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 asymptotic detection boundary is derived in terms of parameters such as the sparsity of non-null cases in each sequence, the effect sizes of the signals, 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 genetic pleiotropy in 10 pediatric autoimmune diseases.

中文翻译：

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多个测试的两个序列之间弱正潜在依赖性的最佳检测


联合分析多个测试的两个配对序列常常是令人感兴趣的。本文研究了检测两个序列中是否存在比偶然预期更多的显着检验对的问题。渐近检测边界是根据参数导出的，例如每个序列中非空情况的稀疏性、信号的效应大小以及两个序列之间的依赖性大小。还提出了一种用于检测弱依赖性的新测试，该测试被证明是渐进自适应最优的，并在模拟中进行了研究，并应用于研究 10 种儿科自身免疫性疾病的遗传多效性。