Development of a marker panel for genotyping of domestic soybean cultivars for genes controlling the duration of vegetation and response to photoperiod

Soybean, Glycine max L., is one of the most important agricultural crops grown in a wide range of latitude. In this regard, in soybean breeding, it is necessary to pay attention to the set of genes that control the transition to the flowering stage, which will make it possible to adapt genotypes to local growing conditions as accurately as possible. The possibilities of soybean breeding for this trait have now significantly expanded due to identification of the main genes (E1–E4, GmFT2a, GmFT5a) that control the processes of flowering and maturation in soybean, depending on the day length. The aim of this work was to develop a panel of markers for these genes, which could be used for a rapid and efficient genotyping of domestic soybean cultivars and selection of plant material based on sensitivity to photoperiod and the duration of vegetation. Combinations of 10 primers, both previously developed and our own, were tested to identify different alleles of the E1–E4, GmFT2a, and GmFT5a genes using 10 soybean cultivars from different maturity groups. As a result, 5 combinations of dominant and recessive alleles for the E1–E4 genes were identified: (1) e1-nl(e1-as)/ e2-ns/e3-tr(e3-fs)/e4; (2) e1-as/e2-ns/e3-tr/E4; (3) e1-as/e2-ns/E3-Ha/e4; (4) E1/e2-ns/e3-tr/E4; (5) e1-nl/e2-ns/E3-Ha/E4. The studied cultivars contained the most common alleles of the GmFT2a and GmFT5a genes, with the exception of the ‘Cassidi’ cultivar having a rare dominant allele GmFT5a-H4. The degree of earliness of cultivars positively correlated with the number of recessive genes E1–E4, which is consistent with the data of foreign authors on different sets of cultivars from Japan and North China. Thus, the developed panel of markers can

Selection of the Salt Tolerance Gene GmSALT3 During Six Decades of Soybean Breeding in China

Salt tolerance is an important trait that affects the growth and yield of plants growing in saline environments. The salt tolerance gene GmSALT3 was cloned from the Chinese soybean cultivar Tiefeng 8, and its variation evaluated in Chinese wild soybeans and landraces. However, the potential role of GmSALT3 in cultivation, and its genetic variation throughout the history of Chinese soybean breeding, remains unknown. Here we identified five haplotypes of GmSALT3 in 279 Chinese soybean landraces using a whole genome resequencing dataset. Additionally, we developed five PCR-based functional markers: three indels and two cleaved amplified polymorphic sequences (CAPS) markers. A total of 706 Chinese soybean cultivars (released 1956–2012), and 536 modern Chinese breeding lines, were genotyped with these markers. The Chinese landraces exhibited relatively high frequencies of the haplotypes H1, H4, and H5. H1 was the predominant haplotype in both the northern region (NR) and Huanghuai region (HHR), and H5 and H4 were the major haplotypes present within the southern region (SR). In the 706 cultivars, H1, H2, and H5 were the common haplotypes, while H3 and H4 were poorly represented. Historically, H1 gradually decreased in frequency in the NR but increased in the HHR; while the salt-sensitive haplotype, H2, increased in frequency in the NR during six decades of soybean breeding. In the 536 modern breeding lines, H2 has become the most common haplotype in the NR, while H1 has remained the highest frequency haplotype in the HHR, and H5 and H1 were highest in the SR. Frequency changes resulting in geographically favored haplotypes indicates that strong selection has occurred over six decades of soybean breeding. Our molecular markers could precisely identify salt tolerant (98.9%) and sensitive (100%) accessions and could accurately trace the salt tolerance gene in soybean pedigrees. Our study, therefore, not only identified effective molecular markers for use in soybean, but also demonstrated how these markers can distinguish GmSALT3 alleles in targeted breeding strategies for specific ecoregions.

Identification and Molecular Mapping of a Major Quantitative Trait Locus Underlying Branch Angle in Soybean

Soybean branch angle is a critical architectural trait that affects many other traits of agronomic importance associated with the plant’s productivity and grain yield, and is thus a vital consideration in soybean breeding. However, the genetic basis for modulating this important trait in soybean and many other crops remain unknown. Previously, we developed a recombinant inbred line (RIL) population derived from a cross between a domesticated soybean (Glycine max) variety, Williams 82, and a wild soybean (Glycine soja) accession, PI 479752, and observed drastic variation in plant architecture including branch angle among individual RILs. In this study, one of the RILs possessing extremely wide branch angle (WBA) was crossed with an elite soybean cultivar (LD00-3309) possessing narrow branch angle (NBA) to produce an F2 population composed of 147 plants and F2-derived F3 families for inheritance analysis and QTL mapping. We found that branch angle is controlled by a major QTL located on chromosome 19, designated qGmBa1, and that WBA – derived from the wild soybean accession – is dominant over NBA. This locus was also detected as a major one underlying branch angle by QTL mapping using a subset of the soybean nested association mapping (SoyNAM) population composed of 140 RILs, which were derived from a cross between a landrace, PI 437169B, possessing WBA and an elite variety, IA3023, possessing NBA. Molecular markers located in the QTL region defined by both mapping populations can be used for marker-assisted selection of branch angle in soybean breeding.

