Genetic mapping reveals the complex genetic architecture controlling slow canopy wilting in soybean.

In soybean [Glycine max (L.) Merr.], drought stress is the leading cause of yield loss from abiotic stress in rain-fed US growing areas. Only 10% of the US soybean production is irrigated; therefore, plants must possess physiological mechanisms to tolerate drought stress. Slow canopy wilting is a physiological trait that is observed in a few exotic plant introductions (PIs) and may lead to yield improvement under drought stress. Canopy wilting of 130 recombinant inbred lines (RILs) derived from Hutcheson × PI 471938 grown under drought stress was visually evaluated and genotyped with the SoySNP6K BeadChip. Over four years, field evaluations of canopy wilting were conducted under rainfed conditions at three locations across the US (Georgia, Kansas, and North Carolina). Due to the variation in weather among locations and years, the phenotypic data were collected from seven environments. Substantial variation in canopy wilting was observed among the genotypes in the RIL population across environments. Three QTLs were identified for canopy wilting from the RIL population using composite interval mapping on chromosomes (Chrs) 2, 8, and 9 based on combined environmental analyses. These QTLs inherited the favorable alleles from PI 471938 and accounted for 11, 10, and 14% of phenotypic variation, respectively. A list of 106 candidate genes were narrowed down for these three QTLs based on the published information. The QTLs identified through this research can be used as targets for further investigation to understand the mechanisms of slow canopy wilting. These QTLs could be deployed to improve drought tolerance through a targeted selection of the genomic regions from PI 471938.

Multi-level perception fusion dehazing network.

Image dehazing models are critical in improving the recognition and classification capabilities of image-related artificial intelligence systems. However, existing methods often ignore the limitations of receptive field size during feature extraction and the loss of important information during network sampling, resulting in incomplete or structurally flawed dehazing outcomes. To address these challenges, we propose a multi-level perception fusion dehazing network (MPFDN) that effectively integrates feature information across different scales, expands the perceptual field of the network, and fully extracts the spatial background information of the image. Moreover, we employ an error feedback mechanism and a feature compensator to address the loss of features during the image dehazing process. Finally, we subtract the original hazy image from the generated residual image to obtain a high-quality dehazed image. Based on extensive experimentation, our proposed method has demonstrated outstanding performance not only on synthesizing dehazing datasets, but also on non-homogeneous haze datasets.

Genomic selection of soybean (Glycine max) for genetic improvement of yield and seed composition in a breeding context.

Genomic selection has been utilized for genetic improvement in both plant and animal breeding and is a favorable technique for quantitative trait development. Within this study, genomic selection was evaluated within a breeding program, using novel validation methods in addition to plant materials and data from a commercial soybean (Glycine max) breeding program. A total of 1501 inbred lines were used to test multiple genomic selection models for multiple traits. Validation included cross-validation, inter-environment, and empirical validation. The results indicated that the extended genomic best linear unbiased prediction (EGBLUP) model was the most effective model tested for yield, protein, and oil in cross-validation with accuracies of 0.50, 0.68, and 0.64, respectively. Increasing marker number from 1000 to 3000 to 6000 single nucleotide polymorphism markers leads to statistically significant increases in accuracy. Cross-environment predictions were statistically lower than cross-validation with accuracies of 0.24, 0.54, and 0.42 for yield, protein, and oil, respectively, using the extended genomic BLUP model. Empirical validation, predicting the yield of 510 soybean lines, had a prediction accuracy of 0.34, with the inclusion of a maturity covariate leading to a notable increase in accuracy. Genomic selection identified high-performance lines in inter-environment predictions: 34% of lines within the upper quartile of yield, and 51% and 48% of the highest quartile protein and oil lines, respectively. Statistically similar results occurred comparing rankings in empirical validation and selection for advancements in yield trials. These results indicate that genomic selection is a useful tool for selection decisions.

Pinpointing Rcs3 for frogeye leaf spot resistance and tracing its origin in soybean breeding.

