Genome-wide association study uncovers major genetic loci associated with seed flooding tolerance in soybean

Abstract

Background Seed flooding stress is one of the threatening environmental stressors that adversely limits soybean at the germination stage across the globe. The knowledge on the genetic basis underlying seed-flooding tolerance is limited. Therefore, we performed a genome-wide association study (GWAS) using 34,718 single nucleotide polymorphism (SNPs) in a panel of 243 worldwide soybean collections to identify genetic loci linked to soybean seed flooding tolerance at the germination stage. Results In the present study, GWAS was performed with two contrasting models, Mixed Linear Model (MLM) and Multi-Locus Random-SNP-Effect Mixed Linear Model (mrMLM) to identify significant SNPs associated with electrical conductivity (EC), germination rate (GR), shoot length (ShL), and root length (RL) traits at germination stage in soybean. With MLM, a total of 20, 40, 4, and 9 SNPs associated with EC, GR, ShL and RL, respectively, whereas in the same order mrMLM detected 27, 17, 13, and 18 SNPs. Among these SNPs, two major SNPs, Gm_08_11971416, and Gm_08_46239716 were found to be consistently connected with seed-flooding tolerance related traits, namely EC and GR across two environments. We also detected two SNPs, Gm_05_1000479 and Gm_01_53535790 linked to ShL and RL, respectively. Based on Gene Ontology enrichment analysis, gene functional annotations, and protein-protein interaction network analysis, we predicted eight candidate genes and three hub genes within the regions of the four SNPs with Cis-elements in promoter regions which may be involved in seed-flooding tolerance in soybeans and these warrant further screening and functional validation. Conclusions Our findings demonstrate that GWAS based on high-density SNP markers is an efficient approach to dissect the genetic basis of complex traits and identify candidate genes in soybean. The trait associated SNPs could be used for genetic improvement in soybean breeding programs. The candidate genes could help researchers better understand the molecular mechanisms underlying seed-flooding stress tolerance in soybean.

The intervention of classical and molecular breeding approaches to enhance flooding stress tolerance in soybean – An review

“…The earlier study detected the SNPs on chromosomes 1 and 5, linked to RL and SL. These novel regions regulating flooding tolerance in soybean may assist in further molecular studies ( Sharmin et al., 2021 ). The mining of flood-tolerant genes has been the main goal of the breeders.…”

“…Various QTL were identified using different mapping populations to accelerate marker-assisted selection programs ( Dhungana et al., 2021b ; Zhang et al., 2022 ). Genome-wise association studies (GWAS) have identified new genes for soybean response to flood stress ( Sharmin et al., 2021 ). In fact application of GWAS is a powerful alternative to genes/QTL identification to strengthen the tolerance mechanism in soybean as proven by the study of Yu et al.…”

Proteomic Analysis Unravels Response and Antioxidation Defense Mechanism of Rice Plants to Copper Oxide Nanoparticles: Comparison with Bulk Particles and Dissolved Cu Ions

“…Abiotic stress usually reduces ribosomal protein in plants, resulting in slower growth and reduced productivity . Meanwhile, the ribosomal protein genes can elevate the tolerance of plants to heavy metal stress . The downregulated EF-2 by CuO NPs can affect the translation, GTP binding, and nucleotide binding processes and further restrict the protein synthesis of rice roots .…”

“…39 Meanwhile, the ribosomal protein genes can elevate the tolerance of plants to heavy metal stress. 40 The downregulated EF-2 by CuO NPs can affect the translation, GTP binding, and nucleotide binding processes and further restrict the protein synthesis of rice roots. 41 CuO NPs may affect the protein phosphorylation process via downregulating GPB-LR A and GPB-LR B, thereby affecting the normal regulation of protein activity and function and hindering the information transmission.…”

Genetic diversity and interaction between the maintainers of commercial soybean cultivars using self‐organizing maps

“…The choice of the most appropriate methodology to assess genetic diversity depends on the precision desired by the researcher, the simplicity of the analysis, and how the data were obtained (Cruz, 2012). Multivariate techniques such as the Tocher method (Matsuo et al., 2012), principal component analysis (Lam et al., 2010; Li et al., 2021; Sharmin et al., 2021), and Nei diversity index (Kao et al., 2021; Ristova et al., 2010) have been used as alternatives for simultaneous comparisons of qualitative and quantitative traits, and they are the most used in studies of soybean diversity (Resende et al., 2020). The self‐organizing maps (SOMs) method is a promising approach for genetic diversity studies.…”