Testing Whether Pre-Pod-Fill Symbiotic Nitrogen Fixation in Soybean Is Subject to Drift or Selection Over 100 Years of Soybean Breeding

Soybean [Glycine max (L.) Merr.] is the world's leading legume crop and the largest oilseed crop. It forms a symbiotic relationship with rhizobia bacteria residing in root nodules that provide fixed nitrogen to host plants through symbiotic nitrogen fixation (SNF). In soybean, it has been widely reported that the highest SNF occurs at the pod-filling stage, associated with the peak demand for nitrogen. However, the majority of seed nitrogen is derived from remobilizing root/shoot nitrogen, representing cumulative SNF from the seedling stage to the pre-pod-fill stage. Therefore, the question arises as to whether there has also been selection for improved SNF at these earlier stages, or whether pre-pod-fill SNF traits have drifted. To test this hypothesis, in this study, pre-pod SNF-related traits were evaluated in soybean cultivars that span 100 years of breeding selection in the Canadian Province of Ontario. Specifically, we evaluated SNF traits in 19 pedigree-related historical cultivars and 25 modern cultivars derived from the University of Guelph soybean breeding program. Field trials were conducted at Woodstock, Ontario, Canada in 2016 and 2017, and various SNF-related traits were measured at pre-pod-fill stages (R1-R3), including nitrogen fixation capacity. Considerable variation was observed among Canadian soybean cultivars released over the past 100 years for pre-pod-fill nitrogen fixation. The modern soybean cultivars had similar or moderately higher pre-pod-fill SNF compared to the historical lines in terms of the percentage of nitrogen derived from the atmosphere (%Ndfa) and total shoot fixed nitrogen. These findings suggest that, despite no direct selection by breeders, pre-pod-fill nitrogen fixation, and associated SNF traits have been maintained and possibly improved in modern soybean breeding. However, the low level of pre-pod-fill SNF in some modern cultivars, and generally wide variation observed in SNF between them, suggest some level of genetic drift for this trait in some pedigrees. Specific historical and modern soybean cultivars were identified as potential parents to enable targeted breeding for improved pre-pod-fill SNF. This retrospective study sheds light on our understanding of the impact of decades of recent selective breeding on pre-pod-fill nitrogen fixation traits in soybean in a temperate environment.

Dissection The Practical Soybean Breeding Pipeline By Developing High Throughput Functional Array ZDX1

Microarray technology facilitates rapid, accurate, and economical genotyping. Here, using resequencing data from 2,214 representative soybean accessions, we developed the ZDX1 high-throughput functional soybean array, containing 158,959 SNPs, covering 90.92% of soybean genes and sites related to agronomically important traits. We genotyped 817 soybean accessions using ZDX1, including parental lines, non-parental lines, and progeny from a practical breeding pipeline. It was clarified that non-parental lines had highest genetic diversity, and 235 SNPs were identified to be fixed in the progeny. The unknown soybean cyst nematode-resistant and early maturity accessions were identified by using allele combinations. Notably, we found that breeding index was a good indicator for progeny selection, in which the superior progeny were derived from the crossing more distantly related parents with at least one parent having a higher breeding index. Based on this rule, two varieties were directionally developed. Meanwhile, redundant parents were screened out and potential combinations were formulated. GBLUP analysis displayed that the markers in genic regions had priority to be higher accuracy on predicting four agronomic traits compared with either whole genome or intergenic markers. Then we used progeny to expand the training population to increase the prediction accuracy of breeding selection by 32.1%. Collectively, our work provided a versatile array for high accuracy selecting and predicting both parents and progeny that can greatly accelerate soybean breeding.

Impact of Genomic Prediction Model, Selection Intensity, and Breeding Strategy on the Long-Term Genetic Gain and Genetic Erosion in Soybean Breeding

