Genome-wide association studies (GWASs) are powerful and flexible tools for identifying single nucleotide polymorphisms (SNPs) associated with quantitative traits (yield, stress resistance) in plants. Variable selection and machine learning are two effective approaches in GWAS. However, both face limitations in complex, noisy data analysis in the big-data era. In this study, we integrated variable selection and machine learning under the mixed linear model framework, proposing a novel method, the improved LASSO screening and sparse Bayesian learning algorithm (ILSBL). The ILSBL first corrects the polygenic and environmental noise, then reduces genotypic dimensionality by LASSO-based variable selection, and finally performs parameter estimation using sparse Bayesian learning. Two simulation experiments and association analyses of three flowering-time-related traits in Arabidopsis thaliana were conducted to validate the new algorithm. The results showed that, compared to established methods, the ILSBL exhibited flexibility in simulation studies and maintained robust performance under complex genetic backgrounds, achieving a favorable balance among statistical power, parameter estimation accuracy, runtime efficiency, and false-positive rate. The analysis of the real Arabidopsis datasets further confirmed the advantages of ILSBL for GWASs, with 30 candidate genes adjacent to significant quantitative trait nucleotides (QTNs) associated with flowering-related traits. These results provide valuable insights for a better understanding of the genetic basis underlying flowering-related traits in Arabidopsis.

Genome-wide association studies (GWASs) are flexible and comprehensive tools for identifying single nucleotide polymorphisms (SNPs) associated with complex traits or diseases. The whole-genome Bayesian models are an effective way of incorporating important prior information into modeling. Bayesian methods have been widely used in association analysis. However, Bayesian analysis is often not feasible due to the high-throughput genotype and large sample sizes involved. In this study, we propose a new Bayesian algorithm under the mixed linear model framework: the expectation and maximization BayesB Improved algorithm (emBBI). The emBBI algorithm corrects polygenic and environmental noise and reduces dimensions; then, it estimates and tests marker effects using emBayesB and the LOD test, respectively. We conducted two simulation experiments and analyzed a real dataset related to flowering time in Arabidopsis to demonstrate the validation of the new algorithm. The results show that the emBBI algorithm is more flexible and accurate in simulation studies compared to established methods, and it performs well under complex genetic backgrounds. The analysis of the Arabidopsis real dataset further illustrates the advantages of the emBBI algorithm for GWAS by detecting known genes. Furthermore, 12 candidate genes are identified in the neighborhood of the significant quantitative trait nucleotides (QTNs) of flowering-related QTNs in Arabidopsis. In addition, we also performed enrichment analysis and tissue expression analysis of candidate genes, which will help us better understand the genetic basis of flowering-related traits in Arabidopsis.