Performance of LASSO and Ridge Regression for Variable Selection in Genome-Wide Association Studies of Maize Flowering Time

Abstract

Genome-Wide Association Studies (GWAS) are instrumental in identifying genetic variants linked to complex traits, providing valuable insights into trait heritability and biological mechanisms. This study applies GWAS to investigate flowering time in maize, a critical adaptive trait, using a diverse dataset of 5,000 recombinant inbred lines across eight environments. Traditional GWAS methods often encounter challenges in high-dimensional datasets due to the presence of multiple small-effect genetic loci. To address this, we compared two penalized regression methods—LASSO and Ridge regression—to perform variable selection and regression analysis within a GWAS framework. LASSO effectively reduced the number of predictors by selecting the most impactful variables, while Ridge regression retained more features, offering a broader genetic context for predicting flowering time. Results demonstrated that Ridge regression yielded slightly better predictive performance, achieving a lower Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) than LASSO