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Key messages What is the key question? Do low frequency exonic variants influence susceptibility to COPD, and severity of airflow limitation? What is the bottom line? Low frequency single nucleotide polymorphisms (SNPs) in MOCS3 and IFIT3 were associated with risk of COPD and a rare splice variant in SERPINA12 was associated with severity of airflow limitation. Why read on? These genomic regions have not previously been implicated in lung function or COPD and these findings could therefore provide further insight into COPD susceptibility and severity.

What is the bottom line? Low frequency single nucleotide polymorphisms (SNPs) in MOCS3 and IFIT3 were associated with risk of COPD and a rare splice variant in SERPINA12 was associated with severity of airflow limitation. Why read on? These genomic regions have not previously been implicated in lung function or COPD and these findings could therefore provide further insight into COPD susceptibility and severity. Introduction COPD is a major public health concern, being a leading cause of morbidity and mortality worldwide.1 The Global Initiative for Chronic Obstructive Lung Disease (GOLD) recommends that the impact of COPD on an individual patient should assessed by considering breathlessness, symptoms and exacerbation risk, in combination with the severity of airflow limitation, which can be graded using %predicted FEV1.2 Approximately 1%–2% of COPD cases can be attributed to α1-antitrypsin (AAT) deficiency, a rare inherited disorder, caused by mutations within the SERPINA1 gene.3 4 For the remainder of COPD cases, cigarette smoking is recognised as the most significant risk factor5; however, there is also a genetic component, with several genomic regions showing association with COPD risk or airflow limitation to date, including CHRNA3/5, HHIP,3 HTR4, GSTCD, TNS1,6 MMP127 8 and FAM13A.9 COPD diagnosis is confirmed using measures of lung function, so it is likely that the genetic determinants of COPD and lung function will overlap. Indeed, many loci identified in large genome-wide association studies (GWAS) of FEV1 and the ratio of FEV1 to forced vital capacity (FEV1/FVC) in general population samples10–13 have subsequently being shown to be associated with COPD or airflow limitation.6 9 14 15

determinants of COPD and lung function will overlap. Indeed, many loci identified in large genome-wide association studies (GWAS) of FEV1 and the ratio of FEV1 to forced vital capacity (FEV1/FVC) in general population samples10–13 have subsequently being shown to be associated with COPD or airflow limitation.6 9 14 15 Despite the successes in identifying genes associated with lung function and COPD, these known loci only explain a small proportion of the expected heritability.13 Large GWAS undertaken to date have generally focused on common variants (typically >5% minor allele frequency (MAF))3 9–14; one hypothesis is that some of the so-called ‘missing heritability’ might be accounted for by variants of lower frequencies. In this study, we set out to investigate the role of low frequency, functional variants in COPD, and to confirm the role of single nucleotide polymorphisms (SNPs) previously showing association with lung function. It is hypothesised that rare variants are more likely than common variants to have deleterious effects; identifying such SNPs could lead to greater understanding of the pathways and biological mechanisms underlying airflow obstruction and COPD, and could translate to novel targets for treatment.

ion with lung function. It is hypothesised that rare variants are more likely than common variants to have deleterious effects; identifying such SNPs could lead to greater understanding of the pathways and biological mechanisms underlying airflow obstruction and COPD, and could translate to novel targets for treatment. We genotyped cases with a history of smoking and airflow limitation, indicative of GOLD 2 COPD or worse, and control samples using an exome chip array to which we had added custom content comprising 2585 SNPs tagging regions which had shown suggestive association (p<2.21×10−3) with lung function in a previous large genome-wide HapMap-imputed study.13 The exome chip genotyping array design contains mostly non-synonymous, splice or stop codon altering variants that are likely to affect protein structure and function, with the majority of variants being low frequency (MAF 1%–5%) or rare (MAF<1%).

th lung function in a previous large genome-wide HapMap-imputed study.13 The exome chip genotyping array design contains mostly non-synonymous, splice or stop codon altering variants that are likely to affect protein structure and function, with the majority of variants being low frequency (MAF 1%–5%) or rare (MAF<1%). In this study, we carried out discovery case–control analyses (COPD cases vs controls) and analyses of %predicted FEV1 in cases, as a measure of severity of airflow limitation. Replication was undertaken using a subset of the UK Biobank Lung Exome Variant Evaluation (BiLEVE) study, a collection of 48 931 individuals from UK Biobank with high-quality lung function and smoking data who were genotyped on an array that includes substantial overlap with the exome chip.16 We also adopted a more powerful discovery strategy for COPD risk and severity of airflow limitation, by meta-analysing data for the subset of exome chip variants that were measured in both the COPD exome chip consortium and the UK BiLEVE study.

otyped on an array that includes substantial overlap with the exome chip.16 We also adopted a more powerful discovery strategy for COPD risk and severity of airflow limitation, by meta-analysing data for the subset of exome chip variants that were measured in both the COPD exome chip consortium and the UK BiLEVE study. Methods Study participants and phenotypes A total of 3487 ever smokers with airflow limitation indicative of GOLD 22 COPD or worse were identified from 12 UK collections as cases (case collections described in online supplementary table S1). Individuals met case criteria if they had FEV1/FVC ≤0.7 and %predicted FEV1 ≤80% (according to the National Health and Nutrition Examination Survey (NHANES) III spirometric reference equations17), did not have a doctor diagnosis of asthma and had reported current, or former smoking. Five of the sample collections (n=1398 samples, table 1) were COPD cohorts, with all individuals having irreversible airflow limitation, and meeting GOLD 2 criteria based on postbronchodilator spirometry. The remaining cases were taken from general population cohorts; for these samples, only prebronchodilator spirometry measures were available. We used general population controls with exome chip data, from Generation Scotland: Scottish Family Health Study (GS:SFHS), British 1958 Birth Cohort (1958BC), Oxford Biobank and GoDARTS (Genetics of Diabetes and Audit Research Tayside Study), listed in table 1 with clinical characteristics. All controls were current or former smokers and were free of lung disease, according to available spirometry and phenotype information.

