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Genetic association meta-analysis is susceptible to confounding by between-study cryptic relatedness
Tiffany Tu1,2,3, Alejandro Ochoa1,2,3
1Program of Computational Biology and Bioinformatics, Duke University, Durham, NC.
Biorxiv : the Preprint Server for Biology
|June 4, 2025
Summary
Cryptic relatedness between studies inflates meta-analysis results, particularly in sex-stratified analyses of family studies. Joint or subpopulation analyses are recommended to maintain accuracy in genetic studies.
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, while intended to correct bias, can exacerbate inflation due to cryptic relatedness.
Purpose of the Study:
- To theoretically and empirically characterize the impact of between-study relatedness on meta-analysis.
- To evaluate the performance of sex-stratified meta-analysis in the presence of relatedness.
- To provide recommendations for robust meta-analysis practices in genetic studies.
Main Methods:
- Developed a theoretical framework to model the effects of cryptic relatedness on GWAS meta-analysis.
- Simulated genetic data under various relatedness scenarios (population structure, family relatedness).
- Performed joint and meta-analyses on simulated and real datasets for binary and quantitative traits.
Main Results:
- Cryptic relatedness leads to correlated test statistics and inflated meta-analysis results.
- Sex-stratified meta-analysis showed severe inflation and reduced AUC in family-related scenarios.
- Genomic control corrected inflation but did not impact calibrated power; effect was negligible in large population studies.
Conclusions:
- Meta-analyzing studies sharing populations increases the risk of inflation due to cryptic relatedness.
- Sex-stratified meta-analysis is not suitable when family relatedness is present.
- Joint or subpopulation meta-analyses are recommended for studies with relatedness to ensure accurate results.
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