Development, validation and genetic analysis of a large soybean SNP genotyping array

Genetic diversity and population structure of soybean (Glycine max (L.) Merril) germplasm

Soybean (Glycine max (L.) Merril) is a significant legume crop for oil and protein. However, its yield in Africa is less than half the global average resulting in low production, which is inadequate for satisfying the continent's needs. To address this disparity in productivity, it is crucial to develop new high-yielding cultivars by utilizing the genetic diversity of existing germplasms. Consequently, the genetic diversity and population structure of various soybean accessions were evaluated in this study. To achieve this objective, a collection of 147 soybean accessions was genotyped using the Diversity Array Technology Sequencing method, enabling high-throughput analysis of 7,083 high-quality single-nucleotide polymorphisms (SNPs) distributed across the soybean genome. The average values observed for polymorphism information content (PIC), minor allele frequency, expected heterozygosity and observed heterozygosity were 0.277, 0.254, 0.344, and 0.110, respectively. The soybean genotypes were categorized into four groups on the basis of model-based population structure, principal component analysis, and discriminant analysis of the principal component. Alternatively, hierarchical clustering was used to organize the accessions into three distinct clusters. Analysis of molecular variance indicated that the genetic variance (77%) within the populations exceeded the variance (23%) among them. The insights gained from this study will assist breeders in selecting parental lines for genetic recombination. The present study demonstrates that soybean improvement is viable within the IITA breeding program, and its outcome will help to optimize the genetic enhancement of soybeans.

Identification of candidate genes for drought tolerance in soybean through QTL mapping and gene expression analysis

Introduction

Drought stress significantly reduces soybean yield, underscoring the need to develop drought-resistant varieties and identify the underlying genetic mechanisms. However, the specific genes and pathways contributing to drought tolerance remain poorly understood. This study aimed to identify candidate genes associated with drought tolerance in soybean using a recombinant inbred line (RIL) population derived from PI416937 and Cheongsang.

Methods

A quantitative trait loci (QTL) mapping study using a 180K high-quality SNP array and composite interval mapping on 140 recombinant inbred lines, coupled with RNA sequencing of treated and control groups, was conducted to identify candidate genes for drought tolerance in soybean.

Results and Discussion

Through QTL mapping and differential gene expression profiling, five candidate genes were identified, with two (Glyma.06G076100 and Glyma.10G029600) highlighted as putative candidates based on functional annotations. These genes appear to play critical roles in stress tolerance, including ion homeostasis and the regulation of plasma membrane ATPase, as well as the synthesis of heat shock proteins (HSPs) that mitigate dehydration and thermal stress. These findings advance our understanding of the genetic basis of drought tolerance in soybean and provide valuable targets for breeding programs aimed at developing resilient cultivars.

Unlocking soybean potential: genetic resources and omics for breeding

Soybean (Glycine max) is a vital foundation of global food security, providing a primary source of high-quality protein and oil for human consumption and animal feed. The rising global population has significantly increased the demand for soybeans, emphasizing the urgency of developing high-yield, stress-tolerant, and nutritionally superior cultivars. The extensive collection of soybean germplasm resources-including wild relatives, landraces, and cultivars-represents a valuable reservoir of genetic diversity critical for breeding advancements. Recent breakthroughs in genomic technologies, particularly high-throughput sequencing and multi-omics approaches, have revolutionized the identification of key genes associated with essential agronomic traits within these resources. These innovations enable precise and strategic utilization of genetic diversity, empowering breeders to integrate traits that improve yield potential, resilience to biotic and abiotic stresses, and nutritional quality. This review highlights the critical role of genetic resources and omics-driven innovations in soybean breeding. It also offers insights into strategies for accelerating the development of elite soybean cultivars to meet the growing demands of global soybean production.

New perspectives of post-GWAS analyses: From markers to causal genes for more precise crop breeding

Crop breeding advancement is hindered by the imperfection of methods to reveal genes underlying key traits. Genome-wide Association Study (GWAS) is one such method, identifying genomic regions linked to phenotypes. Post-GWAS analyses predict candidate genes and assist in causative mutation (CM) recognition. Here, we assess post-GWAS approaches, address limitations in omics data integration and stress the importance of evaluating associated variants within a broader context of publicly available datasets. Recent advances in bioinformatics tools and genomic strategies for CM identification and allelic variation exploration are reviewed. We discuss the role of markers and marker panel development for more precise breeding. Finally, we highlight the perspectives and challenges of GWAS-based CM prediction for complex quantitative traits.

Aflatoxins in cereal based products-an overview of occurrence, detection and health implication

Aflatoxins are naturally produced toxins by specific molds, namely Aspergillus flavus and Aspergillus parasiticus. These toxins can be found in various agricultural products, including crops like maize, peanuts, cottonseed, and tree nuts. They have the potential to contaminate the food supply during different stages of production, processing, and storage. Aflatoxin is a very poisonous substance that has been linked to adverse health effects in both humans and animals. It is essential to detect and monitor aflatoxins to ensure the safety of food. Efficient and precise analytical techniques, such as chromatography and immunoassays, have been used to accurately measure the levels of aflatoxins in different substances. Regulatory bodies and worldwide associations have determined maximum permissible limits for aflatoxins in food and nourishment products to protect the well-being of the general public. Effectively addressing aflatoxin contamination necessitates a comprehensive approach that encompasses various strategies in agriculture, post-harvest practices, and regulatory measures. Continuous research and collaborative endeavors are crucial in order to minimize aflatoxin exposure and mitigate the associated risks. This review offers a comprehensive examination of the presence, health consequences, and elimination techniques associated with aflatoxins.

Genetic dissection of resistance to Phytophthora sojae using genome-wide association and linkage analysis in soybean [Glycine max (L.) Merr.]

KEY MESSAGE

Two novel and one known genomic regions associated with R-gene resistance to Phytophthora sojae were identified by genome-wide association analysis and linkage analysis in soybean. Phytophthora root and stem rot (PRR) caused by Phytophthora sojae is a severe disease that causes substantial economic losses in soybean [Glycine max (L.) Merr.]. The primary approach for successful disease management of PRR is using R-gene-mediated resistance. Based on the phenotypic evaluation of 376 cultivated soybean accessions for the R-gene type resistance to P. sojae (isolate 2457), a genome-wide association analysis identified two regions on chromosomes 3 and 8. The most significant genomic region (20.7-21.3 Mbp) on chromosome 8 was a novel resistance locus where no Rps gene was previously reported. Instead, multiple copies of the UDP-glycosyltransferase superfamily protein-coding gene, associated with disease resistance, were annotated in this new locus. Another genomic region on chromosome 3 was a well-known Rps cluster. Using the Daepung × Ilpumgeomjeong RIL population, a linkage analysis confirmed these two resistance loci and identified a resistance locus on chromosome 2. A unique feature of the resistance in Ilpumgeomjeong was discovered when phenotypic distribution was projected upon eight groups of RILs carrying different combinations of resistance alleles for the three loci. Interestingly, the seven groups carrying at least one resistance allele statistically differed from the other with none, regardless of the number of resistance alleles. This suggests that the respective three different resistance genes can confer resistance to P. sojae isolate 2457. Deployment of the three regions via marker-assisted selection will facilitate effectively improving resistance to particular P. sojae isolates in soybean breeding programs.

Exploration and genetic analyses of canopy leaf pigmentation changes in soybean (Glycine max L.): unveiling a novel phenotype

KEY MESSAGE

Pigmentation changes in canopy leaves were first reported, and subsequent genetic analyses identified a major QTL associated with levels of pigmentation changes, suggesting Glyma.06G202300 as a candidate gene. An unexpected reddish-purple pigmentation in upper canopy leaves was discovered during the late reproductive stages in soybean (Glycine max L.) genotypes. Two sensitive genotypes, 'Uram' and PI 96983, exhibited anomalous canopy leaf pigmentation changes (CLPC), while 'Daepung' did not. The objectives of this study were to: (i) characterize the physiological features of pigmented canopy leaves compared with non-pigmented leaves, (ii) evaluate phenotypic variation in a combined recombinant inbred line (RIL) population (N = 169 RILs) under field conditions, and (iii) genetically identify quantitative trait loci (QTL) for CLPC via joint population linkage analysis. Comparison between pigmented and normal leaves revealed different Fv/Fm of photosystem II, hyperspectral reflectance, and cellular properties, suggesting the pigmentation changes occur in response to an undefined abiotic stress. A highly significant QTL was identified on chromosome 6, explaining ~ 62.8% of phenotypic variance. Based on the QTL result, Glyma.06G202300 encoding flavonoid 3'-hydroxylase (F3'H) was identified as a candidate gene. In both Uram and PI 96983, a 1-bp deletion was confirmed in the third exon of Glyma.06G202300 that results in a premature stop codon in both Uram and PI 96983 and a truncated F3'H protein lacking important domains. Additionally, gene expression analyses uncovered significant differences between pigmented and non-pigmented leaves. This is the first report of a novel symptom and an associated major QTL. These results will provide soybean geneticists and breeders with valuable knowledge regarding physiological changes that may affect soybean production. Further studies are required to elucidate the causal environmental stress and the underlying molecular mechanisms.