Frogeye leaf spot is a yield-reducing disease of soybean caused by the pathogen Cercospora sojina. Rcs3 has provided durable resistance to all known races of C. sojina since its discovery in the cultivar Davis during the 1980s. Using a recombinant inbred line population derived from a cross between Davis and the susceptible cultivar Forrest, Rcs3 was fine-mapped to a 1.15 Mb interval on chromosome 16. This single locus was confirmed by tracing Rcs3 in resistant and susceptible progeny derived from Davis, as well as three near-isogenic lines. Haplotype analysis in the ancestors of Davis indicated that Davis has the same haplotype at the Rcs3 locus as susceptible cultivars in its paternal lineage. On the basis of these results, it is hypothesized that the resistance allele in Davis resulted from a mutation of a susceptibility allele. Tightly linked SNP markers at the Rcs3 locus identified in this research can be used for effective marker-assisted selection. Supplementary Information: The online version contains supplementary material available at 10.1007/s11032-023-01397-x.

Genomic prediction of optimal cross combinations to accelerate genetic improvement of soybean (Glycine max).

Improving yield is a primary soybean breeding goal, as yield is the main determinant of soybean's profitability. Within the breeding process, selection of cross combinations is one of most important elements. Cross prediction will assist soybean breeders in identifying the best cross combinations among parental genotypes prior to crossing, increasing genetic gain and breeding efficiency. In this study optimal cross selection methods were created and applied in soybean and validated using historical data from the University of Georgia soybean breeding program, under multiple training set compositions and marker densities utilizing multiple genomic selection models for marker evaluation. Plant materials consisted of 702 advanced breeding lines evaluated in multiple environments and genotyped using SoySNP6k BeadChips. An additional marker set, the SoySNP3k marker set, was tested in this study as well. Optimal cross selection methods were used to predict the yield of 42 previously made crosses and compared to the performance of the cross's offspring in replicated field trials. The best prediction accuracy was obtained when using Extended Genomic BLUP with the SoySNP6k marker set, consisting of 3,762 polymorphic markers, with an accuracy of 0.56 with a training set maximally related to the crosses predicted and 0.4 in a training set with minimized relatedness to predicted crosses. Prediction accuracy was most significantly impacted by training set relatedness to the predicted crosses, marker density, and the genomic model used to predict marker effects. The usefulness criterion selected had an impact on prediction accuracy within training sets with low relatedness to the crosses predicted. Optimal cross prediction provides a useful method that assists plant breeders in selecting crosses in soybean breeding.

A chromosome 16 deletion conferring a high sucrose phenotype in soybean.

KEY MESSAGE: Sucrose in soybean seeds is desirable for many end-uses. Increased sucrose contents were discovered to associate with a chromosome 16 deletion resulting from fast neutron irradiation. Soybean is one of the most economically important crops in the United States. A primary end-use of soybean is for livestock feed. Therefore, genetic improvement of seed composition is one of the most important goals in soybean breeding programs. Sucrose is desired in animal feed due to its role as an easily digestible energy source. An elite soybean line was irradiated with fast neutrons and the seed from plants were screened for altered seed composition with near-infrared spectroscopy (NIR). One mutant line, G15FN-54, was found to have higher sucrose content (8-9%) than the parental line (5-6%). Comparative genomic hybridization (CGH) revealed three large deletions on chromosomes (Chrs) 10, 13, and 16 in the mutant, which were confirmed through whole genome sequencing (WGS). A bi-parental population derived from the mutant G15FN-54 and the cultivar Benning was developed to conduct a bulked segregant analysis (BSA) with SoySNP50K BeadChips, revealing that the deletion on Chr 16 might be responsible for the altered phenotype. The mapping result using the bi-parental population confirmed that the deletion on Chr 16 conferred elevated sucrose content and a total of 21 genes are located within this Chr 16 deletion. NIR and high-pressure liquid chromatography (HPLC) were used to confirm the stability of the phenotype across generations in the bi-parental population. The mutation will be useful to understand the genetic control of soybean seed sucrose content.

Genome-wide association study reveals novel loci and a candidate gene for resistance to frogeye leaf spot (Cercospora sojina) in soybean.

