  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.
