ArticleScientific reports2017
An Integrative Computational Approach to Evaluate Genetic Markers for Bipolar Disorder.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Combined cellomics and proteomics analysis reveals shared neuronal morphology and molecular pathway phenotypes for multiple schizophrenia risk genes.Molecular psychiatry · 2021Article
- Weighted Gene Coexpression Network Analysis Reveals Essential Genes and Pathways in Bipolar Disorder.Frontiers in psychiatry · 2021Article
- Genetic Modifiers at the Crossroads of Personalised Medicine for Haemoglobinopathies.Journal of clinical medicine · 2019Article
- Assessment of complementarity of WGCNA and NERI results for identification of modules associated to schizophrenia spectrum disorders.PloS one · 2019Article
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Authors and funding
15 authors.
Funding
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Abstract
Studies to date have reported hundreds of genes connected to bipolar disorder (BP). However, many studies identifying candidate genes have lacked replication, and their results have, at times, been inconsistent with one another. This paper, therefore, offers a computational workflow that can curate and evaluate BP-related genetic data. Our method integrated large-scale literature data and gene expression data that were acquired from both postmortem human brain regions (BP case/control: 45/50) and peripheral blood mononuclear cells (BP case/control: 193/593). To assess the pathogenic profiles of candidate genes, we conducted Pathway Enrichment, Sub-Network Enrichment, and Gene-Gene Interaction analyses, with 4 metrics proposed and validated for each gene. Our approach developed a scalable BP genetic database (BP_GD), including BP related genes, drugs, pathways, diseases and supporting references. The 4 metrics successfully identified frequently-studied BP genes (e.g. GRIN2A, DRD1, DRD2, HTR2A, CACNA1C, TH, BDNF, SLC6A3, P2RX7, DRD3, and DRD4) and also highlighted several recently reported BP genes (e.g. GRIK5, GRM1 and CACNA1A). The computational biology approach and the BP database developed in this study could contribute to a better understanding of the current stage of BP genetic research and assist further studies in the field.
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