Selective Genotyping and Phenotyping for Optimization of Genomic Prediction Models for Populations with Different Diversity.

To overcome the different challenges to food security caused by a growing population and climate change, soybean (Glycine max (L.) Merr.) breeders are creating novel cultivars that have the potential to improve productivity while maintaining environmental sustainability. Genomic selection (GS) is an advanced approach that may accelerate the rate of genetic gain in breeding using genome-wide molecular markers. The accuracy of genomic selection can be affected by trait architecture and heritability, marker density, linkage disequilibrium, statistical models, and training set. The selection of a minimal and optimal marker set with high prediction accuracy can lower genotyping costs, computational time, and multicollinearity. Selective phenotyping could reduce the number of genotypes tested in the field while preserving the genetic diversity of the initial population. This study aimed to evaluate different methods of selective genotyping and phenotyping on the accuracy of genomic prediction for soybean yield. The evaluation was performed on three populations: recombinant inbred lines, multifamily diverse lines, and germplasm collection. Strategies adopted for marker selection were as follows: SNP (single nucleotide polymorphism) pruning, estimation of marker effects, randomly selected markers, and genome-wide association study. Reduction of the number of genotypes was performed by selecting a core set from the initial population based on marker data, yet maintaining the original population's genetic diversity. Prediction ability using all markers and genotypes was different among examined populations. The subsets obtained by the model-based strategy can be considered the most suitable for marker selection for all populations. The selective phenotyping based on makers in all cases had higher values of prediction ability compared to minimal values of prediction ability of multiple cycles of random selection, with the highest values of prediction obtained using AN approach and 75% population size. The obtained results indicate that selective genotyping and phenotyping hold great potential and can be integrated as tools for improving or retaining selection accuracy by reducing genotyping or phenotyping costs for genomic selection.

High-throughput phenotyping for non-destructive estimation of soybean fresh biomass using a machine learning model and temporal UAV data.

BACKGROUND: Biomass accumulation as a growth indicator can be significant in achieving high and stable soybean yields. More robust genotypes have a better potential for exploiting available resources such as water or sunlight. Biomass data implemented as a new trait in soybean breeding programs could be beneficial in the selection of varieties that are more competitive against weeds and have better radiation use efficiency. The standard techniques for biomass determination are invasive, inefficient, and restricted to one-time point per plot. Machine learning models (MLMs) based on the multispectral (MS) images were created so as to overcome these issues and provide a non-destructive, fast, and accurate tool for in-season estimation of soybean fresh biomass (FB). The MS photos were taken during two growing seasons of 10 soybean varieties, using six-sensor digital camera mounted on the unmanned aerial vehicle (UAV). For model calibration, canopy cover (CC), plant height (PH), and 31 vegetation index (VI) were extracted from the images and used as predictors in the random forest (RF) and partial least squares regression (PLSR) algorithm. To create a more efficient model, highly correlated VIs were excluded and only the triangular greenness index (TGI) and green chlorophyll index (GCI) remained. RESULTS: More precise results with a lower mean absolute error (MAE) were obtained with RF (MAE = 0.17 kg/m2) compared to the PLSR (MAE = 0.20 kg/m2). High accuracy in the prediction of soybean FB was achieved using only four predictors (CC, PH and two VIs). The selected model was additionally tested in a two-year trial on an independent set of soybean genotypes in drought simulation environments. The results showed that soybean grown under drought conditions accumulated less biomass than the control, which was expected due to the limited resources. CONCLUSION: The research proved that soybean FB could be successfully predicted using UAV photos and MLM. The filtration of highly correlated variables reduced the final number of predictors, improving the efficiency of remote biomass estimation. The additional testing conducted in the independent environment proved that model is capable to distinguish different values of soybean FB as a consequence of drought. Assessed variability in FB indicates the robustness and effectiveness of the proposed model, as a novel tool for the non-destructive estimation of soybean FB.

Bio-Priming of Soybean with Bradyrhizobium japonicum and Bacillus megaterium: Strategy to Improve Seed Germination and the Initial Seedling Growth.

