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

Abstract

Genetic variants, including mobile element insertions (MEIs), are known to impact the epigenome. We hypothesized that genome graphs, which encapsulate genetic diversity, could reveal missing epigenomic signals. To test this, we sequenced the epigenome of monocyte-derived macrophages from 35 ancestrally diverse individuals before and after influenza infection, allowing us to investigate the role of MEIs in immunity. We characterized genetic variants and MEIs using linked reads and built a genome graph. Mapping epigenetic data revealed 2.3%-3% novel peaks for H3K4me1, H3K27ac chromatin immunoprecipitation sequencing (ChIP-seq), and ATAC-seq. Additionally, the use of a genome graph modified some quantitative trait loci estimates and revealed 375 polymorphic MEIs in an active epigenomic state. Among these is an AluYh3 polymorphism whose chromatin state changed after infection and was associated with the expression of TRIM25, a gene that restricts influenza RNA synthesis. Our results demonstrate that graph genomes can reveal regulatory regions that would have been overlooked by other approaches.