Overview
Common neuropsychiatric illnesses, such as schizophrenia, bipolar disorder and Alzheimer’s disease, carry considerable morbidity, mortality, and personal and societal cost. While recent large-scale genetic association studies have identified numerous risk loci, the mechanisms through which they contribute to disease remain largely unknown. By applying single cell molecular approaches to the affected tissue (in this case, the human brain), we can uncover novel cellular subpopulations that are associated with disease. In parallel, cell type-specific molecular studies allow us to characterize the effect of genetic variation on the 3-dimensional configuration of the genome and on the complex mechanisms that regulate gene expression in those cells relevant to disease. All these brain-specific multi-scale data are leveraged to interpret the genetic architecture of neuropsychiatric illnesses in large-scale biobank datasets. Our team is part of studies conducted by the Million Veteran Program, PsychENCODE project, CommonMind Consortium and Accelerating Medicine Partnership for Alzheimer’s Disease.
Functional characterization of non-coding variants for Alzheimer’s disease
Supported by National Institute of Aging (R01AG050986 and R01AG057440).
The majority of the common risk variants for Alzheimer’s Disease (AD) are within non-coding regions. Many of these sequences are thought to exert their effects through disruption of regulatory functions, including effects on long range cis regulatory elements (CREs) that physically interact with transcription start sites (TSS). In this project, we generate additional data using samples from the Accelerating Medicine Partnership for Alzheimer’s Disease (AMP-AD) that involve multiple brain regions from hundreds of postmortem brain samples with no know neuropsychiatric history or cases with AD. Our ultimate goal is to:
- Characterize and annotate the putative functional role of risk variants on the regulation of gene expression by generating cell type-specific multiscale-omics data, including RNAseq, ATACseq, multiple different ChIPseq and HiC in a small cohort of control samples.
- Generate cell type-specific ATACseq data in a large cohort of controls and AD cases, where WGS and RNAseq exist, followed by integration of gene expression and epigenome quantitative trait loci (QTL) maps with genome wide association studies (GWAS).
Multi-scale analysis in schizophrenia and bipolar disorder
Supported by National Institute of Mental Health (U01MH116442, R01MH109897, R01MH109677, R01MH110921) and Veterans Administration (Merit BX002395).
The majority of the common risk variants for schizophrenia (SCZ) and bipolar disorder (BD) are within non-coding regions and affect regulatory functions, including effects on long range cis regulatory elements (CREs) that physically interact with transcription start sites (TSS). In this project, we generate additional data using samples from the CommonMind Consortium (CMC) and PsychENCODE project that involve multiple brain regions from 2,000 postmortem brain samples with no know neuropsychiatric history or cases with SCZ and BD. Our ultimate goal is to generate and integrate cell type-specific omics data in a large cohort of controls and SCZ/BD cases, followed by integration of gene expression, epigenome and proteome quantitative trait loci (QTL) maps with genome wide association studies (GWAS). The following data are being generated:
- Cell type-specific ATACseq in 2 brain regions (dorsolateral prefrontal cortex and anterior cingulate gyrus) of >500 controls and SCZ/BD cases.
- Multiple omics data (ATACseq, RNAseq, HiC and proteomics) in 4 cell types derived from the dorsolateral prefrontal cortex of 100 controls and SCZ cases.
These datasets will complement existing data in the CMC samples, such as WGS, RNAseq and cell type-specific ChIPseq and will be leveraged for downstream integrative analysis for multi-scale network modeling and colocalization of GWAS risk variants with QTLs.
Large-scale transcriptome and epigenome association analysis across multiple traits
Supported by Veterans Administration (Merit BX004189).
Precision Medicine refers to the customization of medical treatment to the individual characteristics of each patient. The Million Veteran Program (MVP) provides a unique opportunity to perform large-scale genome-wide association studies (GWAS) across multiple traits and diseases towards the successful application of Precision Medicine. While well powered GWAS have identified multiple risk variants, due to their small effect sizes there has been limited conclusive findings on the genetic factors contributing to complex traits. In addition, the majority of common risk variants are within non-coding regions of the genome and, as such, the functional relevance of most discovered loci remains unclear. Our group and others have shown that a large proportion of phenotypic variability in disease risk can be explained by regulatory variants, i.e. genetic variants that affect epigenetic mechanisms and the expression levels of genes. The study of gene expression and epigenome changes directly in the MVP samples is not feasible as such data are not available.
To overcome these limitations, we propose to apply a machine learning approach that leverages existing molecular data (unrelated to MVP) as a reference panel to directly impute multi-tissue and genome-wide gene expression and epigenome profiles in MVP samples using existing MVP genotypes. As reference panel, we will use large-scale datasets with genotyping and molecular profiling that our group and others have generated, including, but not limited to, the PsychENCODE project, CommonMind Consortium and Accelerating Medicine Partnership for Alzheimer’s Disease. Imputed MVP gene expression and epigenome data provides a powerful cohort to “translate” genetic findings to the dysregulation of specific molecular pathways across multiple traits that will enhance drug discovery.
