Genetics of COPD

Abstract

Although chronic obstructive pulmonary disease (COPD) risk is strongly influenced by cigarette smoking, genetic factors are also important determinants of COPD. In addition to Mendelian syndromes such as alpha-1 antitrypsin deficiency, many genomic regions that influence COPD susceptibility have been identified in genome-wide association studies. Similarly, multiple genomic regions associated with COPD-related phenotypes, such as quantitative emphysema measures, have been found. Identifying the functional variants and key genes within these association regions remains a major challenge. However, newly identified COPD susceptibility genes are already providing novel insights into COPD pathogenesis. Network-based approaches that leverage these genetic discoveries have the potential to assist in decoding the complex genetic architecture of COPD.

Keywords: COPD; genetic association; network medicine; sequencing; subtyping.

Figure 1

The overall approach for genome-wide association studies of complex diseases. DNA samples and phenotypic information are obtained from cases and controls, families including affected individuals, or subjects from the general population. Subsequently, standard single nucleotide polymorphism (SNP) genotyping panels are tested. Quality control is performed at the level of both the subject and the SNP, and then statistical associations between genotypes and phenotypes are assessed. Adjustment for genetic ancestry is necessary in case-control or population-based studies. To achieve genome-wide statistical significance, meta-analysis of multiple study populations is often required. Adapted with permission from Reference .

Figure 2

International COPD Genetics Consortium and UK Biobank genome-wide association studies for COPD. Manhattan plot demonstrating 82 genome-wide significant associations to COPD. Novel associations (not previously reported for COPD or lung function) are labeled with the nearest gene, and replication in the SpiroMeta cohort for lung function phenotypes is indicated. Adapted with permission from Reference .

Figure 3

Phenogram plot of representative GWAS for COPD susceptibility and COPD-related phenotypes on different chromosomes. Genetic associations are denoted by the nearest gene to the most significantly associated SNP. COPD susceptibility associations (blue) include 22 loci reported by Hobbs et al. (26). Lung function (FEV1, FVC, and FEV1/FVC) associations depicted here (red) include associations from the study conducted by Wain et al. (36). Abbreviations: BDR, bronchodilator response; BMI, body mass index; CB, chronic bronchitis; EMPH.DIST.RATIO, emphysema distribution ratio of upper divided by lower lung fields; FEV1, forced expiratory volume in 1 s; FFMI, fat-free mass index; FVC, forced vital capacity; GWAS, genome-wide association study; LAA-950, percentage of lung density histogram below −950 HU; LHE, local histogram–based emphysema; MOD, moderate centrilobular emphysema on LHE; NORM, normal/nonemphysematous on LHE; OS, resting oxygen saturation; PAE, pulmonary artery enlargement; PAN, panlobular emphysema on LHE; PB, postbronchodilator; PCT.GAS, percent gas-trapping at −856 HU on expiratory computed tomography; PERC15, fifteenth percentile point of the lung density histogram; SEV, severe centrilobular emphysema on LHE; SNP, single nucleotide polymorphism; VIS.EMPH, visual emphysema (presence/absence). Adapted with permission from Reference .

Figure 4

Long-range interaction detected between the COPD GWAS region and HHIP promoter (labeled as Anchor). Chromosome conformation capture assay demonstrated a 7-kb region of interaction upstream from the HHIP gene with the HHIP promoter. This upstream interacting genomic region is located within a frequently replicated COPD GWAS locus. Abbreviation: GWAS, genome-wide association study. Adapted with permission from Reference .

Figure 5

Venn diagram demonstrating the overlap of genes implicated by murine emphysema models and genes located near genome-wide significant associations to COPD. Out of a total of approximately 20,000 mammalian genes, only eight are located in both COPD genome-wide association regions and have been supported by a gene-targeted murine model of emphysema.

Figure 6

Comparison of top-down and bottom-up approaches to build biological networks. Biological networks can be built from the bottom up, starting with GWAS regions and identifying the key genes and the functional variants that impact those genes. Networks can be extended from those genes using tools that assess binding with other proteins (e.g., tandem affinity purification, coimmunoprecipitation) as well as hypothesis-based molecular biology experiments. Top-down approaches begin with Big Data assessments of key biological molecules such as DNA, RNA, proteins, and metabolites. Various types of networks can be built, including correlation-based networks, gene regulatory networks, and protein–protein interaction networks. Ultimately, bottom-up and top-down approaches may converge to give insights into gene regulation, biological function, and disease pathobiology relevant to COPD. The top-down approach is encompassed by the omics data and network methods shown above the rectangles labeled “Understand gene regulation,” “Determine biological function,” and “Define disease pathobiology,” whereas the bottom-up approach includes the components below those rectangles. Abbreviations: AP-MS, affinity purification–mass spectrometry; GWAS, genome-wide association study; SNP, single nucleotide polymorphism.