Genetic discoveries from genome-wide association studies have led to important insights on human disease mechanisms. In particular, disease-associated variants are enriched in regions of the genome that are active in gene regulation. However, most of these analyses have focused on individual variants with the strongest evidence of association, and on broadly defined functional annotations, which provide limited scope for understanding disease mechanisms. In this proposal we analyze a broader set of genome-wide variants, in conjunction with functionally specialized annotations with potential mechanistic interpretations, such as context-specific regulatory elements or binding sites for specific transcription factors. We utilize methods that ascribe heritability to specific segments of the genome, leveraging polygenic signals distributed across the entire genome instead of a limited number of known genetic associations. These methods can pinpoint disease heritability to smaller and more specific subsets of the genome defined by precise context-specific functional annotations. In addition to highlighting specific mechanisms of disease, localizing to precise annotations will offer the ability to identify causal variants. We will take advantage of large databases of genetic data, in addition to a vast array of functional genomics data from ENCODE and other consortia. Specifically, we will (1) develop new statistical methods to partition disease heritability across functional categories, (2) build new annotations enriched for disease heritability from existing functional data sets, and (3) develop new computational methods to integrate ATAC-seq data with ENCODE data. The proposal represents a collaboration between Drs. Alkes Price, Soumya Raychaudhuri, and Nick Patterson, bringing together expertise in functional genomics, human disease genetics, and polygenic modeling. The investigators have a strong track record of integrating functional genomic data with human genetic data in recent publications.
Defining how disease alleles influence precise disease mechanisms is critical to understanding disease mechanisms and deriving therapeutics. In this proposal we will devise strategies to interpret genetic data on human diseases in the context of functionally specialized annotations (FSAs) derived from large-scale ENCODE data. These functionally specialized annotations will implicate specific cellular mechanisms that can be prioritized for mechanistic investigation.
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