A Simple New Approach to Variable Selection in Regression, with Application to Genetic Fine Mapping

TLDR

The methods target settings with highly correlated variables and sparse detectable effects, typical of genetic fine‑mapping studies. The study introduces a new variable‑selection approach and its iterative Bayesian stepwise fitting procedure, aiming to quantify uncertainty and assess applicability to generic variable‑selection problems. The method uses the SuSiE model, decomposing the coefficient vector into single‑effect components, and fits it via iterative Bayesian stepwise selection (IBSS), a fast algorithm that optimizes a variational approximation to the posterior and yields a distribution over variables to capture selection uncertainty. The approach produces credible variable sets and, in simulations, outperforms existing methods for fine‑mapping, as shown by its application to genetic variants affecting alternative splicing in human cell lines.

Abstract

Summary We introduce a simple new approach to variable selection in linear regression, with a particular focus on quantifying uncertainty in which variables should be selected. The approach is based on a new model—the ‘sum of single effects’ model, called ‘SuSiE’—which comes from writing the sparse vector of regression coefficients as a sum of ‘single-effect’ vectors, each with one non-zero element. We also introduce a corresponding new fitting procedure—iterative Bayesian stepwise selection (IBSS)—which is a Bayesian analogue of stepwise selection methods. IBSS shares the computational simplicity and speed of traditional stepwise methods but, instead of selecting a single variable at each step, IBSS computes a distribution on variables that captures uncertainty in which variable to select. We provide a formal justification of this intuitive algorithm by showing that it optimizes a variational approximation to the posterior distribution under SuSiE. Further, this approximate posterior distribution naturally yields convenient novel summaries of uncertainty in variable selection, providing a credible set of variables for each selection. Our methods are particularly well suited to settings where variables are highly correlated and detectable effects are sparse, both of which are characteristics of genetic fine mapping applications. We demonstrate through numerical experiments that our methods outperform existing methods for this task, and we illustrate their application to fine mapping genetic variants influencing alternative splicing in human cell lines. We also discuss the potential and challenges for applying these methods to generic variable-selection problems.