BrainGENIE: The Brain Gene Expression and Network Imputation Engine

Hess, J. L.; Chen, S.; Quinn, T. P.; Neuropsychiatric Consortium for Analysis and Sharing of Transcriptomes,; Kong, S. W.; Cairns, M.; Tsuang, M.; Faraone, S. V.; Glatt, S. J.

doi:10.1101/2020.10.27.356766

bioRxiv · 10.1101/2020.10.27.356766

BrainGENIE: The Brain Gene Expression and Network Imputation Engine

Hess, J. L.·Chen, S.·Quinn, T. P.·Neuropsychiatric Consortium for Analysis and Sharing of Transcriptomes,·Kong, S. W.·Cairns, M.·Tsuang, M.·Faraone, S. V.·Glatt, S. J.

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Abstract

In vivo experimental analysis of human brain tissue poses substantial challenges and ethical concerns. We developed a novel method called the Brain Gene Expression and Network Imputation Engine (BrainGENIE) that uses peripheral-blood transcriptomes to predict brain-tissue-specific gene-expression levels. BrainGENIE reliably predicted brain-tissue-specific expression levels for 1,733 - 11,569 genes (false-discovery rate-adjusted p<0.05), including many transcripts that cannot be predicted reliably by a transcriptome imputation method such as PrediXcan. We tested the generalizability of BrainGENIE in external within-individual data from ex vivo peripheral blood and postmortem brain samples from the Religious Orders Study and Memory and Aging Project, wherein we validated 39% of predicted gene expression levels as concordant with observed expression levels in dorsolateral prefrontal cortex and 23% in caudate. BrainGENIE recapitulated diagnosis-related gene expression changes in brain better than direct correlations from blood and predictions from PrediXcan. BrainGENIE complements and, in some ways, outperforms existing transcriptome-imputation tools, providing biologically meaningful predictions and opening new research avenues.

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Hess, J. L., Chen, S., Quinn, T. P., Neuropsychiatric Consortium for Analysis and Sharing of Transcriptomes,, Kong, S. W., Cairns, M., Tsuang, M., Faraone, S. V., Glatt, S. J.. 2020-10-27. BrainGENIE: The Brain Gene Expression and Network Imputation Engine. https://doi.org/10.1101/2020.10.27.356766

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