Integrating pan-genome analysis, GWAS, and interpretable machine learning to prioritize trait-associated structural variations in Setaria italica.

Abstract excerpt

Structural variations (SVs), especially presence-absence variations (PAVs), are crucial in crop domestication and trait improvement. Although pan-genome analysis provides an exhaustive view of PAVs, it is often limited by high costs and restricted sample sizes. In contrast, genome-wide association studies (GWASs) can effectively identify trait-marker associations in large populations but typically overlook PAVs...

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• Genome, Plant
• Genome-Wide Association Study
• Machine Learning
• Phenotype
• Plant Leaves