tudy (GS:SFHS), British 1958 Birth Cohort (1958BC), Oxford Biobank and GoDARTS (Genetics of Diabetes and Audit Research Tayside Study), listed in table 1 with clinical characteristics. All controls were current or former smokers and were free of lung disease, according to available spirometry and phenotype information. 10.1136/thoraxjnl-2015-207876.supp1Supplementary data Table 1 Clinical characteristics of samples passing genotype QC

tudy (GS:SFHS), British 1958 Birth Cohort (1958BC), Oxford Biobank and GoDARTS (Genetics of Diabetes and Audit Research Tayside Study), listed in table 1 with clinical characteristics. All controls were current or former smokers and were free of lung disease, according to available spirometry and phenotype information. 10.1136/thoraxjnl-2015-207876.supp1Supplementary data Table 1 Clinical characteristics of samples passing genotype QC Sex Age %Predicted FEV1 FEV1/FVC Pack-years Sample collection n Male, n (%) Mean (SD) Mean (SD) Mean (SD) Samples with data (n) Mean (SD) Discovery analyses airflow limitation cases (total n=3226, with pack-years n=2517) GS:SFHS 508 224 (44.1%) 58.9 (8.94) 64.84 (12.64) 0.580 (0.108) 482 29.32 (24.96) British Regional Heart Study 425 425 (100%) 70.1 (5.46) 59.41 (14.66) 0.597 (0.084) 0 – British Women's Heart and Health Study 254 0 (0%) 69.3 (5.46) 64.26 (12.40) 0.603 (0.074) 203 28.1 (18.36) UK COPD cohort* 209 129 (61.7%) 68.7 (8.11) 37.94 (15.29) 0.447 (0.119) 199 50.07 (27.79 Hertfordshire Cohort Study 317 203 (64.0%) 66.1 (2.79) 62.89 (13.57) 0.589 (0.101) 312 32.25 (23.37) COPDBEAT* 87 62 (71.3%) 67.6 (8.77) 45.19 (16.24) 0.480 (0.115) 86 38.69 (21.24) Nottingham COPD study* 76 48 (63.2%) 67.2 (8.97) 50.29 (15.04) 0.482 (0.111) 74 49.02 (26.86) Nottingham smokers 125 78 (62.4%) 63.1 (8.60) 46.27 (17.65) 0.503 (0.125) 124 41.75 (20.61) Gedling study 33 26 (78.8%) 69.0 (8.23) 59.67 (16.81) 0.593 (0.103) 31 45.47 (33.40) English Longitudinal Study of Aging 166 75 (45.2%) 66.0 (8.17) 54.84 (17.24) 0.526 (0.149) 0 – EU COPD Gene Scan* 277 155 (56.0%) 67.0 (8.68) 38.51 (14.74) 0.467 (0.120) 277 46.43 (20.56) GoTARDIS Study* 749 412 (55.0%) 68.8 (8.97) 52.16 (14.14) 0.509 (0.110) 729 43.26 (21.59) Discovery analyses controls (total n=4784, with pack-years n=3889) GS:SFHS 961 552 (57.4%) 54.5 (8.41) 98.18 (10.92) 0.783 (0.051) 961 28.92 (16.86) British 1958 Birth Cohort 1429 888 (62.1%) 44 (0) 100.90 (13.46) 0.809 (0.060) 1046 14.74 (10.07) Oxford Biobank 1770 832 (47.0%) 41.6 (5.77) – – 1682 9.09 (9.34) GoDARTS 624 402 (64.4%) 59.0 (10.75) – – 200 35.46 (25.89) UK Biobank Lung Exome Variant Evaluation samples (meta-analysis and replication) Airflow limitation cases 4231 2379 (56.2%) 59.54 (6.86) 61.76 (11.8) 0.607 (0.076) 4231 42.41 (21.10) Controls 8979 4260 (47.4%) 56.19 (7.92) 101.40 (8.1) 0.773 (0.038) 8979 30.43 (14.41) *Sample collection is COPD case cohort.

.46 (25.89) UK Biobank Lung Exome Variant Evaluation samples (meta-analysis and replication) Airflow limitation cases 4231 2379 (56.2%) 59.54 (6.86) 61.76 (11.8) 0.607 (0.076) 4231 42.41 (21.10) Controls 8979 4260 (47.4%) 56.19 (7.92) 101.40 (8.1) 0.773 (0.038) 8979 30.43 (14.41) *Sample collection is COPD case cohort. GS:SFHS, Generation Scotland: Scottish Family Health Study; GoTARDIS, Tayside Allergy and Respiratory Disease Information System; QC, quality control. We used a subset of the UK BiLEVE study16 for replication of novel signals, and for a larger discovery meta-analysis. A total of 24 457 heavy smokers (mean 35 pack-years) were genotyped as part of the UK BiLEVE study, selected such that 9748 individuals formed a low FEV1 group (based on %predicted FEV1), 4906 individuals formed a high FEV1 group and 9803 had average FEV1. We selected 4231 samples from the low FEV1 group, with airflow limitation consistent with GOLD 2 or worse as cases and 8979 samples from the high and average FEV1 groups with FEV1/FVC >0.7, %predicted FEV1 >80% and no doctor diagnosis of COPD for use as controls. All spirometry measures were prebronchodilator, all samples were heavy smokers and individuals with a doctor diagnosis of asthma or other lung diseases were excluded. The %predicted FEV1 was estimated using NHANES III spirometric reference equations.17 An overview of the full study design is shown in figure 1.

We used a subset of the UK BiLEVE study16 for replication of novel signals, and for a larger discovery meta-analysis. A total of 24 457 heavy smokers (mean 35 pack-years) were genotyped as part of the UK BiLEVE study, selected such that 9748 individuals formed a low FEV1 group (based on %predicted FEV1), 4906 individuals formed a high FEV1 group and 9803 had average FEV1. We selected 4231 samples from the low FEV1 group, with airflow limitation consistent with GOLD 2 or worse as cases and 8979 samples from the high and average FEV1 groups with FEV1/FVC >0.7, %predicted FEV1 >80% and no doctor diagnosis of COPD for use as controls. All spirometry measures were prebronchodilator, all samples were heavy smokers and individuals with a doctor diagnosis of asthma or other lung diseases were excluded. The %predicted FEV1 was estimated using NHANES III spirometric reference equations.17 An overview of the full study design is shown in figure 1. Figure 1 Two-stage study design. Stage 1: exome discovery analyses. Stage 2: Follow-up in UK BiLEVE: A. Replication of signals; B. meta-analysis of UK COPD exome chip consortium and UK BiLEVE. Genotyping All 3487 cases and 1032 GS:SFHS controls were genotyped together using the Illumina Human Exome BeadChip with additional custom content for regions which have previously shown modest association with lung function (description of custom content design in online supplementary methods). The remaining discovery analyses control samples were genotyped separately using the Illumina Human Exome BeadChip.