Bio-priming is a new technique of seed treatment that improves seed germination, vigor, crop growth and yield. The objective of this study was to evaluate the effectiveness of Bradyrhizobium japonicum (commercial strains) and Bacillus megaterium (newly isolated strains) as a single inoculant and co-inoculant during seed bio-priming to improve seed germination and initial seedling growth of two soybean cultivars. The treated seeds were subjected to germination test (GT), cold test (CT) and accelerated aging test (AAT). B. megaterium significantly improved all parameters in GT and CT; final germination, shoot length, root length, root dry weight, and seedling vigor index in AAT, as compared to control. In addition, co-inoculation significantly increased all parameters except shoot dry weight in GT; all parameters in CT; germination energy, shoot length, root length, and seedling vigor index in AAT, in comparison to the control. Moreover, Br. japonicum significantly improved the germination energy, shoot length, shoot dry weight, root dry weight, and seedling vigor index in GT; all parameters in CT; shoot length, root length, and seedling vigor index in AAT, compared with non-primed seeds. Thus, B. megaterium strains could be used in soybean bio-priming as a potential single inoculant and co-inoculant, following proper field evaluation.

Author Correction: Increase of isoflavones in the aglycone form in soybeans by targeted crossings of cultivated breeding material.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Increase of isoflavones in the aglycone form in soybeans by targeted crossings of cultivated breeding material.

Isoflavones are a group of phytoestrogens, naturally-occurring substances important for their role in human health. Legumes, particularly soybeans (Glycine max (L.) Merr.), are the richest source of isoflavones in human diet. Since there is not much current data on genetics of isoflavones in soybean, particularly in the aglycone form, elucidation of the mode of inheritance is necessary in order to design an efficient breeding strategy for the development of high-isoflavone soybean genotypes. Based on the isoflavone content in 23 samples of soybeans from four different maturity groups (00, 0, I and II), three crosses were made in order to determine the inheritance pattern and increase the content of total isoflavones and their aglycone form. Genotype with the lowest total isoflavone content (NS-L-146) was crossed with the low- (NS Zenit), medium (NS Maximus), and high- (NS Virtus) isoflavone genotypes. There were no significant differences in the total isoflavone content (TIF) between F2 populations, and there was no transgression among genotypes within the populations. Each genotype within all three populations had a higher TIF value than the lower parent (NS-L-146), while genotypes with a higher TIF value than the better parent were found only in the NS-L-146 × NS Zenit cross. However, significant differences in the aglycone ratio (ratio of aglycone to glycone form of isoflavones) were found between the populations. The highest aglycone ratio was found in the NS-L-146 × NS Maximus cross. The results indicate that the genetic improvement for the trait is possible.

Allelic Variation and Distribution of the Major Maturity Genes in Different Soybean Collections.

Soybean time of flowering and maturity are genetically controlled by E genes. Different allelic combinations of these genes determine soybean adaptation to a specific latitude. The paper describes the first attempt to assess adaptation of soybean genotypes developed and realized at Institute of Field and Vegetable Crops, Novi Sad, Serbia [Novi Sad (NS) varieties and breeding lines] based on E gene variation, as well as to comparatively assess E gene variation in North-American (NA), Chinese, and European genotypes, as most of the studies published so far deal with North-American and Chinese cultivars and breeding material. Allelic variation and distribution of the major maturity genes (E1, E2, E3, and E4) has been determined in 445 genotypes from soybean collections of NA ancestral lines, Chinese germplasm, and European varieties, as well as NS varieties and breeding lines. The study showed that allelic combinations of E1-E4 genes significantly determined the adaptation of varieties to different geographical regions, although they have different impacts on maturity. In general, each collection had one major E genotype haplogroup, comprising over 50% of the lines. The exceptions were European varieties that had two predominant haplogroups and NA ancestral lines distributed almost evenly among several haplogroups. As e1-as/e2/E3/E4 was the most common genotype in NS population, present in the best-performing genotypes in terms of yield, this specific allele combination was proposed as the optimal combination for the environments of Central-Eastern Europe.

Threshold Level and Traceability of Roundup Ready® Soybeans in Tofu Production.

