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

Sean K Wang; Surag Nair; Rui Li; Katerina Kraft; Anusri Pampari; Aman Patel; Joyce B Kang; Christy Luong; Anshul Kundaje; Howard Y Chang

doi:10.1016/j.xgen.2022.100164

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

Cell Genom. 2022 Aug 10;2(8):100164. doi: 10.1016/j.xgen.2022.100164. Epub 2022 Jul 27.

Authors

Sean K Wang^{1

2}, Surag Nair³, Rui Li¹, Katerina Kraft¹, Anusri Pampari³, Aman Patel³, Joyce B Kang^{4

5}, Christy Luong^{1

6}, Anshul Kundaje^{3

7}, Howard Y Chang^{1

8

9}

Affiliations

¹ Center for Personal Dynamic Regulomes, Stanford University, Stanford, CA, USA.
² Department of Ophthalmology, Stanford University School of Medicine, Stanford, CA, USA.
³ Department of Computer Science, Stanford University, Stanford, CA, USA.
⁴ Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
⁵ Center for Data Sciences, Brigham and Women's Hospital, Boston, MA, USA.
⁶ Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, CA, USA.
⁷ Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.
⁸ Howard Hughes Medical Institute, Stanford University, Stanford, CA, USA.
⁹ Lead contact.

Abstract

Genome-wide association studies (GWASs) 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 more than 50,000 cells, which we used to analyze single-nucleotide polymorphisms (SNPs) implicated by GWASs 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.

Abstract

Grants and funding