ArticleFrontiers in genetics2019
Detecting Weak Signals by Combining Small P-Values in Genetic Association Studies.
Article in Frontiers in genetics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- A high-dimensional omnibus test for set-based association analysis.Briefings in bioinformatics · 2024Article
- GWAS supported by computer vision identifies large numbers of candidate regulators of in planta regeneration in Populus trichocarpa.G3 (Bethesda, Md.) · 2024Article
- GWAS identifies candidate genes controlling adventitious rooting inHorticulture research · 2023Article
- A comprehensive comparison of multilocus association methods with summary statistics in genome-wide association studies.BMC bioinformatics · 2022Article
- Interpretable network-guided epistasis detection.GigaScience · 2022Article
- DOT: Gene-set analysis by combining decorrelated association statistics.PLoS computational biology · 2020Article
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Authors and funding
3 authors.
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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.
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