The aim of this study is to assess DNA degradation, DNA amplification, and GMO quantity during tofu production. Soybean seeds were spiked with Roundup Ready® soybeans (RRS) at 0.9, 2, 3 and 5% (by mass), to assess the level of RSS that would be of practical interest for threshold labelling. Real-time polymerase chain reaction (PCR) was more effective than conventional PCR in the analysis of raw soymilk, okara, boiled soymilk and tofu. The negative effect of grinding and mechanical manipulation was obvious in the okara sample prepared with 3 and 5% RRS, where GMO content was reduced to (2.28±0.23) and (2.74±0.26) %, respectively. However, heating at 100 °C for 10 min did not cause significant degradation of DNA in all samples. The content of RRS in the final product, tofu, was reduced tenfold during processing, ranging from 0.07 to 0.46%, which was below the labelling threshold level. The results are discussed in terms of global harmonization of GMO standards, which could have the positive effect on the trade of lightly processed foodstuffs such as tofu, especially regarding the labelling policies.

Beauty will save the world, but will the world save beauty? The case of the highly endangered Vavilovia formosa (Stev.) Fed.

MAIN CONCLUSION: Vavilovia formosa (Stev.) Fed. is a scientifically valuable common ancestor of the plant tribe Fabeae and also important in breeding and agronomy studies of the cultivated Fabeae, but it is close to extinction. A concerted academic and geovernmental effort is needed to save it. Since 2007, an informal international group of researchers on legumes has been working to increase awareness of Vavilovia formosa (Stev.) Fed., a relict and endangered wild-land relative to crop plant species. A majority of the modern botanical classifications place it within the tribe Fabeae, together with the genera vetchling (Lathyrus L.), lentil (Lens Mill.), pea (Pisum L.) and vetch (Vicia L.). V. formosa is encountered at altitudes from 1,500 m up to 3,500 m in Armenia, Azerbaijan, Georgia, Iran, Iraq, Lebanon, Russia, Syria and Turkey. This species may be of extraordinary importance for broadening current scientific knowledge on legume evolution and taxonomy because of its proximity to the hypothetical common ancestor of the tribe Fabeae, as well as for breeding and agronomy of the cultivated Fabeae species due to its perenniality and stress resistance. All this may be feasible only if a concerted and long-term conservation strategy is established and carried out by both academic and geovernmental authorities. The existing populations of V. formosa are in serious danger of extinction. The main threats are domestic and wild animal grazing, foraging, and early frosts in late summer. A long-term strategy to save V. formosa from extinction and to sustain its use in both basic and applied research comprises much improved in situ preservation, greater efforts for an ex situ conservation, and novel approaches of in vitro propagation.

Detection of genetically modified soybean in crude soybean oil.

In order to detect presence and quantity of Roundup Ready (RR) soybean in crude oil extracted from soybean seed with a different percentage of GMO seed two extraction methods were used, CTAB and DNeasy Plant Mini Kit. The amplifications of lectin gene, used to check the presence of soybean DNA, were not achieved in all CTAB extracts of DNA, while commercial kit gave satisfactory results. Comparing actual and estimated GMO content between two extraction methods, root mean square deviation for kit is 0.208 and for CTAB is 2.127, clearly demonstrated superiority of kit over CTAB extraction. The results of quantification evidently showed that if the oil samples originate from soybean seed with varying percentage of RR, it is possible to monitor the GMO content at the first stage of processing crude oil.

Advantages of the lab-on-a-chip method in the determination of the kunitz trypsin inhibitor in soybean varieties.

Qualitative and quantitative determination of the Kunitz trypsin inhibitor (KTI) in soybean experimental lines is very important in processes of selecting and breeding of new varieties. The total enzyme activity assay, sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE), and Lab-on-a-Chip (LoaC) method were used for the determination of the presence and quantity of KTI in 15 soybean experimental lines and varieties. From the total trypsin inhibitor enzyme assay, inhibitor activities were registered in all samples, even in a Kunitz variety that was a negative control. The SDS-PAGE method did not detect the presence of the KTI protein band in seven soybean experimental lines and Kunitz variety, while the LoaC method showed the absence of KTI only in the Kunitz variety sample. Results confirmed the superiority of the LoaC method over other two methods in selectivity and sensitivity when KTI determination is concerned. Relationships between the KTI content obtained by the LoaC method and total trypsin inhibitor enzyme activity were established and statistically confirmed.