SUnSeT: spectral unmixing of hyperspectral images for phenotyping soybean seed traits

KEY MESSAGE

Hyperspectral features enable accurate classification of soybean seeds using linear discriminant analysis and GWAS for novel seed trait genes. Evaluating crop seed traits such as size, shape, and color is crucial for assessing seed quality and improving agricultural productivity. The introduction of the SUnSet toolbox, which employs hyperspectral sensor-derived image analysis, addresses this necessity. In a validation test involving 420 seed accessions from the Korean Soybean Core Collections, the pixel purity index algorithm identified seed- specific hyperspectral endmembers to facilitate segmentation. Various metrics extracted from ventral and lateral side images facilitated the categorization of seeds into three size groups and four shape groups. Additionally, quantitative RGB triplets representing seven seed coat colors, averaged reflectance spectra, and pigment indices were acquired. Machine learning models, trained on a dataset comprising 420 accession seeds and 199 predictors encompassing seed size, shape, and reflectance spectra, achieved accuracy rates of 95.8% for linear discriminant analysis model. Furthermore, a genome-wide association study utilizing hyperspectral features uncovered associations between seed traits and genes governing seed pigmentation and shapes. This comprehensive approach underscores the effectiveness of SUnSet in advancing precision agriculture through meticulous seed trait analysis.

Developing an SNP dataset for efficiently evaluating soybean germplasm resources using the genome sequencing data of 3,661 soybean accessions

BACKGROUND

Single nucleotide polymorphism (SNP) markers play significant roles in accelerating breeding and basic crop research. Several soybean SNP panels have been developed. However, there is still a lack of SNP panels for differentiating between wild and cultivated populations, as well as for detecting polymorphisms within both wild and cultivated populations.

RESULTS

This study utilized publicly available resequencing data from over 3,000 soybean accessions to identify differentiating and highly conserved SNP and insertion/deletion (InDel) markers between wild and cultivated soybean populations. Additionally, a naturally occurring mutant gene library was constructed by analyzing large-effect SNPs and InDels in the population.

CONCLUSION

The markers obtained in this study are associated with numerous genes governing agronomic traits, thus facilitating the evaluation of soybean germplasms and the efficient differentiation between wild and cultivated soybeans. The natural mutant gene library permits the quick identification of individuals with natural mutations in functional genes, providing convenience for accelerating soybean breeding using reverse genetics.

Genomic Analysis of Romanian Lycium Genotypes: Exploring BODYGUARD Genes for Stress Resistance Breeding

Goji berries, long valued in Traditional Chinese Medicine and Asian cuisine for their wide range of medicinal benefits, are now considered a 'superfruit' and functional food worldwide. Because of growing demand, Europe and North America are increasing their goji berry production, using goji berry varieties that are not originally from these regions. European breeding programs are focusing on producing Lycium varieties adapted to local conditions and market demands. By 2023, seven varieties of goji berries were successfully registered in Romania, developed using germplasm that originated from sources outside the country. A broader project focused on goji berry breeding was initiated in 2014 at USAMV Bucharest. In the present research, five cultivated and three wild L. barbarum genotypes were compared to analyse genetic variation at the whole genome level. In addition, a case study presents the differences in the genomic coding sequences of BODYGUARD (BDG) 3 and 4 genes from chromosomes 4, 8, and 9, which are involved in cuticle-related resistance. All three BDG genes show distinctive differences between the cultivated and wild-type genotypes at the SNP level. In the BDG 4 gene located on chromosome 8, 69% of SNPs differentiate the wild from the cultivated genotypes, while in BDG 3 on chromosome 4, 64% of SNPs could tell the difference between the wild and cultivated goji berry. The research also uncovered significant SNP and InDel differences between cultivated and wild genotypes, in the entire genome, providing crucial insights for goji berry breeders to support the development of goji berry cultivation in Romania.

QTL mapping reveals key factors related to the isoflavone contents and agronomic traits of soybean (Glycine max)

BACKGROUND

Soybean is a valuable source of edible protein and oil, as well as secondary metabolites that can be used in food products, cosmetics, and medicines. However, because soybean isoflavone content is a quantitative trait influenced by polygenes and environmental interactions, its genetic basis remains unclear.

RESULTS

This study was conducted to identify causal quantitative trait loci (QTLs) associated with soybean isoflavone contents. A mutant-based F2 population (190 individuals) was created by crossing the Korean cultivar Hwanggeum with low isoflavone contents (1,558 µg g-1) and the soybean mutant DB-088 with high isoflavone contents (6,393 µg g-1). A linkage map (3,049 cM) with an average chromosome length of 152 cM was constructed using the 180K AXIOM® SoyaSNP array. Thirteen QTLs related to agronomic traits were mapped to chromosomes 2, 3, 11, 13, 19, and 20, whereas 29 QTLs associated with isoflavone contents were mapped to chromosomes 1, 3, 8, 11, 14, 15, and 17. Notably, the qMGLI11, qMGNI11, qADZI11, and qTI11, which located Gm11_9877690 to Gm11_9955924 interval on chromosome 11, contributed to the high isoflavone contents and explained 11.9% to 20.1% of the phenotypic variation. This QTL region included four candidate genes, encoding β-glucosidases 13, 14, 17-1, and 17-2. We observed significant differences in the expression levels of these genes at various seed developmental stages. Candidate genes within the causal QTLs were functionally characterized based on enriched GO terms and KEGG pathways, as well as the results of a co-expression network analysis. A correlation analysis indicated that certain agronomic traits (e.g., days to flowering, days to maturity, and plant height) are positively correlated with isoflavone content.

CONCLUSIONS

Herein, we reported that the major QTL associated with isoflavone contents was located in the interval from Gm11_9877690 to Gm11_9955924 (78 kb) on chromosome 11. Four β-glucosidase genes were identified that may be involved in high isoflavone contents of soybean DB-088. Thus, the mutant alleles from soybean DB-088 may be useful for marker-assisted selection in developing soybean lines with high isoflavone contents and superior agronomic traits.

QTLs and Candidate Genes for Seed Protein Content in Two Recombinant Inbred Line Populations of Soybean

This study aimed to discover the quantitative trait loci (QTL) associated with a high seed protein content in soybean and unravel the potential candidate genes. We developed two recombinant inbred line populations: YS and SI, by crossing Saedanbaek (high protein) with YS2035-B-91-1-B-1 (low protein) and Saedanbaek with Ilmi (low protein), respectively, and evaluated the protein content for three consecutive years. Using single-nucleotide polymorphism (SNP)-marker-based linkage maps, four QTLs were located on chromosomes 15, 18, and 20 with high logarithm of odds values (5.9-55.0), contributing 5.5-66.0% phenotypic variance. In all three experimental years, qPSD20-1 and qPSD20-2 were stable and identified in overlapping positions in the YS and SI populations, respectively. Additionally, novel QTLs were identified on chromosomes 15 and 18. Considering the allelic sequence variation between parental lines, 28 annotated genes related to soybean seed protein-including starch, lipid, and fatty acid biosynthesis-related genes-were identified within the QTL regions. These genes could potentially affect protein accumulation during seed development, as well as sucrose and oil metabolism. Overall, this study offers insights into the genetic mechanisms underlying a high soybean protein content. The identified potential candidate genes can aid marker-assisted selection for developing soybean lines with an increased protein content.

