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 and apply them to define causal alleles, (2) identify the genes that are acting downstream of those causal allele, and characterize the transcription factor mediated mechanisms that are being disrupted by those causal alleles, and (3) define the cell-state specific regulatory mechanisms that are altered by the causal alleles. The proposal represents a collaboration between Drs. Alkes Price and Soumya Raychaudhuri, bringing together expertise in functional genomics, human disease genetics, and polygenic modeling. The investigators have a strong track record of integrating and applying strategies to exploit functional genomic data to define human genetic mechanisms.
Defining how disease alleles influence precise disease mechanisms is critical to understanding disease mechanisms and deriving therapeutics. In this research program we have devised strategies to interpret genetic data on human diseases in the context of functionally specialized annotations (FSAs) derived from large-scale ENCODE data. Now, we will apply these annotations to define specific causal alleles, characterize upstream transcriptional regulation that is potentially disrupted by those alleles, identify the genes acting downstream of those alleles, and demonstrate how the regulation of those genes is altered.
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