrhizal fungal inoculation, quantitative trait loci (QTLs), functional genomics and advanced strategies (CRISPR-Cas9) are also critically discussed. We propose that the integration of several approaches such as agronomic and biotechnological strategies as well as advanced genome editing tools is needed to develop drought-tolerant legume cultivars.

Integration of lodging resistance QTL in soybean

Poor lodging resistance could limit increases in soybean yield. Previously, a considerable number of observations of quantitative trait loci (QTL) for lodging resistance have been reported by independent studies. The integration of these QTL into a consensus map will provide further evidence of their usefulness in soybean improvement. To improve informative QTL in soybean, a mapping population from a cross between the Harosoy and Clark cultivars, which inherit major U.S. soybean genetic backgrounds, was used along with previous mapping populations to identify QTL for lodging resistance. Together with 78 QTL for lodging collected from eighteen independent studies, a total of 88 QTL were projected onto the soybean consensus map. A total of 16 significant QTL clusters were observed; fourteen of them were confirmed in either two or more mapping populations or a single population subjected to different environmental conditions. Four QTL (one on chromosome 7 and three on 10) were newly identified in the present study. Further, meta-analysis was used to integrate QTL across different studies, resulting in two significant meta-QTL each on chromosomes 6 and 19. Our results provide deeper knowledge of valuable lodging resistance QTL in soybean, and these QTL could be used to increase lodging resistance.

Identification and mapping of two independent recessive loci for the root hairless mutant phenotype in soybean

KEY MESSAGE

Two functional complementation QTLs were identified for root hairless formation in soybean. Root hairs play critical roles not only in nutrient/water uptake from soils, but also in plant-microorganism interactions. However, genetic information about root hair development remains fragmented. We previously identified a soybean natural mutant (RBC-HL) with the root hairless (HL) phenotype. In order to reveal the genetic basis for this phenotype, a polymorphic population was constructed using RBC-HL and a genotype (RBC-NH) with normal root hairs (NH). Three representative phenotypes of root hair formation were observed in the progeny, including NH, medium (MH) and HL. All F1 plants were of the NH type, and the respective segregation ratios in F2, F2:3 and RIL (F5:7) plants fit the theoretical ratio of 15:1, 7:8:1 and 3:1, indicating that the HL mutation is controlled by two independent recessive loci. In order to map HL-associated loci, a high-density genetic map was constructed using 8784 bin markers covering a total genetic distance of 3108.2 cM, and an average distance between adjacent markers of 0.4 cM. Two major QTLs, qRHLa and qRHLb, were identified and mapped on chromosome 01 and 11, and further delimited to interval regions of ~ 289 kb and ~ 1120 kb, respectively. Phylogenetic analysis suggested that the two candidate regions originated from soybean duplication events, where seven pairs of homologous genes shared 86-97% sequence identify. In conclusion, we partially uncovered the genetic mechanism underlying root hair formation in soybean. Namely, two independent recessive loci, qRHLa and qRHLb, containing several candidate genes were predicted to control the root hairless mutant RBC-HL.

Understanding genetic control of root system architecture in soybean: Insights into the genetic basis of lateral root number

Developing crops with better root systems is a promising strategy to ensure productivity in both optimum and stress environments. Root system architectural traits in 397 soybean accessions were characterized and a high-density single nucleotide polymorphisms (SNPs)-based genome-wide association study was performed to identify the underlying genes associated with root structure. SNPs associated with root architectural traits specific to landraces and elite germplasm pools were detected. Four loci were detected in landraces for lateral root number (LRN) and distribution of root thickness in diameter Class I with a major locus on chromosome 16. This major loci was detected in the coding region of unknown protein, and subsequent analyses demonstrated that root traits are affected with mutated haplotypes of the gene. In elite germplasm pool, 3 significant SNPs in alanine-glyoxalate aminotransferase, Leucine-Rich Repeat receptor/No apical meristem, and unknown functional genes were found to govern multiple traits including root surface area and volume. However, no major loci were detected for LRN in elite germplasm. Nucleotide diversity analysis found evidence of selective sweeps around the landraces LRN gene. Soybean accessions with minor and mutated allelic variants of LRN gene were found to perform better in both water-limited and optimal field conditions.

