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

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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.