PROJECT SUMMARY/ABSTRACT Genome-wide association studies (GWAS) for complex traits have often revealed a significant proportion of risk attributed to the non-coding genome. This has led our group to question whether single nucleotide polymorphisms (SNPs) conferring disease risk, could be affecting the function of genetic elements which are commonly excluded from omic studies, and whose functional role is largely unknown. Human endogenous retroviruses (HERVs) are often found in the non-coding and intergenic regions of the genome, and due to their incomplete annotation and repetitive nature, have been largely overlooked in both genomic and transcriptomic analyses. This project utilizes a novel annotation, developed by our team, of 14,968 HERVs that contain internal regions (which are therefore, more likely to generate functional transcripts), to better understand their role in relation to 20 polygenic disease traits. First, we will integrate our HERV annotation with GWAS summary statistics via gene level enrichment analyses, which will allow us to prioritize HERVs of etiological importance. Second, we will perform HERV quantification in 51 tissues using our recently developed bioinformatic tool, “Telescope”, which quantifies HERVs with single-locus specificity using RNA-seq data. Tissue-specific expression quantitative trait loci (eQTL) and transcriptome-wide association studies (TWAS) will then be used to assess the effect disease risk SNPs have on expression profiles in each tissue, and to prioritize risk-associated HERVs for each trait on a whole-genome level, respectively. The intersection between HERVs implicated in the etiology of each disease, and those functionally affected by cis-eQTL risk SNPs will aid in identifying biological mechanisms pertaining to HERVs in disease risk, which may ultimately lead to new biomarkers or treatment targets.