QTL analyses for tolerance to abiotic stresses in a common bean (Phaseolus vulgaris L.) population

Common bean productivity is reduced by several abiotic stress factors like drought and low soil fertility, leading to yield losses particularly in low input smallholder farming systems in the tropics. To understand the genetics of stress tolerance, and to improve adaptation of common bean to adverse environments, the BAT 881 x G21212 population of 95 recombinant inbred lines (RILs) was evaluated under different abiotic stress conditions in 15 trials across four locations in Colombia, representing two higher altitude (Darién, Popayán) and two lower altitude (Palmira, Quilichao) locations. Stress vs non-stress treatments showed that yields were reduced in drought trials in Palmira by 13 and 31%, respectively, and observed yield reductions in low phosphorus stress were 39% in Quilichao, 16% in Popayán, and 71% in Darién, respectively. Yield components and biomass traits were also reduced. Traits linked to dry matter redistribution from stems, leaves and pods to seed, such as pod harvest index and total non-structural carbohydrates, were found to be important factors contributing to yield in all conditions. In contrast, early maturity was correlated with improved yield only in lower altitude locations, whereas in higher altitudes delayed maturity promoted yield. Superior RILs that combine stress tolerance and high cross-location productivity were identified. Lines that showed good yield under strong stress conditions also performed well under non-stress conditions, indicating that breeder's selection can be applied for both conditions at the same time. Quantitative trait loci (QTL) analyses revealed a stable yield QTL on chromosome Pv04, detected individually in all locations, several stress treatments and in best linear unbiased predictions (BLUPs) across all trials. Furthermore, two QTL hotspots for maturity traits were identified on Pv01 and Pv08, which are the most stable QTL. The constitutive yield QTL could serve as a good candidate for marker development and could be used in marker assisted selection. Increased understanding of the physiology of abiotic stress tolerance, combined with the availability of superior germplasm and molecular tools, will aid breeding efforts for further improvement of these plant traits.

Acid phosphatase gene GmHAD1 linked to low phosphorus tolerance in soybean, through fine mapping

KEY MESSAGE

Map-based cloning identified GmHAD1, a gene which encodes a HAD-like acid phosphatase, associated with soybean tolerance to low phosphorus stress. Phosphorus (P) deficiency in soils is a major limiting factor for crop growth worldwide. Plants may adapt to low phosphorus (LP) conditions via changes to root morphology, including the number, length, orientation, and branching of the principal root classes. To elucidate the genetic mechanisms for LP tolerance in soybean, quantitative trait loci (QTL) related to root morphology responses to LP were identified via hydroponic experiments. In total, we identified 14 major loci associated with these traits in a RIL population. The log-likelihood scores ranged from 2.81 to 7.43, explaining 4.23-13.98% of phenotypic variance. A major locus on chromosome 08, named qP8-2, was co-localized with an important P efficiency QTL (qPE8), containing phosphatase genes GmACP1 and GmACP2. Another major locus on chromosome 10 named qP10-2 explained 4.80-13.98% of the total phenotypic variance in root morphology. The qP10-2 contains GmHAD1, a gene which encodes an acid phosphatase. In the transgenic soybean hairy roots, GmHAD1 overexpression increased P efficiency by 8.4-16.5% relative to the control. Transgenic Arabidopsis plants had higher biomass than wild-type plants across both short- and long-term P reduction. These results suggest that GmHAD1, an acid phosphatase gene, improved the utilization of organic phosphate by soybean and Arabidopsis plants.

Genetic diversity of root system architecture in response to drought stress in grain legumes

Climate change has increased the occurrence of extreme weather patterns globally, causing significant reductions in crop production, and hence threatening food security. In order to meet the food demand of the growing world population, a faster rate of genetic gains leading to productivity enhancement for major crops is required. Grain legumes are an essential commodity in optimal human diets and animal feed because of their unique nutritional composition. Currently, limited water is a major constraint in grain legume production. Root system architecture (RSA) is an important developmental and agronomic trait, which plays vital roles in plant adaptation and productivity under water-limited environments. A deep and proliferative root system helps extract sufficient water and nutrients under these stress conditions. The integrated genetics and genomics approach to dissect molecular processes from genome to phenome is key to achieve increased water capture and use efficiency through developing better root systems. Success in crop improvement under drought depends on discovery and utilization of genetic variations existing in the germplasm. In this review, we summarize current progress in the genetic diversity in major legume crops, quantitative trait loci (QTLs) associated with RSA, and the importance and applications of recent discoveries associated with the beneficial root traits towards better RSA for enhanced drought tolerance and yield.