Frogeye leaf spot, caused by the fungus Cercospora sojina, is a threat to soybeans in the southeastern and midwestern United States that can be controlled by crop genetic resistance. Limited genetic resistance to the disease has been reported, and only three sources of resistance have been used in modern soybean breeding. To discover novel sources and identify the genomic locations of resistance that could be used in soybean breeding, a GWAS was conducted using a panel of 329 soybean accessions selected to maximize genetic diversity. Accessions were phenotyped using a 1-5 visual rating and by using image analysis to count lesion number and measure the percent of leaf area diseased. Eight novel loci on eight chromosomes were identified for three traits utilizing the FarmCPU or BLINK models, of which a locus on chromosome 11 was highly significant across all model-trait combinations. KASP markers were designed using the SoySNP50K Beadchip and variant information from 65 of the accessions that have been sequenced to target SNPs in the gene model Glyma.11g230400, a LEUCINE-RICH REPEAT RECEPTOR-LIKE PROTEIN KINASE. The association of a KASP marker, GSM990, designed to detect a missense mutation in the gene was the most significant with all three traits in a genome-wide association, and the marker may be useful to select for resistance to frogeye leaf spot in soybean breeding.

Introgression of a Danbaekkong high-protein allele across different genetic backgrounds in soybean.

Soybean meal is a major component of livestock feed due to its high content and quality of protein. Understanding the genetic control of protein is essential to develop new cultivars with improved meal protein. Previously, a genomic region on chromosome 20 significantly associated with elevated protein content was identified in the cultivar Danbaekkong. The present research aimed to introgress the Danbaekkong high-protein allele into elite lines with different genetic backgrounds by developing and deploying robust DNA markers. A multiparent population consisting of 10 F5-derived populations with a total of 1,115 recombinant inbred lines (RILs) was developed using "Benning HP" as the donor parent of the Danbaekkong high-protein allele. A new functional marker targeting the 321-bp insertion in the gene Glyma.20g085100 was developed and used to track the Danbaekkong high-protein allele across the different populations and enable assessment of its effect and stability. Across all populations, the high-protein allele consistently increased the content, with an increase of 3.3% in seed protein. A total of 103 RILs were selected from the multiparent population for yield testing in five environments to assess the impact of the high-protein allele on yield and to enable the selection of new breeding lines with high protein and high yield. The results indicated that the high-protein allele impacts yield negatively in general; however, it is possible to select high-yielding lines with high protein content. An analysis of inheritance of the Chr 20 high-protein allele in Danbaekkong indicated that it originated from a Glycine soja line (PI 163453) and is the same as other G. soja lines studied. A survey of the distribution of the allele across 79 G. soja accessions and 35 Glycine max ancestors of North American soybean cultivars showed that the high-protein allele is present in all G. soja lines evaluated but not in any of the 35 North American soybean ancestors. These results demonstrate that G. soja accessions are a valuable source of favorable alleles for improvement of protein composition.

Soybean microbiome composition and the impact of host plant resistance.

Microbial communities play an important role in the growth and development of plants, including plant immunity and the decomposition of complex substances into absorbable nutrients. Hence, utilizing beneficial microbes becomes a promising strategy for the optimization of plant growth. The objective of this research was to explore the root bacterial profile across different soybean genotypes and the change in the microbial community under soybean cyst nematode (SCN) infection in greenhouse conditions using 16S rRNA sequencing. Soybean genotypes with soybean cyst nematode (SCN) susceptible and resistant phenotypes were grown under field and greenhouse conditions. Bulked soil, rhizosphere, and root samples were collected from each replicate. Sequencing of the bacterial 16S gene indicated that the bacterial profile of soybean root and soil samples partially overlapped but also contained different communities. The bacterial phyla Proteobacteria, Actinobacteria, and Bacteroidetes dominate the soybean root-enriched microbiota. The structure of bacteria was significantly affected by sample year (field) or time point (greenhouse). In addition, the host genotype had a small but significant effect on the diversity of the root microbiome under SCN pressure in the greenhouse test. These differences may potentially represent beneficial bacteria or secondary effects related to SCN resistance.

Identification of Functional Genetic Variations Underlying Flooding Tolerance in Brazilian Soybean Genotypes.

