The Sequential SNP Prioritization Algorithm (SSPA) refines GWAS results by leveraging correlation-based feature engineering to prioritize SNPs associated with complex traits in sorghum, providing valuable insights for functional genomics research and enabling downstream machine learning applications.
Researchers analyzed sorghum grain development across genotypes and growing seasons, highlighting differences in grain filling, phenolic compound synthesis, and antioxidant capacity, with phenolic accumulation shifting from early-stage phenolic acids to flavonoid dominance in mature grains.
Conserved non-coding sequences (CNS) play a crucial role in maize gene regulation by interacting with chromatin accessibility and epigenetic modifications, offering potential targets for crop improvement through molecular breeding.
This study identifies novel and known genetic loci associated with sorghum root system architecture traits, highlighting their role in drought tolerance and demonstrating the potential of genomic selection for sorghum improvement.
This study identifies key transcription factors, metabolic pathways, and hormone signaling mechanisms that differentiate salt-tolerant and salt-sensitive sorghum cultivars, providing insights for breeding salt-resistant varieties.
Researchers investigated sorghum’s proteomic responses to combined drought and heat stress, identifying key differentially expressed proteins and molecular pathways that contribute to its resilience, with insights that may aid future crop improvement.
