Dissecting genetic architecture and improving machine learning‑based genomic prediction of flowering time in Osmanthus fragrans by integrating structural variants.

Abstract

Sweet osmanthus (Osmanthus fragrans), a traditional ornamental plant in China, exhibits substantial variation in autumn flowering time, which significantly affects landscape application and cultivation efficiency. Here, we performed a genome-wide association study on 127 resequenced accessions classified into early, intermediate, and late flowering types, using a set of 2,325,410 single-nucleotide polymorphisms (SNPs) and 246,824 structural variants (SVs). By integrating SNP/insertion and deletion (Indel) and SV data with weighted gene co-expression network analysis, machine learning, and genomic prediction, we dissected the genetic architecture of flowering time. We identified 24 associated SNP/Indels and six SVs, mapping to 30 candidate genes, including known flowering regulators FLK, LOS1, Y14, MIF2, and GID1B. These genes showed tissue-specific expression, with some responding to low temperature. The two hub genes, GUX1 and LYG027904, were located within modules of the co-expression network associated with low-temperature treatment. Haplotype analysis revealed a specific three-SNP haplotype associated with late flowering and linked to LOS1, and epistatic interactions among combined genotypes contributed to phenotypic variation. Notably, integrating SVs with SNP/Indels improved genomic prediction accuracy; the gradient boosting decision tree model outperformed other machine learning algorithms, achieving a mean accuracy of 0.859 and an AUC > 0.8 (where AUC is area under receiver operating characteristic curve) for all flowering types. These findings provide insights into the genetic mechanisms underlying flowering time variation in O. fragrans, offer candidate genes and haplotypes for molecular breeding, and highlight the value of integrating SVs with machine learning for genomic prediction in woody ornamentals.