Inferring on joint associations from marginal associations and a reference sample

TL;DR

This paper introduces a method to accurately infer joint regression coefficients from marginal associations using a reference sample, addressing issues of false discoveries in genetic studies.

Contribution

It derives the asymptotic distribution of joint regression coefficients from marginal data and provides a valid inference framework accounting for reference panel uncertainty.

Findings

Ignoring reference panel uncertainty inflates false discoveries.

The method produces valid inference in GWAS scenarios.

Real data and simulations confirm the approach's effectiveness.

Abstract

We present a method to infer on joint regression coefficients obtained from marginal regressions using a reference panel. This type of scenario is common in genetic fine-mapping, where the estimated marginal associations are reported in genomewide association studies (GWAS), and a reference panel is used for inference on the association in a joint regression model. We show that ignoring the uncertainty due to the use of a reference panel instead of the original design matrix, can lead to a severe inflation of false discoveries and a lack of replicable findings. We derive the asymptotic distribution of the estimated coefficients in the joint regression model, and show how it can be used to produce valid inference. We address two settings: inference within regions that are pre-selected, as well as within regions that are selected based on the same data. By means of real data examples and simulations we demonstrate the usefulness of our suggested methodology.