Detecting Weak Signals by Combining Small P-Values in Genetic Association Studies

Olga A. Vsevolozhskaya, Fengjiao Hu, Dmitri V. Zaykin

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

We approach the problem of combining top-ranking association statistics or P-values 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 single nucleotide polymorphisms (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 and 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 μ-opioid receptor variants with sensitivity to pain.

Original languageEnglish
Article number1051
JournalFrontiers in Genetics
Volume10
DOIs
StatePublished - Nov 20 2019

Bibliographical note

Publisher Copyright:
© Copyright © 2019 Vsevolozhskaya, Hu and Zaykin.

Keywords

  • a rank truncated product RTP
  • adaptive augmented rank truncation
  • augmented rank truncation
  • combining evidence
  • rank truncated product

ASJC Scopus subject areas

  • Molecular Medicine
  • Genetics
  • Genetics(clinical)

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