Abstract: Grain appearance quality in rice is a critical factor affecting consumer acceptance. Chalkiness and milling quality are complex traits controlled by multiple QTLs. In this study, we performed an integrative meta-QTL (MQTL) analysis to identify robust consensus genomic regions and biologically supported candidate genes (CGs) underlying rice grain chalkiness and milling quality. A total of 124 QTLs related to 31 chalkiness and milling traits, collected from 19 independent mapping studies, were projected onto a high-density consensus map and summarized into 40 MQTLs distributed across 9 chromosomes. On average, MQTL confidence intervals (CIs) were reduced from 92.36 to 13.74 cM, revealing several genomic hotspots on chromosomes 1, 3, 5, 6, and 7. To evaluate their robustness, we compared MQTL intervals with marker-trait associations from 9 genome-wide association studies (GWAS). Among the 40 identified MQTLs, 17 (42.5%) showed positional colocalization with at least one GWAS locus associated with chalkiness or milling traits, providing independent evidence for the relevance of these genomic regions to grain quality. Notably, MQTL5.1, MQTL6.2, MQTL7.1, and MQTL7.2 overlapped with association signals across multiple studies, indicating recurrent genomic regions of interest. Candidate genes were identified within ±100-kb genomic regions surrounding each MQTL peak, resulting in a total of 974 candidate genes. Comparison with transcriptomic data from developing grains of the high-chalkiness variety HK300 and the low-chalkiness variety ZR24D, sampled at 5, 7, and 10 d after flowering, identified 379 MQTL-derived candidate genes with significant differential expression. Functional annotation and Gene Ontology (GO) enrichment analyses revealed that the transcriptomically supported candidate genes were involved in a range of cellular and molecular functions, including metabolic and regulatory processes. Integration of positional, transcriptomic, and functional evidence highlighted Os03g0686900 (FLO6), 20 transcription factors, and 1 receptor-like protein kinase as high-priority candidates. These MQTLs and candidate genes provide refined targets for fine-mapping, functional validation, and genomics-assisted breeding aimed at improving rice grain appearance and milling quality.

Key words: Meta-QTL, chalkiness, milling quality, rice, marker-assisted selection, GWAS colocalization