na Human Exome BeadChip with additional custom content for regions which have previously shown modest association with lung function (description of custom content design in online supplementary methods). The remaining discovery analyses control samples were genotyped separately using the Illumina Human Exome BeadChip. The UK BiLEVE samples were genotyped using the Affymetrix UK BiLEVE array, which includes rare variants selected from the same sequencing project as the Illumina Human Exome BeadChip alongside additional content.16 Of the 807 411 SNPs included on the Affymetrix UK BiLEVE array, 74 891 were also present on the Illumina Human Exome BeadChip; this subset of SNPs, which were directly genotyped on both arrays, was selected for the discovery meta-analysis. Quality control of genotype data Discovery exome analysis Genotypes were called using Illumina's Gencall algorithm in Genomestudio18 with refinement of rare variants with missing calls undertaken using zCall.19 Standard quality control (QC) filters were applied, in accordance with the Exome-chip Quality Control SOP V.5, as developed within the UK exome chip consortium20 and are fully described in online supplementary methods. In brief, SNPs were excluded if they had low call rate (<99%) or deviated from Hardy Weinberg Equilibrium (p<10−4) and samples were excluded if they were duplicates, sex mismatches, heterozygosity outliers (>3 SD from mean), had an excess of singleton SNPs, or were ancestral outliers. Clusterplots for all SNPs of interest were inspected, to ensure accuracy of genotype calling.

%) or deviated from Hardy Weinberg Equilibrium (p<10−4) and samples were excluded if they were duplicates, sex mismatches, heterozygosity outliers (>3 SD from mean), had an excess of singleton SNPs, or were ancestral outliers. Clusterplots for all SNPs of interest were inspected, to ensure accuracy of genotype calling. UK BiLEVE data The QC procedure of the UK BiLEVE genotype data is described elsewhere.16

%) or deviated from Hardy Weinberg Equilibrium (p<10−4) and samples were excluded if they were duplicates, sex mismatches, heterozygosity outliers (>3 SD from mean), had an excess of singleton SNPs, or were ancestral outliers. Clusterplots for all SNPs of interest were inspected, to ensure accuracy of genotype calling. UK BiLEVE data The QC procedure of the UK BiLEVE genotype data is described elsewhere.16 Statistical analyses SNP associations with COPD risk were carried out using a logistic regression model, adjusting for age, sex and pack-years and assuming an additive genetic model. Associations with untransformed %predicted FEV1 in cases were tested, using a linear regression model, with adjustment for pack-years (analysis of severity of airflow limitation). Since not all samples had pack-years data available, secondary analyses were carried out without adjustment for pack-years, for both the COPD risk and severity of airflow limitation analyses, allowing the inclusion of all samples. Single variant analyses were carried out using PLINK V.1.07.21 Using a Bonferroni correction for the number of tests undertaken, a significance level of p<3.7×10−7 would be required in the exome single variant analysis to retain a type 1 error of 5%. We defined SNPs of interest as those with p<10−5 in the discovery exome analysis; for these SNPs, we undertook replication analyses in the UK BiLEVE study to corroborate findings (see online supplementary methods). We set a Bonferroni corrected significance level for replication, for the number of SNPs in novel loci taken forward to replication (p<0.017 for analysis of COPD risk). Gene-based analyses using SKAT-O were additionally undertaken; the methods and results of these analyses are described in the online supplementary information.

Bonferroni corrected significance level for replication, for the number of SNPs in novel loci taken forward to replication (p<0.017 for analysis of COPD risk). Gene-based analyses using SKAT-O were additionally undertaken; the methods and results of these analyses are described in the online supplementary information. Custom content single variant analyses Custom content comprising 2585 SNPs tagging regions which had shown suggestive association (p<2.21×10−3) with lung function in a previous large genome-wide HapMap-imputed study13 were also included on the array for cases and GS:SFHS controls. Additional controls from 1958BC and Busselton Health Study (BHS) with genome-wide data were also used; full methods and results of this analysis are given in the supplementary information. Meta-analysis with UK BiLEVE data Single variant associations with COPD risk and severity of airflow limitation in the UK BiLEVE samples were carried out using PLINK v1.07,21 identically to the corresponding discovery analysis with pack-years adjustment. We carried out an inverse-variance–weighted meta-analysis of the union of SNPs included in the discovery exome and UK BiLEVE analyses (described in online supplementary methods).

n the UK BiLEVE samples were carried out using PLINK v1.07,21 identically to the corresponding discovery analysis with pack-years adjustment. We carried out an inverse-variance–weighted meta-analysis of the union of SNPs included in the discovery exome and UK BiLEVE analyses (described in online supplementary methods). Results Discovery exome analysis 3226 cases and 4784 controls passed all sample and SNP genotype QC and were used in the exome analysis (exclusions in online supplementary table S1). Clinical characteristics of these samples are summarised in table 1. Of the SNPs which passed all QC criteria in both cases and controls, 135 818 were polymorphic, of which 101 308 (74.6%) had a MAF<1%. Analyses of COPD risk We carried out pack-years adjusted analysis of COPD risk, including 2517 cases and 3889 controls, in addition to an unadjusted analysis, using all 3226 cases and 4784 controls (quantile–quantile plots shown in online supplementary figure S1). A total of four SNPs in three regions met the p<10−5 significance threshold in the pack-years adjusted analysis, with five SNPs in four regions showing p<10−5 in the unadjusted analysis (figure 2). Figure 2 (A) Analysis of COPD risk, with pack-years adjustment (single nucleotide polymorphisms (SNPs) with minor allele frequency (MAF) >0.05% only; SNPs with p<10−5 highlighted). (B) Analysis of COPD risk, without pack-years adjustment (SNPs with MAF >0.05% only; SNPs with p<10−5 highlighted).

