Benchmarking Feature Selection Methods and Prediction Models for Flowering Time Prediction in Maize

Flowering time is a fundamental trait that determines crop adaptation and yield stability. To accurately predict flowering time and identify key regulatory factors, it is necessary to extract biologically meaningful signals from high-dimensional and multi-omics datasets. Although machine learning has been increasingly applied in plant genomics, there is still limited research on how feature selection (FS) methods and genomic prediction (GP) models affect the prediction of flowering time and gene discovery, particularly regarding the combination of different FS and GP approaches and the interpretability of prediction models. To address this gap, we conducted a large-scale benchmarking study that jointly evaluated seven feature selection methods and six prediction models, resulting in 42 FS–GP combinations. By integrating SNP and transcriptomic data, we assessed predictive performance and further interpreted model outputs using SHAP (SHapley Additive exPlanations) within a random forest (RF) framework to quantify feature contributions. This strategy successfully identified known flowering time regulators in maize, including ZmMADS69 and ZmRap2.7, and revealed additional candidate genes potentially involved in the flowering regulatory network. Overall, this study offers valuable insights into the genetic regulation of flowering time in maize and provides an effective framework for discovering candidate genes from multi-omics data for crop improvement.