da Cruz, A. C.; Vianello, R. P.; Valdisser, P. A. M. R.; Bueno, L. G.; Brondani, C.

Grain yield is a highly complex quantitative trait in rice, resulting from the interaction of multiple genetic, physiological and environmental factors. Although genome-wide association studies (GWAS) have successfully identified loci associated with grain yield, translating statistical associations into biologically meaningful candidate variants remains a major challenge, particularly for variants located in regulatory regions. This study aimed to identify genomic variants associated with grain yield in upland rice and to develop an integrative framework for functionally prioritizing candidate variants through the combination of genome-wide association analysis, functional annotation and regulatory genomics. A panel of 252 accessions from the Brazilian Rice Core Collection was phenotyped for grain yield and genotyped with 35,763 single nucleotide polymorphism (SNP) markers. GWAS identified 29 SNPs significantly associated with grain yield, including 16 variants located within or near annotated genes and 13 located in intergenic regions. The identified candidate genes were involved in signal perception, metabolite transport, amino acid and energy metabolism, hormone biosynthesis, protein turnover, RNA processing and disease resistance, highlighting the polygenic architecture of grain yield. Functional characterization of the intergenic regions revealed enrichment of cis-regulatory elements recognized by transcription factors associated with hormonal signaling, drought response, carbon metabolism, photosynthesis and reproductive development, indicating that regulatory variation represents an important component of grain yield determination. By integrating GWAS signals, candidate gene annotation, cis-regulatory element characterization and the physical proximity between SNPs and cis-regulatory elements, an integrative prioritization strategy identified seven intergenic SNPs as the most promising candidates for functional validation. Together, these findings establish a robust framework for discovering, prioritizing and functionally validating regulatory variants, bridging the gap between statistical associations and biological function while providing a rational strategy for translating GWAS discoveries into molecular breeding of complex traits.