The 3D genome in transcriptional regulation across the postnatal life span
Supported by National Institute of Mental Health (U01MH116442).
Schizophrenia (SCZ) and bipolar disorder (BD) risk loci are preferentially located within promoter and enhancer regulatory sequences of neurons and that they co-localize with expression Quantitative Traits Loci (eQTL), thus implicating specific genes. However, work that has been performed to-date has limited spatiotemporal resolution as: (1) only a few cortical regions have been examined, (2) the effect of 3D genome on transcriptional regulation across the lifespan has never been examined, and (3) studies have been limited to homogenate brain tissue or include only broadly defined neuronal and non-neuronal populations. To address these limitations, we will generate cell type-, brain region- and age period-specific high-dimensional data that will inform us of the effect of 3D genome on the transcriptional regulation and will link regulatory elements with specific transcripts. More specifically, we use fluorescence activated nuclei sorting to isolate glutamatergic and GABAergic neuronal as well as oligodendrocyte and astrocyte nuclei from five human cortical and subcortical regions relevant to SCZ and BD across five postnatal age periods. We will then generate cell-type specific annotations for gene expression and enhancer RNA (RNA-seq and CAGE-seq), open chromatin (ATAC-seq), insulators (CTCF ChIP-seq), active enhancers and promoters (H3K27ac and H3K4me3 ChIP-seq), and chromatin loop interactions (HiC and Capture-C). Using the resulting data, we will delineate cis transcriptional regulation associated with the 3D genome (including promoter-enhancer loopings) and uncover the functional consequences of SCZ and BD risk loci on enhancer-transcript units.
In parallel, we examine the impact of SCZ and BD risk variants on cell type-specific gene expression and epigenome QTLs. We map RNAseq and ATACseq at the single cell level and use cell type-specific markers and deconvolution approaches to the existing large scale transcriptome and epigenome datasets, from CommonMind Consortium, PsychENCODE and other projects, in order to generate cell type-specific expression and epigenome QTLs. We then co-localize SCZ and BD risk loci with expression and fine map epigenome QTLs to define disease-associated enhancer-transcript units. It is our expectation that these integrated analyses will enable us to assign specific regulatory units within SCZ and BD risk haplotypes to specific cell types, brain regions and age windows, thereby providing insight into the mechanisms of genetic risk for SCZ and BD. 
Understanding the role of human brain immune cells in Alzheimer Disease vulnerability
Supported by National Institute of Aging (R01AG050986) and funds from the Presidential Early Career Awards for Scientists and Engineers.
Microglia and other myeloid origin cells (collectively called human brain immune cells, or HBICs) have recently emerged as crucial players in the pathogenesis of Alzheimer’s disease (AD). This is supported through genetic association studies, where many of the common and rare risk loci affect genes that are preferentially or selectively expressed in HBICs, emphasizing the pivotal role of the innate immune system in AD. In addition, previous studies have shown enrichment of AD common risk loci in non-coding, regulatory sequences of peripheral immune cells but not in annotations generated from homogenate brain tissue. As HBICs constitute a small proportion of total brain cells, homogenate-based studies in human brain tissue are therefore unlikely to capture the full spectrum of HBIC molecular signatures, especially in light of the growing appreciation for the diversity of HBICs in the brain. Our study addresses some of the limitations of previous research and is focused on: (1) cell type specific studies in immune cells isolated from human brain tissue; and (2) a systematic study of the regulatory effects of non-coding DNA on gene and protein expression, which is necessary given that the majority of common risk variants are situated in non-coding regions of the genome. More specifically, we: (1) apply innovative genomic approaches and generate multi-omics data from HBICs, including whole genome sequencing, RNAseq, ATACseq, HiC chromosome conformation capture and proteomics; (2)perform state-of-the-art single cell analysis that will allow us to assess the diversity of HBIC subpopulations, as well as detect those that are associated with AD; (3) connect AD risk loci with changes in the regulatory mechanisms of gene and protein expression in HBICs; and (4) organize HBIC multiscale data in functional networks and identify key drivers for AD.
Multiregional assessment of gene expression and chromatin accessibility in human brain tissue
Supported by funds from Boehringer Ingelheim, LTD and the Presidential Early Career Awards for Scientists and Engineers.
Most common genetic risk variants associated with neuropsychiatric disease are noncoding and are thought to exert their effects by disrupting the function of cis regulatory elements (CREs), including promoters and enhancers. Within each cell, chromatin is arranged in specific patterns to expose the repertoire of CREs required for optimal spatiotemporal regulation of gene expression. To further understand the complex mechanisms that modulate transcription in the brain, we use frozen postmortem samples to generate an atlas of human brain and cell-type-specific open chromatin and gene expression data set. More specifically, we create maps of chromatin accessibility (ATACseq) and gene expression (RNAseq) in two cell types (neurons and non-neurons) across 25 distinct brain regions of six individuals. The aim of this project is to: (1) define cell type- and region-specific changes in gene expression and chromatin accessibility; and (2) examine whether differentially accessible chromatin and gene expression overlaps with the genetic architecture of neuropsychiatric traits and identifies differences in molecular pathways and biological functions.