Genomic-assisted breeding has become an important tool in soybean breeding. However, the impact of different genomic selection (GS) approaches on short- and long-term gains is not well understood. Such gains are conditional on the breeding design and may vary with a combination of the prediction model, family size, selection strategies, and selection intensity. To address these open questions, we evaluated various scenarios through a simulated closed soybean breeding program over 200 breeding cycles. Genomic prediction was performed using genomic best linear unbiased prediction (GBLUP), Bayesian methods, and random forest, benchmarked against selection on phenotypic values, true breeding values (TBV), and random selection. Breeding strategies included selections within family (WF), across family (AF), and within pre-selected families (WPSF), with selection intensities of 2.5, 5.0, 7.5, and 10.0%. Selections were performed at the F4 generation, where individuals were phenotyped and genotyped with a 6K single nucleotide polymorphism (SNP) array. Initial genetic parameters for the simulation were estimated from the SoyNAM population. WF selections provided the most significant long-term genetic gains. GBLUP and Bayesian methods outperformed random forest and provided most of the genetic gains within the first 100 generations, being outperformed by phenotypic selection after generation 100. All methods provided similar performances under WPSF selections. A faster decay in genetic variance was observed when individuals were selected AF and WPSF, as 80% of the genetic variance was depleted within 28–58 cycles, whereas WF selections preserved the variance up to cycle 184. Surprisingly, the selection intensity had less impact on long-term gains than did the breeding strategies. The study supports that genetic gains can be optimized in the long term with specific combinations of prediction models, family size, selection strategies, and selection intensity. A combination of strategies may be necessary for balancing the short-, medium-, and long-term genetic gains in breeding programs while preserving the genetic variance.

Mega-Environment Analysis and Identify Stable and High-Yielding of New Promising Black Soybean Lines in Indonesia

Stable and high-yielding are the major goals of black soybean breeding. Testing new lines in a mega-environment is one of the development processes in black soybean breeding. The aims of the research were (i) to identify the effects of genotype, environment, and GEIs on the grain yield of soybean lines in Java Island; (ii) to select stable and high yielding soybean lines; and (iii) to determine the discriminative environments, and (iv) to determine the concept of stability measurements on black soybean grain yields. Field trials of 10 new F8 promising lines and three check varieties were conducted under eight different environments during four years (2016–2019). The measurement results showed that the grain yield was influenced by genotype (8.35%), environment (59.49%), and GEIs (32.16%). Grain yield stability measurements showed that the four newly lines was identified had high yields and stable in eight environments, they were A-5A-PSJ (S2), DB-96-CTY (S5), UP 161 (S6), and UP 162 (S7). The Ngawi (2017) followed by Bogor (2019) and Banyuwangi (2016) has the strongest interactive capabilities, and was suitable for used as a trial environments. Grain yield (Y) was identified as having a positive and significant correlation (p < 0.05) with S(3), S(6), NP(2), NP(3), NP(4), KR, and YSI stability measurements, which indicated that they were included in the concept of dynamic stability measurement.

Benchmarking Nutraceutical Soybean Composition Relative to Protein and Oil

The aim of this study was to explore relationships between protein, oil, and seed weight with seed nutraceutical composition, focused on total isoflavone (TI) and total tocopherol (TT) contents across genotypic and environmental combinations in soybean. We conducted a synthesis-analysis of peer-reviewed published field studies reporting TI, TT, protein, oil, and seed weight (n = 1,908). The main outcomes from this synthesis-analysis were: (i) relationship of TI-to-protein concentration was positive, though for the upper boundary, TI decreases with increases in protein; (ii) relationship of TT-to-oil concentration was positive, but inconsistent when oil was expressed in mg per seed; and (iii) as seed weight increased, TI accumulation was less than proportional relative to protein concentration and TT decreased more proportional relative to oil concentration. Association between nutraceuticals and protein, oil, and seed weight for soybean reported in the present study can be used as a foundational knowledge for soybean breeding programs interested on predicting and selecting enhanced meal isoflavone and/or oil tocopherol contents.

Qualification of Soybean Responses to Flooding Stress Using UAV-Based Imagery and Deep Learning

Soybean is sensitive to flooding stress that may result in poor seed quality and significant yield reduction. Soybean production under flooding could be sustained by developing flood-tolerant cultivars through breeding programs. Conventionally, soybean tolerance to flooding in field conditions is evaluated by visually rating the shoot injury/damage due to flooding stress, which is labor-intensive and subjective to human error. Recent developments of field high-throughput phenotyping technology have shown great potential in measuring crop traits and detecting crop responses to abiotic and biotic stresses. The goal of this study was to investigate the potential in estimating flood-induced soybean injuries using UAV-based image features collected at different flight heights. The flooding injury score (FIS) of 724 soybean breeding plots was taken visually by breeders when soybean showed obvious injury symptoms. Aerial images were taken on the same day using a five-band multispectral and an infrared (IR) thermal camera at 20, 50, and 80 m above ground. Five image features, i.e., canopy temperature, normalized difference vegetation index, canopy area, width, and length, were extracted from the images at three flight heights. A deep learning model was used to classify the soybean breeding plots to five FIS ratings based on the extracted image features. Results show that the image features were significantly different at three flight heights. The best classification performance was obtained by the model developed using image features at 20 m with 0.9 for the five-level FIS. The results indicate that the proposed method is very promising in estimating FIS for soybean breeding.