Single-cell multiome of the human retina and deep learning nominate causal variants in complex eye diseases
Abstract
Genome-wide association studies (GWAS) of eye disorders have identified hundreds of genetic variants associated with ocular disease. However, the vast majority of these variants are noncoding, making it challenging to interpret their function. Here, we present a joint single-cell atlas of gene expression and chromatin accessibility of the adult human retina with >50,000 cells, which we used to analyze single-nucleotide polymorphisms (SNPs) implicated by GWAS of age-related macular degeneration, glaucoma, diabetic retinopathy, myopia, and type 2 macular telangiectasia. We integrate this atlas with a HiChIP enhancer connectome, expression quantitative trait loci (eQTL) data, and base-resolution deep learning models to predict noncoding SNPs with causal roles in eye disease, assess SNP impact on transcription factor binding, and define their known and novel target genes. Our efforts nominate pathogenic SNP-target gene interactions for multiple vision disorders and provide a potentially powerful resource for interpreting noncoding variation in the eye.
Explore
| Cell-type gene expression browser | Single Cell Portal UMAP | Chromatin accessibility tracks and H3K27ac HiChIP loops |
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Data
- Raw and processed scRNA-seq, scATAC-seq, and HiChIP files: GSE196235
- Deep learning (BPNet) models: https://doi.org/10.5281/zenodo.6330053
- scRNA-seq/scATAC-seq code: https://github.com/seankwang/retina-multiome
- BPNet training code: https://github.com/kundajelab/retina-models
Citation
If you use this resource in your research, please cite:
| Sean K. Wang, Surag Nair, Rui Li, Katerina Kraft, Anusri Pampari, Aman Patel, Joyce B. Kang, Christy Luong, Anshul Kundaje, Howard Y. Chang (2022). Single-cell multiome of the human retina and deep learning nominate causal variants in complex eye diseases. Cell Genomics (2022). DOI: 10.1016/j.xgen.2022.100164 |
© 2022 Chang lab at Stanford University