Genetic architecture of heterosis in maize NCII breeding populations

Despite the extensive utilization of heterosis in modern breeding, its genetic architecture remains incompletely understood.

This study aimed to decipher the genetic basis of heterosis in maize.

Four heterosis indices derived from seven traits in a 2023 maize NCII breeding population were analyzed in association with 36,671,063 genetic markers.

Among the 119 candidate genes identified through multi-omics integration and haplotype analyses, 101 exhibited consistent and significant haplotype-heterosis associations across both the 2019 and 2023 NCII breeding populations. Five high-confidence candidate genes were highlighted, with HDA108, MADS3, and Zm00001eb080510 repeatedly detected across multiple environments and populations. eGWAS linked the known heterosis-related genes ZAR1, bZIP29, and ACCO2 to one, two, and two candidate genes, respectively. Five candidate genes showed co-localization of eQTLs and heterosis-associated QTNs within linkage disequilibrium windows (2.60 to 8.63 kb). Integration of multi-dimensional networks using a graph neural network framework identified six hub genes, including HDA108 (degree = 224). Notably, complementary genetic backgrounds of parental lines enabled more complete gene functionality in hybrid offspring. Candidate genes exhibited substantially more significant haplotype-trait associations in F₁ hybrids (98) than in parental lines (35), and 97 candidates showed significant allele frequency divergence (Fst > 0.2) between parental groups. The number of superior haplotypes was significantly correlated with both best-parent and mid-parent heterosis across the two breeding populations. These findings demonstrate that heterosis-related genes exert stronger functional effects in hybrids than in inbred parents, thereby enhancing trait performance in offspring. Furthermore, variation in the number of superior haplotypes directly contributed to heterosis differences among hybrids. All heterosis-associated QTNs primarily influenced trait phenotypes through epistatic interactions, which emerged as the predominant genetic driver of heterosis.

This study has identified some novel candidate genes and elucidated some key genetic mechanisms underlying heterosis, providing some valuable insight and resource for precision hybrid breeding.