A Dehydration-Induced Eukaryotic Translation Initiation Factor iso4G Identified in a Slow Wilting Soybean Cultivar Enhances Abiotic Stress Tolerance in Arabidopsis

Water is usually the main limiting factor for soybean productivity worldwide and yet advances in genetic improvement for drought resistance in this crop are still limited. In the present study, we investigated the physiological and molecular responses to drought in two soybean contrasting genotypes, a slow wilting N7001 and a drought sensitive TJS2049 cultivars. Measurements of stomatal conductance, carbon isotope ratios and accumulated dry matter showed that N7001 responds to drought by employing mechanisms resulting in a more efficient water use than TJS2049. To provide an insight into the molecular mechanisms that these cultivars employ to deal with water stress, their early and late transcriptional responses to drought were analyzed by suppression subtractive hybridization. A number of differentially regulated genes from N7001 were identified and their expression pattern was compared between in this genotype and TJS2049. Overall, the data set indicated that N7001 responds to drought earlier than TJ2049 by up-regulating a larger number of genes, most of them encoding proteins with regulatory and signaling functions. The data supports the idea that at least some of the phenotypic differences between slow wilting and drought sensitive plants may rely on the regulation of the level and timing of expression of specific genes. One of the genes that exhibited a marked N7001-specific drought induction profile encoded a eukaryotic translation initiation factor iso4G (GmeIFiso4G-1a). GmeIFiso4G-1a is one of four members of this protein family in soybean, all of them sharing high sequence identity with each other. In silico analysis of GmeIFiso4G-1 promoter sequences suggested a possible functional specialization between distinct family members, which can attain differences at the transcriptional level. Conditional overexpression of GmeIFiso4G-1a in Arabidopsis conferred the transgenic plants increased tolerance to osmotic, salt, drought and low temperature stress, providing a strong experimental evidence for a direct association between a protein of this class and general abiotic stress tolerance mechanisms. Moreover, the results of this work reinforce the importance of the control of protein synthesis as a central mechanism of stress adaptation and opens up for new strategies for improving crop performance under stress.

Using genomic information to improve soybean adaptability to climate change

Climate change has brought severe challenges to agriculture. It is anticipated that there will be a drop in crop yield - including that of soybean - due to climatic stress factors that include drastic fluctuations in temperature, drought, flooding and high salinity. Genomic information on soybean has been accumulating rapidly since initial publication of its reference genome, providing a valuable tool for the improvement of cultivated soybean. Not only are many molecular markers that are associated with important quantitative trait loci now identified, but we also have a more detailed picture of the genomic variations among soybean germplasms, enabling us to utilize these as tools to assist crop breeding. In this review, we will summarize and discuss the currently available soybean genomic approaches, including whole-genome sequencing, sequencing-based genotyping, functional genomics, proteomics, and epigenomics. The information uncovered through these techniques will help further pinpoint important gene candidates and genomic loci associated with adaptive traits, as well as achieving a better understanding of how soybeans cope with the changing climate.

Root xylem plasticity to improve water use and yield in water-stressed soybean

We tested the hypothesis that increasing the number of metaxylem vessels would enhance the efficiency of water uptake in soybean (Glycine max) and decrease the yield gap in water-limited environments. A panel of 41 soybean accessions was evaluated in greenhouse, rainout shelter, and rain-fed field environments. The metaxylem number influenced the internal capture of CO2 and improved stomatal conductance, enhancing water uptake/use in soybeans exposed to stress during the reproductive stage. We determined that other root anatomical features, such as cortex cell area and the percentage of stele that comprised cortical cells, also affected seed yield under similar growth parameters. Seed yield was also impacted by pod retention rates under drought stress (24-80 pods/plant). We surmise that effective biomass allocation, that is, the transport of available photosynthates to floral structures at late reproductive growth stages (R6-R7), enables yield protection under drought stress. A mesocosm study of contrasting lines for yield under drought stress and root anatomical features revealed that increases in metaxylem number as an adaptation to drought in the high-yielding lines improved root hydraulic conductivity, which reduced the metabolic cost of exploring water in deeper soil strata and enhanced water transport. This allowed the maintenance of shoot physiological processes under water-limited conditions.

