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

Yin-Chun Lin1, Yu-Jen Liang1, Hsin-Chou Yang1,2,3,4

  • 1Institute of Statistical Science, Academia Sinica, Taipei 11529, Taiwan.

Computational and Structural Biotechnology Journal
|July 21, 2022
PubMed
Summary

A new numerical integration method enhances statistical power by combining p-values from multiple studies. This approach improves accuracy and efficiency for genome-wide association studies (GWASs) and transcriptome-wise association studies (TWASs).

Keywords:
DecorrelationFisher’s methodGWAS, Genome-Wide Association StudyGenome-wide association studyMHC, Major Histocompatibility CommplexMeta-analysisNARAC, North American Rheumatoid Arthritis ConsortiumP-value combinationPermutationSNP, Single Nucleotide PolymorphismTWAS, Transcriptome-Wide Association StudyTranscriptome-wise association studyWTCCC, Wellcome Trust Case Control Consortium

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Statistical Genetics

Background:

  • Meta-analysis integrates data from multiple studies to increase statistical power.
  • Existing p-value integration methods (Fisher's, permutation, decorrelation) have limitations in statistical assumptions, computational efficiency, and accuracy.
  • These limitations impact analyses in bioinformatics and computational biotechnology.

Purpose of the Study:

  • To propose and evaluate a novel numerical integration method for p-values.
  • To assess the theoretical properties, simulation performance, and real-world applicability of the new method.
  • To compare the proposed method against existing p-value integration techniques.

Main Methods:

  • Developed a numerical integration method for combining p-values.
  • Conducted simulation studies to evaluate Type I error, statistical power, computational efficiency, and estimation accuracy.
  • Applied the method to large-scale genome-wide association studies (GWASs) and transcriptome-wise association studies (TWASs).

Main Results:

  • The proposed method demonstrates robust performance in controlling Type I error and maintaining statistical power.
  • It offers superior computational efficiency, irrespective of sample size, and accurate statistical significance estimation.
  • Analysis of GWAS and TWAS data identified genomic regions associated with rheumatoid arthritis and asthma, enhancing significance and controlling false positives.

Conclusions:

  • The novel numerical integration method provides a more effective and efficient approach for p-value combination in large-scale genetic studies.
  • The method successfully identifies disease-associated genomic regions and improves statistical rigor compared to traditional methods like Fisher's.
  • A software package, Pbine, has been developed to facilitate the application of this new method.