SoyDBean: a database for SNPs reconciliation by multiple versions of soybean reference genomes

Due to the development of sequence technology and decreased cost, many whole genome sequences have been obtained. As a result, extensive genetic variations have been discovered from many populations and germplasms to understand the genetic diversity of soybean (Glycine max [L.] Merr.). However, assessing the quality of variation is essential because the published variants were collected using different bioinformatic methods and parameters. Furthermore, despite the enhanced genome contiguity and more efficient filling of "N" stretches in the new reference genome, there remains a dearth of endeavors to verify the caliber of variations present in it. The primary goal of this research was to discern a dependable set of SNPs that can withstand reconciliation across multiple reference genomes. Additionally, the investigation aimed to reconfirm the variations through the utilization of numerous whole genome sequencing data obtained from publicly available databases. Based on the result, we created datasets that comprised the thoroughly verified SNP coordinates between the reference assemblies. The resulting "SoyDBean" database is now publicly accessible through the following URL: http://soydbean.plantprofile.net/ .

On the usefulness of mock genomes to define heterotic pools, testers, and hybrid predictions in orphan crops

The advances in genomics in recent years have increased the accuracy and efficiency of breeding programs for many crops. Nevertheless, the adoption of genomic enhancement for several other crops essential in developing countries is still limited, especially for those that do not have a reference genome. These crops are more often called orphans. This is the first report to show how the results provided by different platforms, including the use of a simulated genome, called the mock genome, can generate in population structure and genetic diversity studies, especially when the intention is to use this information to support the formation of heterotic groups, choice of testers, and genomic prediction of single crosses. For that, we used a method to assemble a reference genome to perform the single-nucleotide polymorphism (SNP) calling without needing an external genome. Thus, we compared the analysis results using the mock genome with the standard approaches (array and genotyping-by-sequencing (GBS)). The results showed that the GBS-Mock presented similar results to the standard methods of genetic diversity studies, division of heterotic groups, the definition of testers, and genomic prediction. These results showed that a mock genome constructed from the population's intrinsic polymorphisms to perform the SNP calling is an effective alternative for conducting genomic studies of this nature in orphan crops, especially those that do not have a reference genome.

The elite variations in germplasms for soybean breeding

The genetic base of soybean cultivars (Glycine max (L.) Merr.) has been narrowed through selective domestication and specific breeding improvement, similar to other crops. This presents challenges in breeding new cultivars with improved yield and quality, reduced adaptability to climate change, and increased susceptibility to diseases. On the other hand, the vast collection of soybean germplasms offers a potential source of genetic variations to address those challenges, but it has yet to be fully leveraged. In recent decades, rapidly improved high-throughput genotyping technologies have accelerated the harness of elite variations in soybean germplasm and provided the important information for solving the problem of a narrowed genetic base in breeding. In this review, we will overview the situation of maintenance and utilization of soybean germplasms, various solutions provided for different needs in terms of the number of molecular markers, and the omics-based high-throughput strategies that have been used or can be used to identify elite alleles. We will also provide an overall genetic information generated from soybean germplasms in yield, quality traits, and pest resistance for molecular breeding.

A Genome-Wide Association Study of Protein, Oil, and Amino Acid Content in Wild Soybean (Glycine soja)

Soybean (Glycine max L.) is a globally important source of plant proteins, oils, and amino acids for both humans and livestock. Wild soybean (Glycine soja Sieb. and Zucc.), the ancestor of cultivated soybean, could be a useful genetic source for increasing these components in soybean crops. In this study, 96,432 single-nucleotide polymorphisms (SNPs) across 203 wild soybean accessions from the 180K Axiom® Soya SNP array were investigated using an association analysis. Protein and oil content exhibited a highly significant negative correlation, while the 17 amino acids exhibited a highly significant positive correlation with each other. A genome-wide association study (GWAS) was conducted on the protein, oil, and amino acid content using the 203 wild soybean accessions. A total of 44 significant SNPs were associated with protein, oil, and amino acid content. Glyma.11g015500 and Glyma.20g050300, which contained SNPs detected from the GWAS, were selected as novel candidate genes for the protein and oil content, respectively. In addition, Glyma.01g053200 and Glyma.03g239700 were selected as novel candidate genes for nine of the amino acids (Ala, Asp, Glu, Gly, Leu, Lys, Pro, Ser, and Thr). The identification of the SNP markers related to protein, oil, and amino acid content reported in the present study is expected to help improve the quality of selective breeding programs for soybeans.

Development of SNP marker panels for genotyping by target sequencing (GBTS) and its application in soybean

A high-throughput genotyping platform with customized flexibility, high genotyping accuracy, and low cost is critical for marker-assisted selection and genetic mapping in soybean. Three assay panels were selected from the SoySNP50K, 40K, 20K, and 10K arrays, containing 41,541, 20,748, and 9670 SNP markers, respectively, for genotyping by target sequencing (GBTS). Fifteen representative accessions were used to assess the accuracy and consistency of the SNP alleles identified by the SNP panels and sequencing platform. The SNP alleles were 99.87% identical between technical replicates and 98.86% identical between the 40K SNP GBTS panel and 10× resequencing analysis. The GBTS method was also accurate in the sense that the genotypic dataset of the 15 representative accessions correctly revealed the pedigree of the accessions, and the biparental progeny datasets correctly constructed the linkage maps of the SNPs. The 10K panel was also used to genotype two parent-derived populations and analyze QTLs controlling 100-seed weight, resulting in the identification of the stable associated genetic locus Locus_OSW_06 on chromosome 06. The markers flanking the QTL explained 7.05% and 9.83% of the phenotypic variation, respectively. Compared with GBS and DNA chips, the 40K, 20K, and 10K panels reduced costs by 5.07% and 58.28%, 21.44% and 65.48%, and 35.74% and 71.76%, respectively. Low-cost genotyping panels could facilitate soybean germplasm assessment, genetic linkage map construction, QTL identification, and genomic selection.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11032-023-01372-6.

Genetic Diversity of Korean Wild Soybean Core Collections and Genome-Wide Association Study for Days to Flowering

The utilization of wild soybean germplasms in breeding programs increases genetic diversity, and they contain the rare alleles of traits of interest. Understanding the genetic diversity of wild germplasms is essential for determining effective strategies that can improve the economic traits of soybeans. Undesirable traits make it challenging to cultivate wild soybeans. This study aimed to construct a core subset of 1467 wild soybean accessions of the total population and analyze their genetic diversity to understand their genetic variations. Genome-wild association studies were conducted to detect the genetic loci underlying the time to flowering for a core subset collection, and they revealed the allelic variation in E genes for predicting maturity using the available resequencing data of wild soybean. Based on principal component and cluster analyses, 408 wild soybean accessions in the core collection covered the total population and were explained by 3 clusters representing the collection regions, namely, Korea, China, and Japan. Most of the wild soybean collections in this study had the E1e2E3 genotype according to association mapping and a resequencing analysis. Korean wild soybean core collections can provide helpful genetic resources to identify new flowering and maturity genes near the E gene loci and genetic materials for developing new cultivars, facilitating the introgression of genes of interest from wild soybean.

Identification of hub genes regulating isoflavone accumulation in soybean seeds via GWAS and WGCNA approaches

INTRODUCTION

Isoflavones are the secondary metabolites synthesized by the phenylpropanoid biosynthesis pathway in soybean that benefits human and plant health.

METHODS

In this study, we have profiled seed isoflavone content by HPLC in 1551 soybean accessions grown in Beijing and Hainan for two consecutive years (2017 and 2018) and in Anhui for one year (2017).

RESULTS

A broad range of phenotypic variations was observed for individual and total isoflavone (TIF) content. The TIF content ranged from 677.25 to 5823.29 µg g^-1 in the soybean natural population. Using a genome-wide association study (GWAS) based on 6,149,599 single nucleotide polymorphisms (SNPs), we identified 11,704 SNPs significantly associated with isoflavone contents; 75% of them were located within previously reported QTL regions for isoflavone. Two significant regions on chromosomes 5 and 11 were associated with TIF and malonylglycitin across more than 3 environments. Furthermore, the WGCNA identified eight key modules: black, blue, brown, green, magenta, pink, purple, and turquoise. Of the eight co-expressed modules, brown (r = 0.68***), magenta (r = 0.64***), and green (r = 0.51**) showed a significant positive association with TIF, as well as with individual isoflavone contents. By combining the gene significance, functional annotation, and enrichment analysis information, four hub genes Glyma.11G108100, Glyma.11G107100, Glyma.11G106900, and Glyma.11G109100 encoding, basic-leucine zipper (bZIP) transcription factor, MYB4 transcription factor, early responsive to dehydration, and PLATZ transcription factor respectively were identified in brown and green modules. The allelic variation in Glyma.11G108100 significantly influenced individual and TIF accumulation.