Analyses of COPD risk We carried out pack-years adjusted analysis of COPD risk, including 2517 cases and 3889 controls, in addition to an unadjusted analysis, using all 3226 cases and 4784 controls (quantile–quantile plots shown in online supplementary figure S1). A total of four SNPs in three regions met the p<10−5 significance threshold in the pack-years adjusted analysis, with five SNPs in four regions showing p<10−5 in the unadjusted analysis (figure 2). Figure 2 (A) Analysis of COPD risk, with pack-years adjustment (single nucleotide polymorphisms (SNPs) with minor allele frequency (MAF) >0.05% only; SNPs with p<10−5 highlighted). (B) Analysis of COPD risk, without pack-years adjustment (SNPs with MAF >0.05% only; SNPs with p<10−5 highlighted). In the pack-years adjusted analysis (table 2A and figure 2A), the most significant association was for the previously reported COPD/smoking region 15q25 (sentinel SNP rs8034191 OR: 1.38, MAF=34.8%, p=2.42×10−7). This signal was replicated in the UK BiLEVE study. Two novel signals of association with COPD risk (p<10−5) were rs3813803 within SMPDL3B (OR: 1.37, MAF=29.2%, p=1.04×10−6) and low frequency SNP rs7269297 within MOCS3 (OR: 0.25, MAF=1.1%, p=3.08×10−6). There was evidence of replication, just above the Bonferroni corrected level of significance (p<0.017) for rs7269297 in the UK BiLEVE study (p=7.27×10−5 for meta-analysis of discovery and UK BiLEVE results, table 2A). Table 2 Top associations in exome discovery analyses and meta-analysis of COPD risk

In the pack-years adjusted analysis (table 2A and figure 2A), the most significant association was for the previously reported COPD/smoking region 15q25 (sentinel SNP rs8034191 OR: 1.38, MAF=34.8%, p=2.42×10−7). This signal was replicated in the UK BiLEVE study. Two novel signals of association with COPD risk (p<10−5) were rs3813803 within SMPDL3B (OR: 1.37, MAF=29.2%, p=1.04×10−6) and low frequency SNP rs7269297 within MOCS3 (OR: 0.25, MAF=1.1%, p=3.08×10−6). There was evidence of replication, just above the Bonferroni corrected level of significance (p<0.017) for rs7269297 in the UK BiLEVE study (p=7.27×10−5 for meta-analysis of discovery and UK BiLEVE results, table 2A). Table 2 Top associations in exome discovery analyses and meta-analysis of COPD risk (A) SNPs with p<10–5 in either the pack-years adjusted or unadjusted discovery analyses Discovery pack-years adjusted analysis (2517 cases, 3889 controls) Discovery unadjusted analysis (3226 cases, 4784 controls) UK BiLEVE pack-years adjusted analysis (4231 cases, 8979 controls) Meta-analysis of discovery and UK BiLEVE pack-year adjusted analyses MAF (MAC) Association result MAF (MAC) Association result MAF (MAC) Association result Association result rs no.

889 controls) Discovery unadjusted analysis (3226 cases, 4784 controls) UK BiLEVE pack-years adjusted analysis (4231 cases, 8979 controls) Meta-analysis of discovery and UK BiLEVE pack-year adjusted analyses MAF (MAC) Association result MAF (MAC) Association result MAF (MAC) Association result Association result rs no. CHR Position Coded allele Gene Cases Controls OR (95% CI) p Value* Cases Controls OR (95% CI) p Value* Cases Controls OR (95% CI) p Value* OR (95% CI) p Value* rs3813803 1 28282292 C SMPDL3B (non-synonymous) 30.6% (1541) 28.3% (2203) 1.370 (1.207 to 1.554) 2.41×10−6 30.3% (1956) 28.5% (2722) 1.288 (1.160 to 1.430) 2.11×10−6 28.7% (2418) 29.4% (5269) 0.968 (0.911 to 1.029) 0.298 1.033 (0.978 to 1.092) 0.241 rs17368582 11 102738075 C MMP12 (synonymous) 11.1% (561) 12.9% (1001) 0.767 (0.642 to 0.915) 3.22×10−3 11.1% (719) 12.8% (1229) 0.712 (0.615 to 0.824) 5.01×10−6 12.0% (1015) 12.2% (2198) 0.982 (0.902 to 1.069) 0.676 0.938 (0.868 to 1.013) 0.101 rs3827522 12 42853871 A PRICKLE1 (non-synonymous) 0.2% (11) 0.4% (27) 0.184 (0.065 to 0.519) 1.39×10−3 0.2% (14) 0.5% (46) 0.123 (0.057 to 0.266) 1.03×10−7 0.3% (21) 0.3% (45) 0.907 (0.518 to 1.585) 0.731 0.633 (0.386 to 1.039) 0.071 rs8034191 15 78806023 C near AGPHD1 (intergenic) 38.0% (1912) 32.7% (2546) 1.374 (1.218 to 1.550) 2.42×10−7 37.7% (2432) 32.9% (3144) 1.364 (1.234 to 1.507) 1.18×10−9 39.2% (3315) 35.2% (6320) 1.156 (1.092 to 1.224) 6.85×10−7 1.193 (1.133 to 1.257) 2.79×10−11 rs7269297 20 49576664 G MOCS3 (non-synonymous) 0.7% (37) 1.4% (110) 0.251 (0.140 to 0.448) 3.08×10−6 0.8% (54) 1.5% (139) 0.423 (0.262 to 0.680) 3.98×10−4 1.2% (98) 1.4% (252) 0.742 (0.578 to 0.953) 0.019 0.626 (0.497 to 0.789) 7.27×10−5 (B) SNPs with p<10–5 in the meta-analysis (only most statically significant SNP in each region shown) Discovery pack-years adjusted analysis (2517 cases, 3889 controls) UK BiLEVE pack-years adjusted analysis (4231 cases, 8979 controls) Meta-analysis of discovery and UK BiLEVE pack-year adjusted analyses MAF (MAC) Association result MAF (MAC) Association result Association result rs no.

ificant SNP in each region shown) Discovery pack-years adjusted analysis (2517 cases, 3889 controls) UK BiLEVE pack-years adjusted analysis (4231 cases, 8979 controls) Meta-analysis of discovery and UK BiLEVE pack-year adjusted analyses MAF (MAC) Association result MAF (MAC) Association result Association result rs no. CHR Position Coded allele Gene Cases Controls OR (95% CI) p Value* Cases Controls OR (95% CI) p Value* OR (95% CI) p Value* rs1828591 4 145480780 A GYPA/HHIP (intergenic) 35.6% (1794) 39.1% (3042) 0.9167 (0.814, 1.032) 0.153 36.6% (3088) 40.0% (771) 0.867 (0.819, 0.918) 9.88×10−7 0.876 (0.832, 0.922) 5.75×10−7 rs4896582 6 142703877 A GPR126 (intronic) 29.3% (1473) 31.7% (2468) 0.8594 (0.757, 0.974) 0.018 28.0% (2349) 30.2% (5344) 0.879 (0.826, 0.934) 3.87×10−5 0.875 (0.827, 0.925) 2.53×10−6 rs140549288 10 91099466 C IFIT3 (exonic), LIPA (intronic) 0.8% (38) 0.6% (44) 2.156 (1.046, 4.445) 0.037 0.9% (79) 0.6% (100) 1.880 (1.378, 2.565) 6.87×10−5 1.924 (1.441, 2.560) 8.56×10−6 *p Values in bold significant at p<10−5 level. BiLEVE, Biobank Lung Exome Variant Evaluation; MAC, minor allele count; MAF, minor allele frequency; SNPs, single nucleotide polymorphisms.

