Single-cell multiome of the human retina and deep learning nominate causal variants in complex eye diseases

We present a joint single-cell atlas of gene expression and chromatin accessibility of the adult human retina. We integrate this atlas with a HiChIP enhancer connectome, expression quantitative trait loci (eQTL) data, and base-resolution deep learning models to predict noncoding variants with causal roles in eye disease. Overall design: Single-cell RNA-seq, single-cell ATAC-seq, and HiChIP Please note that the processed data for the ATAC-seq and RNA-seq samples were generated from all replicates and are linked to the corresponding rep1 sample records.