DISCUSSION

The present study demonstrated that the GWAS approach, combined with WGCNA, could efficiently identify isoflavone candidate genes in the natural soybean population.

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.

Identification of major quantitative trait loci and candidate genes for seed weight in soybean

Four major quantitative trait loci for 100-seed weight were identified in a soybean RIL population under five environments, and the most likely candidate genes underlying these loci were identified. Seed weight is an important target of soybean breeding. However, the genes underlying the major quantitative trait loci (QTL) controlling seed weight remain largely unknown. In this study, a soybean population of 300 recombinant inbred lines (RILs) derived from a cross between PI595843 (PI) and WH was used to map the QTL and identify candidate genes for seed weight. The RIL population was genotyped through whole genome resequencing, and phenotyped for 100-seed weight under five environments. A total of 38 QTL were detected, and four major QTL, each explained at least 10% of the variation in 100-seed weight, were identified. Six candidate genes within these four major QTL regions were identified by analyses of their tissue expression patterns, gene annotations, and differential gene expression levels in soybean seeds during four developmental stages between two parental lines. Further sequence variation analyses revealed a C to T substitution in the first exon of the Glyma.19G143300, resulting in an amino acid change between PI and WH, and thus leading to a different predicted kinase domain, which might affect its protein function. Glyma.19G143300 is highly expressed in soybean seeds and encodes a leucine-rich repeat receptor-like protein kinase (LRR-RLK). Its predicted protein has typical domains of LRR-RLK family, and phylogenetic analyses reveled its similarity with the known LRR-RLK protein XIAO (LOC_Os04g48760), which is involved in controlling seed size. The major QTL and candidate genes identified in this study provide useful information for molecular breeding of new soybean cultivars with desirable seed weight.

What Is the Relationship between Antioxidant Efficacy, Functional Composition, and Genetic Characteristics in Comparing Soybean Resources by Year?

The aim of this study was to analyze the physiological activity of 48 soybean resources harvested in 2020 to identify the soybean resources’ relationships with individual isoflavone compounds and their genetic properties. These data will subsequently be compared with the research results on soybeans harvested in 2019. Initially, with respect to the physiological activity (6 types) and substances (19 types), this study evaluated the differences between the cultivation year (two years), seed coat color (three colors), and the interaction of the year and seed coat color of soybeans through ANOVA. Among the physiological activities, there were differences in the estrogen, estrogen receptor alpha, and UCP-1 (uncoupling protein-1) activities depending on the cultivation year. Moreover, there were differences in NO (nitric oxide), revealing differences in the ABTS (2, 2′-azino-bis-3ethylbenzo-thiazoline-6-sulfonic acid) and DPPH (2, 2-diphenyl-2-picrylhydrazyl) radical scavenging activities due to the seed coat color and the interaction of the year and seed coat color. Soybeans harvested in 2020 exhibited increased ABTS, DPPH, and NO inhibitory activities and reduced estrogen, estrogen receptor alpha, and UCP-1 activities compared to those harvested in 2019. According to the ANOVA results, eight of the nineteen individual derivatives illustrated yearly differences, while three derivatives displayed differences due to the seed coat color. Secondly, according to the relationship between the efficacy, derivative substances, and genetic properties, it was determined that genistein 7-O-(2″-O-apiosyl)glucoside (F5) is the individual isoflavone derivative that affected the six types of physiological activity, on which the genome-wide association study (GWAS) showed no significant differences for genetic properties. These results were inconsistent with the 2019 data, where three types of individual compounds, including F5, were proposed as substances that correlated with efficacy and there was a high correlation with genetic properties. Therefore, this study selected B17, B23, B15, B24, and Y7 as excellent varieties that are stable and highly functional in the cultivation environment, producing only small annual differences. The results of this study will be utilized as basic data for predicting soybean varieties and their cultivation, which have high environmental stability under climate variation and properly retain the functional substances and efficacy.

Identification of noble candidate gene associated with sensitivity to phytotoxicity of etofenprox in soybean

Phytotoxicity is caused by the interaction between plants and a chemical substance, which can cause critical damage to plants. Understanding the molecular mechanism underlying plant-chemical interactions is important for managing pests in crop fields and avoiding plant phytotoxicity by insecticides. The genomic region responsible for sensitivity to phytotoxicity of etofenprox (PE), controlled by a single dominant gene, was detected by constructing high density genetic map using recombination inbred lines (RILs) in soybean. The genomic region of ~ 80 kbp containing nine genes was identified on chromosome 16 using a high-throughput single nucleotide polymorphism (SNP) genotyping system using two different RIL populations. Through resequencing data of 31 genotypes, nonsynonymous SNPs were identified in Glyma.16g181900, Glyma.16g182200, and Glyma.16g182300. The genetic variation in Glyma.16g182200, encoding glycosylphosphatidylinositol-anchored protein (GPI-AP), caused a critical structure disruption on the active site of the protein. This structural variation of GPI-AP may change various properties of the ion channels which are the targets of pyrethroid insecticide including etofenprox. This is the first study that identifies the candidate gene and develops SNP markers associated with PE. This study would provide genomic information to understand the mechanism of phytotoxicity in soybean and functionally characterize the responsive gene.

Inheritance of somatic mutations by animal offspring

Since 1892, it has been widely assumed that somatic mutations are evolutionarily irrelevant in animals because they cannot be inherited by offspring. However, some nonbilaterians segregate the soma and germline late in development or never, leaving the evolutionary fate of their somatic mutations unknown. By investigating uni- and biparental reproduction in the coral Acropora palmata (Cnidaria, Anthozoa), we found that uniparental, meiotic offspring harbored 50% of the 268 somatic mutations present in their parent. Thus, somatic mutations accumulated in adult coral animals, entered the germline, and were passed on to swimming larvae that grew into healthy juvenile corals. In this way, somatic mutations can increase allelic diversity and facilitate adaptation across habitats and generations in animals.

Genetic and Genomic Resources for Soybean Breeding Research

Soybean (Glycine max) is a legume species of significant economic and nutritional value. The yield of soybean continues to increase with the breeding of improved varieties, and this is likely to continue with the application of advanced genetic and genomic approaches for breeding. Genome technologies continue to advance rapidly, with an increasing number of high-quality genome assemblies becoming available. With accumulating data from marker arrays and whole-genome resequencing, studying variations between individuals and populations is becoming increasingly accessible. Furthermore, the recent development of soybean pangenomes has highlighted the significant structural variation between individuals, together with knowledge of what has been selected for or lost during domestication and breeding, information that can be applied for the breeding of improved cultivars. Because of this, resources such as genome assemblies, SNP datasets, pangenomes and associated databases are becoming increasingly important for research underlying soybean crop improvement.

Dissection of the practical soybean breeding pipeline by developing ZDX1, a high-throughput functional array

KEY MESSAGE

We developed the ZDX1 high-throughput functional soybean array for high accuracy evaluation and selection of both parents and progeny, which can greatly accelerate soybean breeding. Microarray technology facilitates rapid, accurate, and economical genotyping. Here, using resequencing data from 2214 representative soybean accessions, we developed the high-throughput functional array ZDX1, containing 158,959 SNPs, covering 90.92% of soybean genes and sites related to important traits. By application of the array, a total of 817 accessions were genotyped, including three subpopulations of candidate parental lines, parental lines and their progeny from practical breeding. The fixed SNPs were identified in progeny, indicating artificial selection during the breeding process. By identifying functional sites of target traits, novel soybean cyst nematode-resistant progeny and maturity-related novel sources were identified by allele combinations, demonstrating that functional sites provide an efficient method for the rapid screening of desirable traits or gene sources. Notably, we found that the breeding index (BI) was a good indicator for progeny selection. Superior progeny were derived from the combination of distantly related parents, with at least one parent having a higher BI. Furthermore, new combinations based on good performance were proposed for further breeding after excluding redundant and closely related parents. Genomic best linear unbiased prediction (GBLUP) analysis was the best analysis method and achieved the highest accuracy in predicting four traits when comparing SNPs in genic regions rather than whole genomic or intergenic SNPs. The prediction accuracy was improved by 32.1% by using progeny to expand the training population. Collectively, a versatile assay demonstrated that the functional ZDX1 array provided efficient information for the design and optimization of a breeding pipeline for accelerated soybean breeding.

