Optimal detection of weak positive latent dependence between two sequences of multiple tests

TL;DR

This paper develops a new statistically optimal method for detecting weak positive dependence between two sequences of tests, with applications to genetic studies of autoimmune diseases.

Contribution

It derives the detection boundary for weak dependence and proposes a new adaptive test that is optimal in asymptotic scenarios.

Findings

The proposed test is asymptotically adaptively optimal.

Simulation studies demonstrate the test's effectiveness.

Application to genetic data reveals meaningful dependence patterns.

Abstract

It is frequently of interest to jointly analyze two paired sequences of multiple tests. This paper studies the problem of detecting whether there are more pairs of tests that are significant in both sequences than would be expected by chance. The detection boundary is derived in terms of parameters such as the sparsity of the latent significance indicators of the tests in each sequence, the effect sizes of the non-null tests, and the magnitude of the dependence between the two sequences. A new test for detecting weak dependence is also proposed, shown to be asymptotically adaptively optimal, studied in simulations, and applied to study of genetic pleiotropy in 10 pediatric autoimmune diseases.