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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 conventional approach to search for new materials is to synthesize a limited number of candidates. However, this approach might delay or prevent the discovery of better-performing materials due to the narrow composition space explored. Here, we fabricate binary alloy films with a composition gradient in a single shot in less than one minute. We apply this approach to study the stability of halide perovskites. We synthesize all possible binary compositions from MAPbI3 and MAPbBr3 and then study their optical properties, structure, and environmental stability in a high-throughput manner. We find that perovskite alloys experience three different degradation mechanisms depending on halogen content: bromine-rich perovskites degrade by hydration, iodine-rich perovskites by the loss of the organic component, and all other intermediate alloys by phase segregation. The proposed method offers an avenue for discovering new materials and processing parameters for a wide range of applications that rely on compositional engineering.