Applications of Genomic Tools in Plant Breeding: Crop Biofortification

Crop breeding has mainly been focused on increasing productivity, either directly or by decreasing the losses caused by biotic and abiotic stresses (that is, incorporating resistance to diseases and enhancing tolerance to adverse conditions, respectively). Quite the opposite, little attention has been paid to improve the nutritional value of crops. It has not been until recently that crop biofortification has become an objective within breeding programs, through either conventional methods or genetic engineering. There are many steps along this long path, from the initial evaluation of germplasm for the content of nutrients and health-promoting compounds to the development of biofortified varieties, with the available and future genomic tools assisting scientists and breeders in reaching their objectives as well as speeding up the process. This review offers a compendium of the genomic technologies used to explore and create biodiversity, to associate the traits of interest to the genome, and to transfer the genomic regions responsible for the desirable characteristics into potential new varieties. Finally, a glimpse of future perspectives and challenges in this emerging area is offered by taking the present scenario and the slow progress of the regulatory framework as the starting point.

SoySNP618K array: A high-resolution single nucleotide polymorphism platform as a valuable genomic resource for soybean genetics and breeding

Innovations in genomics have enabled the development of low-cost, high-resolution, single nucleotide polymorphism (SNP) genotyping arrays that accelerate breeding progress and support basic research in crop science. Here, we developed and validated the SoySNP618K array (618,888 SNPs) for the important crop soybean. The SNPs were selected from whole-genome resequencing data containing 2,214 diverse soybean accessions; 29.34% of the SNPs mapped to genic regions representing 86.85% of the 56,044 annotated high-confidence genes. Identity-by-state analyses of 318 soybeans revealed 17 redundant accessions, highlighting the potential of the SoySNP618K array in supporting gene bank management. The patterns of population stratification and genomic regions enriched through domestication were highly consistent with previous findings based on resequencing data, suggesting that the ascertainment bias in the SoySNP618K array was largely compensated for. Genome-wide association mapping in combination with reported quantitative trait loci enabled fine-mapping of genes known to influence flowering time, E2 and GmPRR3b, and of a new candidate gene, GmVIP5. Moreover, genomic prediction of flowering and maturity time in 502 recombinant inbred lines was highly accurate (>0.65). Thus, the SoySNP618K array is a valuable genomic tool that can be used to address many questions in applied breeding, germplasm management, and basic crop research.

Progress in soybean functional genomics over the past decade

Soybean is one of the most important oilseed and fodder crops. Benefiting from the efforts of soybean breeders and the development of breeding technology, large number of germplasm has been generated over the last 100 years. Nevertheless, soybean breeding needs to be accelerated to meet the needs of a growing world population, to promote sustainable agriculture and to address future environmental changes. The acceleration is highly reliant on the discoveries in gene functional studies. The release of the reference soybean genome in 2010 has significantly facilitated the advance in soybean functional genomics. Here, we review the research progress in soybean omics (genomics, transcriptomics, epigenomics and proteomics), germplasm development (germplasm resources and databases), gene discovery (genes that are responsible for important soybean traits including yield, flowering and maturity, seed quality, stress resistance, nodulation and domestication) and transformation technology during the past decade. At the end, we also briefly discuss current challenges and future directions.

Mapping Major Disease Resistance Genes in Soybean by Genome-Wide Association Studies

Soybean is one of the most valuable agricultural crops in the world. Besides, this legume is constantly attacked by a wide range of pathogens (fungi, bacteria, viruses, and nematodes) compromising yield and increasing production costs. One of the major disease management strategies is the genetic resistance provided by single genes and quantitative trait loci (QTL). Identifying the genomic regions underlying the resistance against these pathogens on soybean is one of the first steps performed by molecular breeders. In the past, genetic mapping studies have been widely used to discover these genomic regions. However, over the last decade, advances in next-generation sequencing technologies and their subsequent cost decreasing led to the development of cost-effective approaches to high-throughput genotyping. Thus, genome-wide association studies applying thousands of SNPs in large sets composed of diverse soybean accessions have been successfully done. In this chapter, a comprehensive review of the majority of GWAS for soybean diseases published since this approach was developed is provided. Important diseases caused by Heterodera glycines, Phytophthora sojae, and Sclerotinia sclerotiorum have been the focus of the several GWAS. However, other bacterial and fungi diseases also have been targets of GWAS. As such, this GWAS summary can serve as a guide for future studies of these diseases. The protocol begins by describing several considerations about the pathogens and bringing different procedures of molecular characterization of them. Advice to choose the best isolate/race to maximize the discovery of multiple R genes or to directly map an effective R gene is provided. A summary of protocols, methods, and tools to phenotyping the soybean panel is given to several diseases. We also give details of options of DNA extraction protocols and genotyping methods, and we describe parameters of SNP quality to soybean data. Websites and their online tools to obtain genotypic and phenotypic data for thousands of soybean accessions are highlighted. Finally, we report several tricks and tips in Subheading 4, especially related to composing the soybean panel as well as generating and analyzing the phenotype data. We hope this protocol will be helpful to achieve GWAS success in identifying resistance genes on soybean.

A Correlation Study on In Vitro Physiological Activities of Soybean Cultivars, 19 Individual Isoflavone Derivatives, and Genetic Characteristics

The functionality of soybeans is an important factor in the selection and utilization of excellent soybean cultivars, and isoflavones are representative functional substances in soybeans, which exhibit effects on antioxidants, estrogen activity, and cancer, and prevent cardiovascular diseases. This study analyzed ABTS, DPPH, estrogen, ER (ER) alpha, UCP-1, and NO inhibition activities in 48 types of soybean cultivars, as well as the relationship with 19 isolated types of individual isoflavone derivatives. Statistical analysis was conducted to find individual isoflavone derivatives affecting physiological activities, revealing the high correlation of three types of derivatives: genistein 7-O-(6″-O-acetyl)glucoside (6″-O-acetylgenistin), genistein 7-O-(2″-O-apiosyl)glucoside, and glycitein. Based on these results, 15 types of soybean cultivars were selected (one control type, seven yellow types, six black types, and one green type), which have both high physiological activities and a high content of individual isoflavone derivatives. In addition, these high correlations were further verified through a genome-wide association study (GWAS) to determine the association between activities, substances, and genetic characteristics. This study comprehensively describes the relationship between the specific physiological activities of soybean resources, individual isoflavone derivative substances, and SNPs, which will be utilized for in-depth research, such as selection of excellent soybean resources with specific physiological activities.

Genome assembly of the popular Korean soybean cultivar Hwangkeum

Massive resequencing efforts have been undertaken to catalog allelic variants in major crop species including soybean, but the scope of the information for genetic variation often depends on short sequence reads mapped to the extant reference genome. Additional de novo assembled genome sequences provide a unique opportunity to explore a dispensable genome fraction in the pan-genome of a species. Here, we report the de novo assembly and annotation of Hwangkeum, a popular soybean cultivar in Korea. The assembly was constructed using PromethION nanopore sequencing data and two genetic maps and was then error-corrected using Illumina short-reads and PacBio SMRT reads. The 933.12 Mb assembly was annotated as containing 79,870 transcripts for 58,550 genes using RNA-Seq data and the public soybean annotation set. Comparison of the Hwangkeum assembly with the Williams 82 soybean reference genome sequence (Wm82.a2.v1) revealed 1.8 million single-nucleotide polymorphisms, 0.5 million indels, and 25 thousand putative structural variants. However, there was no natural megabase-scale chromosomal rearrangement. Incidentally, by adding two novel subfamilies, we found that soybean contains four clearly separated subfamilies of centromeric satellite repeats. Analyses of satellite repeats and gene content suggested that the Hwangkeum assembly is a high-quality assembly. This was further supported by comparison of the marker arrangement of anthocyanin biosynthesis genes and of gene arrangement at the Rsv3 locus. Therefore, the results indicate that the de novo assembly of Hwangkeum is a valuable additional reference genome resource for characterizing traits for the improvement of this important crop species.

