Univariate-guided sparse regression for Biobank-scale high-dimensional omics data

We present a scalable framework for computing polygenic risk scores (PRS) in high-dimensional genomic settings using the recently introduced Univariate-Guided Sparse Regression (uniLasso). UniLasso is a two-stage penalized regression procedure that leverages univariate coefficients and magnitudes to stabilize feature selection and produce sparse predictive models. Building on its theoretical and empirical advantages, we adapt uniLasso for application to the UK Biobank, a population-based repository comprising over one million genetic variants measured on hundreds of thousands of individuals from the United Kingdom. We further extend the framework to incorporate external summary statistics via uniLasso ES (external scores). These signals guide the regression toward variants with prior evidence of association by informing penalty weights and sign constraints. Both uniLasso ES and uniLasso ultimately fit multivariate models using individual-level target data; the external statistics guide, rather than replace, this fitting. Our results demonstrate that uniLasso attains predictive performance comparable to standard Lasso while selecting substantially fewer variants, yielding sparser and potentially more interpretable models. Moreover, it remains competitive with other PRS estimation methods, such as PRS-CS and lassosum2.