The advent of single-cell chromatin accessibility profiling has accelerated the ability to map gene regulatory landscapes but has outpaced the development of scalable software to rapidly extract biological meaning from these data. Here we present a software suite for single-cell analysis of regulatory chromatin in R (ArchR; https://www.archrproject.com/) that enables fast and comprehensive analysis of single-cell chromatin accessibility data. ArchR provides an intuitive, user-focused interface for complex single-cell analyses, including doublet removal, single-cell clustering and cell type identification, unified peak set generation, cellular trajectory identification, DNA element-to-gene linkage, transcription factor footprinting, mRNA expression level prediction from chromatin accessibility and multi-omic integration with single-cell RNA sequencing (scRNA-seq). Enabling the analysis of over 1.2 million single cells within 8 h on a standard Unix laptop, ArchR is a comprehensive software suite for end-to-end analysis of single-cell chromatin accessibility that will accelerate the understanding of gene regulation at the resolution of individual cells.

Genome-wide association studies (GWAS) of neurological diseases have identified thousands of variants associated with disease phenotypes. However, the majority of these variants do not alter coding sequences, making it difficult to assign their function. Here, we present a multi-omic epigenetic atlas of the adult human brain through profiling of single-cell chromatin accessibility landscapes and three-dimensional (3D) chromatin interactions of diverse adult brain regions across a cohort of cognitively healthy individuals. We developed a machine-learning classifier to integrate this multi-omic framework and predict dozens of functional single-nucleotide polymorphisms (SNPs) for Alzheimer’s disease (AD) and Parkinson’s disease (PD), nominating target genes and cell types for previously orphaned GWAS loci. Moreover, we dissected the complex inverted haplotype of the MAPT (encoding tau) PD risk locus, identifying putative ectopic regulatory interactions in neurons that may mediate this disease association. This work expands our understanding of inherited variation and provides a roadmap for the epigenomic dissection of causal regulatory variation in disease.

The advent of large-scale single-cell chromatin accessibility profiling has accelerated our ability to map gene regulatory landscapes, but has outpaced the development of robust, scalable software to rapidly extract biological meaning from these data. Here we present a software suite for single-cell analysis of regulatory chromatin in R (ArchR; www.ArchRProject.com) that enables fast and comprehensive analysis of single-cell chromatin accessibility data. ArchR provides an intuitive, user-focused interface for complex single-cell analyses including doublet removal, single-cell clustering and cell type identification, robust peak set generation, cellular trajectory identification, DNA element to gene linkage, transcription factor footprinting, mRNA expression level prediction from chromatin accessibility, and multi-omic integration with scRNA-seq. Enabling the analysis of over 1.2 million single cells within 8 hours on a standard Unix laptop, ArchR is a comprehensive analytical suite for end-to-end analysis of single-cell chromatin accessibility data that will accelerate the understanding of gene regulation at the resolution of individual cells.

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...