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.

Marker-Assisted Pyramiding of Downy Mildew-Resistant Gene Ppa3 and Black Rot-Resistant Gene Xca1bo in Popular Early Cauliflower Variety Pusa Meghna

Cauliflower is an important extensively grown cool season vegetable in India. Black rot and downy mildew are major devastating diseases reducing yield and quality of the crop. To tackle these through host plant resistance, a marker-assisted backcross breeding method was followed to pyramid a black rot-resistant gene (Xca1bo) and a downy mildew-resistant gene (Ppa3) from donors BR-161 and BR-2, respectively, into the background of Pusa Meghna cauliflower cultivar. Marker-assisted backcross breeding was followed up to BC₂ generation using SCAR marker ScOPO-04₈₃₃ and SSR marker BoGMS0624 for black rot and downy mildew resistance genes in foreground selection, respectively. In background selection, at each stage of backcrossing, 47 parental polymorphic SSR markers were used. The graphical genotyping of the five two-gene (Xca1boXca1boPpa3Ppa3) homozygous BC₂F₂ plants showed an average recovery of 85.44% of the Pusa Meghna genome with highest genome recovery of 91.7%. The genome contribution of donor parents (BR-161 and BR-2) was 8.26 with 6.34% of residual heterozygousity. The backcross derived pyramided lines BC₂F_2:3-7-16 and BC₂F_2:3-7-33 showed high resistance to both the diseases and exhibited higher yield and vitamin C content as compared with recipient parent Pusa Meghna. It is, therefore, evident from this study that resistant genes can be introgressed successfully into a Pusa Meghna cultivar without any yield penalty, benefitting farmers with reduced input cost and consumers with chemical residue free produce. Besides, the pyramided lines carrying dominant resistant genes can be exploited in a hybridization programme to develop hybrid(s) in cauliflower.

Genetic diversity analysis of Korean peanut germplasm using 48 K SNPs 'Axiom_Arachis' Array and its application for cultivar differentiation

Cultivated peanut (Arachis hypogaea) is one of the important legume oilseed crops. Cultivated peanut has a narrow genetic base. Therefore, it is necessary to widen its genetic base and diversity for additional use. The objective of the present study was to assess the genetic diversity and population structure of 96 peanut genotypes with 9478 high-resolution SNPs identified from a 48 K 'Axiom_Arachis' SNP array. Korean set genotypes were also compared with a mini-core of US genotypes. These sets of genotypes were used for genetic diversity analysis. Model-based structure analysis at K = 2 indicated the presence of two subpopulations in both sets of genotypes. Phylogenetic and PCA analysis clustered these genotypes into two major groups. However, clear genotype distribution was not observed for categories of subspecies, botanical variety, or origin. The analysis also revealed that current Korean genetic resources lacked variability compared to US mini-core genotypes. These results suggest that Korean genetic resources need to be expanded by creating new allele combinations and widening the genetic pool to offer new genetic variations for Korean peanut improvement programs. High-quality SNP data generated in this study could be used for identifying varietal contaminant, QTL, and genes associated with desirable traits by performing mapping, genome-wide association studies.

Fine-mapping and candidate gene analysis for the foxglove aphid resistance gene Raso2 from wild soybean PI 366121

KEY MESSAGE

The foxglove aphid resistance gene Raso2 from PI 366121 was fine-mapped to 77 Kb region, and one candidate gene was identified. The foxglove aphid (FA: Aulacorthum solani Kaltenbach) is an important insect pest that causes serious yield losses in soybean. The FA resistance gene Raso2 from wild soybean PI 366121 was previously mapped to a 13 cM interval on soybean chromosome 7. However, fine-mapping of Raso2 was needed to improve the effectiveness of marker-assisted selection (MAS) and to eventually clone it. The objectives of this study were to fine-map Raso2 from PI 366121 using Axiom® 180 K SoyaSNP array, to confirm the resistance and inheritance of Raso2 in a different background, and to identify candidate gene(s). The 105 F4:8 recombinant inbred lines were used to fine-map the gene and to test antibiosis and antixenosis of Raso2 to FA. These efforts resulted in the mapping of Raso2 on 1 cM interval which corresponds to 77 Kb containing eight annotated genes based on the Williams 82 reference genome assembly (Wm82.a2.v1). Interestingly, all nonsynonymous substitutions were in Glyma.07g077700 which encodes the disease resistance protein containing LRR domain and expression of the gene in PI 366121 was significantly higher than that in Williams 82. In addition, distinct SNPs within Glyma.07g077700 that can distinguish PI 366121 and diverse FA-susceptible soybeans were identified. We also confirmed that Raso2 presented the resistance to FA and the Mendelian inheritance for single dominant gene in a different background. The results of this study would provide fundamental information on MAS for development of FA-resistant cultivars as well as functional study and cloning of the candidate gene in soybean.

Genome-Wide Association Study for Ultraviolet-B Resistance in Soybean (Glycine max L.)

The depletion of the stratospheric ozone layer is a major environmental issue and has increased the dosage of ultraviolet-B (UV-B) radiation reaching the Earth’s surface. Organisms are negatively affected by enhanced UV-B radiation, and especially in crop plants this may lead to severe yield losses. Soybean (Glycine max L.), a major legume crop, is sensitive to UV-B radiation, and therefore, it is required to breed the UV-B-resistant soybean cultivar. In this study, 688 soybean germplasms were phenotyped for two categories, Damage of Leaf Chlorosis (DLC) and Damage of Leaf Shape (DLS), after supplementary UV-B irradiation for 14 days. About 5% of the germplasms showed strong UV-B resistance, and GCS731 was the most resistant genotype. Their phenotypic distributions showed similar patterns to the normal, suggesting UV-B resistance as a quantitative trait governed by polygenes. A total of 688 soybean germplasms were genotyped using the Axiom^® Soya 180K SNP array, and a genome-wide association study (GWAS) was conducted to identify SNPs significantly associated with the two traits, DLC and DLS. Five peaks on chromosomes 2, 6, 10, and 11 were significantly associated with either DLC or DLS, and the five adjacent genes were selected as candidate genes responsible for UV-B resistance. Among those candidate genes, Glyma.02g017500 and Glyma.06g103200 encode cryptochrome (CRY) and cryptochrome 1 (CRY1), respectively, and are known to play a role in DNA repair during photoreactivation. Real-time quantitative RT-PCR (qRT-PCR) results revealed that CRY1 was expressed significantly higher in the UV-B-resistant soybean compared to the susceptible soybean after 6 h of UV-B irradiation. This study is the first GWAS report on UV-B resistance in soybean, and the results will provide valuable information for breeding UV-B-resistant soybeans in preparation for climate change.

Designing Future Crops: Genomics-Assisted Breeding Comes of Age

Over the past decade, genomics-assisted breeding (GAB) has been instrumental in harnessing the potential of modern genome resources and characterizing and exploiting allelic variation for germplasm enhancement and cultivar development. Sustaining GAB in the future (GAB 2.0) will rely upon a suite of new approaches that fast-track targeted manipulation of allelic variation for creating novel diversity and facilitate their rapid and efficient incorporation in crop improvement programs. Genomic breeding strategies that optimize crop genomes with accumulation of beneficial alleles and purging of deleterious alleles will be indispensable for designing future crops. In coming decades, GAB 2.0 is expected to play a crucial role in breeding more climate-smart crop cultivars with higher nutritional value in a cost-effective and timely manner.

Integration of Infinium and Axiom SNP array data in the outcrossing species Malus × domestica and causes for seemingly incompatible calls

Background

Single nucleotide polymorphism (SNP) array technology has been increasingly used to generate large quantities of SNP data for use in genetic studies. As new arrays are developed to take advantage of new technology and of improved probe design using new genome sequence and panel data, a need to integrate data from different arrays and array platforms has arisen. This study was undertaken in view of our need for an integrated high-quality dataset of Illumina Infinium® 20 K and Affymetrix Axiom® 480 K SNP array data in apple (Malus × domestica). In this study, we qualify and quantify the compatibility of SNP calling, defined as SNP calls that are both accurate and concordant, across both arrays by two approaches. First, the concordance of SNP calls was evaluated using a set of 417 duplicate individuals genotyped on both arrays starting from a set of 10,295 robust SNPs on the Infinium array. Next, the accuracy of the SNP calls was evaluated on additional germplasm (n = 3141) from both arrays using Mendelian inconsistent and consistent errors across thousands of pedigree links. While performing this work, we took the opportunity to evaluate reasons for probe failure and observed discordant SNP calls.

