A computational framework to assess genome-wide distribution of polymorphic human endogenous retrovirus-K in human populations

Human Endogenous Retrovirus type K (HERV-K) is the only HERV known to be insertionally polymorphic. It is possible that HERV-Ks contribute to human disease because people differ in both number and genomic location of these retroviruses. Indeed viral transcripts, proteins, and antibody against HERV-K are detected in cancers, auto-immune, and neurodegenerative diseases. However, attempts to link a polymorphic HERV-K with any disease have been frustrated in part because population frequency of HERV-K provirus at each site is lacking and it is challenging to identify closely related elements such as HERV-K from short read sequence data. We present an integrated and computationally robust approach that uses whole genome short read data to determine the occupation status at all sites reported to contain a HERV-K provirus. Our method estimates the proportion of fixed length genomic sequence (k-mers) from whole genome sequence data matching a reference set of k-mers unique to each HERV-K loci and applies mixture model-based clustering to account for low depth sequence data. Our analysis of 1000 Genomes Project Data (KGP) reveals numerous differences among the five KGP super-populations in the frequency of individual and co-occurring HERV-K proviruses; we provide a visualization tool to easily depict the prevalence of any combination of HERV-K among KGP populations. Further, the genome burden of polymorphic HERV-K is variable in humans, with East Asian (EAS) individuals having the fewest integration sites. Our study identifies population-specific sequence variation for several HERV-K proviruses. We expect these resources will advance research on HERV-K contributions to human diseases.