CHR Position Coded allele Gene Cases Controls OR (95% CI) p Value* Cases Controls OR (95% CI) p Value* OR (95% CI) p Value* rs1828591 4 145480780 A GYPA/HHIP (intergenic) 35.6% (1794) 39.1% (3042) 0.9167 (0.814, 1.032) 0.153 36.6% (3088) 40.0% (771) 0.867 (0.819, 0.918) 9.88×10−7 0.876 (0.832, 0.922) 5.75×10−7 rs4896582 6 142703877 A GPR126 (intronic) 29.3% (1473) 31.7% (2468) 0.8594 (0.757, 0.974) 0.018 28.0% (2349) 30.2% (5344) 0.879 (0.826, 0.934) 3.87×10−5 0.875 (0.827, 0.925) 2.53×10−6 rs140549288 10 91099466 C IFIT3 (exonic), LIPA (intronic) 0.8% (38) 0.6% (44) 2.156 (1.046, 4.445) 0.037 0.9% (79) 0.6% (100) 1.880 (1.378, 2.565) 6.87×10−5 1.924 (1.441, 2.560) 8.56×10−6 *p Values in bold significant at p<10−5 level. BiLEVE, Biobank Lung Exome Variant Evaluation; MAC, minor allele count; MAF, minor allele frequency; SNPs, single nucleotide polymorphisms. A further two loci were associated with COPD risk in the analysis unadjusted for pack-years: rs3827522 within PRICKLE1 (OR: 0.12, MAF=0.4%, p=1.03×10−7) and rs17368582 within MMP12 (OR: 0.712, MAF=12.2% p=5.01×10−6, table 2A and figure 2B); however, there was no evidence of replication of these associations with COPD risk in UK BiLEVE. rs2276109, another SNP within MMP12, (MAF=5.6%) which is strongly correlated with rs17368582 (r2=0.84), has previously been associated with COPD risk in smokers.7 Overall, no associations in novel regions met exome-wide significance (p<3.7×10−7).

vidence of replication of these associations with COPD risk in UK BiLEVE. rs2276109, another SNP within MMP12, (MAF=5.6%) which is strongly correlated with rs17368582 (r2=0.84), has previously been associated with COPD risk in smokers.7 Overall, no associations in novel regions met exome-wide significance (p<3.7×10−7). Analyses of severity of airflow limitation Although no SNPs reached the p<10−5 significance level in either the pack-years adjusted, or the unadjusted analysis (see online supplementary figures S2 and S3), six SNPs showed some evidence of association (p<10−4) in one or both analyses (see online supplementary table S2). Of note, rs28929474, the z-allele within the SERPINA1 gene, showed modest association in the unadjusted analysis (β=−6.17%, MAF=2.0%, p=2.83×10−5). UK BiLEVE meta-analysis results Analyses of COPD risk For the 57 234 polymorphic SNPs common to both the COPD exome chip consortium samples and the UK BiLEVE study, a meta-analysis of discovery and UK BiLEVE study results was undertaken in which three regions showed association with risk of COPD (p<10−5, figure 3, online supplementary figure S4 and table 2B). The GYPA/HHIP and GPR126 regions have previously been reported as showing association with lung function and COPD or airflow limitation risk.3 10 14 The IFIT3 region signal (rs140549288 p.Val352Leu in IFIT3, OR: 1.92, MAF=0.7%, p=7.49×10−6) represents a novel rare variant signal of association with COPD. Figure 3 Meta-analysis of COPD risk in discovery exome analysis and UK Biobank Lung Exome Variant Evaluation samples.

UK BiLEVE meta-analysis results Analyses of COPD risk For the 57 234 polymorphic SNPs common to both the COPD exome chip consortium samples and the UK BiLEVE study, a meta-analysis of discovery and UK BiLEVE study results was undertaken in which three regions showed association with risk of COPD (p<10−5, figure 3, online supplementary figure S4 and table 2B). The GYPA/HHIP and GPR126 regions have previously been reported as showing association with lung function and COPD or airflow limitation risk.3 10 14 The IFIT3 region signal (rs140549288 p.Val352Leu in IFIT3, OR: 1.92, MAF=0.7%, p=7.49×10−6) represents a novel rare variant signal of association with COPD. Figure 3 Meta-analysis of COPD risk in discovery exome analysis and UK Biobank Lung Exome Variant Evaluation samples. Analyses of severity of airflow limitation A total of 54 168 SNPs were included in the meta-analysis of severity of airflow limitation (see online supplementary figures S5 and S6). One SNP showed association with p<10−5: rs140198372, a variant which alters the sequence at a site where the splicing of an intron takes place (splice site) in SERPINA12 (β=−33.51%, MAF=0.03%, p=5.72×10−6, table 3). Table 3 Top associations (p<10−5) in meta-analysis of severity of airflow limitation

Analyses of severity of airflow limitation A total of 54 168 SNPs were included in the meta-analysis of severity of airflow limitation (see online supplementary figures S5 and S6). One SNP showed association with p<10−5: rs140198372, a variant which alters the sequence at a site where the splicing of an intron takes place (splice site) in SERPINA12 (β=−33.51%, MAF=0.03%, p=5.72×10−6, table 3). Table 3 Top associations (p<10−5) in meta-analysis of severity of airflow limitation Severity of airflow limitation, adjusted for pack-years (n=2517) UK BiLEVE pack-years adjusted analysis (n=4231) Meta-analysis of discovery and UK BiLEVE pack-year adjusted analyses rs no. CHR Position Coded allele Gene MAF (MAC) Beta (95% CI) p Value MAF (MAC) Beta (95% CI) p Value Beta (95% CI) p Value rs140198372 14 94953832 A SERPINA12 (splice site) 0.059% (3) −29.23 (−49.50 to −8.96) 2.59×10−5 0.012% (1) −38.35 (−59.88 to −16.82) 4.11×10−4 −33.51 (−48.27 to −18.76) 5.72×10−6 *p Values in bold significant at p<10−5 level. BiLEVE, Biobank Lung Exome Variant Evaluation; MAC, minor allele count; MAF, minor allele frequency.

