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Embracing study heterogeneity for finding genetic interactions in large-scale research consortia
Yulun Liu1, Jing Huang2, Ryan J Urbanowicz3
1Department of Population and Data Sciences, The University of Texas Southwestern Medical Center, Dallas, Texas.
Genetic Epidemiology
|October 5, 2019
Summary
Identifying genetic interactions in complex diseases is challenging. A new method, YETI2, uses distributed genome-wide association study (GWAS) data heterogeneity to find these interactions without sharing personal information.
Area of Science:
- Genetics
- Bioinformatics
- Computational Biology
Background:
- Genetic interactions are crucial for complex disease heritability but difficult to identify due to small effect sizes and stringent multiple-testing corrections.
- Large-scale consortia facilitate genome-wide association study (GWAS) data sharing, but privacy concerns and data heterogeneity pose challenges.
- Heterogeneity in marginal effects across distributed GWAS databases can provide novel insights into genetic interactions.
Purpose of the Study:
- To develop a novel statistical method for detecting genetic interactions in large-scale consortia.
- To address challenges of data privacy and heterogeneity in distributed GWAS databases.
- To leverage marginal effect heterogeneity for prioritizing potential genetic interactions.
Main Methods:
- Developed a novel two-stage testing procedure named phylogenY-based effect-size tests for interactions using first 2 moments (YETI2).
- YETI2 utilizes a meta-analytic framework to analyze both pooled marginal effects and heterogeneity in marginal effects across sites.
- The method is designed for large consortia without requiring the sharing of personal genomic information.
Main Results:
- YETI2 effectively detects genetic interactions by considering both averaged and heterogeneous marginal effects across distributed GWAS databases.
- The method can prioritize potential genetic interactions by leveraging underlying heterogeneity.
- Simulation studies demonstrated the performance of YETI2, and it was applied to bladder cancer data from dbGaP.
Conclusions:
- YETI2 offers a powerful new approach for identifying genetic interactions in complex diseases within large consortia.
- The method overcomes limitations of conventional approaches by utilizing data heterogeneity without compromising privacy.
- YETI2 enhances the discovery of genetic interactions relevant to complex diseases like bladder cancer.
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