soyfungigcn

soyfungigcn integrates GWAS and transcriptomic data to construct and analyze gene coexpression networks in soybean tissues infected by phytopathogenic fungi to prioritize candidate resistance genes.

Key Features:

• Integration of Genomic Data: Integrates genome-wide association studies (GWAS) with transcriptomic data to prioritize candidate genes associated with resistance to Cadophora gregata, Fusarium graminearum, Fusarium virguliforme, Macrophomina phaseolina, and Phakopsora pachyrhizi.
• Identification of High-Confidence Candidates: Prioritized high-confidence candidate gene counts are 188 for Fusarium virguliforme, 56 for Fusarium graminearum, 11 for Cadophora gregata, 8 for Macrophomina phaseolina, and 3 for Phakopsora pachyrhizi.
• Conservation and Defense Focus: Prioritized candidates are highly conserved within the soybean pangenome and are biased toward species-specific defense responses, including recognition, signaling, oxidative stress response, systemic acquired resistance, and physical barrier formation.
• Resistance Allele Analysis: Identifies the most resistant accessions within the USDA soybean germplasm for each pathogen and indicates these accessions have not reached their theoretical maximum resistance, highlighting opportunities for improvement via breeding or genetic engineering.
• Implementation: Implemented as an R package for computational analysis of coexpression networks and candidate prioritization.

Scientific Applications:

• Plant pathology: Dissects transcriptional responses to fungal infection and links coexpression patterns to pathogen-specific resistance mechanisms.
• Genetics: Prioritizes candidate resistance genes for functional validation and genetic studies.
• Breeding and genetic engineering: Informs selection of resistant germplasm and guides targeted breeding or genetic engineering strategies to enhance resistance.
• Pangenome and comparative analyses: Supports conservation analyses of resistance genes across the soybean pangenome and germplasm collections.

Methodology:

Integrates GWAS with transcriptomic data, constructs and analyzes gene coexpression networks, prioritizes candidate genes, performs conservation analysis within the soybean pangenome, and assesses resistance alleles across USDA soybean germplasm.