Genetics of chronic respiratory disease

Abstract

Chronic respiratory diseases, such as chronic obstructive pulmonary disease (COPD), asthma and interstitial lung diseases are frequently occurring disorders with a polygenic basis that account for a large global burden of morbidity and mortality. Recent large-scale genetic epidemiology studies have identified associations between genetic variation and individual respiratory diseases and linked specific genetic variants to quantitative traits related to lung function. These associations have improved our understanding of the genetic basis and mechanisms underlying common lung diseases. Moreover, examining the overlap between genetic associations of different respiratory conditions, along with evidence for gene-environment interactions, has yielded additional biological insights into affected molecular pathways. This genetic information could inform the assessment of respiratory disease risk and contribute to stratified treatment approaches.

Fig. 1. Workflow for variant-to-gene mapping.

a, Genome-wide association studies (GWASs) are used to identify associations between genotype and respiratory phenotypes. These either use disease status as a binary trait, or quantitative traits such as the lung function variables forced expiratory volume in one second (FEV₁), forced vital capacity (FVC), peak expiratory flow rate and the FEV₁/FVC ratio. b, The genetic associations are further refined using fine-mapping approaches, including Bayesian methods^,. Next, identifying the location of the causal variants can provide further insight into the mechanism — although most putative causal single-nucleotide polymorphisms (SNPs) are in non-coding regions, which require specific approaches to address function. c, Having identified the sentinel (lead credible) variant, the implicated gene is investigated using a range of omic and linkage disequilibrium (LD) score regression approaches^,. For example, one can determine whether a variant is in a region important for transcriptional regulation by testing for enrichment of histone marks such as H3K27ac, H3K9ac, H3K4me3 or H3K4me1. Tools such as GARFIELD assess whether a variant is in or near a DNAse 1 hypersensitivity site; variants in such regions are more likely to be driving functional effects. Increasing numbers of expression and protein quantitative trait locus (eQTL and pQTL) datasets can help to identify the gene for which altered expression contributes to a given genetic association^–. Other approaches include searching for nearby rare variants in whole-exome sequencing data (for example, in the UK Biobank^,), prioritizing genes with a known Mendelian disease with a respiratory phenotype, and considering data from mouse knockout models to look for respiratory phenotypes with potential genes of interest. PoPS, polygenic priority score, is a gene prioritization method that leverages GWAS summary statistics and incorporates data from bulk and single-cell expression datasets, curated biological pathways and predicted protein–protein interactions. Finally, biological mechanisms can be further explored using pathway approaches by grouping the genes identified as likely to be driving effects, and by exploring other phenotypes using phenome-wide association study (PheWAS) approaches. U, potential confounders; WES, whole-exome sequencing; WGS, whole-genome sequencing; FDR, false discovery rate; bp, base pair. Panel b is partially adapted from ref. , CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). Panel c is partially adapted from ref. , Springer Nature Limited. Panel c partially adapted from ref. , CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/).

Fig. 2. Spirometry and lung function traits.

Spirometry provides quantitative lung function traits by measuring the expired volume of air over time. Deviations from healthy lungs (black line) caused by either obstructive airway disease (blue line) or restrictive lung disease (red line) are captured by key metrics calculated from the resulting curve: these include the forced expiratory volume in one second (FEV₁), which is the volume of air expelled from the lungs in one second in a forced expiratory manoeuvre; the forced vital capacity (FVC), which is the total volume of air expelled from the lungs during a forced expiratory manoeuvre; the FEV₁/FVC ratio; and the peak expiratory flow rate (PEFR), which is the maximum flow rate recorded during a forced expiratory manoeuvre.

Fig. 3. Key genes and pathways implicated by GWASs of lung function.

Implicated genes are grouped based on organ function. The colour of the gene box indicates the number of criteria met that support variant-to-gene mapping using the approach shown in Fig. 1 and described in detail in ref. . Reprinted from ref. , CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/).

Fig. 4. Genes and pathways identified in GWASs of asthma targeted by recently developed biological therapies.

In asthma, the airway epithelium is stimulated by environmental factors such as viruses or allergens, which lead to the release of alarmins such as IL-25, IL-33 and TSLP. These alarmins then lead to an inflammatory response involving multiple cell types (for example, dendritic cells, basophils, ILC2 and Th2 cells), ultimately resulting in type 2 inflammation characterized by high levels of IL-4, IL-5 and IL-13. Candidate causal genes identified from genome-wide association studies (GWASs) are shown in red boxes and include genes encoding cytokines (for example, IL13, IL4, IL5 and IL33), cytokine receptors (for example, IL1RL1, which is a IL-33 receptor, and IL4RA), signalling molecules (for example, GATA3, RAD50 and STAT6) and downstream effectors (for example, MUC5AC, leading to mucus production). Biological therapies in development and/or in clinical practice targeting these pathways are shown, including anti-IL-13 (lebrikizumab, tralokinumab), anti-IL-4/13 (dupilumab), anti-IL-33 (itepekimab), anti-IL1RL1 (astogolimab), anti-TSLP (tezepelumab), anti-IL-5 (mepolizumab, reslizumab), anti-IL-5R (benralizumab) and anti-IgE (omalizumab). Adapted with permission from ref. , Elsevier.

Fig. 5. Circle plot of genetic loci implicated in one or more respiratory trait.

Only regions of the genome showing association with at least one relevant phenotype are shown. Genetic associations overlapping across respiratory traits were defined using a distance-based approach. Specifically, the sentinel variants for genetic associations must be more than 2 megabase pairs apart to be considered distinct genetic loci. Lung function loci are shown in red, chronic obstructive pulmonary disease (COPD) loci in blue, asthma^{,,–,,,,,,,–} loci in orange and idiopathic pulmonary fibrosis (IPF)^, loci in green. Darker shading indicates that loci were implicated in more respiratory traits. Genes implicated by more than one trait are annotated on the outside of the plot; genes implicated for one trait are shown on the inside of the plot. There are two loci on chromosome 6 and 17 implicating a large number of genes (too many to show in the figure): locus 151 genes implicated by more than one trait (AGER, HLA-B, HLA-DQA1, HLA-DQB1, ITPR3, MICA, SCUBE3); locus 151 genes implicated by only one trait (HIST1H2BD, LOC401242, HCG4B, TRIM26, IER3, MIR6891, MICB, AIF1, HLA-DRB1, HLA-DPA1, HLA-DPB1, GRM4); locus 334 genes implicated by more than one trait (EFCAB5, GSDMA, GSDMB, KANSL1, LOC102724596, ORMDL3, SUZ12P1, THRA); locus 334 genes implicated by only one trait (LRRC37B, ASIC2, SLFN5, LHX1, C17orf96, ERBB2, ZPBP2, PSMD3, SMARCE1, JUP, STAT5B, ATXN7L3, MAP3K14-AS1, MAP3K14, SPPL2C, CDC27, TBX21, ZNF652, LOC101927274). Note that, for clarity, genomic distances are not to scale.