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Updated: Sep 17, 2025
A Simple Method for Isolation of Soybean Protoplasts and Application to Transient Gene Expression Analyses
Published on: January 25, 2018
Genome-wide association analysis study and genomic prediction for resistance to soybean mosaic virus in
Tiantian Zhao1,2, Fengmin Wang1,2, Jin Qi1,2
1Ministry of Education Key Laboratory of Molecular and Cellular Biology, Hebei Research Center of the Basic Discipline of Cell Biology, Hebei Collaboration Innovation Center for Cell Signaling and Environmental Adaptation, Hebei Key Laboratory of Molecular and Cellular Biology, College of Life Sciences, Hebei Normal University, Shijiazhuang, China.
Background:
Soybean (Glycine max (L.) Merr.), a global agricultural staple, faces significant threats from Soybean Mosaic Virus (SMV). Effective resistance to SMV, particularly the SC3 strain, is crucial for sustainable soybean production. This study aims to explore the genetic variability and identify loci associated with SMV SC3 resistance in soybean.
Results:
We assessed the resistance of 290 soybean accessions to the SMV SC3 strain, revealing considerable genetic variability: 19.9% exhibited high resistance, while 11.7% were highly susceptible. This diversity is a valuable asset for breeding programs targeting disease management. Deep sequencing and genome-wide association studies (GWAS) of the accession population structures identified five distinct clusters and 14 significant loci associated with resistance across chromosomes 2, 4, 7, 9, 13, 14, 17, 19, and 20. Notably, a known resistance locus on chromosome 13 and a novel locus on chromosome 4, Loci_04_7299944, were identified. The latter is linked to Glyma.04G086700, a gene encoding a leucine-rich repeat protein kinase integral to pathogen recognition and resistance, showing three distinct haplotypes correlated with varying resistance levels, governed by specific allelic variations at certain SNP sites. Our genomic prediction models demonstrated that expanding SNP feature sets generally improved prediction accuracy, especially with the Top 100 set, although adding more than 8000 SNPs introduced diminishing returns and potential noise. Fourteen effective SNP loci were identified as pivotal for accurately predicting the genetic architecture of complex traits related to SMV resistance.
Conclusions:
Our findings underscore the importance of selecting SNPs closely linked to phenotypic traits to refine prediction accuracy in genomic selection models. The identified loci, particularly Glyma.04G086700, provide a foundation for further exploration of genetic mechanisms underlying SMV SC3 resistance. These insights can guide future enhancements in soybean breeding strategies to combat SMV effectively.
