ArticleEuropean journal of human genetics : EJHG2022
An analysis of genetically regulated gene expression and the role of co-expression networks across 16 psychiatric and substance use phenotypes.
Article in European journal of human genetics : EJHG, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
What it found
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it, 10 citations in OpenAlex.
- Genetic and phenotypic similarity across major psychiatric disorders: a systematic review and quantitative assessment.Translational psychiatry · 2024Pooled it
- Identification of hub genes involved in early-onset schizophrenia: from genetic susceptibility to predicted regulated gene expression.Molecular psychiatry · 2026Article
- Transcription-based identification of uncharacterized genes in the human immune response.European journal of human genetics : EJHG · 2026Article
- Leveraging pleiotropy for the improved treatment of psychiatric disorders.Molecular psychiatry · 2025Review
- The genetic landscape of substance use disorders.Molecular psychiatry · 2024Review
- Association between parental psychiatric disorders and risk of offspring autism spectrum disorder: a Swedish and Finnish population-based cohort study.The Lancet regional health. Europe · 2024Article
- xWAS analysis in neuropsychiatric disorders by integrating multi-molecular phenotype quantitative trait loci and GWAS summary data.Journal of translational medicine · 2024Article
- No gene to predict the future?European journal of human genetics : EJHG · 2022Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 3 countries.
Funding
Abstract
Genome-wide association studies (GWASs) have identified thousands of risk loci for psychiatric and substance use phenotypes, however the biological consequences of these loci remain largely unknown. We performed a transcriptome-wide association study of 10 psychiatric disorders and 6 substance use phenotypes (GWAS sample size range, N = 9725-807,553) using expression quantitative trait loci data from 532 prefrontal cortex samples. We estimated the correlation of genetically regulated expression between phenotype pairs, and compared the results with the genetic correlations. We identified 393 genes with at least one significant phenotype association, comprising 458 significant associations across 16 phenotypes. Overall, the transcriptomic correlations for phenotype pairs were significantly higher than the respective genetic correlations. For example, attention deficit hyperactivity disorder and autism spectrum disorder, both childhood developmental disorders, had significantly higher transcriptomic correlation (r = 0.84) than genetic correlation (r = 0.35). Finally, we tested the enrichment of phenotype-associated genes in gene co-expression networks built from human prefrontal cortex samples. Phenotype-associated genes were enriched in multiple gene co-expression modules and the implicated modules contained genes involved in mRNA splicing and glutamatergic receptors, among others. Together, our results highlight the utility of gene expression data in the understanding of functional gene mechanisms underlying psychiatric disorders and substance use phenotypes.
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Registered trials
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