Integrative analysis of bulk and single-cell RNA-seq data from human retina for age-related macular degeneration

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PROJECT SUMMARY Age-related macular degeneration (AMD) affects over 10 million Americans, twice the number affected by Alzheimer disease and equal to the total of all cancer patients combined. Worldwide, AMD is the third largest cause of vision loss. While there are short-term therapies available for one type of AMD, the underlying disease is by no means cured, and vision loss is an eventual outcome for many individuals. Although advances in retinal disease diagnostics have progressed rapidly, specific treatments for AMD directed at primary genetic or metabolic defects have progressed slowly due to a lack of understanding of the disease pathway. The slow progress is a result of multiple factors including lack of information about cell types involved in the initiation of AMD. Therefore, there is an urgent need to understand what cells are contributing to the development of AMD pathology. Identification of the cellular and gene expression changes occurring in human AMD will facilitate the design of animal and in vitro cell models incorporating the affected cell types for future drug development. While genome-wide association studies (GWAS) have identified strong and highly replicated association of genetic loci for AMD, GWAS findings can only suggest locations of associated variants and not directly link any one gene within a region to disease. Since most GWAS-identified single nucleotide polymorphisms are located in non-coding regions, their influence on disease is believed to be on modulating RNA expression by acting as expression quantitative trait loci. In this project, we propose to perform integrative secondary data analysis of publically available bulk RNA sequencing (RNA-seq) and single-cell RNA-seq (scRNA-seq) data from postmortem human retina, to test the hypothesis that measurable molecular deficits that include cell types and gene expression occur in the retina of AMD eyes. We will further integrate with publically available GWAS data on AMD to advance post-GWAS interpretation of AMD genetic results. By detailed characterization of cell type composition and cell type-specific gene expression changes in human eye, our results will elucidate the functional roles of GWAS findings that are still poorly understood and can power precision therapeutic targeting of AMD. All new computational tools will be released as user-friendly open source software. A visualization and query website will also be created to facilitate dissemination of our findings.