Genome graphs detect human polymorphisms in active epigenomic states during influenza infection

Abstract

AbstractBackgroundEpigenomic experiments can be used to survey the chromatin state of the human genome and find functionally relevant sequences in given cells. However, the reference genome that is typically used to interpret these data does not account for SNPs, indels, and other structural variants present in the individual being profiled. Fortunately, population studies and whole genome sequencing can assemble tens of thousands of sequences that are not in the reference [18], including mobile element insertions (MEIs), which are known to influence the epigenome [66, 60, 1]. We hypothesized that the use of a genome graph, which can capture this genetic diversity, could help identify more peaks and reveal notable regulatory sequences hidden by the use of a biased reference.ResultsGiven the contributions of MEIs to the evolution of human innate immunity, we wanted to test this hypothesis in macrophages derived from 35 individuals of African and European ancestry before and after in-vitro Influenza infection. We used local assembly to resolve non-reference MEIs based on linked reads obtained from these individuals and reconstructed over five thousand Alu, over three hundred L1, and tens of SVA and ERV insertions. Next, we built a genome graph representing SNPs, indels and MEIs in these genomes and demonstrated improved read mapping sensitivity and specificity. Aligning H3K27ac and H3K4me1 ChIP-seq and ATAC-seq data on this genome graph revealed between 2 to 6 thousand novel peaks per sample. Notably, we observed hundreds of polymorphic MEIs that were marked by active histone modifications or accessible chromatin, of which 12 were associated with differential gene expression. Lastly, we found a MEI polymorphism in an active epigenomic state that is associated with the expression of TRIM25, a gene that restricts influenza RNA synthesis [46].ConclusionOur results demonstrate that the use of graph genomes capturing genetic variability can reveal notable regulatory regions that would have been missed by standard analytical approaches.