Severity of airflow limitation, adjusted for pack-years (n=2517) UK BiLEVE pack-years adjusted analysis (n=4231) Meta-analysis of discovery and UK BiLEVE pack-year adjusted analyses rs no. CHR Position Coded allele Gene MAF (MAC) Beta (95% CI) p Value MAF (MAC) Beta (95% CI) p Value Beta (95% CI) p Value rs140198372 14 94953832 A SERPINA12 (splice site) 0.059% (3) −29.23 (−49.50 to −8.96) 2.59×10−5 0.012% (1) −38.35 (−59.88 to −16.82) 4.11×10−4 −33.51 (−48.27 to −18.76) 5.72×10−6 *p Values in bold significant at p<10−5 level. BiLEVE, Biobank Lung Exome Variant Evaluation; MAC, minor allele count; MAF, minor allele frequency. Sensitivity analyses to assess COPD case criteria Of our 3226 COPD cases defined as described above, 1398 also had a GOLD 2 or worse COPD based on postbronchodilator spirometry. We carried out a sensitivity analysis for all SNPs identified in our discovery or meta-analyses of COPD risk, by repeating the discovery analyses including only those 1398 COPD cases which underwent reversibility testing. This analysis showed consistent estimated effect sizes (see online supplementary table S3 and figure S7), and in particular, the ORs were not substantially attenuated for rs7269297 in MOCS3 (sensitivity analysis OR: 0.276; original discovery OR: 0.251), nor rs140549288 in IFIT3 (sensitivity analysis OR: 2.554; original discovery OR: 2.156).

onsistent estimated effect sizes (see online supplementary table S3 and figure S7), and in particular, the ORs were not substantially attenuated for rs7269297 in MOCS3 (sensitivity analysis OR: 0.276; original discovery OR: 0.251), nor rs140549288 in IFIT3 (sensitivity analysis OR: 2.554; original discovery OR: 2.156). Association of novel loci with smoking behaviour Given the disparity of smoking behaviour in our cases and control samples (table 1), we further investigated whether either of the two novel COPD risk loci were associated with smoking behaviour, to ascertain whether the associations with COPD may be explained by differences in smoking. Neither of the sentinel SNPs showed significant association with heavy versus never smoking within UK BiLEVE (p=0.956 for rs7269297 and p=0.945 for rs140549288) study. We further undertook a look-up in the publically available results of a GWAS from the Tobacco and Genetics consortium22 for associations with rs7269297 in MOCS3 (rs140549288 was not available in data) and a number of smoking traits; however, no evidence for association with smoking behaviour was found (cigarettes per day p=0.610; ever vs never smoking p=0.172; current vs former smoking p=0.699).

rom the Tobacco and Genetics consortium22 for associations with rs7269297 in MOCS3 (rs140549288 was not available in data) and a number of smoking traits; however, no evidence for association with smoking behaviour was found (cigarettes per day p=0.610; ever vs never smoking p=0.172; current vs former smoking p=0.699). Discussion We carried out analyses of exome chip variants with COPD risk and %predicted FEV1 among cases, through which we identified a number of SNPs in both known COPD regions and at novel loci that showed suggestive association (p<10−5) with risk of COPD. These novel regions (region plots: online supplementary figure S8) warrant further investigation as they may provide insight into the underlying biological mechanisms of COPD and airflow limitation in smokers and could provide novel therapeutic targets. The most significant associations in both the discovery exome analysis and the meta-analysis were with SNPs in the 15q25 region, previously identified through GWAS as being associated with smoking behaviour,22–24 lung cancer,25 COPD3 and airflow obstruction.14 In addition, we independently replicated previously reported associations of HHIP,3 10 GPR12614 and MMP127 8 with COPD risk.

lysis and the meta-analysis were with SNPs in the 15q25 region, previously identified through GWAS as being associated with smoking behaviour,22–24 lung cancer,25 COPD3 and airflow obstruction.14 In addition, we independently replicated previously reported associations of HHIP,3 10 GPR12614 and MMP127 8 with COPD risk. We identified novel associations between COPD risk and low frequency or rare coding SNPs in two genes: MOCS3 (rs7269297, serine to alanine, MAF=1.3%, pdiscovery=3.08×10−6, PolyPhen prediction: benign) and IFIT3 (rs140549288, valine to leucine, MAF=0.7%, pmeta=8.56×10−6, PolyPhen prediction: benign). The protein encoded by MOCS3 adenylates and activates molybdopterin synthase, an enzyme required to synthesise molybdenum cofactor26 and is expressed in bronchial epithelium and smooth muscle layer of the bronchus.27 IFIT3 is associated with interferon-α antiviral activity and has been found to be up-regulated in respiratory syncytial virus infection28 and in human lung epithelial cells infected with dengue virus.29 The SNP rs140549288 is also located within in an intron of LIPA; the product of this gene is involved in the hydrolysis of cholesteryl esters and triglycerides and other SNPs within this gene have previously been associated with coronary artery disease.30

and in human lung epithelial cells infected with dengue virus.29 The SNP rs140549288 is also located within in an intron of LIPA; the product of this gene is involved in the hydrolysis of cholesteryl esters and triglycerides and other SNPs within this gene have previously been associated with coronary artery disease.30 The z-allele within the SERPINA1 gene was associated with a lower %predicted FEV1 in cases (unadjusted analysis: pdiscovery=2.83×10−5); as well as being a well-established cause of AAT deficiency,3 4 this SNP has also previously been associated with an increased annual decline in FEV1 in a general population sample31 and increased airflow limitation in COPD cases.32 In the present study, the z-allele was associated with an increased risk of COPD, although this was not statistically significant (OR: 1.27, p=0.252). The likely reason for the lack of a significant association with this known COPD locus is that some of the case collections excluded individuals with AAT deficiency, resulting in selection bias. In the meta-analysis of severity of airflow limitation, we identified a very rare SNP within another serine protease inhibitor gene, SERPINA12, not previously associated with COPD (rs140198372, MAF=0.03%, pmeta=5.72×10−6). SERPINA12 and SERPINA1 lie 96.6 kb apart on chromosome 14 (rs140198372 and the z-allele in SERPINA1 are not in linkage disequilibrium (r2=9.0×10−6)). SERPINA12 has been associated with cardiovascular diseases, being implicated in obesity and type 2 diabetes.33

