ArticleBMC bioinformatics2020
Differential gene regulatory pattern in the human brain from schizophrenia using transcriptomic-causal network.
Article in BMC bioinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it, 26 citations in OpenAlex.
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- Gene signatures derived from transcriptomic-causal networks stratify colorectal cancer patients for effective targeted therapy.Communications medicine · 2025Article
- A bootstrap model comparison test for identifying genes with context-specific patterns of genetic regulation.The annals of applied statistics · 2024Article
- 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
- Gene expression biomarkers differentiate overall survival of colorectal cancer upon targeted therapies.Research square · 2024Article
- Artificial Intelligence in Cardiology and Atherosclerosis in the Context of Precision Medicine: A Scoping Review.Applied bionics and biomechanics · 2024Article
- Gene signatures derived from transcriptomic-causal networks stratified colorectal cancer patients for effective targeted therapy.Research square · 2023Article
- DeepGAMI: deep biologically guided auxiliary learning for multimodal integration and imputation to improve genotype-phenotype prediction.Genome medicine · 2023Article
- A BOOTSTRAP MODEL COMPARISON TEST FOR IDENTIFYING GENES WITH CONTEXT-SPECIFIC PATTERNS OF GENETIC REGULATION.bioRxiv : the preprint server for biology · 2023Article
- From classical mendelian randomization to causal networks for systematic integration of multi-omics.Frontiers in genetics · 2022Review
- Assisted estimation of gene expression graphical models.Genetic epidemiology · 2021Article
- Inherited L1 Retrotransposon Insertions Associated With Risk for Schizophrenia and Bipolar Disorder.Schizophrenia bulletin open · 2021Article
- Genetic variations analysis for complex brain disease diagnosis using machine learning techniques: opportunities and hurdles.PeerJ. Computer science · 2021Article
- Molecular linkage between post-traumatic stress disorder and cognitive impairment: a targeted proteomics study of World Trade Center responders.Translational psychiatry · 2020Article
Corrections and comments
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Authors and funding
5 authors at 4 institutions in 1 country.
Funding
Abstract
backgroundCommon and complex traits are the consequence of the interaction and regulation of multiple genes simultaneously, therefore characterizing the interconnectivity of genes is essential to unravel the underlying biological networks. However, the focus of many studies is on the differential expression of individual genes or on co-expression analysis.
methodsGoing beyond analysis of one gene at a time, we systematically integrated transcriptomics, genotypes and Hi-C data to identify interconnectivities among individual genes as a causal network. We utilized different machine learning techniques to extract information from the network and identify differential regulatory pattern between cases and controls. We used data from the Allen Brain Atlas for replication.
resultsEmploying the integrative systems approach on the data from CommonMind Consortium showed that gene transcription is controlled by genetic variants proximal to the gene (cis-regulatory factors), and transcribed distal genes (trans-regulatory factors). We identified differential gene regulatory patterns in SCZ-cases versus controls and novel SCZ-associated genes that may play roles in the disorder since some of them are primary expressed in human brain. In addition, we observed genes known associated with SCZ are not likely (OR = 0.59) to have high impacts (degree > 3) on the network.
conclusionsCausal networks could reveal underlying patterns and the role of genes individually and as a group. Establishing principles that govern relationships between genes provides a mechanistic understanding of the dysregulated gene transcription patterns in SCZ and creates more efficient experimental designs for further studies. This information cannot be obtained by studying a single gene at the time.
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