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Inferring a directed acyclic graph of phenotypes from GWAS summary statistics
Rachel Zilinskas1, Chunlin Li2, Xiaotong Shen3
1Statistics and Data Corporation, Tempe, AZ 85288, United States.
Biometrics
|March 12, 2024
Summary
This study introduces a new computational biology method to build phenotype networks using genetic data and summary statistics. The approach aids in understanding disease causes, like linking cardiovascular proteins to Alzheimer's disease.
Area of Science:
- Computational biology
- Genetics
- Network analysis
Background:
- Phenotype network estimation is crucial for understanding disease etiology.
- Existing methods may require individual-level genetic data.
Purpose of the Study:
- To present a novel method for constructing phenotype networks using summary statistics from genome-wide association studies (GWAS).
- To enable causal inference on directed edges within these networks.
Main Methods:
- Utilizes a Gaussian linear structure model with a directed acyclic graph (DAG).
- Employs genetic variants as instrumental variables.
- Requires only GWAS summary statistics and reference genotype data.
Main Results:
- Successfully estimated a causal network of 29 cardiovascular-related proteins.
- Linked the cardiovascular protein network to Alzheimer's disease (AD).
- Simulation studies confirmed the method's effectiveness.
Conclusions:
- The developed method provides an efficient way to estimate phenotype networks from summary statistics.
- Facilitates the study of complex disease relationships, such as cardiovascular factors and AD.
- An R package (sumdag) and Shiny application are available for public use.
Keywords:
Alzheimer’s disease (AD)directed acyclic graph (DAG)genome-wide association study (GWAS)likelihood ratio testproteomicsMore Related Videos
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