A large-scale genome-wide association analysis of lung function in the Chinese population identifies novel loci and highlights shared genetic aetiology with obesity

Abstract

Background: Lung function is a heritable complex phenotype with obesity being one of its important risk factors. However, knowledge of their shared genetic basis is limited. Most genome-wide association studies (GWASs) for lung function have been based on European populations, limiting the generalisability across populations. Large-scale lung function GWASs in other populations are lacking.

Methods: We included 100 285 subjects from the China Kadoorie Biobank (CKB). To identify novel loci for lung function, single-trait GWAS analyses were performed on forced expiratory volume in 1 s (FEV₁), forced vital capacity (FVC) and FEV₁/FVC in the CKB. We then performed genome-wide cross-trait analysis between lung function and obesity traits (body mass index (BMI), BMI-adjusted waist-to-hip ratio and BMI-adjusted waist circumference) to investigate the shared genetic effects in the CKB. Finally, polygenic risk scores (PRSs) of lung function were developed in the CKB and their interaction with BMI's association on lung function were examined. We also conducted cross-trait analysis in parallel with the CKB using up to 457 756 subjects from the UK Biobank (UKB) for replication and investigation of ancestry-specific effects.

Results: We identified nine genome-wide significant novel loci for FEV₁, six for FVC and three for FEV₁/FVC in the CKB. FEV₁ and FVC showed significant negative genetic correlation with obesity traits in both the CKB and UKB. Genetic loci shared between lung function and obesity traits highlighted important biological pathways, including cell proliferation, embryo, skeletal and tissue development, and regulation of gene expression. Mendelian randomisation analysis suggested significant negative causal effects of BMI on FEV₁ and on FVC in both the CKB and UKB. Lung function PRSs significantly modified the effect of change in BMI on change in lung function during an average follow-up of 8 years.

Conclusion: This large-scale GWAS of lung function identified novel loci and shared genetic aetiology between lung function and obesity. Change in BMI might affect change in lung function differently according to a subject's polygenic background. These findings may open new avenues for the development of molecular-targeted therapies for obesity and lung function improvement.

FIGURE 1

Overall study design. FEV₁: forced expiratory volume in 1 s; FVC: forced vital capacity; BMI: body mass index; WHRadjBMI: BMI-adjusted waist-to-hip ratio; WCadjBMI: BMI-adjusted waist circumference.

FIGURE 2

Manhattan plots for genome-wide association analysis of 100 285 Chinese subjects in the China Kadoorie Biobank cohort for three lung function traits: a) forced expiratory volume in 1 s (FEV₁), b) forced vital capacity (FVC) and c) FEV₁/FVC. The x-axis denotes the genomic position (chromosomes 1–22); the y-axis denotes the –log₁₀(p-value) of association test and starts at –log₁₀(p-value)=3. The most significant novel variant in each independent clump is highlighted by an orange diamond symbol. Genes in black were previously reported and genes in red are novel. An asterisk on some genes indicates a novel variant. The genome-wide significance level (p=5×10⁻⁸) is denoted by the red line.

FIGURE 3

Genome-wide genetic correlation between three lung function traits and three obesity traits in the a) China Kadoorie Biobank (CKB) and b) UK Biobank (UKB) cohorts. FEV₁: forced expiratory volume in 1 s; FVC: forced vital capacity; BMI: body mass index; WHRadjBMI: BMI-adjusted waist-to-hip ratio; WCadjBMI: BMI-adjusted waist circumference. The colour of each box scales with the magnitude of the genetic correlation (R_g). *: pairs of traits with nominal significant genetic correlation (p<0.05); **: pairs of traits with significant genetic correlation after correcting for multiple testing (p<0.05/9). Boxes without labelling are trait pairs with nonsignificant genetic correlation.

FIGURE 4

Relationship of the distribution of three lung function polygenic risk scores (PRSs) with body mass index (BMI) in the China Kadoorie Biobank for a, c, e) the baseline model and b, d, f) the change model: a, b) forced expiratory volume in 1 s (FEV₁), c, d) forced vital capacity (FVC) and e, f) FEV₁/FVC. For the baseline model, we set normal BMI and the deciles 2–9 group as reference; for the change model, we set BMI stable and the deciles 2–9 group as reference. For the baseline model, the x-axis denotes different BMI categories by the following definitions: underweight BMI <18.5 kg·m⁻², normal BMI 18.5–24.9 kg·m⁻², overweight BMI 25.0–29.9 kg·m⁻² and obese BMI ≥30.0 kg·m⁻². The y-axis denotes the difference between lung function measurements for each group and the reference group. For the change model, the x-axis denotes different BMI change categories: BMI decrease is defined as BMI_t1−BMI_t0≤ −1 kg·m⁻², BMI stable is defined as −1 kg·m⁻²<BMI_t1−BMI_t0≤1 kg·m⁻² and BMI increase is defined as BMI_t1−BMI_t0>1 kg·m⁻². The y-axis denotes the difference between lung function measurements change (lung function_t1−lung function_t0) for each group and the reference group. The PRS groups were defined as: bottom decile, deciles 2–9 and top decile. The p-value on each plot represents the lung function and baseline BMI or BMI change interaction p-value from baseline or change models.