Host genetics and infectious disease: new tools, insights and translational opportunities

Abstract

Understanding how human genetics influence infectious disease susceptibility offers the opportunity for new insights into pathogenesis, potential drug targets, risk stratification, response to therapy and vaccination. As new infectious diseases continue to emerge, together with growing levels of antimicrobial resistance and an increasing awareness of substantial differences between populations in genetic associations, the need for such work is expanding. In this Review, we illustrate how our understanding of the host-pathogen relationship is advancing through holistic approaches, describing current strategies to investigate the role of host genetic variation in established and emerging infections, including COVID-19, the need for wider application to diverse global populations mirroring the burden of disease, the impact of pathogen and vector genetic diversity and a broad array of immune and inflammation phenotypes that can be mapped as traits in health and disease. Insights from study of inborn errors of immunity and multi-omics profiling together with developments in analytical methods are further advancing our knowledge of this important area.

Fig. 1. Signalling pathways crucial to the immune response and consequences of inborn errors of immunity for infectious disease.

Examples of specific proteins are shown (highlighted in colour), which when present as mutants give rise to monogenic inborn errors of immunity, with the main infectious disease phenotypes noted. a | Pattern recognition receptors (PRRs) responsible for detecting pathogen-associated molecular patterns (PAMPs) and damage-associated molecular patterns (DAMPs) with examples of PRR pathways illustrated for retinoic acid-inducible gene I protein (RIG-I)-like receptor (RLR) and Toll-like receptor (TLR). b | Receptor-interacting protein kinase (RIPK) signalling. RIPK1 and RIPK3 regulate inflammation and cell death (necroptosis). c | Interferon pathways. Type I interferons (for example, interferon-α (IFNα) and IFNβ) and type II interferons (for example, IFNγ) regulate the immune response to viral and bacterial infections. d | Antigen presentation pathways. Major histocompatibility (MHC) class I molecules present antigens (derived from intracellular proteins such as viruses and some bacteria) to cytotoxic CD8⁺ T cells via the endogenous pathway (left). MHC class II molecules present antigens from bacteria, parasites and other extracellular pathogens endocytosed into antigen-presenting cells to CD4⁺ T cells via the exogenous pathway (right). CARD, caspase activation and recruitment domain; CLIP, class II-associated invariant chain peptide; CMV, cytomegalovirus; CTD, carboxy-terminal domain; ERK, extracellular signal-regulated kinase; GAS, gamma-activated sequence; HSV, herpes simplex virus; IFNAR, interferon-α/β receptor; IKK, IκB kinase; IRAK, interleukin-1 receptor-associated kinase; IRF, interferon response factor; ISGs, interferon-stimulated genes; ISRE, interferon-stimulated response element; JNK, JUN amino-terminal kinase; MAPK, mitogen-activated protein kinase; MAPKKK, mitogen-activated protein kinase kinase kinase; NF-κB, nuclear factor-κB; TAB, transforming growth factor-β-activated kinase 1 (MAP3K7)-binding protein; TAK, transforming growth factor-β-activated kinase; TAP, transporter associated with antigen processing; TCR, T cell receptor; TIRAP, Toll–interleukin-1 receptor domain-containing adaptor protein; TRAFs, tumour necrosis factor receptor-associated factors; TRAM, Toll–interleukin-1 receptor domain-containing adapter-inducing interferon-β (TRIF)-related adaptor molecule; TRIF, Toll–interleukin-1 receptor domain-containing adapter-inducing interferon-β.

Fig. 2. Precision medicine approaches in infectious disease informed by human genetics.

Examples of how understanding of human genetic information can be or has begun to be leveraged for improved patient care. Strategies include identifying specific molecular targets based on genetic understanding, stratifying patients to decide on use of certain drugs and using genetic knowledge to predict severe adverse reactions to medications. GWAS, genome-wide association studies; HCV, hepatitis C virus; HIV, human immunodeficiency virus; JAK, Janus kinase.

Fig. 3. Omics and intermediate phenotypes as part of the toolkit for investigating the basis of infectious disease susceptibility.

a | Traditional case–control genome-wide association study (GWAS) approaches compare allele frequencies of genetic variants in cases versus controls. b | Mendelian disease mapping with pedigree analysis (including case–parent trio analyses) and use of whole-exome or whole-genome sequencing. c | Multi-omics approaches, which enable intermediate phenotypes to be quantified by various -omics technologies. d | Leveraging genetic information to interrogate or leverage intermediate phenotypes. Differences in intermediate phenotypes such as gene expression can be mapped to genetic variation by quantitative trait locus (QTL) mapping. Mendelian randomization methods can use intermediate phenotypes that are risk factors for disease, with genetic variants that affect the intermediate phenotype allocated randomly to allow confounders to also be randomly distributed.