Abstract Genome-wide association studies (GWAS) have highlighted that disease-associated human genetic variants are prevalent in noncoding regions and for most of them the biological function or gene target remain uncharacterized. To better annotate such disease variants, NIH-funded consortia created comprehensive maps of putative regulatory elements and identified SNPs associated with gene expression (eQTLs) for different tissues and primary cell types. In parallel, breakthroughs in capturing the 3D genome structure have demonstrated the importance of cell-type-specific physical proximity between genes and their regulatory elements. This 3D view provided a new way through which disease-associations of certain variants can be explained. There is an increasing interest in utilization of chromatin loops for GWAS variant annotation, however, to the best of our knowledge, there is no comprehensive study incorporating eQTL data and high-resolution chromatin looping information across many different matched/related cell types and tissues to interpret GWAS variants identified for a large set of diseases. To goal of this proposal is to utilize NIH Common Fund datasets (GTEx and 4D Nucleome) as well as other published chromatin loop and eQTL data to carry out different integrative approaches for better annotation of disease-associated genetic variants. This will lead to the development of a framework and best practices for integrative analysis of loops, eQTLs and GWAS signals. The developed framework will be tested on a large number of diseases and disease-relevant cell types to create a substantial online resource for researchers. For a subset of the studied diseases, for which we have ongoing research interests, we will further analyze the identified novel genes, genetic variants and overlapping regulatory elements to determine potential targets that warrant further investigation.

Narrative Genome-wide association studies (GWAS) cataloged disease-associated genetic differences (i.e., variants) between individuals, however, the precise role of these differences in noncoding regions remains largely uncharacterized. This characterization requires identifying the specific causal variants with their correct target genes in the relevant cell types and at the right cellular state, an ongoing effort by multiple different NIH-funded projects including large consortia such as Encyclopedia of DNA Elements (ENCODE), Genotype-Tissue Expression (GTEx) and 4D Nucleome program (4DN). Utilizing existing data from primary cell types, cell lines and tissues, we propose to combine correlation-based analysis of genetic variants with their physical localization in the nucleus with respect to potential gene targets, in order to better characterize their cell-type-specific roles in relation to disease.