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Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
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A correction for sample overlap in genome-wide association studies in a polygenic pleiotropy-informed framework.
Marissa LeBlanc1, Verena Zuber2, Wesley K Thompson3
1Oslo Centre for Biostatistics and Epidemiology, Oslo University Hospital, Oslo universitetssykehus HF, Sogn Arena, PB 4950 Nydalen, Oslo, 0424, Norway. marissa.leblanc@medisin.uio.no.
BMC Genomics
|June 27, 2018
Summary
We developed a method to correct for overlapping subjects in genome-wide association studies (GWAS). This improves pleiotropy estimation by adjusting for spurious correlations, ensuring accurate false discovery rates.
Area of Science:
- Genetics
- Statistical Genetics
- Bioinformatics
Background:
- Complex traits often share genetic underpinnings (pleiotropy).
- Integrating genome-wide association study (GWAS) summary statistics across traits is valuable.
- Overlapping subjects in GWAS pose a challenge for methods like covariate-modulated false discovery rate (cmfdr).
Purpose of the Study:
- To propose a method for correcting sample overlap in GWAS summary statistics.
- To address the issue of spurious correlations affecting pleiotropy estimation.
- To ensure proper control of the false discovery rate when integrating GWAS data.
Main Methods:
- Quantified spurious correlation due to sample overlap in GWAS summary statistics.
- Developed a linear correction to adjust the joint distribution of test statistics.
- Applied the correction to GWAS with case-control or quantitative outcomes.
Main Results:
- Uncorrected sample overlap leads to improper control of cmfdr and excessive false discoveries.
- The proposed correction effectively restores control of the false discovery rate.
- The method shows minimal loss in statistical power.
Conclusions:
- The proposed correction enables the integration of GWAS summary statistics with overlapping samples.
- This facilitates accurate pleiotropy estimation within statistical frameworks dependent on joint GWAS distributions.
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