Flooding is a frequent environmental stress that reduces soybean (Glycine max) growth and grain yield in many producing areas in the world, such as, e.g., in the United States, Southeast Asia and Southern Brazil. In these regions, soybean is frequently cultivated in lowland areas by rotating with rice (Oryza sativa), which provides numerous technical, economic and environmental benefits. Given these realities, this work aimed to characterize physiological responses, identify genes differentially expressed under flooding stress in Brazilian soybean genotypes with contrasting flooding tolerance, and select SNPs with potential use for marker-assisted selection. Soybean cultivars TECIRGA 6070 (flooding tolerant) and FUNDACEP 62 (flooding sensitive) were grown up to the V6 growth stage and then flooding stress was imposed. Total RNA was extracted from leaves 24 h after the stress was imposed and sequenced. In total, 421 induced and 291 repressed genes were identified in both genotypes. TECIRGA 6070 presented 284 and 460 genes up- and down-regulated, respectively, under flooding conditions. Of those, 100 and 148 genes were exclusively up- and down-regulated, respectively, in the tolerant genotype. Based on the RNA sequencing data, SNPs in differentially expressed genes in response to flooding stress were identified. Finally, 38 SNPs, located in genes with functional annotation for response to abiotic stresses, were found in TECIRGA 6070 and absent in FUNDACEP 62. To validate them, 22 SNPs were selected for designing KASP assays that were used to genotype a panel of 11 contrasting genotypes with known phenotypes. In addition, the phenotypic and grain yield impacts were analyzed in four field experiments using a panel of 166 Brazilian soybean genotypes. Five SNPs possibly related to flooding tolerance in Brazilian soybean genotypes were identified. The information generated from this research will be useful to develop soybean genotypes adapted to poorly drained soils or areas subject to flooding.

Automated, image-based disease measurement for phenotyping resistance to soybean frogeye leaf spot.

BACKGROUND: Frogeye leaf spot is a disease of soybean, and there are limited sources of crop genetic resistance. Accurate quantification of resistance is necessary for the discovery of novel resistance sources, which can be accelerated by using a low-cost and easy-to-use image analysis system to phenotype the disease. The objective herein was to develop an automated image analysis phenotyping pipeline to measure and count frogeye leaf spot lesions on soybean leaves with high precision and resolution while ensuring data integrity. RESULTS: The image analysis program developed measures two traits: the percent of diseased leaf area and the number of lesions on a leaf. Percent of diseased leaf area is calculated by dividing the number of diseased pixels by the total number of leaf pixels, which are segmented through a series of color space transformations and pixel value thresholding. Lesion number is determined by counting the number of objects remaining in the image when the lesions are segmented. Automated measurement of the percent of diseased leaf area deviates from the manually measured value by less than 0.05% on average. Automatic lesion counting deviates by an average of 1.6 lesions from the manually counted value. The proposed method is highly correlated with a conventional method using a 1-5 ordinal scale based on a standard area diagram. Input image compression was optimal at a resolution of 1500 × 1000 pixels. At this resolution, the image analysis method proposed can process an image in less than 10 s and is highly concordant with uncompressed images. CONCLUSION: Image analysis provides improved resolution over conventional methods of frogeye leaf spot disease phenotyping. This method can improve the precision and resolution of phenotyping frogeye leaf spot, which can be used in genetic mapping to identify QTLs for crop genetic resistance and in breeding efforts for resistance to the disease.

Genomic regions associated with resistance to soybean rust (Phakopsora pachyrhizi) under field conditions in soybean germplasm accessions from Japan, Indonesia and Vietnam.

KEY MESSAGE: Eight soybean genomic regions, including six never before reported, were found to be associated with resistance to soybean rust (Phakopsora pachyrhizi) in the southeastern USA. Soybean rust caused by Phakopsora pachyrhizi is one of the most important foliar diseases of soybean [Glycine max (L.) Merr.]. Although seven Rpp resistance gene loci have been reported, extensive pathotype variation in and among fungal populations increases the importance of identifying additional genes and loci associated with rust resistance. One hundred and ninety-one soybean plant introductions from Japan, Indonesia and Vietnam, and 65 plant introductions from other countries were screened for resistance to P. pachyrhizi under field conditions in the southeastern USA between 2008 and 2015. The results indicated that 84, 69, and 49% of the accessions from southern Japan, Vietnam or central Indonesia, respectively, had negative BLUP values, indicating less disease than the panel mean. A genome-wide association analysis using SoySNP50K Infinium BeadChip data identified eight genomic regions on seven chromosomes associated with SBR resistance, including previously unreported regions of Chromosomes 1, 4, 6, 9, 13, and 15, in addition to the locations of the Rpp3 and Rpp6 loci. The six unreported genomic regions might contain novel Rpp loci. The identification of additional sources of rust resistance and associated genomic regions will further efforts to develop soybean cultivars with broad and durable resistance to soybean rust in the southern USA.

