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Genetic association meta-analysis is susceptible to confounding by between-study cryptic relatedness
1Program of Computational Biology and Bioinformatics, Duke University, Durham, NC, USA; Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, USA; Duke Center for Statistical Genetics and Genomics, Duke University, Durham, NC, USA.
HGG Advances
|July 27, 2026
Summary
Cryptic relatedness between studies inflates meta-analysis results, especially in family studies. A new method (metalcor) corrects for this inflation, improving accuracy for genome-wide association studies (GWAS).
Area of Science:
- Genetics
- Statistical Genetics
- Bioinformatics
Background:
- Meta-analysis of Genome-Wide Association Studies (GWAS) assumes study independence.
- Relatedness between studies, such as population structure or family ties, violates this assumption.
- Sex-stratified meta-analysis, used to correct participation bias, can be severely affected by relatedness within populations.
Purpose of the Study:
- To theoretically and empirically characterize the impact of between-study relatedness on meta-analysis.
- To develop and evaluate a novel meta-analysis method (metalcor) that accounts for correlated test statistics.
- To compare the performance of standard meta-analysis, joint GWAS, and metalcor under various relatedness scenarios.
Main Methods:
- Developed a theoretical framework to model the effects of cryptic relatedness on meta-analysis.
- Simulated GWAS data across diverse relatedness scenarios (no relatedness, within-subpopulation, across-subpopulation, single population with family relatedness).
- Evaluated standard meta-analysis, joint GWAS, and the proposed metalcor method for binary and quantitative traits, comparing Type I error, power, and AUC.
Main Results:
- Cryptic relatedness significantly inflates meta-analysis Type I error, particularly in family studies.
- Standard sex-stratified meta-analysis shows severe inflation and reduced AUC in related studies.
- The metalcor method effectively controls inflation and improves performance compared to standard methods, especially with increasing sample size.
- Genomic control corrects inflation but may not improve power and can fail under certain conditions.
Conclusions:
- Standard meta-analysis is unreliable when studies share cryptic relatedness, especially within the same population or in family-based studies.
- The metalcor R package provides a robust solution for meta-analysis in the presence of between-study correlation.
- Careful consideration of study design and relatedness is crucial for accurate GWAS meta-analysis.
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