Univariate-Guided Sparse Regression for Biobank-Scale High-Dimensional Omics Data

TL;DR

This paper introduces uniLasso, a scalable and interpretable sparse regression method for high-dimensional genomic data, demonstrating its effectiveness in predicting polygenic risk scores in the UK Biobank with fewer variants and comparable or better accuracy.

Contribution

The paper adapts uniLasso for large-scale biobank data, incorporating external summary statistics to improve prediction and interpretability of polygenic risk scores.

Findings

uniLasso achieves similar predictive performance to Lasso with fewer variants.

It outperforms competitors like PRS-CS in estimating PRS.

Incorporating external scores enhances prediction accuracy and sparsity.

Abstract

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 enhance interpretability. 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 to increase predictive accuracy. Our results demonstrate that uniLasso attains predictive performance comparable to standard Lasso while selecting substantially fewer variants, yielding sparser and more interpretable models. Moreover, it exhibits superior performance in estimating PRS relative to its competitors, such as PRS-CS. Integrating external scores further improves prediction while maintaining sparsity.