Utilization of Plant Architecture Genes in Soybean to Positively Impact Adaptation to High Yield Environments.

Optimization of plant architecture by modifying stem termination and timing of flowering and maturity of soybean is a promising strategy to improve its adaptability to specific production environments. Therefore, it is important to choose a proper stem termination type and to understand morphological differences between each stem termination type under various environmental conditions. Variations in abruptness of stem termination have been generally classified into three classical genetic types, indeterminate (Dt1), determinate (dt1), and semi-determinate (Dt2). However, an additional stem termination type, termed tall determinate, and its genetic symbol, dt1-t, were introduced about 25 years ago. The tall determinate soybean lines show delayed cessation of apical stem growth and about 50% taller plant heights than the typical determinate soybeans, even though the genetic control of the tall determinate phenotype was found to be allelic to dt1. Despite the potential agronomic merits of the alternative stem termination type, knowledge about the tall determinate soybean remains limited. We clarified the molecular basis of the tall determinate stem termination type and examined potential agronomic merits of the alternative stem type under three different production environments in the US. Sequence analysis of the classical tall determinate soybean lines revealed that the dt1-t allele responsible for tall determinate stem architecture is caused by two of the identified independent missense alleles of dt1, dt1-t1 (R130K), and dt1-t2 (R62S). Also, from the comparison among soybean accessions belonging to each of the genotype categories for stem termination types, soybean accessions with tall determinate alleles were found to have a high discrepancy rate in phenotyping. Newly developed tall determinate late-maturing soybean germplasm lines had taller plant heights and a greater number of nodes with a similar stem diameter and similar pod density at the apical stem compared to typical determinate soybeans having dt1 (R166W) alleles in Southern environments in the US. The phenotype of increased pod-bearing nodes with lodging resistance has the potential to improve yield, especially grown in high yield environments. This study suggests an alternative strategy to remodel the shape of soybean plants, which can possibly lead to yield improvement through the modification of soybean plant architecture.

Exploring Machine Learning Algorithms to Unveil Genomic Regions Associated With Resistance to Southern Root-Knot Nematode in Soybeans.

Southern root-knot nematode [SRKN, Meloidogyne incognita (Kofold & White) Chitwood] is a plant-parasitic nematode challenging to control due to its short life cycle, a wide range of hosts, and limited management options, of which genetic resistance is the main option to efficiently control the damage caused by SRKN. To date, a major quantitative trait locus (QTL) mapped on chromosome (Chr.) 10 plays an essential role in resistance to SRKN in soybean varieties. The confidence of discovered trait-loci associations by traditional methods is often limited by the assumptions of individual single nucleotide polymorphisms (SNPs) always acting independently as well as the phenotype following a Gaussian distribution. Therefore, the objective of this study was to conduct machine learning (ML)-based genome-wide association studies (GWAS) utilizing Random Forest (RF) and Support Vector Machine (SVM) algorithms to unveil novel regions of the soybean genome associated with resistance to SRKN. A total of 717 breeding lines derived from 330 unique bi-parental populations were genotyped with the Illumina Infinium BARCSoySNP6K BeadChip and phenotyped for SRKN resistance in a greenhouse. A GWAS pipeline involving a supervised feature dimension reduction based on Variable Importance in Projection (VIP) and SNP detection based on classification accuracy was proposed. Minor effect SNPs were detected by the proposed ML-GWAS methodology but not identified using Bayesian-information and linkage-disequilibrium Iteratively Nested Keyway (BLINK), Fixed and Random Model Circulating Probability Unification (FarmCPU), and Enriched Compressed Mixed Linear Model (ECMLM) models. Besides the genomic region on Chr. 10 that can explain most of SRKN resistance variance, additional minor effects SNPs were also identified on Chrs. 10 and 11. The findings in this study demonstrated that overfitting in GWAS may lead to lower prediction accuracy, and the detection of significant SNPs based on classification accuracy limited false-positive associations. The expansion of the basis of the genetic resistance to SRKN can potentially reduce the selection pressure over the major QTL on Chr. 10 and achieve higher levels of resistance.

