A frequentist test of proportional colocalization after selecting relevant genetic variants

TL;DR

This paper introduces a new frequentist statistical test for colocalization analysis that complements existing Bayesian methods by providing an alternative approach based on proportionality of genetic associations, useful when Bayesian results are inconclusive.

Contribution

The paper proposes a novel conditional frequentist test, prop-coloc-cond, for proportional colocalization that accounts for variant selection uncertainty using summary data.

Findings

The proposed test maintains accurate type I error control.

Simulation studies demonstrate the test's effectiveness.

Application to GLP1R gene expression offers valuable biological insights.

Abstract

Colocalization analyses assess whether two traits are affected by the same or distinct causal genetic variants in a single gene region. A class of Bayesian colocalization tests are now routinely used in practice; for example, for genetic analyses in drug development pipelines. In this work, we consider an alternative frequentist approach to colocalization testing that examines the proportionality of genetic associations with each trait. The proportional colocalization approach uses markedly different assumptions to Bayesian colocalization tests, and therefore can provide valuable complementary evidence in cases where Bayesian colocalization results are inconclusive or sensitive to priors. We propose a novel conditional test of proportional colocalization, prop-coloc-cond, that aims to account for the uncertainty in variant selection, in order to recover accurate type I error control. The test can be implemented straightforwardly, requiring only summary data on genetic associations. Simulation evidence and an empirical investigation into GLP1R gene expression demonstrates how tests of proportional colocalization can offer important insights in conjunction with Bayesian colocalization tests.