Computational approaches for functional annotations of non-coding sequences in immune disease

University Of California, San Francisco, San Francisco CA

Investigators

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Abstract

PROJECT SUMMARY/ABSTRACT Here, we propose to collect new experimental data and develop a computational strategy to improve the power and resolution of identifying non-coding variants causal for type 1 diabetes by integrating functional genomic and high-density genotyping data. My proposal addresses the important problem of understanding how disease-associated genetic variants affect the function of primary human immune cell subsets, specifically inflammatory CD4+ T cells, and thus contribute to type 1 diabetes disease processes. We choose to develop our project with generation and analysis of experimental data from inflammatory CD4+ T cells in healthy and type 1 diabetes patient donors because of the relevance of this subset to type 1 diabetes pathology, ready availability of matched samples through the Network for Pancreatic Organ Donors with Diabetes (nPOD) cohort, and our laboratory's previous experience generating and analyzing functional genomic data from primary T cells and related cell types. The two aims are: 1) Profile the genetic variation (genotyping), chromatin state (ATAC-seq) and gene expression (RNA-seq) from CD4+ T cells in type 1 diabetes patients and control donors, and 2) integrate analysis of functional genomic and disease genetic data to interpret type 1 diabetes-associated variants using intermediate functional genomic phenotypes. This proposal will deliver a foundational experimental dataset for studying the contribution of genetic variation in immune cell subsets relevant to type 1 diabetes. Using these datasets, we will apply models that make use of inter-individual variation in functional genomic data for improved annotation of non-coding variants. The application of the strategy to the generated data will (i) identify variants that contribute to disease via effects on chromatin accessibility or gene expression and (ii) characterize how disease-associated variants combine to influence disease risk.