Genetic diversity and genomic strategies for improving drought and waterlogging tolerance in soybeans

Drought and its interaction with high temperature are the major abiotic stress factors affecting soybean yield and production stability. Ongoing climate changes are anticipated to intensify drought events, which will further impact crop production and food security. However, excessive water also limits soybean production. The success of soybean breeding programmes for crop improvement is dependent on the extent of genetic variation present in the germplasm base. Screening for natural genetic variation in drought- and flooding tolerance-related traits, including root system architecture, water and nitrogen-fixation efficiency, and yield performance indices, has helped to identify the best resources for genetic studies in soybean. Genomic resources, including whole-genome sequences of diverse germplasms, millions of single-nucleotide polymorphisms, and high-throughput marker genotyping platforms, have expedited gene and marker discovery for translational genomics in soybean. This review highlights the current knowledge of the genetic diversity and quantitative trait loci associated with root system architecture, canopy wilting, nitrogen-fixation ability, and flooding tolerance that contributes to the understanding of drought- and flooding-tolerance mechanisms in soybean. Next-generation mapping approaches and high-throughput phenotyping will facilitate a better understanding of phenotype-genotype associations and help to formulate genomic-assisted breeding strategies, including genomic selection, in soybean for tolerance to drought and flooding stress.

Characterization of Genetic Basis on Synergistic Interactions between Root Architecture and Biological Nitrogen Fixation in Soybean

Soybean [Glycine max (L.) Merr] is an important legume crop and its yield largely depends on root architecture (RA) and biological nitrogen fixation (BNF). However, the relationship between RA and BNF, and its genetics behind remain unclear. Here, two soybean genotypes contrasting in RA and their 175 F_9:11 recombinant inbred lines (RILs) were evaluated in field. The shallow-root parent, JD12, had better nodulation and higher yield than the deep-root parent, NF58. Strong correlations between shoot dry weight (SDW) and RA or BNF traits existed in the RILs, and the shallow-root group had more and heavier nodules, as well as higher SDW. After inoculating with rhizobia, roots became shallower and bigger, showing strong synergistic interactions between RA and BNF. In total, 70 QTLs were identified for the 21 tested traits. Among them, qBNF-RA-C2, qBNF-RA-O, and qBNF-RA-B1, were newly identified QTLs for BNF and/or RA traits in soybean, which co-located with the QTLs for SDW detected presently, and with the QTLs for yield identified previously. The results together suggest that there are synergistic interactions between RA and BNF, and the QTLs identified here could be used for breeding new soybean varieties with higher yields through optimization of RA traits and BNF capacity.

Evaluation of high yielding soybean germplasm under water limitation

Limited information is available for soybean root traits and their plasticity under drought stress. To date, no studies have focused on examining diverse soybean germplasm for regulation of shoot and root response under water limited conditions across varying soil types. In this study, 17 genetically diverse soybean germplasm lines were selected to study root response to water limited conditions in clay (trial 1) and sandy soil (trial 2) in two target environments. Physiological data on shoot traits was measured at multiple crop stages ranging from early vegetative to pod filling. The phenotypic root traits, and biomass accumulation data are collected at pod filling stage. In trial 1, the number of lateral roots and forks were positively correlated with plot yield under water limitation and in trial 2, lateral root thickness was positively correlated with the hill plot yield. Plant Introduction (PI) 578477A and 088444 were found to have higher later root number and forks in clay soil with higher yield under water limitation. In sandy soil, PI458020 was found to have a thicker lateral root system and higher yield under water limitation. The genotypes identified in this study could be used to enhance drought tolerance of elite soybean cultivars through improved root traits specific to target environments.