Genetic control and allele variation among soybean maturity groups 000 through IX.

Soybean [Glycinemax (L.) Merr.] maturity determines the growing region of a given soybean variety and is a primary factor in yield and other agronomic traits. The objectives of this research were to identify the quantitative trait loci (QTL) associated with maturity groups (MGs) and determine the genetic control of soybean maturity in each MG. Using data from 16,879 soybean accessions, genome-wide association (GWA) analyses were conducted for each paired MG and across MGs 000 through IX. Genome-wide association analyses were also performed using 184 genotypes (MGs V-IX) with days to flowering (DTF) and maturity (DTM) collected in the field. A total of 58 QTL were identified to be significantly associated with MGs in individual GWAs, which included 12 reported maturity loci and two stem termination genes. Genome-wide associations across MGs 000-IX detected a total of 103 QTL and confirmed 54 QTL identified in the individual GWAs. Of significant loci identified, qMG-5.2 had effects on the highest number (9) of MGs, followed by E2, E3, Dt2, qMG-15.5, E1, qMG-13.1, qMG-7.1, and qMG-16.1, which affected five to seven MGs. A high number of genetic loci (8-25) that affected MGs 0-V were observed. Stem termination genes Dt1 and Dt2 mainly had significant allele variation in MGs II-V. Genome-wide associations for DTF, DTM, and reproductive period (RP) in the diversity panel confirmed 15 QTL, of which seven were observed in MGs V-IX. The results generated can help soybean breeders manipulate the maturity loci for genetic improvement of soybean yield.

Assessment of Quinone Outside Inhibitor Sensitivity and Frogeye Leaf Spot Race of Cercospora sojina in Georgia Soybean.

Frogeye leaf spot (FLS), caused by the fungal pathogen Cercospora sojina K. Hara, is a foliar disease of soybean (Glycine max L. [Merr.]) responsible for yield reductions throughout the major soybean-producing regions of the world. In the United States, management of FLS relies heavily on the use of resistant cultivars and in-season fungicide applications, specifically within the class of quinone outside inhibitors (QoIs), which has resulted in the development of fungicide resistance in many states. In 2018 and 2019, 80 isolates of C. sojina were collected from six counties in Georgia and screened for QoI fungicide resistance using molecular and in vitro assays, with resistant isolates being confirmed from three counties. Additionally, 50 isolates, including a "baseline isolate" with no prior fungicide exposure, were used to determine the percent reduction of mycelial growth to two fungicides, azoxystrobin and pyraclostrobin, at six concentrations: 0.0001, 0.001, 0.01, 0.1, 1, and 10 µg ml-1. Mycelial growth observed for resistant isolates varied significantly from both sensitive isolates and baseline isolate for azoxystrobin concentrations of 10, 1, 0.1, and 0.01 µg ml-1 and for pyraclostrobin concentrations of 10, 1, 0.1, 0.01, and 0.001 µg ml-1. Moreover, 40 isolates were used to evaluate pathogen race on six soybean differential cultivars by assessing susceptible or resistant reactions. Isolate reactions suggested 12 races of C. sojina present in Georgia, 4 of which have not been previously described. Species richness indicators (rarefaction and abundance-based coverage estimators) indicated that within-county C. sojina race numbers were undersampled in this study, suggesting the potential for the presence of either additional undescribed races or known but unaccounted for races in Georgia. However, no isolates were pathogenic on 'Davis', a differential cultivar carrying the Rcs3 resistance allele, suggesting that the gene is still an effective source of resistance in Georgia.

Mining QTLs for elevated protein and other major seed composition traits from diverse soybean germplasm.

