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.

Phenotypic and Genotypic Characterization and Correlation Analysis of Pea (Pisum sativum L.) Diversity Panel.

Phenotypic and genotypic characterization were performed to assess heritability, variability, and seed yield stability of pea genotypes used in breeding to increase the pea production area. A European pea diversity panel, including genotypes from North America, Asia, and Australia consisting of varieties, breeding lines, pea, and landraces was examined in 2019 and 2020 in Serbia and Belgium using augmented block design. The highest heritability was for thousand seed weight; the highest coefficient of variation was for seed yield. The highest positive correlation was between number of seeds per plant and number of pods per plant; the highest negative correlation was between seed yield and protein content. Hierarchical clustering separated pea germplasm based on use and type. Different Principal component analysis grouping of landraces, breeding lines, and varieties, as well as forage types and garden and dry peas, confirms that there was an apparent decrease in similarity between the genotypes, which can be explained by their different purposes. Pea breeding should be focused on traits with consistent heritability and a positive effect on seed yield when selecting high-yielding genotypes, and on allowing for more widespread use of pea in various agricultural production systems.

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.