Confirmation of delayed canopy wilting QTLs from multiple soybean mapping populations

KEY MESSAGE

QTLs for delayed canopy wilting from five soybean populations were projected onto the consensus map to identify eight QTL clusters that had QTLs from at least two independent populations. Quantitative trait loci (QTLs) for canopy wilting were identified in five recombinant inbred line (RIL) populations, 93705 KS4895 × Jackson, 08705 KS4895 × Jackson, KS4895 × PI 424140, A5959 × PI 416937, and Benning × PI 416937 in a total of 15 site-years. For most environments, heritability of canopy wilting ranged from 0.65 to 0.85 but was somewhat lower when averaged over environments. Putative QTLs were identified with composite interval mapping and/or multiple interval mapping methods in each population and positioned on the consensus map along with their 95% confidence intervals (CIs). We initially found nine QTL clusters with overlapping CIs on Gm02, Gm05, Gm11, Gm14, Gm17, and Gm19 identified from at least two different populations, but a simulation study indicated that the QTLs on Gm14 could be false positives. A QTL on Gm08 in the 93705 KS4895 × Jackson population co-segregated with a QTL for wilting published previously in a Kefeng1 × Nannong 1138-2 population, indicating that this may be an additional QTL cluster. Excluding the QTL cluster on Gm14, results of the simulation study indicated that the eight remaining QTL clusters and the QTL on Gm08 appeared to be authentic QTLs. QTL × year interactions indicated that QTLs were stable over years except for major QTLs on Gm11 and Gm19. The stability of QTLs located on seven clusters indicates that they are possible candidates for use in marker-assisted selection.

Dynamic QTL and epistasis analysis on seedling root traits in upland cotton

Roots are involved in acquisition of water and nutrients, as well as in providing structural support to plant. The root system provides a dynamic model for developmental analysis. Here, we investigated quantitative trait loci (QTL), dynamic conditional QTL and epistatic interactions for seedling root traits using an upland cotton F2 population and a constructed genetic map. Totally, 37 QTLs for root traits, 35 dynamic conditional QTLs based on the net increased amount of root traits (root tips, forks, length, surface area and volume) (i) after transplanting 10 days compared to 5 days, and (ii) after transplanting 15 days to 10 days were detected. Obvious dynamic characteristic of QTL and dynamic conditional QTL existed at different developmental stages of root because QTL and dynamic conditional QTL had not been detected simultaneously. We further confirmed that additive and dominance effects of QTL qRSA-chr1-1 in interval time 5 to 10 DAT (days after transplant) offset the effects in 10 to 15 DAT. Lots of two-locus interactions for root traits were identified unconditionally or dynamically, and a few epistatic interactions were only detected simultaneously in interval time of 5-10 DAT and 10-15 DAT, suggesting different interactive genetic mechanisms on root development at different stages. Dynamic conditional QTL and epistasis effects provide new attempts to understand the dynamics of roots and provide clues for root architecture selection in upland cotton.

Identification of novel QTL governing root architectural traits in an interspecific soybean population

Cultivated soybean (Glycine max L.) cv. Dunbar (PI 552538) and wild G. soja (PI 326582A) exhibited significant differences in root architecture and root-related traits. In this study, phenotypic variability for root traits among 251 BC2F5 backcross inbred lines (BILs) developed from the cross Dunbar/PI 326582A were identified. The root systems of the parents and BILs were evaluated in controlled environmental conditions using a cone system at seedling stage. The G. max parent Dunbar contributed phenotypically favorable alleles at a major quantitative trait locus on chromosome 8 (Satt315-I locus) that governed root traits (tap root length and lateral root number) and shoot length. This QTL accounted for >10% of the phenotypic variation of both tap root and shoot length. This QTL region was found to control various shoot- and root-related traits across soybean genetic backgrounds. Within the confidence interval of this region, eleven transcription factors (TFs) were identified. Based on RNA sequencing and Affymetrix expression data, key TFs including MYB, AP2-EREBP and bZIP TFs were identified in this QTL interval with high expression in roots and nodules. The backcross inbred lines with different parental allelic combination showed different expression pattern for six transcription factors selected based on their expression pattern in root tissues. It appears that the marker interval Satt315-I locus on chromosome 8 contain an essential QTL contributing to early root and shoot growth in soybean.

Genetic variants in root architecture-related genes in a Glycine soja accession, a potential resource to improve cultivated soybean

BACKGROUND

Root system architecture is important for water acquisition and nutrient acquisition for all crops. In soybean breeding programs, wild soybean alleles have been used successfully to enhance yield and seed composition traits, but have never been investigated to improve root system architecture. Therefore, in this study, high-density single-feature polymorphic markers and simple sequence repeats were used to map quantitative trait loci (QTLs) governing root system architecture in an inter-specific soybean mapping population developed from a cross between Glycine max and Glycine soja.

