Genetic Association and Risk Scores in a Chronic Obstructive Pulmonary Disease Meta-analysis of 16,707 Subjects

Abstract

The heritability of chronic obstructive pulmonary disease (COPD) cannot be fully explained by recognized genetic risk factors identified as achieving genome-wide significance. In addition, the combined contribution of genetic variation to COPD risk has not been fully explored. We sought to determine: (1) whether studies of variants from previous studies of COPD or lung function in a larger sample could identify additional associated variants, particularly for severe COPD; and (2) the impact of genetic risk scores on COPD. We genotyped 3,346 single-nucleotide polymorphisms (SNPs) in 2,588 cases (1,803 severe COPD) and 1,782 control subjects from four cohorts, and performed association testing with COPD, combining these results with existing genotyping data from 6,633 cases (3,497 severe COPD) and 5,704 control subjects. In addition, we developed genetic risk scores from SNPs associated with lung function and COPD and tested their discriminatory power for COPD-related measures. We identified significant associations between SNPs near PPIC (P = 1.28 × 10^-8) and PPP4R4/SERPINA1 (P = 1.01 × 10^-8) and severe COPD; the latter association may be driven by recognized variants in SERPINA1. Genetic risk scores based on SNPs previously associated with COPD and lung function had a modest ability to discriminate COPD (area under the curve, ∼0.6), and accounted for a mean 0.9-1.9% lower forced expiratory volume in 1 second percent predicted for each additional risk allele. In a large genetic association analysis, we identified associations with severe COPD near PPIC and SERPINA1. A risk score based on combining genetic variants had modest, but significant, effects on risk of COPD and lung function.

Keywords: alpha-1 antitrypsin; chronic obstructive pulmonary disease; genetic epidemiology; genetic risk factors; genetic risk score.

Figure 1.

Unadjusted FEV₁% predicted by number of COPD7 and LUNG30 risk alleles. Boxplots showing FEV₁ % predicted stratified by number of risk alleles in the International COPD Genetics Network pedigree-based cohort. For each boxplot, the black line represents the median data point, the upper and lower edges of the light blue box represent data within the 25th to 75th percentile of the distribution, the upper and lower “whiskers” represent the upper and lower limits of the data, and open circles represent outliers. The figure on the left shows the COPD7 risk score, whereas the figure on the right shows the LUNG30 risk score. COPD7, genetic risk score composed of seven COPD risk SNPs (ranging from 0 to 14 scoring alleles); LUNG30, genetic risk score compsed of thirty lung function associated risk SNPs (ranging from 0 to 60 scoring alleles); SNP, single-nucleotide polymorphism.

Figure 2.

Severe COPD diagnosis using COPD7 and LUNG30. Receiver operator characteristic curves showing diagnostic accuracy of models based on clinical variables (age and pack-years of smoking alone, shown in light blue), COPD7 or LUNG30 risk allele data alone (green), and the combination of clinical and genetic risk score data (dark blue) for predicting Global Initiative for Chronic Obstructive Lung Disease spirometric stages III–IV COPD affection status in the International COPD Genetics Network cohort. The differences between the clinical and combined curves were statistically significant in both the COPD7 (difference 0.010; P = 4.4 × 10⁻³) and the LUNG30 scores (difference 0.012; P = 4.7 × 10⁻³).