An advanced systems biology framework of feature engineering for cold tolerance genes discovery from integrated omics and non-omics data in soybean.

Soybean is sensitive to low temperatures during the crop growing season. An urgent demand for breeding cold-tolerant cultivars to alleviate the production loss is apparent to cope with this scenario. Cold-tolerant trait is a complex and quantitative trait controlled by multiple genes, environmental factors, and their interaction. In this study, we proposed an advanced systems biology framework of feature engineering for the discovery of cold tolerance genes (CTgenes) from integrated omics and non-omics (OnO) data in soybean. An integrative pipeline was introduced for feature selection and feature extraction from different layers in the integrated OnO data using data ensemble methods and the non-parameter random forest prioritization to minimize uncertainties and false positives for accuracy improvement of results. In total, 44, 143, and 45 CTgenes were identified in short-, mid-, and long-term cold treatment, respectively, from the corresponding gene-pool. These CTgenes outperformed the remaining genes, the random genes, and the other candidate genes identified by other approaches in an independent RNA-seq database. Furthermore, we applied pathway enrichment and crosstalk network analyses to uncover relevant physiological pathways with the discovery of underlying cold tolerance in hormone- and defense-related modules. Our CTgenes were validated by using 55 SNP genotype data of 56 soybean samples in cold tolerance experiments. This suggests that the CTgenes identified from our proposed systematic framework can effectively distinguish cold-resistant and cold-sensitive lines. It is an important advancement in the soybean cold-stress response. The proposed pipelines provide an alternative solution to biomarker discovery, module discovery, and sample classification underlying a particular trait in plants in a robust and efficient way.

A Modified Roger's Distance Algorithm for Mixed Quantitative-Qualitative Phenotypes to Establish a Core Collection for Taiwanese Vegetable Soybeans.

Vegetable soybeans [Glycine max (L.) Merr.] have characteristics of larger seeds, less beany flavor, tender texture, and green-colored pods and seeds. Rich in nutrients, vegetable soybeans are conducive to preventing neurological disease. Due to the change of dietary habits and increasing health awareness, the demand for vegetable soybeans has increased. To conserve vegetable soybean germplasms in Taiwan, we built a core collection of vegetable soybeans, with minimum accessions, minimum redundancy, and maximum representation. Initially, a total of 213 vegetable soybean germplasms and 29 morphological traits were used to construct the core collection. After redundant accessions were removed, 200 accessions were retained as the entire collection, which was grouped into nine clusters. Here, we developed a modified Roger's distance for mixed quantitative-qualitative phenotypes to select 30 accessions (denoted as the core collection) that had a maximum pairwise genetic distance. No significant differences were observed in all phenotypic traits (p-values > 0.05) between the entire and the core collections, except plant height. Compared to the entire collection, we found that most traits retained diversities, but seven traits were slightly lost (ranged from 2 to 9%) in the core collection. The core collection demonstrated a small percentage of significant mean difference (3.45%) and a large coincidence rate (97.70%), indicating representativeness of the entire collection. Furthermore, large values in variable rate (149.80%) and coverage (92.5%) were in line with high diversity retained in the core collection. The results suggested that phenotype-based core collection can retain diversity and genetic variability of vegetable soybeans, providing a basis for further research and breeding programs.

Prioritization and Evaluation of Flooding Tolerance Genes in Soybean [Glycine max (L.) Merr.].

Soybean [Glycine max (L.) Merr.] is one of the most important legume crops abundant in edible protein and oil in the world. In recent years there has been increasingly more drastic weather caused by climate change, with flooding, drought, and unevenly distributed rainfall gradually increasing in terms of the frequency and intensity worldwide. Severe flooding has caused extensive losses to soybean production and there is an urgent need to breed strong soybean seeds with high flooding tolerance. The present study demonstrates bioinformatics big data mining and integration, meta-analysis, gene mapping, gene prioritization, and systems biology for identifying prioritized genes of flooding tolerance in soybean. A total of 83 flooding tolerance genes (FTgenes), according to the appropriate cut-off point, were prioritized from 36,705 test genes collected from multidimensional genomic features linking to soybean flooding tolerance. Several validation results using independent samples from SoyNet, genome-wide association study, SoyBase, GO database, and transcriptome databases all exhibited excellent agreement, suggesting these 83 FTgenes were significantly superior to others. These results provide valuable information and contribution to research on the varieties selection of soybean.