Detecting weak signals by combining small P-values in genetic association studies

TL;DR

This paper introduces the Augmented Rank Truncation (ART) method, a simple and powerful approach for combining small P-values in genetic association studies to detect weak signals more effectively.

Contribution

The paper proposes the ART method, simplifying the combination of top-ranking association statistics and enhancing the adaptive algorithm for improved power in genetic studies.

Findings

ART is simpler to implement than RTP.

ART improves detection power in genetic association studies.

Application to opioid receptor variants strengthened previous associations.

Abstract

We approach the problem of combining top-ranking association statistics or P-value from a new perspective which leads to a remarkably simple and powerful method. Statistical methods, such as the Rank Truncated Product (RTP), have been developed for combining top-ranking associations and this general strategy proved to be useful in applications for detecting combined effects of multiple disease components. To increase power, these methods aggregate signals across top ranking SNPs, while adjusting for their total number assessed in a study. Analytic expressions for combined top statistics or P-values tend to be unwieldy, which complicates interpretation, practical implementation, and hinders further developments. Here, we propose the Augmented Rank Truncation (ART) method that retains main characteristics of the RTP but is substantially simpler to implement. ART leads to an efficient form of the adaptive algorithm, an approach where the number of top ranking SNPs is varied to optimize power. We illustrate our methods by strengthening previously reported associations of $\mu$-opioid receptor variants with sensitivity to pain.