Epigenetic variation impacts individual differences in the transcriptional response to influenza infection

Widespread gene-environment interactions shape the immune response to SARS-CoV-2 infection in hospitalized COVID-19 patients

Genome-wide association studies performed in patients with coronavirus disease 2019 (COVID-19) have uncovered various loci significantly associated with susceptibility to SARS-CoV-2 infection and COVID-19 disease severity. However, the underlying cis-regulatory genetic factors that contribute to heterogeneity in the response to SARS-CoV-2 infection and their impact on clinical phenotypes remain enigmatic. Here, we used single-cell RNA-sequencing to quantify genetic contributions to cis-regulatory variation in 361,119 peripheral blood mononuclear cells across 63 COVID-19 patients during acute infection, 39 samples collected in the convalescent phase, and 106 healthy controls. Expression quantitative trait loci (eQTL) mapping across cell types within each disease state group revealed thousands of cis-associated variants, of which hundreds were detected exclusively in immune cells derived from acute COVID-19 patients. Patient-specific genetic effects dissipated as infection resolved, suggesting that distinct gene regulatory networks are at play in the active infection state. Further, 17.2% of tested loci demonstrated significant cell state interactions with genotype, with pathways related to interferon responses and oxidative phosphorylation showing pronounced cell state-dependent variation, predominantly in CD14+ monocytes. Overall, we estimate that 25.6% of tested genes exhibit gene-environment interaction effects, highlighting the importance of environmental modifiers in the transcriptional regulation of the immune response to SARS-CoV-2. Our findings underscore the importance of expanding the study of regulatory variation to relevant cell types and disease contexts and argue for the existence of extensive gene-environment effects among patients responding to an infection.

Shaping immunity: The influence of natural selection on population immune diversity

Humans exhibit considerable variability in their immune responses to the same immune challenges. Such variation is widespread and affects individual and population-level susceptibility to infectious diseases and immune disorders. Although the factors influencing immune response diversity are partially understood, what mechanisms lead to the wide range of immune traits in healthy individuals remain largely unexplained. Here, we discuss the role that natural selection has played in driving phenotypic differences in immune responses across populations and present-day susceptibility to immune-related disorders. Further, we touch on future directions in the field of immunogenomics, highlighting the value of expanding this work to human populations globally, the utility of modeling the immune response as a dynamic process, and the importance of considering the potential polygenic nature of natural selection. Identifying loci acted upon by evolution may further pinpoint variants critically involved in disease etiology, and designing studies to capture these effects will enrich our understanding of the genetic contributions to immunity and immune dysregulation.

Improved multi-ancestry fine-mapping identifies cis-regulatory variants underlying molecular traits and disease risk

Multi-ancestry statistical fine-mapping of cis-molecular quantitative trait loci (cis-molQTL) aims to improve the precision of distinguishing causal cis-molQTLs from tagging variants. However, existing approaches fail to reflect shared genetic architectures. To solve this limitation, we present the Sum of Shared Single Effects (SuShiE) model, which leverages LD heterogeneity to improve fine-mapping precision, infer cross-ancestry effect size correlations, and estimate ancestry-specific expression prediction weights. We apply SuShiE to mRNA expression measured in PBMCs (n=956) and LCLs (n=814) together with plasma protein levels (n=854) from individuals of diverse ancestries in the TOPMed MESA and GENOA studies. We find SuShiE fine-maps cis-molQTLs for 16% more genes compared with baselines while prioritizing fewer variants with greater functional enrichment. SuShiE infers highly consistent cis-molQTL architectures across ancestries on average; however, we also find evidence of heterogeneity at genes with predicted loss-of-function intolerance, suggesting that environmental interactions may partially explain differences in cis-molQTL effect sizes across ancestries. Lastly, we leverage estimated cis-molQTL effect-sizes to perform individual-level TWAS and PWAS on six white blood cell-related traits in AOU Biobank individuals (n=86k), and identify 44 more genes compared with baselines, further highlighting its benefits in identifying genes relevant for complex disease risk. Overall, SuShiE provides new insights into the cis-genetic architecture of molecular traits.