Computational Methods for Integrative Analysis of Multi-omic and Single-cell Data to Elucidate Cell Type-specific Gene Regulatory Architecture for Mental Disorders

Presenter:

Alexander Arguello, Ph.D.
Division of Neuroscience and Basic Behavioral Science

Goal:

The goal of this initiative is to encourage research that will advance our understanding of brain cell type-specific gene regulation to elucidate the pathogenesis of mental disorders.

Rationale:

Like most complex traits, psychiatric disorders are influenced by 1000s of genetic variants individually conferring small to moderate effects on risk. Understanding the functional impact of these variants is limited by our understanding of the molecular and cellular complexity of the brain and how it is shaped by genetic variation. Advances in analysis of single-cell technologies, while still in development, are leading investigators to map the cellular diversity of the brain at single-cell resolution (e.g., BRAIN Cell Census, Allen Brain Atlas, and Single Cell Analysis Consortium). Despite these efforts, significant conceptual, technical and analytic challenges remain for fully harnessing the power of multi-omic, single-cell approaches in understanding healthy and diseased brain function.

In silico methods could be utilized to leverage the strengths of high-depth multi-omic information generated from bulk tissue brain samples in conjunction with the more precise genomic information provided by single-cell approaches. It is thus a prime opportunity to integrate new single-cell datasets with existing bulk tissue brain data resources such as GTEx, ENCODE, and the PsychENCODE Consortium (PEC).

This initiative encourages research to develop new, or adapt existing, computational and statistical approaches to aggregate, mine, and conduct integrative analyses of single-cell and bulk tissue multi-omic datasets from brain samples (e.g., gene expression, chromatin state, histone modifications, DNA methylation, etc.) to achieve new insights into the biology of psychiatric disorders. Examples of relevant research include, but are not limited to:

• Determining how gene expression and regulation in various human brain cell types are affected in or influenced by genetic risk for psychiatric disorders
• Identifying cell types and cell lineages that are involved in disease pathogenesis and/or pathophysiology
• Identifying shared and distinct gene regulatory networks and molecular pathways across cell types and development that impact disease risk
• Integrating genetic variation with cell-type specific variation in transcript and protein abundance to make causal inferences about potential molecular targets.