RESULTS

Wild and cultivated soybean both contributed alleles towards significant additive large effect QTLs on chromosome 6 and 7 for a longer total root length and root distribution, respectively. Epistatic effect QTLs were also identified for taproot length, average diameter, and root distribution. These root traits will influence the water and nutrient uptake in soybean. Two cell division-related genes (D type cyclin and auxin efflux carrier protein) with insertion/deletion variations might contribute to the shorter root phenotypes observed in G. soja compared with cultivated soybean. Based on the location of the QTLs and sequence information from a second G. soja accession, three genes (slow anion channel associated 1 like, Auxin responsive NEDD8-activating complex and peroxidase), each with a non-synonymous single nucleotide polymorphism mutation were identified, which may also contribute to changes in root architecture in the cultivated soybean. In addition, Apoptosis inhibitor 5-like on chromosome 7 and slow anion channel associated 1-like on chromosome 15 had epistatic interactions for taproot length QTLs in soybean.

CONCLUSION

Rare alleles from a G. soja accession are expected to enhance our understanding of the genetic components involved in root architecture traits, and could be combined to improve root system and drought adaptation in soybean.

Transcriptomic changes due to water deficit define a general soybean response and accession-specific pathways for drought avoidance

BACKGROUND

Among abiotic stresses, drought is the most common reducer of crop yields. The slow-wilting soybean genotype PI 416937 is somewhat robust to water deficit and has been used previously to map the trait in a bi-parental population. Since drought stress response is a complex biological process, whole genome transcriptome analysis was performed to obtain a deeper understanding of the drought response in soybean.

RESULTS

Contrasting data from PI 416937 and the cultivar 'Benning', we developed a classification system to identify genes that were either responding to water-deficit in both genotypes or that had a genotype x environment (GxE) response. In spite of very different wilting phenotypes, 90% of classifiable genes had either constant expression in both genotypes (33%) or very similar response profiles (E genes, 57%). By further classifying E genes based on expression profiles, we were able to discern the functional specificity of transcriptional responses at particular stages of water-deficit, noting both the well-known reduction in photosynthesis genes as well as the less understood up-regulation of the protein transport pathway. Two percent of classifiable genes had a well-defined GxE response, many of which are located within slow-wilting QTLs. We consider these strong candidates for possible causal genes underlying PI 416937's unique drought avoidance strategy.

CONCLUSIONS

There is a general and functionally significant transcriptional response to water deficit that involves not only known pathways, such as down-regulation of photosynthesis, but also up-regulation of protein transport and chromatin remodeling. Genes that show a genotypic difference are more likely to show an environmental response than genes that are constant between genotypes. In this study, at least five genes that clearly exhibited a genotype x environment response fell within known QTL and are very good candidates for further research into slow-wilting.

Challenges of modifying root traits in crops for agriculture

Roots play an essential role in the acquisition of water and minerals from soils. Measuring crop root architecture and assaying for changes in function can be challenging, but examples have emerged showing that modifications to roots result in higher yield and increased stress tolerance. In this review, we focus mainly on the molecular genetic advances that have been made in altering root system architecture and function in crop plants, as well as phenotyping methods. The future for the modification of crop plant roots looks promising based on recent advances, but there are also important challenges ahead.

A new simple method for improving QTL mapping under selective genotyping

The selective genotyping approach, where only individuals from the high and low extremes of the trait distribution are selected for genotyping and the remaining individuals are not genotyped, has been known as a cost-saving strategy to reduce genotyping work and can still maintain nearly equivalent efficiency to complete genotyping in QTL mapping. We propose a novel and simple statistical method based on the normal mixture model for selective genotyping when both genotyped and ungenotyped individuals are fitted in the model for QTL analysis. Compared to the existing methods, the main feature of our model is that we first provide a simple way for obtaining the distribution of QTL genotypes for the ungenotyped individuals and then use it, rather than the population distribution of QTL genotypes as in the existing methods, to fit the ungenotyped individuals in model construction. Another feature is that the proposed method is developed on the basis of a multiple-QTL model and has a simple estimation procedure similar to that for complete genotyping. As a result, the proposed method has the ability to provide better QTL resolution, analyze QTL epistasis, and tackle multiple QTL problem under selective genotyping. In addition, a truncated normal mixture model based on a multiple-QTL model is developed when only the genotyped individuals are considered in the analysis, so that the two different types of models can be compared and investigated in selective genotyping. The issue in determining threshold values for selective genotyping in QTL mapping is also discussed. Simulation studies are performed to evaluate the proposed methods, compare the different models, and study the QTL mapping properties in selective genotyping. The results show that the proposed method can provide greater QTL detection power and facilitate QTL mapping for selective genotyping. Also, selective genotyping using larger genotyping proportions may provide roughly equivalent power to complete genotyping and that using smaller genotyping proportions has difficulties doing so. The R code of our proposed method is available on http://www.stat.sinica.edu.tw/chkao/.

