- Title
- BrainGENIE: The Brain Gene Expression and Network Imputation Engine
- Creator
- Hess, Jonathan L.; Quinn, Thomas P.; Kumarasinghe, Nishantha; Ophoff, Roel; Schall, Ulrich; Scott, Rodney; Stamova, Boryana; Tooney, Paul; Kong, Sek Won; Cairns, Murray; Tsuang, Ming T.; Faraone, Stephen V.; Zhang, Chunling; Glatt, Stephen J.; Hearn, Gentry C.; Chen, Samuel; Beveridge, Natalie Jane; Carr, Vaughan; de Jong, Simone; Gardiner, Erin; Kelly, Brian
- Relation
- NHMRC.1121474 http://purl.org/au-research/grants/nhmrc/1121474 & 1147644 http://purl.org/au-research/grants/nhmrc/1147644 & 1188493
- Relation
- Translational Psychiatry Vol. 13, Issue 1, no. 98
- Publisher Link
- http://dx.doi.org/10.1038/s41398-023-02390-w
- Publisher
- Nature Publishing Group
- Resource Type
- journal article
- Date
- 2023
- Description
- In vivo experimental analysis of human brain tissue poses substantial challenges and ethical concerns. To address this problem, we developed a computational method called the Brain Gene Expression and Network-Imputation Engine (BrainGENIE) that leverages peripheral-blood transcriptomes to predict brain tissue-specific gene-expression levels. Paired blood–brain transcriptomic data collected by the Genotype-Tissue Expression (GTEx) Project was used to train BrainGENIE models to predict gene-expression levels in ten distinct brain regions using whole-blood gene-expression profiles. The performance of BrainGENIE was compared to PrediXcan, a popular method for imputing gene expression levels from genotypes. BrainGENIE significantly predicted brain tissue-specific expression levels for 2947–11,816 genes (false-discovery rate-adjusted p < 0.05), including many transcripts that cannot be predicted significantly by a transcriptome-imputation method such as PrediXcan. BrainGENIE recapitulated measured diagnosis-related gene-expression changes in the brain for autism, bipolar disorder, and schizophrenia better than direct correlations from blood and predictions from PrediXcan. We developed a convenient software toolset for deploying BrainGENIE, and provide recommendations for how best to implement models. BrainGENIE complements and, in some ways, outperforms existing transcriptome-imputation tools, providing biologically meaningful predictions and opening new research avenues.
- Subject
- human brain tissue; gene-expression prediction; transcriptome prediction; Genotype-Tissue Expression (GTEx)
- Identifier
- http://hdl.handle.net/1959.13/1484470
- Identifier
- uon:51344
- Identifier
- ISSN:2158-3188
- Rights
- This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
- Language
- eng
- Full Text
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