Related Experiment Video
Updated: May 25, 2026

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization
Published on: July 27, 2021
Assumption weighting for incorporating heterogeneity into meta-analysis of genomic data
1Department of Statistics, Penn State University, University Park, PA 16802, USA.
Motivation:
There is now a large literature on statistical methods for the meta-analysis of genomic data from multiple studies. However, a crucial assumption for performing many of these analyses is that the data exhibit small between-study variation or that this heterogeneity can be sufficiently modelled probabilistically.
Results:
In this article, we propose 'assumption weighting', which exploits a weighted hypothesis testing framework proposed by Genovese et al. to incorporate tests of between-study variation into the meta-analysis context. This methodology is fast and computationally simple to implement. Several weighting schemes are considered and compared using simulation studies. In addition, we illustrate application of the proposed methodology using data from several high-profile stem cell gene expression datasets.
Related Concept Videos
Weighted Mean
For example, consider the number of goals scored in the matches of a tournament. While computing the average number of goals scored in the tournament, it may be more important to...
Genome-wide Association Studies-GWAS
GWAS does not require the identification of the target gene involved in...
Hardy-Weinberg Principle
Assumptions of Survival Analysis
Strategies for Assessing and Addressing Confounding
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
Bias in Epidemiological Studies