Single-cell epigenomic identification of inherited risk loci in Alzheimer’s and Parkinson’s disease

Abstract

42Genome-wide association studies (GWAS) have identified thousands of variants associated with 43 disease phenotypes. However, the majority of these variants do not alter coding sequences, making 44 it difficult to assign their function. To this end, we present a multi-omic epigenetic atlas of the 45 adult human brain through profiling of the chromatin accessibility landscapes and three-46 dimensional chromatin interactions of seven brain regions across a cohort of 39 cognitively healthy 47 individuals. Single-cell chromatin accessibility profiling of 70,631 cells from six of these brain 48 regions identifies 24 distinct cell clusters and 359,022 cell type-specific regulatory elements, 49 capturing the regulatory diversity of the adult brain. We develop a machine learning classifier to 50 integrate this multi-omic framework and predict dozens of functional single nucleotide 51 polymorphisms (SNPs), nominating gene and cellular targets for previously orphaned GWAS loci. 52These predictions both inform well-studied disease-relevant genes, such as BIN1 in microglia for 53 Alzheimer's disease (AD) and reveal novel gene-disease associations, such as STAB1 in microglia 54 and MAL in oligodendrocytes for Parkinson's disease (PD). Moreover, we dissect the complex 55 inverted haplotype of the MAPT (encoding tau) PD risk locus, identifying ectopic enhancer-gene 56 contacts in neurons that increase MAPT expression and may mediate this disease association. This 57 work greatly expands our understanding of inherited variation in AD and PD and provides a 58 roadmap for the epigenomic dissection of noncoding regulatory variation in disease. 59 60 61 62Alzheimer's disease (AD) and Parkinson's disease (PD) affect ~50 and ~10 million individuals 63 world-wide, as two of the most common neurodegenerative disorders. Several large consortia have 64 assembled genome-wide association studies (GWAS) that associate genetic variants with clinical 65 diagnoses of probable AD dementia 1-4 or probable PD 5-7 , or with their characteristic pathologic 66 features. These efforts have led to the identification of dozens of potential risk loci for these 67 prevalent neurodegenerative diseases. One goal of these studies was to build more precise 68 molecular biomarkers of AD or PD, efforts that are beginning to yield encouraging results with 69 polygenic risk scores 8 . The other major goal was to gain deeper insight into the molecular 70 pathogenesis of disease and thereby inform novel therapeutic targets. Some of the risk loci contain 71 coding variants and so have credibility as putative disease mediators. However, most risk loci are 72 in noncoding regions and so it remains unclear if the nominated (often nearest) gene is the 73 functional disease-relevant gene, or if some other gene is involved 9 . Furthermore, even if the 74 nominated gene is a true positive, the noncoding risk locus might regulate additional genes. These 75 challenges remain a fundamental gap in interpreting the etiology of neurodegenerative diseases 76 and d...

Integration of Alzheimer’s disease genetics and myeloid genomics identifies disease risk regulatory elements and genes

“…These observations suggest that hiPSC-derived microglia might not be the best model for in-depth studies of the effects of genetic variation on gene expression and chromatin architecture at the MS4A and other AD risk loci. Since a recent single-cell ATAC-seq study in the brain revealed that rs636317 resides in a microglia specific ATAC-seq peak 49 , we utilized brain ATAC-seq data from CommonMind 50 to test if the ATAC-seq imbalance that we observed in hiPSC-derived microglia can be replicated in primary brain microglia. Indeed, we saw a significant imbalance in normalized ATAC-seq reads consistent with our computational and experimental data (P-value = 0.006, one-sided paired t-test) (Fig.…”

Mapping the genetic architecture of human traits to cell types in the kidney identifies mechanisms of disease and potential treatments

“…Bulk tissue analysis coupled with computational cellular deconvolution have recently been developed for in silico estimation of cell type eQTLs, however, the accuracy of the computational methods has not been validated (21)(22)(23)(24)(25). Single cell epigenome studies, such as single-nucleus ATAC-seq (snATAC-seq), could provide potential insights into causal variants identification by overlapping GWAS variants with cell-type specific open chromatin regions (26)(27)(28).…”

Cardiac Cell Type-Specific Gene Regulatory Programs and Disease Risk Association

“…Non-coding genetic variants contributing to risk of complex diseases are enriched within cCREs in a cell type-specific fashion 20,[37][38][39][40] . To examine the enrichment of cardiovascular disease variants within cCREs active in specific cardiac cell types, we performed cell type-stratified LD (Linkage disequilibrium) score regression analysis 41 using GWAS summary statistics for cardiovascular diseases [42][43][44][45][46] ( Figure 5A Figure 5A).…”