Evaluating statistical significance in a meta-analysis by using numerical integration

Yin-Chun Lin; Yu-Jen Liang; Hsin-Chou Yang

doi:10.1016/j.csbj.2022.06.055

Evaluating statistical significance in a meta-analysis by using numerical integration

Comput Struct Biotechnol J. 2022 Jul 4:20:3615-3620. doi: 10.1016/j.csbj.2022.06.055. eCollection 2022.

Authors

Yin-Chun Lin¹, Yu-Jen Liang¹, Hsin-Chou Yang^{1

2

3

4}

Affiliations

¹ Institute of Statistical Science, Academia Sinica, Taipei 11529, Taiwan.
² Biomedical Translation Research Center, Academia Sinica, Taipei 11529, Taiwan.
³ Institute of Statistics, National Cheng Kung University, Tainan 70101, Taiwan.
⁴ Institute of Public Health, National Yang-Ming University, Taipei 11221, Taiwan.

Abstract

Meta-analysis is a method for enhancing statistical power through the integration of information from multiple studies. Various methods for integrating p-values (i.e., statistical significance), including Fisher's method under an independence assumption, the permutation method, and the decorrelation method, have been broadly used in bioinformatics and computational biotechnology studies. However, these methods have limitations related to statistical assumption, computing efficiency, and accuracy of statistical significance estimation. In this study, we proposed a numerical integration method and examined its theoretical properties. Simulation studies were conducted to evaluate its Type I error, statistical power, computational efficiency, and estimation accuracy, and the results were compared with those of other methods. The results demonstrate that our proposed method performs well in terms of Type I error, statistical power, computing efficiency (regardless of sample size), and statistical significance estimation accuracy. P-value data from multiple large-scale genome-wide association studies (GWASs) and transcriptome-wise association studies (TWASs) were analyzed. The results demonstrate that our proposed method can be used to identify critical genomic regions associated with rheumatoid arthritis and asthma, increase statistical significance in individual GWASs and TWASs, and control for false-positives more effectively than can Fisher's method under an independence assumption. We created the software package Pbine, available at GitHub (https://github.com/Yinchun-Lin/Pbine).

Keywords: Decorrelation; Fisher’s method; GWAS, Genome-Wide Association Study; Genome-wide association study; MHC, Major Histocompatibility Commplex; Meta-analysis; NARAC, North American Rheumatoid Arthritis Consortium; P-value combination; Permutation; SNP, Single Nucleotide Polymorphism; TWAS, Transcriptome-Wide Association Study; Transcriptome-wise association study; WTCCC, Wellcome Trust Case Control Consortium.