Multi-locus genome-wide association study reveals the genetic architecture of kernel fat content in maize

Kernel fat content (KFC) is a key determinant of oil quality and nutritional value in maize, yet the genetic architecture underlying its natural variation remains incompletely understood. Here, we performed a genome-wide association study (GWAS) using 239 maize inbred lines genotyped with 1.25 million single nucleotide polymorphisms (SNPs), applying six complementary statistical models: MLM, MLMM, FarmCPU, BLINK, SUPER, and 3VmrMLM.

KFC exhibited substantial phenotypic variation across the three environments, ranging from 5.48% to 8.72%, with a broad-sense heritability (H²) of 0.81. In total, 200 significant quantitative trait nucleotides (QTNs) were detected, each explaining 0.53%-23.49% of the phenotypic variance. Twenty QTNs were consistently identified across multiple models and/or environments and were consolidated into 18 non-redundant QTL intervals. Within these intervals, 58 candidate genes were annotated. Gene Ontology and KEGG enrichment analyses revealed significant enrichment in catalytic activities and metabolic pathways, notably α-linolenic acid metabolism-a core pathway in fatty acid biosynthesis. Based on multi-model co-localization and pathway evidence, Zm00001d025166, encoding a plastid-localized putative quinone-oxidoreductase, was prioritized as the primary candidate gene. Haplotype analysis identified two major haplotypes, with Hap1 conferring significantly higher KFC than Hap2 (7.09% vs. 6.46%, p = 0.003), although the difference was modest. Selective sweep analysis detected moderate selection pressure on this gene in U.S. breeding germplasm compared with Chinese and CIMMYT accessions, while weak differentiation between tropical and temperate ecotypes suggested a conserved role in fatty acid metabolism.

These findings provide a foundation for marker-assisted selection in high-oil maize breeding and demonstrate the value of multi-model GWAS strategies for dissecting complex quantitative traits.