ABSTRACT
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(s)
Gene Expression Profiling , Genome-Wide Association Study , Humans , Genome-Wide Association Study/methods , Genotype , Gene Expression Profiling/methods , Transcriptome , BrainABSTRACT
The ORTEC digiBASE-E (ORTEC, 801 S. Illinos Ave., Oak Ridge TN 37831) is a compact photomultiplier tube endcap designed to handle all of the necessary power and signal processing requirements of a scintillation gamma-ray detector. The list mode feature of this device was used by a custom software package (CraneWow, Department of Nuclear Engineering, Texas A&M University, College Station, TX 77843) to gather data during seaport operations unloading cargo containers. A number of difficulties were encountered in creating the software and are catalogued here to aid future users of the device.
