Leveraging single-cell ATAC-seq to identify disease-critical fetal and adult brain cell types

Abstract

Prioritizing disease-critical cell types by integrating genome-wide association studies (GWAS) with functional data is a fundamental goal. Single-cell chromatin accessibility (scATAC-seq) and gene expression (scRNA-seq) have characterized cell types at high resolution, and early work on integrating GWAS with scRNA-seq has shown promise, but work on integrating GWAS with scATAC-seq has been limited. Here, we identify disease-critical fetal and adult brain cell types by integrating GWAS summary statistics from 28 brain-related diseases and traits (average N=298K) with 3.2 million scATAC-seq and scRNA-seq profiles from 83 cell types. We identified disease-critical fetal (resp. adult) brain cell types for 22 (resp. 23) of 28 traits using scATAC-seq data, and for 8 (resp. 17) of 28 traits using scRNA-seq data. Notable findings using scATAC-seq data included highly significant enrichments of fetal photoreceptor cells for major depressive disorder, fetal ganglion cells for BMI, fetal astrocytes for ADHD, and adult VGLUT2 excitatory neurons for schizophrenia. Our findings improve our understanding of brain-related diseases and traits, and inform future analyses of other diseases/traits.

Gene regulatory networks controlling temporal patterning, neurogenesis, and cell-fate specification in mammalian retina

“…Our work fills an important gap in our understanding of how temporal patterning is controlled. Although several recent studies have used scATAC-seq to identify active TF motifs in the developing brain (Domcke et al, 2020;Kim et al, 2021;Sarropoulos et al, 2021), only one recent study has systematically integrated these data to identify GRNs controlling neurogenesis and specification of major cell types (Di Bella et al, 2021). Moreover, although previous studies have used ATACseq, ChIP-seq, and HiC analysis to profile changes in chromatin accessibility, conformation, and covalent modification during retinal development, the information in these data has been limited by high cellular heterogeneity (Aldiri et al, 2017;Norrie et al, 2019;Xie et al, 2020).…”

The genetic relationships between brain structure and schizophrenia

“…Single-nucleotide polymorphisms were mapped to genes using the default settings in H-MAGMA with fetal and adult brain Hi-C annotation datafiles provided by the developers of H-MAGMA (https://github.com/thewonlab/H-MAGMA) and the reference data file for a European ancestry population downloaded from https://ctg.cncr.nl/software/magma. Since the genetic signal for both schizophrenia and brain structure are enriched for regulatory regions active in the fetal brain [3, 68], we report findings based on fetal Hi-C datasets. We restricted our analyses to protein-coding genes and excluded genes within the major histocompatibility region due to the complexity of linkage disequilibrium, which can override the overall pattern [21].…”

“…Since the genetic signal for both schizophrenia and brain structure are enriched for regulatory regions active in the fetal brain [3,68], we report findings based on fetal Hi-C datasets. We restricted our analyses to protein-coding genes and excluded genes within the major histocompatibility region due to the complexity of linkage disequilibrium, which can override the overall pattern [21].…”

Partitioning heritability using single-cell multi-omics identifies a novel macrophage subpopulation conveying genetic risks of coronary artery disease

“…Advances in single-cell techniques provide new opportunities for investigating the function of non-coding variants by identifying the cis-regulatory interactions in cellular subpopulations [23][24][25][26], and using this information to prioritize candidate causal genetic variants [27][28][29][30]. However, transcriptome-based single cell approaches generally rely on public enhancer-gene maps.…”