Results

Concordance among the duplicate individuals was on average of 97.1% across 10,295 SNPs. Of these SNPs, 35% had discordant call(s) that were further curated, leading to a final set of 8412 (81.7%) SNPs that were deemed compatible. Compatibility was highly influenced by the presence of alternate probe binding locations and secondary polymorphisms. The impact of the latter was highly influenced by their number and proximity to the 3′ end of the probe.

Conclusions

The Infinium and Axiom SNP array data were mostly compatible. However, data integration required intense data filtering and curation. This work resulted in a workflow and information that may be of use in other data integration efforts. Such an in-depth analysis of array concordance and accuracy as ours has not been previously described in the literature and will be useful in future work on SNP array data integration and interpretation, and in probe/platform development.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-07565-7.

Detection of ploidy and chromosomal aberrations in commercial oil palm using high-throughput SNP markers

Karyotyping using high-density genome-wide SNP markers identified various chromosomal aberrations in oil palm (Elaeis guineensis Jacq.) with supporting evidence from the 2C DNA content measurements (determined using FCM) and chromosome counts. Oil palm produces a quarter of the world's total vegetable oil. In line with its global importance, an initiative to sequence the oil palm genome was carried out successfully, producing huge amounts of sequence information, allowing SNP discovery. High-capacity SNP genotyping platforms have been widely used for marker-trait association studies in oil palm. Besides genotyping, a SNP array is also an attractive tool for understanding aberrations in chromosome inheritance. Exploiting this, the present study utilized chromosome-wide SNP allelic distributions to determine the ploidy composition of over 1,000 oil palms from a commercial F₁ family, including 197 derived from twin-embryo seeds. Our method consisted of an inspection of the allelic intensity ratio using SNP markers. For palms with a shifted or abnormal distribution ratio, the SNP allelic frequencies were plotted along the pseudo-chromosomes. This method proved to be efficient in identifying whole genome duplication (triploids) and aneuploidy. We also detected several loss of heterozygosity regions which may indicate small chromosomal deletions and/or inheritance of identical by descent regions from both parents. The SNP analysis was validated by flow cytometry and chromosome counts. The triploids were all derived from twin-embryo seeds. This is the first report on the efficiency and reliability of SNP array data for karyotyping oil palm chromosomes, as an alternative to the conventional cytogenetic technique. Information on the ploidy composition and chromosomal structural variation can help to better understand the genetic makeup of samples and lead to a more robust interpretation of the genomic data in marker-trait association analyses.

Genomic resources in plant breeding for sustainable agriculture

Climate change during the last 40 years has had a serious impact on agriculture and threatens global food and nutritional security. From over half a million plant species, cereals and legumes are the most important for food and nutritional security. Although systematic plant breeding has a relatively short history, conventional breeding coupled with advances in technology and crop management strategies has increased crop yields by 56 % globally between 1965-85, referred to as the Green Revolution. Nevertheless, increased demand for food, feed, fiber, and fuel necessitates the need to break existing yield barriers in many crop plants. In the first decade of the 21st century we witnessed rapid discovery, transformative technological development and declining costs of genomics technologies. In the second decade, the field turned towards making sense of the vast amount of genomic information and subsequently moved towards accurately predicting gene-to-phenotype associations and tailoring plants for climate resilience and global food security. In this review we focus on genomic resources, genome and germplasm sequencing, sequencing-based trait mapping, and genomics-assisted breeding approaches aimed at developing biotic stress resistant, abiotic stress tolerant and high nutrition varieties in six major cereals (rice, maize, wheat, barley, sorghum and pearl millet), and six major legumes (soybean, groundnut, cowpea, common bean, chickpea and pigeonpea). We further provide a perspective and way forward to use genomic breeding approaches including marker-assisted selection, marker-assisted backcrossing, haplotype based breeding and genomic prediction approaches coupled with machine learning and artificial intelligence, to speed breeding approaches. The overall goal is to accelerate genetic gains and deliver climate resilient and high nutrition crop varieties for sustainable agriculture.

The soybean (Glycine max L.) cytokinin oxidase/dehydrogenase multigene family; Identification of natural variations for altered cytokinin content and seed yield

Cytokinins (CKs) play a fundamental role in regulating dynamics of organ source/sink relationships during plant development, including flowering and seed formation stages. As a result, CKs are key drivers of seed yield. The cytokinin oxidase/dehydrogenase (CKX) is one of the critical enzymes responsible for regulating plant CK levels by causing their irreversible degradation. Variation of CKX activity is significantly correlated with seed yield in many crop species while in soybean (Glycine max L.), the possible associations between CKX gene family members (GFMs) and yield parameters have not yet been assessed. In this study, 17 GmCKX GFMs were identified, and natural variations among GmCKX genes were probed among soybean cultivars with varying yield characteristics. The key CKX genes responsible for regulating CK content during seed filling stages of reproductive development were highlighted using comparative phylogenetics, gene expression analysis and CK metabolite profiling. Five of the seventeen identified GmCKX GFMs, showed natural variations in the form of single nucleotide polymorphisms (SNPs). The gene GmCKX7-1, with high expression during critical seed filling stages, was found to have a non-synonymous mutation (H105Q), on one of the active site residues, Histidine 105, previously reported to be essential for co-factor binding to maintain structural integrity of the enzyme. Soybean lines with this mutation had higher CK content and desired yield characteristics. The potential for marker-assisted selection based on the identified natural variation within GmCKX7-1, is discussed in the context of hormonal control that can result in higher soybean yield.

A new decade and new data at SoyBase, the USDA-ARS soybean genetics and genomics database

SoyBase, a USDA genetic and genomics database, holds professionally curated soybean genetic and genomic data, which is integrated and made accessible to researchers and breeders. The site holds several reference genome assemblies, as well as genetic maps, thousands of mapped traits, expression and epigenetic data, pedigree information, and extensive variant and genotyping data sets. SoyBase displays include genetic, genomic, and epigenetic maps of the soybean genome. Gene expression data is presented in the genome viewer as heat maps and pictorial and tabular displays in gene report pages. Millions of sequence variants have been added, representing variations across various collections of cultivars. This variant data is explorable using new interactive tools to visualize the distribution of those variants across the genome, between selected accessions. SoyBase holds several reference-quality soybean genome assemblies, accessible via various query tools and browsers, including a new visualization system for exploring the soybean pan-genome. SoyBase also serves as a nexus of announcements pertinent to the greater soybean research community. The database also includes a soybean-specific anatomic and biochemical trait ontology. The database can be accessed at https://soybase.org.

The patterns of deleterious mutations during the domestication of soybean

Globally, soybean is a major protein and oil crop. Enhancing our understanding of the soybean domestication and improvement process helps boost genomics-assisted breeding efforts. Here we present a genome-wide variation map of 10.6 million single-nucleotide polymorphisms and 1.4 million indels for 781 soybean individuals which includes 418 domesticated (Glycine max), 345 wild (Glycine soja), and 18 natural hybrid (G. max/G. soja) accessions. We describe the enhanced detection of 183 domestication-selective sweeps and the patterns of putative deleterious mutations during domestication and improvement. This predominantly selfing species shows 7.1% reduction of overall deleterious mutations in domesticated soybean relative to wild soybean and a further 1.4% reduction from landrace to improved accessions. The detected domestication-selective sweeps also show reduced levels of deleterious alleles. Importantly, genotype imputation with this resource increases the mapping resolution of genome-wide association studies for seed protein and oil traits in a soybean diversity panel.