ciated with COPD (rs140198372, MAF=0.03%, pmeta=5.72×10−6). SERPINA12 and SERPINA1 lie 96.6 kb apart on chromosome 14 (rs140198372 and the z-allele in SERPINA1 are not in linkage disequilibrium (r2=9.0×10−6)). SERPINA12 has been associated with cardiovascular diseases, being implicated in obesity and type 2 diabetes.33 One of the primary challenges associated with identifying low frequency variants associated with disease is limited statistical power, and this could explain our lack of strong statistically significant findings. Indeed, none of the reported associations in novel regions met a stringent exome-wide significance level (p<3.8×10−7) overall. In the present study, we would have just 54% power to detect an association with an SNP associated with COPD risk with a MAF of 1% and an OR of 2, at the p<3.8×10−7 level. Furthermore, recent analyses undertaken by the UK10K Consortium found no evidence of low frequency SNPs having large effects, upon a series of traits.34 Due to the limited power to detect single variant associations of rare variants with modest effect sizes, we additionally adopted gene-based analyses using SKAT-O, a method which combines information from several rare variants (see online supplementary information). In these analyses, we only identified one gene meeting our elected significance level (p<10−5); this gene-based signal in PRICKLE1 was found however, to be driven by a single SNP, which was identified as being associated with COPD risk in the single variant discovery analysis, but which was not replicated in the UK BiLEVE data.

analyses, we only identified one gene meeting our elected significance level (p<10−5); this gene-based signal in PRICKLE1 was found however, to be driven by a single SNP, which was identified as being associated with COPD risk in the single variant discovery analysis, but which was not replicated in the UK BiLEVE data. Another limitation of this study is that a number of our cases had only prebronchodilator spirometry; for these samples, it could not be determined whether their airflow limitation was reversible, and so a proportion of these cases may not have met the clinical definition of COPD. We undertook case–control sensitivity analyses using our discovery samples, restricting cases to the subset of 1398 individuals taken from COPD cohorts and who had known irreversible airflow limitation. The effect estimates of our top hits did not substantially change in this sensitivity analysis, suggesting that our broader case definition, including samples that did not undergo reversibility testing, did not result in substantial misclassification bias. A further potential source of bias in this study was the heavier smoking history in our cases compared with the control samples. For the two SNPs identified through the analyses of COPD risk, we found no evidence of association with smoking in data from the UK BiLEVE study, suggesting that the associations with COPD risk were not driven by the imbalances in smoking behaviour.

eavier smoking history in our cases compared with the control samples. For the two SNPs identified through the analyses of COPD risk, we found no evidence of association with smoking in data from the UK BiLEVE study, suggesting that the associations with COPD risk were not driven by the imbalances in smoking behaviour. Finally, it was not possible to validate the findings of this study through additional genotyping; however for the three reported loci, consistent results were observed in both the discovery and the UK BiLEVE samples. It would not be expected to see the same false positive result in these two independent samples, therefore, strengthening the evidence for these being true associations.

through additional genotyping; however for the three reported loci, consistent results were observed in both the discovery and the UK BiLEVE samples. It would not be expected to see the same false positive result in these two independent samples, therefore, strengthening the evidence for these being true associations. In summary, we have identified potentially interesting associations with low frequency and rare SNPs and COPD risk in two regions not previously implicated in COPD or lung function. We further identified an association of %predicted FEV1 in individuals with COPD with a very rare SNP in SERPINA12. Further confirmation of these associations in larger independent collections of COPD cases and controls is needed. This study also provides further evidence that the z-allele within SERPINA1 may be related to severity of airflow limitation in COPD. While large sample sizes may be required to definitively identify novel loci, we present evidence to support the notion that the genetic contribution to COPD risk comprises polygenic contributions of rare, low frequency and common genetic variants. Future studies, alone or in combination, should aim to target the full allele frequency range to unravel the genetic architecture of COPD.

ovel loci, we present evidence to support the notion that the genetic contribution to COPD risk comprises polygenic contributions of rare, low frequency and common genetic variants. Future studies, alone or in combination, should aim to target the full allele frequency range to unravel the genetic architecture of COPD. This research used the ALICE and SPECTRE High Performance Computing Facilities at the University of Leicester and was supported by the National Institute for Health Research (NIHR) Leicester Respiratory Biomedical Research Unit. This article/paper/report presents independent research funded partially by the NIHR. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health. This research has been conducted using the UK Biobank Resource. Contributors: Case collection study concept, or data acquisition and quality control: IS, IPH, DPS, RM, PW, JPC, AA, MC, CD, MK, JE, NK, SC, TGB, TMM, CNAP, RT, JWH, AAS, EMD, CC, MB, BB, CB, CEB, MEJ, SGP, MFM, AJW, MJC, BJP, BHS, SP and LH. Genotype data acquisition and QC: KES and PD. Central study design, analysis and writing of manuscript: VEJ, IN, LVW, MDT, IPH and IS.

isition and quality control: IS, IPH, DPS, RM, PW, JPC, AA, MC, CD, MK, JE, NK, SC, TGB, TMM, CNAP, RT, JWH, AAS, EMD, CC, MB, BB, CB, CEB, MEJ, SGP, MFM, AJW, MJC, BJP, BHS, SP and LH. Genotype data acquisition and QC: KES and PD. Central study design, analysis and writing of manuscript: VEJ, IN, LVW, MDT, IPH and IS. Funding: British Women's Heart and Health Study is funded by the Department of Health grant no. 90049 and the British Heart Foundation grant no. PG/09/022. British Regional Heart Study is supported by the British Heart Foundation (grant RG/13/16/30528). CB (COPDBEAT) received funding from the Medical Research Council UK (grant no. G0601369), CB (COPDBEAT) and AJW (UKCOPD) were supported by the National Institute for Health Research (NIHR Leicester Biomedical Research Unit). MB (COPDBEAT) received funding from the NIHR (grant no. PDF-2013-06-052). Hertfordshire Cohort Study received support from the Medical Research Council, Arthritis Research UK, the International Osteoporosis Foundation and the British Heart Foundation; NIHR Biomedical Research Centre in Nutrition, University of Southampton; NIHR Musculoskeletal Biomedical Research Unit, University of Oxford. Generation Scotland: Scottish Family Health Study is funded by the Chief Scientist Office, Scottish Government Health Directorates, grant number CZD/16/6 and the Scottish Funding Council grant HR03006. EU COPD Gene Scan is funded by the European Union, grant no. QLG1-CT-2001-01012. English Longitudinal Study of Aging is funded by the Institute of Aging, NIH grant No. AG1764406S1. GoDARTs is funded by the Wellcome Trust grants 072960, 084726 and 104970. MDT has been supported by MRC fellowship G0902313. UK Biobank Lung Exome Variant Evaluation study was funded by a Medical Research Council strategic award to MDT, IPH, DPS and LVW (MC_PC_12010).

the Institute of Aging, NIH grant No. AG1764406S1. GoDARTs is funded by the Wellcome Trust grants 072960, 084726 and 104970. MDT has been supported by MRC fellowship G0902313. UK Biobank Lung Exome Variant Evaluation study was funded by a Medical Research Council strategic award to MDT, IPH, DPS and LVW (MC_PC_12010). Competing interests: None declared. Ethics approval: Several (meta-analysis design). Provenance and peer review: Not commissioned; externally peer reviewed.

