{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Zilinskas R"],"funding":["University of Minnesota","National Institutes of Health","NIGMS NIH HHS","NIH HHS"],"pagination":["ujad039"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC10928990"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["80(1)"],"pubmed_abstract":["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."],"journal":["Biometrics"],"pubmed_title":["Inferring a directed acyclic graph of phenotypes from GWAS summary statistics."],"pmcid":["PMC10928990"],"funding_grant_id":["R01 AG065636","R01 AG069895","R01 HL116720","RF1 AG067924","U01 AG073079","R01 GM126002"],"pubmed_authors":["Pan W","Zilinskas R","Li C","Yang T","Shen X"],"additional_accession":[]},"is_claimable":false,"name":"Inferring a directed acyclic graph of phenotypes from GWAS summary statistics.","description":"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.","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Jan","modification":"2025-04-22T11:16:29.787Z","creation":"2025-04-05T23:54:04.966Z"},"accession":"S-EPMC10928990","cross_references":{"pubmed":["38470257"],"doi":["10.1093/biomtc/ujad039"]}}