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Empirically adjusted fixed-effects meta-analysis methods in genomic studies

  • Wimarsha T. Jayanetti and Sinjini Sikdar EMAIL logo
Published/Copyright: September 30, 2024
Statistical Applications in Genetics and Molecular Biology
From the journal Statistical Applications in Genetics and Molecular Biology Volume 23 Issue 1

Abstract

In recent years, meta-analyzing summary results from multiple studies has become a common practice in genomic research, leading to a significant improvement in the power of statistical detection compared to an individual genomic study. Meta analysis methods that combine statistical estimates across studies are known to be statistically more powerful than those combining statistical significance measures. An approach combining effect size estimates based on a fixed-effects model, called METAL, has gained extreme popularity to perform the former type of meta-analysis. In this article, we discuss the limitations of METAL due to its dependence on the theoretical null distribution, leading to incorrect significance testing results. Through various simulation studies and real genomic data application, we show how modifying the z-scores in METAL, using an empirical null distribution, can significantly improve the results, especially in presence of hidden confounders. For the estimation of the null distribution, we consider two different approaches, and we highlight the scenarios when one null estimation approach outperforms the other. This article will allow researchers to gain an insight into the importance of using an empirical null distribution in the fixed-effects meta-analysis as well as in choosing the appropriate empirical null distribution estimation approach.

Keywords: meta-analysis; empirical null distribution; genomic studies; large-scale hypothesis testing
Corresponding author: Sinjini Sikdar, Department of Mathematics and Statistics, Old Dominion University, Norfolk, VA 23529, USA, E-mail: 

  1. Research ethics: Not applicable.

  2. Author contributions: The authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  3. Use of Large Language Models, AI and Machine Learning Tools: None declared.

  4. Conflict of interest: The authors state no conflict of interest.

  5. Research funding: None declared.

  6. Data availability: Not applicable.

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Supplementary Material

The online version of this article offers supplementary material (https://doi.org/10.1515/sagmb-2023-0041).

Received: 2023-12-14
Accepted: 2024-09-10
Published Online: 2024-09-30

© 2024 Walter de Gruyter GmbH, Berlin/Boston

Articles in the same Issue

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https://doi.org/10.1515/sagmb-2023-0041
Keywords for this article
meta-analysis; empirical null distribution; genomic studies; large-scale hypothesis testing

Articles in the same Issue

  1. Frontmatter
  2. Research Articles
  3. Empirically adjusted fixed-effects meta-analysis methods in genomic studies
  4. A CNN-CBAM-BIGRU model for protein function prediction
  5. A heavy-tailed model for analyzing miRNA-seq raw read counts
  6. Flexible model-based non-negative matrix factorization with application to mutational signatures
  7. Choice of baseline hazards in joint modeling of longitudinal and time-to-event cancer survival data
  8. Assessing the feasibility of statistical inference using synthetic antibody-antigen datasets
  9. A global test of hybrid ancestry from genome-scale data
  10. Integrative pathway analysis with gene expression, miRNA, methylation and copy number variation for breast cancer subtypes
  11. Bayesian LASSO for population stratification correction in rare haplotype association studies
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