Integrating GWAS and Machine Learning to Dissect the Genetic Basis of Flowering Time and Vernalization-Type Differentiation in Brassica napus

Flowering time is an important trait affecting regional adaptation and yield stability in rapeseed (Brassica napus). To dissect the genetic basis of flowering time and vernalization-type differentiation in B. napus, this study integrated genome-wide SNPs, flowering-time phenotypes, vernalization types, and geographic origin information to establish a candidate QTL discovery framework combining genome-wide association study (GWAS) and machine learning. Based on 8,387,529 high-quality SNPs, GWAS was performed using the FarmCPU model under two covariate settings: a PC-corrected model and a PC + vernalization type-corrected model. These two models identified 32 and 27 suggestive SNPs, respectively. In parallel, ExtraTreesRegressor was used to estimate the feature contribution of SNPs to flowering-time prediction, leading to the identification of 18,854 ML_delta10SD high-contribution SNPs, which were further merged into 997 ML-derived candidate QTL intervals, hereafter referred to as ML-QTLs. By integrating GWAS-derived QTLs and ML-QTLs at the interval level, 34 GWAS- and ML-supported candidate regions were prioritized. Candidate gene annotation revealed that these regions contained genes related to flowering regulation, including FLC, FRIGIDA-like, VIN3-like, SOC1, ELF3, CONSTANS-like, APETALA1-like, and genes involved in the gibberellin pathway. Top-SNP genotypic effect analysis and multi-SNP genotype combination analysis further showed that genetic variation within representative candidate regions was significantly associated with flowering-time differences and vernalization-type differentiation. Predictive modeling indicated that candidate markers selected by GWAS and machine learning showed predictive potential within the present population. Overall, this study prioritized a set of GWAS- and ML-supported candidate regions and putative candidate genes associated with flowering time in B. napus from the complementary perspectives of statistical association and machine learning-based predictive contribution, providing candidate resources for future marker development, functional validation, and adaptive improvement in rapeseed.