NCMP-04. BRAIN-UMAP: THE GENETIC INTERSECTION BETWEEN NEUROSCIENCE, NEUROLOGY, PSYCHIATRY, AND ONCOLOGY

Abstract

Abstract Whole transcriptome sequencing (RNA-seq) is an important tool for understanding genetic mechanisms underlying human diseases and gaining a better insight into complex human diseases. Several ground-breaking projects have uniformly processed RNASeq data from publicly available studies to enable cross-comparison. One noteworthy study is the recount2 pipeline, which in 2017, has reprocessed ~70,0000 samples from Short Read Archive(SRA), The Cancer Genome Atlas (TCGA), and Genotype-Tissue Expression (GTEx). This vast dataset also includes gene expression data for GTEx-defined brain regions, neurological and psychiatric disorders (such as Parkinson's, Alzheimer’s, Huntington’s) and gliomas (such as TCGA, Chinese Glioma Genome Atlas (CGGA)). We apply uniform manifold approximation and projection (UMAP), a non-linear dimension reduction tool, to bulk gene expression data from brain-related diseases to build a BRAIN-UMAP, which allows for visualization of gene expression profiles across datasets. This UMAP shows that while gliomas form a distinct cluster, the neurological and psychiatric diseases are similar to GTEX-defined normal brain regions which exhibit tissue-specific profiles and patterns. Incorporating gliomas from various publicly available datasets also allows for the ability to observe unique clustering of particular subtypes, which can increase our genetic understanding of the disease. We also present a resource where researchers interested in mechanisms, can easily compare, and contrast the expression of a given gene and/or pathway of interest across various diseases, gliomas, and normal brain regions. Our current study, focusing on brain related diseases, offers insight into what may be possible for the broader neuroscientific community if we continually reprocess newly available brain related RNASeq samples using recount2. Additionally, if we build similar uniformly processing pipelines for other kinds of next-generation sequencing data, we would be able to use multi-omic sequencing data to find novel associations between biological entities and increase our mechanistic knowledge of the disease.