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PLEIO: a method to map and interpret pleiotropic loci with GWAS summary statistics.
Cue Hyunkyu Lee1, Huwenbo Shi2, Bogdan Pasaniuc3
1Department of Biomedical Sciences, BK21 Plus Biomedical Science Project, Seoul National University College of Medicine, Seoul 03080, Republic of Korea; Department of Convergence Medicine, University of Ulsan College of Medicine, Asan Medical Center, Seoul 05505, Republic of Korea.
American Journal of Human Genetics
|December 22, 2020
Summary
This study introduces PLEIO, a new framework for identifying shared genetic influences across multiple diseases and traits. It improves the analysis of pleiotropic loci by accounting for genetic correlations and heritabilities.
Area of Science:
- Genetics
- Bioinformatics
- Statistical genomics
Background:
- Identifying pleiotropic loci is crucial for understanding shared genetic underpinnings of diseases and complex traits.
- Current meta-analysis methods for mapping pleiotropic loci do not fully account for complex genetic architectures like correlations and heritabilities.
- Interpreting results is challenging due to varying phenotype characteristics and units.
Purpose of the Study:
- To develop a novel framework, PLEIO (Pleiotropic Locus Exploration and Interpretation using Optimal test), for mapping and interpreting pleiotropic loci.
- To enhance the power of association tests by systematically incorporating genetic correlations and heritabilities.
- To provide tools for seamless integration and interpretation of diverse phenotypes.
Main Methods:
- Developed PLEIO, a summary-statistic-based framework for joint analysis of multiple diseases and complex traits.
- The framework systematically accounts for genetic correlations and heritabilities in association testing.
- Integrated interpretation and visualization tools for downstream analysis.
Main Results:
- Applied PLEIO to 18 cardiovascular disease-related traits.
- Identified 13 pleiotropic loci with significant associations.
- Observed four distinct patterns of pleiotropic associations.
Conclusions:
- PLEIO offers a powerful and flexible approach for mapping and interpreting pleiotropic loci across diverse traits.
- The framework effectively integrates quantitative and binary traits with different units.
- The identified loci and association patterns provide insights into the shared etiology of cardiovascular diseases.
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