SINGLE-CELL MULTI-OMIC APPROACHES TO MECHANISTICALLY CHARACTERIZE PSYCHIATRIC DISORDER RISK LOCI IN THE HUMAN BRAIN

NIH Pandemic-Era Grants

Pandemic Era Grants

2024

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Principal Investigator: Chongyuan  Luo
Organization: UNIVERSITY OF CALIFORNIA LOS ANGELES
Fiscal Year: 2024
Award: $739,969
Funding agency: National Institute of Mental Health

PROJECT SUMMARY
Large-scale genome-wide association studies (GWAS) have identified between a handful to hundreds of risk loci
for each major type of neuropsychiatric disorders. One of the main challenges for the post-GWAS era is to
determine the causal variants and dissect the regulatory mechanism in each of the risk loci. The analysis of
causal genetic mechanisms for psychiatric diseases is confounded by the highly heterogeneous brain structures
and cell types. We hypothesize that brain regions and cell types are selectively vulnerable to mental disorders
and cell-type-specific gene regulation underlies such selectivity. In this proposed project, we aim to determine
the causal probability of individual genetic variants with high spatial resolution with respect to brain regions and
cell types. To this end, we will generate a unique dataset of single-nucleus joint profiling of chromatin
conformation and DNA methylation (sn-m3C-seq) for 10 adult brain regions, allowing the cell-type-specific
identification of regulatory elements, enhancer-gene looping and linking non-coding variants to their regulatory
target. To further identify the genetic mechanisms for cell-type-specific regulation of gene expression, we will
develop and apply cutting-edge statistical methods to existing and newly generated population single-nucleus
RNA-seq datasets for the human brain cortex and hippocampus. We will develop CONtexT spEcific geNeTics
(CONTENT) to distinguish tissue- or cell-type-specific from the tissue-shared genetic component of gene
expression regulation. We will also apply the recently developed PopuLation Allele-Specific MApping (PLASMA)
that integrates QTL and allele-specific QTL for regulatory variant fine-mapping. To validate our findings, we will
experimentally determine the function of non-coding variants using both high-throughput CRISPR interference
and precise variant replacement experiments, as well as apply orthogonal statistical approaches to link the
functional properties of variants to disease causality. Our proposed project integrates diverse approaches
including single-cell multi-omics, statistical fine-mapping, and genetic engineering and will likely provide new
insights into the genetic mechanism of mental disorders.

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