Inferring a directed acyclic graph of phenotypes from GWAS summary statistics
Zilinskas, R.; Li, C.; Shen, X.; Pan, W.; Yang, T.
Show 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.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
Similar papers in this journal
- Fast Lasso method for Large-scale and Ultrahigh-dimensional Cox Model with applications to UK Biobank 96%
- Survival Analysis on Rare Events Using Group-Regularized Multi-Response Cox Regression 95%
- A mixed-model approach for powerful testing of genetic associations with cancer risk incorporating tumor characteristics 95%
Similar papers in this journal
- Estimating the effect size of a hidden causal factor between SNPs and a continuous trait: a mediation model approach 96%
- DAGBagM: Learning directed acyclic graphs of mixed variables with an application to identify prognostic protein biomarkers in ovarian cancer 95%
- Mixed Logistic Regression in Genome-WideAssociation Studies 95%
Similar papers in this journal
- A novel method for multiple phenotype association studies based on genotype and phenotype network 96%
- Robust Inference of Bi-Directional Causal Relationships in Presence of Correlated Pleiotropy with GWAS Summary Data 96%
- Inferring Causal Direction Between Two Traits in the Presence of Horizontal Pleiotropy with GWAS Summary Data 95%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.