Soybean is the world's largest source of protein for animal feed and the second largest source of vegetable oil. Improving the seed protein of soybean without negatively affecting yield and oil content is an important goal for soybean breeders. A population consisting of 132 recombinant inbred lines (RILs) was developed by crossing an elite breeding line, G00-3213 with a plant introduction, PI 594458A, with elevated protein content. In 2016 and 2017, each of the RILs was grown as a single row in Watkinsville, GA, while in 2018, the population was grown at two locations. The seed composition of RILs was analyzed with near-infrared (NIR) spectroscopy. The RIL population was genotyped using the SoySNP6k BeadChip for quantitative trait locus (QTL) mapping. Significant genotype × environment interaction was observed. QTL analyses in and across four environments identified 16, 10, 10, 16, and 5 QTLs for protein, oil, sucrose, and normalized cysteine and methionine contents, respectively. QTLs for protein content identified on chromosomes (Chrs) 3, 6, 13, and 20 were detected in multiple environments. Eight genomic regions on Chrs 3, 6, 8, 10, 13, 17, and 20 were detected that influenced two to four traits, indicating that pleiotropic or linkage effects of these loci may influence multiple seed composition traits. The results of this research provide additional genomic resources for genetic improvement of seed composition and help breeders to better understand the environmental impacts on these QTLs. Supplementary Information: The online version contains supplementary material available at 10.1007/s11032-021-01242-z.

RhizoVision Crown: An Integrated Hardware and Software Platform for Root Crown Phenotyping.

Root crown phenotyping measures the top portion of crop root systems and can be used for marker-assisted breeding, genetic mapping, and understanding how roots influence soil resource acquisition. Several imaging protocols and image analysis programs exist, but they are not optimized for high-throughput, repeatable, and robust root crown phenotyping. The RhizoVision Crown platform integrates an imaging unit, image capture software, and image analysis software that are optimized for reliable extraction of measurements from large numbers of root crowns. The hardware platform utilizes a backlight and a monochrome machine vision camera to capture root crown silhouettes. The RhizoVision Imager and RhizoVision Analyzer are free, open-source software that streamline image capture and image analysis with intuitive graphical user interfaces. The RhizoVision Analyzer was physically validated using copper wire, and features were extensively validated using 10,464 ground-truth simulated images of dicot and monocot root systems. This platform was then used to phenotype soybean and wheat root crowns. A total of 2,799 soybean (Glycine max) root crowns of 187 lines and 1,753 wheat (Triticum aestivum) root crowns of 186 lines were phenotyped. Principal component analysis indicated similar correlations among features in both species. The maximum heritability was 0.74 in soybean and 0.22 in wheat, indicating that differences in species and populations need to be considered. The integrated RhizoVision Crown platform facilitates high-throughput phenotyping of crop root crowns and sets a standard by which open plant phenotyping platforms can be benchmarked.

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.

Genome-Wide Association Analyses Reveal Genomic Regions Controlling Canopy Wilting in Soybean.

Drought stress causes the greatest soybean [Glycine max (L.) Merr.] yield losses among the abiotic stresses in rain-fed U.S. growing areas. Because less than 10% of U.S. soybean hectares are irrigated, combating this stress requires soybean plants which possess physiological mechanisms to tolerate drought for a period of time. Phenotyping for these mechanisms is challenging, and the genetic architecture for these traits is poorly understood. A morphological trait, slow or delayed canopy wilting, has been observed in a few exotic plant introductions (PIs), and may lead to yield improvement in drought stressed fields. In this study, we visually scored wilting during stress for a panel of 162 genetically diverse maturity group VI-VIII soybean lines genotyped with the SoySNP50K iSelect BeadChip. Field evaluation of canopy wilting was conducted under rain-fed conditions at two locations (Athens, GA and Salina, KS) in 2015 and 2016. Substantial variation in canopy wilting was observed among the genotypes. Using a genome-wide association mapping approach, 45 unique SNPs that tagged 44 loci were associated with canopy wilting in at least one environment with one region identified in a single environment and data from across all environments. Several new soybean accessions were identified with canopy wilting superior to those of check genotypes. The germplasm and genomic regions identified can be used to better understand the slow canopy wilting trait and be incorporated into elite germplasm to improve drought tolerance in soybean.