ArticleJournal of biomedical informatics2016
Generating a robust statistical causal structure over 13 cardiovascular disease risk factors using genomics data.
Article in Journal of biomedical informatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Metabolomic Profiles in Jamaican Children With and Without Autism Spectrum Disorder.Journal of autism and developmental disorders · 2025Article
- Immune and inflammatory resolution pathways through multi-omics using the AI-based Network Integration.Research square · 2025Article
- Causal network inference of cis- and trans-gene regulation of expression quantitative trait loci across human tissues.Genetics · 2025Article
- Understanding 30-Day Mortality After First STEMI Through DAGs: Unravelling Epidemiological Cause-Effect Links.Cureus · 2025Article
- Broadcasters, receivers, functional groups of metabolites, and the link to heart failure by revealing metabolomic network connectivity.Metabolomics : Official journal of the Metabolomic Society · 2024Article
- Article
- Deep neural networks with knockoff features identify nonlinear causal relations and estimate effect sizes in complex biological systems.GigaScience · 2022Article
- A review of causal discovery methods for molecular network analysis.Molecular genetics & genomic medicine · 2022Review
- Connectivity in eQTL networks dictates reproducibility and genomic properties.Cell reports methods · 2022Article
- The Inflamm-Aging Model Identifies Key Risk Factors in Atherosclerosis.Frontiers in genetics · 2022Article
- From classical mendelian randomization to causal networks for systematic integration of multi-omics.Frontiers in genetics · 2022Review
- Differential gene regulatory pattern in the human brain from schizophrenia using transcriptomic-causal network.BMC bioinformatics · 2020Article
- Pathways leading to prevention of fatal and non-fatal cardiovascular disease: An interaction model on 15 years population-based cohort study.Lipids in health and disease · 2020Article
- Article
- Genome analysis and pleiotropy assessment using causal networks with loss of function mutation and metabolomics.BMC genomics · 2019Article
- A Multi-Trait Approach Identified Genetic Variants Including a Rare Mutation in RGS3 with Impact on Abnormalities of Cardiac Structure/Function.Scientific reports · 2019Article
- An unsupervised machine learning method for discovering patient clusters based on genetic signatures.Journal of biomedical informatics · 2018Article
- Arachidonic acid as a target for treating hypertriglyceridemia reproduced by a causal network analysis and an intervention study.Metabolomics : Official journal of the Metabolomic Society · 2018Article
- A comparison of methods for inferring causal relationships between genotype and phenotype using additional biological measurements.Genetic epidemiology · 2017Article
- A Causal Network Analysis of the Fatty Acid Metabolome in African-Americans Reveals a Critical Role for Palmitoleate and Margarate.Omics : a journal of integrative biology · 2016Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Understanding causal relationships among large numbers of variables is a fundamental goal of biomedical sciences and can be facilitated by Directed Acyclic Graphs (DAGs) where directed edges between nodes represent the influence of components of the system on each other. In an observational setting, some of the directions are often unidentifiable because of Markov equivalency. Additional exogenous information, such as expert knowledge or genotype data can help establish directionality among the endogenous variables. In this study, we use the method of principle component analysis to extract information across the genome in order to generate a robust statistical causal network among phenotypes, the variables of primary interest. The method is applied to 590,020 SNP genotypes measured on 1596 individuals to generate the statistical causal network of 13 cardiovascular disease risk factor phenotypes. First, principal component analysis was used to capture information across the genome. The principal components were then used to identify a robust causal network structure, GDAG, among the phenotypes. Analyzing a robust causal network over risk factors reveals the flow of information in direct and alternative paths, as well as determining predictors and good targets for intervention. For example, the analysis identified BMI as influencing multiple other risk factor phenotypes and a good target for intervention to lower disease risk.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.