Fine mapping and identification of candidate genes controlling the resistance to southern root-knot nematode in PI 96354

Meloidogyne incognita (Kofoid and White) Chitwood (Mi) is the most economically damaging species of the root-knot nematode to soybean and other crops in the southern USA. PI 96354 was identified to carry a high level of resistance to galling and Mi egg production. Two Quantitative Trait Locus (QTLs) were found to condition the resistance in PI 96354 including a major QTL and a minor QTL on chromosome 10 and chromosome 18, respectively. To fine map the major QTL on chromosome 10, F5:6 recombinant inbred lines from the cross between PI 96354 and susceptible genotype Bossier were genotyped with Simple Sequence Repeats (SSR) markers to identify recombinational events. Analysis of lines carrying key recombination events placed the Mi-resistant allele on chromosome 10 to a 235-kb region of the 'Williams 82' genome sequence with 30 annotated genes. Candidate gene analysis identified four genes with cell wall modification function that have several mutations in promoter, exon, 5', and 3'UTR regions. qPCR analysis showed significant difference in expression levels of these four genes in Bossier compared to PI 96354 in the presence of Mi. Thirty Mi-resistant soybean lines were found to have same SNPs in these 4 candidate genes as PI 96354 while 12 Mi-susceptible lines possess the 'Bossier' genotype. The mutant SNPs were used to develop KASP assays to detect the resistant allele on chromosome 10. The four candidate genes identified in this study can be used in further studies to investigate the role of cell wall modification genes in conferring Mi resistance in PI 96354.

Potentials toward genetic engineering of drought-tolerant soybean

Soybean (Glycine max) is one of the most important crops in legume family. Soybean and soybean-based products are also considered as popular food for human and animal husbandry. With its high oil content, soybean has become a potential resource for the production of renewable fuel. However, soybean is considered one of the most drought-sensitive crops, with approximately 40% reduction of the yield in the worst years. Recent research progresses in elucidation of biochemical, morphological and physiological responses as well as molecular mechanisms of plant adaptation to drought stress in model plants have provided a solid foundation for translational genomics of soybean toward drought tolerance. In this review, we will summarize the recent advances in development of drought-tolerant soybean cultivars by gene transfer.

Mapping of quantitative trait loci for canopy-wilting trait in soybean (Glycine max L. Merr)

Drought stress adversely affects [Glycine max (L.) Merr] soybean at most developmental stages, which collectively results in yield reduction. Little information is available on relative contribution and chromosomal locations of quantitative trait loci (QTL) conditioning drought tolerance in soybean. A Japanese germplasm accession, PI 416937, was found to possess drought resistance. Under moisture-deficit conditions, PI 416937 wilted more slowly in the field than elite cultivars and has been used as a parent in breeding programs to improve soybean productivity. A recombinant inbred line (RIL) population was derived from a cross between PI 416937 and Benning, and the population was phenotyped for canopy wilting under rain-fed field conditions in five distinct environments to identify the QTL associated with the canopy-wilting trait. In a combined analysis over environments, seven QTL that explained 75 % of the variation in canopy-wilting trait were identified on different chromosomes, implying the complexity of this trait. Five QTL inherited their positive alleles from PI 416937. Surprisingly, the other two QTL inherited their positive alleles from Benning. These putative QTL were co-localized with other QTL previously identified as related to plant abiotic stresses in soybean, suggesting that canopy-wilting QTL may be associated with additional morpho-physiological traits in soybean. A locus on chromosome 12 (Gm12) from PI 416937 was detected in the combined analysis as well as in each individual environment, and explained 27 % of the variation in canopy-wilting. QTL identified in PI 416937 could provide an efficient means to augment field-oriented development of drought-tolerant soybean cultivars.