biostudies-literatureZilinskas RUniversity of MinnesotaNational Institutes of HealthNIGMS NIH HHSNIH HHSujad039https://www.ebi.ac.uk/biostudies/studies/S-EPMC10928990biostudies-literatureUnknown80(1)Estimating 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 Alzheimer's 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.BiometricsInferring a directed acyclic graph of phenotypes from GWAS summary statistics.PMC10928990R01 AG065636R01 AG069895R01 HL116720RF1 AG067924U01 AG073079R01 GM126002Pan WZilinskas RLi CYang TShen XfalseInferring a directed acyclic graph of phenotypes from GWAS summary statistics.Estimating 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 Alzheimer's 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.2024-01-01T00:00:00Z2024 Jan2025-04-22T11:16:29.787Z2025-04-05T23:54:04.966ZS-EPMC109289903847025710.1093/biomtc/ujad039