A Genome-Wide Genetic Diversity Scan Reveals Multiple Signatures of Selection in a European Soybean Collection Compared to Chinese Collections of Wild and Cultivated Soybean Accessions

Targeted and untargeted selections including domestication and breeding efforts can reduce genetic diversity in breeding germplasm and create selective sweeps in crop genomes. The genomic regions at which selective sweeps are detected can reveal important information about signatures of selection. We have analyzed the genetic diversity within a soybean germplasm collection relevant for breeding in Europe (the EUCLEG collection), and have identified selective sweeps through a genome-wide scan comparing that collection to Chinese soybean collections. This work involved genotyping of 480 EUCLEG soybean accessions, including 210 improved varieties, 216 breeding lines and 54 landraces using the 355K SoySNP microarray. SNP calling of 477 EUCLEG accessions together with 328 Chinese soybean accessions identified 224,993 high-quality SNP markers. Population structure analysis revealed a clear differentiation between the EUCLEG collection and the Chinese materials. Further, the EUCLEG collection was sub-structured into five subgroups that were differentiated by geographical origin. No clear association between subgroups and maturity group was detected. The genetic diversity was lower in the EUCLEG collection compared to the Chinese collections. Selective sweep analysis revealed 23 selective sweep regions distributed over 12 chromosomes. Co-localization of these selective sweep regions with previously reported QTLs and genes revealed that various signatures of selection in the EUCLEG collection may be related to domestication and improvement traits including seed protein and oil content, phenology, nitrogen fixation, yield components, diseases resistance and quality. No signatures of selection related to stem determinacy were detected. In addition, absence of signatures of selection for a substantial number of QTLs related to yield, protein content, oil content and phenological traits suggests the presence of substantial genetic diversity in the EUCLEG collection. Taken together, the results obtained demonstrate that the available genetic diversity in the EUCLEG collection can be further exploited for research and breeding purposes. However, incorporation of exotic material can be considered to broaden its genetic base.

Genetic Mapping of a Resistance Locus to Phytophthora sojae in the Korean Soybean Cultivar Daewon

Phytophthora root and stem rot reduce soybean yields worldwide. The use of R-gene type resistance is currently crucial for protecting soybean production. The present study aimed to identify the genomic location of a gene conferring resistance to Phytophthora sojae isolate 2457 in the recombinant inbred line population developed by a cross of Daepung × Daewon. Single-marker analysis identified 20 single nucleotide polymorphisms associated with resistance to the P. sojae isolate 2457, which explained ~67% of phenotypic variance. Daewon contributed a resistance allele for the locus. This region is a well-known location for Rps1 and Rps7. The present study is the first, however, to identify an Rps gene locus from a major soybean variety cultivated in South Korea. Linkage analysis also identified a 573 kb region on chromosome 3 with high significance (logarithm of odds = 13.7). This genomic region was not further narrowed down due to lack of recombinants within the interval. Based on the latest soybean genome, ten leucine-rich repeat coding genes and four serine/threonine protein kinase-coding genes are annotated in this region, which all are well-known types of genes for conferring disease resistance in crops. These genes would be candidates for molecular characterization of the resistance in further studies. The identified R-gene locus would be useful in developing P. sojae resistant varieties in the future. The results of the present study provide foundational knowledge for researchers who are interested in soybean-P. sojae interaction.

GCViT: a method for interactive, genome-wide visualization of resequencing and SNP array data

Background

Large genotyping datasets have become commonplace due to efficient, cheap methods for SNP identification. Typical genotyping datasets may have thousands to millions of data points per accession, across tens to thousands of accessions. There is a need for tools to help rapidly explore such datasets, to assess characteristics such as overall differences between accessions and regional anomalies across the genome.

Results

We present GCViT (Genotype Comparison Visualization Tool), for visualizing and exploring large genotyping datasets. GCViT can be used to identify introgressions, conserved or divergent genomic regions, pedigrees, and other features for more detailed exploration. The program can be used online or as a local instance for whole genome visualization of resequencing or SNP array data. The program performs comparisons of variants among user-selected accessions to identify allele differences and similarities between accessions and a user-selected reference, providing visualizations through histogram, heatmap, or haplotype views. The resulting analyses and images can be exported in various formats.

Conclusions

GCViT provides methods for interactively visualizing SNP data on a whole genome scale, and can produce publication-ready figures. It can be used in online or local installations. GCViT enables users to confirm or identify genomics regions of interest associated with particular traits.

GCViT is freely available at https://github.com/LegumeFederation/gcvit. The 1.0 version described here is available at 10.5281/zenodo.4008713.

QTL Mapping and Candidate Gene Analysis for Pod Shattering Tolerance in Soybean (Glycine max)

Pod shattering is an important reproductive process in many wild species. However, pod shattering at the maturing stage can result in severe yield loss. The objectives of this study were to discover quantitative trait loci (QTLs) for pod shattering using two recombinant inbred line (RIL) populations derived from an elite cultivar having pod shattering tolerance, namely "Daewonkong", and to predict novel candidate QTL/genes involved in pod shattering based on their allele patterns. We found several QTLs with more than 10% phenotypic variance explained (PVE) on seven different chromosomes and found a novel candidate QTL on chromosome 16 (qPS-DS16-1) from the allele patterns in the QTL region. Out of the 41 annotated genes in the QTL region, six were found to contain SNP (single-nucleotide polymorphism)/indel variations in the coding sequence of the parents compared to the soybean reference genome. Among the six potential candidate genes, Glyma.16g076600, one of the genes with known function, showed a highly differential expression levels between the tolerant and susceptible parents in the growth stages R3 to R6. Further, Glyma.16g076600 is a homolog of AT4G19230 in Arabidopsis, whose function is related to abscisic acid catabolism. The results provide useful information to understand the genetic mechanism of pod shattering and could be used for improving the efficiency of marker-assisted selection for developing varieties of soybeans tolerant to pod shattering.

STAGdb: a 30K SNP genotyping array and Science Gateway for Acropora corals and their dinoflagellate symbionts

Standardized identification of genotypes is necessary in animals that reproduce asexually and form large clonal populations such as coral. We developed a high-resolution hybridization-based genotype array coupled with an analysis workflow and database for the most speciose genus of coral, Acropora, and their symbionts. We designed the array to co-analyze host and symbionts based on bi-allelic single nucleotide polymorphisms (SNP) markers identified from genomic data of the two Caribbean Acropora species as well as their dominant dinoflagellate symbiont, Symbiodinium ‘fitti’. SNPs were selected to resolve multi-locus genotypes of host (called genets) and symbionts (called strains), distinguish host populations and determine ancestry of coral hybrids between Caribbean acroporids. Pacific acroporids can also be genotyped using a subset of the SNP loci and additional markers enable the detection of symbionts belonging to the genera Breviolum, Cladocopium, and Durusdinium. Analytic tools to produce multi-locus genotypes of hosts based on these SNP markers were combined in a workflow called the Standard Tools for Acroporid Genotyping (STAG). The STAG workflow and database are contained within a customized Galaxy environment (https://coralsnp.science.psu.edu/galaxy/), which allows for consistent identification of host genet and symbiont strains and serves as a template for the development of arrays for additional coral genera. STAG data can be used to track temporal and spatial changes of sampled genets necessary for restoration planning and can be applied to downstream genomic analyses. Using STAG, we uncover bi-directional hybridization between and population structure within Caribbean acroporids and detect a cryptic Acroporid species in the Pacific.

Recommendations for Choosing the Genotyping Method and Best Practices for Quality Control in Crop Genome-Wide Association Studies

High-throughput genotyping boosts genome-wide association studies (GWAS) in crop species, leading to the identification of single-nucleotide polymorphisms (SNPs) associated with economically important traits. Choosing a cost-effective genotyping method for crop GWAS requires careful examination of several aspects, namely, the purpose and the scale of the study, crop-specific genomic features, and technical and economic matters associated with each genotyping option. Once genotypic data have been obtained, quality control (QC) procedures must be applied to avoid bias and false signals in genotype–phenotype association tests. QC for human GWAS has been extensively reviewed; however, QC for crop GWAS may require different actions, depending on the GWAS population type. Here, we review most popular genotyping methods based on next-generation sequencing (NGS) and array hybridization, and report observations that should guide the investigator in the choice of the genotyping method for crop GWAS. We provide recommendations to perform QC in crop species, and deliver an overview of bioinformatics tools that can be used to accomplish all needed tasks. Overall, this work aims to provide guidelines to harmonize those procedures leading to SNP datasets ready for crop GWAS.