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bioRxiv · 10.1101/2023.02.10.528092

Inferring a directed acyclic graph of phenotypes from GWAS summary statistics

Zilinskas, R.·Li, C.·Shen, X.·Pan, W.·Yang, T.
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Abstract

SO_SCPLOWUMMARYC_SCPLOWEstimating phenotype networks is a growing field in computational biology. It deepens the understanding of disease etiology and is useful in many applications. In this study, we present a method that constructs a phenotype network by assuming a Gaussian linear structure model embedding a directed acyclic graph (DAG). We utilize genetic variants as instrumental variables and show how our method only requires access to summary statistics from a genome-wide association study (GWAS) and a reference panel of genotype data. Besides estimation, a distinct feature of the method is its summary statistics-based likelihood ratio test on directed edges. We applied our method to estimate a causal network of 29 cardiovascular-related proteins and linked the estimated network to Alzheimers disease (AD). A simulation study was conducted to demonstrate the effectiveness of this method. An R package sumdag implementing the proposed method, all relevant code, and a Shiny application are available at https://github.com/chunlinli/sumdag.

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Zilinskas, R., Li, C., Shen, X., Pan, W., Yang, T.. 2023-02-11. Inferring a directed acyclic graph of phenotypes from GWAS summary statistics. https://doi.org/10.1101/2023.02.10.528092

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