BrainGENIE: The Brain Gene Expression and Network Imputation Engine
Jonathan L. Hess,
Thomas P. Quinn,
Neuropsychiatric Consortium for Analysis and Sharing of Transcriptomes,
Sek Won Kong,
Murray J Cairns,
Ming T Tsuang,
Stephen V Faraone,
Stephen J Glatt
Posted 27 Oct 2020
bioRxiv DOI: 10.1101/2020.10.27.356766
Posted 27 Oct 2020
Ex vivo molecular analysis of the human brain is virtually impossible given major risks and ethical concerns. Transcriptome imputation offers a promising and non-invasive alternative for developing models (albeit imperfect) of brain gene expression in lieu of biopsying brain tissue. Popular tools such as FUSION (Gusev et al., 2016) and PrediXcan (Gamazon et al., 2015) use genotypes at common cis-expression quantitative trait loci (eQTLs) to predict tissue-specific gene expression levels. However, those tools cannot reliably predict expression levels for a majority of genes in the brain. This raises the question of whether an alternative modeling approach should be evaluated to capture greater variance in more genes in the brain that are not yet imputable with existing cis-eQTL imputation tools. To address this problem, we developed a novel transcriptome-imputation method called the Brain Gene Expression and Network Imputation Engine (BrainGENIE) that imputes brain-region-specific gene expression levels from peripheral blood gene expression. BrainGENIE predicted brain-region-specific expression levels for 1,733 - 11,569 genes (cross-validation R2≥0.01, false-discovery rate-adjusted p<0.05), few of which are imputable by PrediXcan. Disease-related transcriptome signals detected by BrainGENIE showed stronger agreement with known transcriptome signatures from postmortem brain when compared with findings from analyses of peripheral blood or S-PrediXcan. BrainGENIE complements and outperforms existing transcriptome-imputation tools, provides biologically meaningful predictions, and opens avenues for studying brain transcriptomes longitudinally. BrainGENIE was developed using R (v.3.6.3, tested in 4.0.2) and is freely available at: https://github.com/hessJ/BrainGENIE. ### Competing Interest Statement In the past year, Dr. Faraone received income, potential income, travel expenses continuing education support and/or research support from, Akili, Arbor, Genomind, Ironshore, Ondosis, Otsuka, Rhodes, Shire/Takeda, Sunovion, Supernus, Tris, and Vallon. With his institution, he has US patent US20130217707 A1 for the use of sodium-hydrogen exchange inhibitors in the treatment of ADHD. In previous years, he received support from: Alcobra, CogCubed, Eli Lilly, Enzymotec, Janssen, KemPharm, Lundbeck/Takeda, McNeil, Neurolifesciences, Neurovance, Novartis, Pfizer, and Vaya. Dr. Faraone also receives royalties from books published by Guilford Press: Straight Talk about Your Child's Mental Health; Oxford University Press: Schizophrenia: The Facts; and Elsevier: ADHD: Non-Pharmacologic Interventions. He is also principal investigator of www.adhdinadults.com. In the past year, Dr. Glatt has received royalties from a book published by Oxford University Press: Schizophrenia: The Facts, and consulting fees from Cohen Veterans Bioscience.
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