Key messages What is the key question? This work evaluates the performance of the first health-related quality of life measure for adults with primary ciliary dyskinesia (QOL-PCD), to overcome the lack of a reliable outcome measure in this illness. What is the bottom line? Psychometric validation has shown that QOL-PCD is a reliable and valid patient-reported outcome measure for adults with PCD. Why read on? This work has led to the first validated health-related QOL instrument specific for PCD, providing a promising outcome measure for use in clinical trials and clinical practice. Introduction Primary ciliary dyskinesia (PCD) is a rare, heterogeneous genetic disorder characterised by impaired mucociliary clearance due to abnormal ciliary function. Individuals with PCD usually present with unexplained neonatal respiratory symptoms in the first few days of life,1 2 have early onset of persistent sinopulmonary infections, bronchiectasis during childhood3 and a progressive decline in lung function.4 This can lead to end-stage lung disease with a report that 25% of adult patients with PCD in the USA required long-term oxygen or lung transplantation.5 Male infertility is common since sperm flagella have a similar ultrastructure to cilia; the incidence of female infertility and of ectopic pregnancy is unclear and is explained by immotile fallopian tube cilia. Motile embryonic nodal cilia contribute to left-right asymmetry and nearly half of patients with PCD exhibit situs inversus, and 12% heterotaxy syndromes, sometimes associated with complex congenital cardiac defects.6

ale infertility and of ectopic pregnancy is unclear and is explained by immotile fallopian tube cilia. Motile embryonic nodal cilia contribute to left-right asymmetry and nearly half of patients with PCD exhibit situs inversus, and 12% heterotaxy syndromes, sometimes associated with complex congenital cardiac defects.6 Outcome measures that have been used to assess disease severity in PCD include spirometry,4 chest CT,3 7 MRI8 and lung clearance index.9–12 These physiological and radiological measures all have limitations in terms of their sensitivity or feasibility to monitor disease progression. The Food and Drug Administration (FDA) and the European Medicines Agency strongly endorse the use of outcome measures in clinical trials, assessing the impact of the disease on the patient's daily symptoms and functioning (eg, physical, respiratory, social) in addition to physiological measures.13–15 Treatment strategies for PCD have necessarily been applied from other diseases,16 17 particularly cystic fibrosis, since no medications have specifically been tested and approved for PCD. A major obstacle to evaluating new treatments and monitoring disease progression is the lack of a disease-specific outcome measure.18 Thus, we developed health-related quality of life (HRQOL) measures to assess the impact of PCD for children, teenagers and adults from the patient perspective. 19 Here, we present the psychometric validation of the English version of QOL-PCD in adults from the UK, the USA and Canada.

Outcome measures that have been used to assess disease severity in PCD include spirometry,4 chest CT,3 7 MRI8 and lung clearance index.9–12 These physiological and radiological measures all have limitations in terms of their sensitivity or feasibility to monitor disease progression. The Food and Drug Administration (FDA) and the European Medicines Agency strongly endorse the use of outcome measures in clinical trials, assessing the impact of the disease on the patient's daily symptoms and functioning (eg, physical, respiratory, social) in addition to physiological measures.13–15 Treatment strategies for PCD have necessarily been applied from other diseases,16 17 particularly cystic fibrosis, since no medications have specifically been tested and approved for PCD. A major obstacle to evaluating new treatments and monitoring disease progression is the lack of a disease-specific outcome measure.18 Thus, we developed health-related quality of life (HRQOL) measures to assess the impact of PCD for children, teenagers and adults from the patient perspective. 19 Here, we present the psychometric validation of the English version of QOL-PCD in adults from the UK, the USA and Canada. Methods Participants Participants were recruited from PCD diagnostic centres across the UK, the USA and Canada. Adults (aged ≥18 years) with a positive diagnosis of PCD were eligible to participate. Information about the study was provided at a clinic appointment or by telephone. The following inclusion criteria had to be met by patients: (1) diagnosis of PCD in one of the specified diagnostic centres, (2) age ≥18 years and (3) ability to read and speak English fluently. The UK participants had been diagnosed at one of the English diagnostic centres20 21 based on clinical phenotype plus high-speed video analysis of ciliary function and/or assessment of ciliary ultrastructure by electron microscopy. North American participants were diagnosed at a specialised PCD research centre, based on: a compatible clinical phenotype plus defect in ciliary ultrastructure and/or identification of biallelic disease-causing mutations in one of the PCD genes.

ction and/or assessment of ciliary ultrastructure by electron microscopy. North American participants were diagnosed at a specialised PCD research centre, based on: a compatible clinical phenotype plus defect in ciliary ultrastructure and/or identification of biallelic disease-causing mutations in one of the PCD genes. QOL-PCD scales The QOL-PCD adult version consisted of 49 items,19 which were self-completed electronically at home or in the clinic, where possible; ‘pen and paper’ copies were provided to those without access to internet. Development of QOL-PCD followed the procedures and analyses recommended by the FDA guidance on patient-reported outcome measures.15 Previously reported content validity, clinical relevance score and cognitive testing conducted on interim versions supported QOL-PCD concepts, items and scale options.19 Participants were provided with a unique study number and a link to the online survey. No identifiable information was included, and the data were captured on a server of University of Southampton. Most responses used a 4-point Likert scale: ‘not at all true’ to ‘very true’ or ‘never’ to ‘always’. The first time QOL-PCD was completed, participants also completed generic questionnaires: Short-Form 36 Health Survey (SF-36), the shortened St George Respiratory Questionnaire (SGRQ-C) and a measure focusing on rhinosinus symptoms: Sino-Nasal Outcome Test 20 (SNOT-20). Further details on this protocol and these measures can be found in the online supplementary data. 10.1136/thoraxjnl-2016-209356.supp1supplementary data

QOL-PCD scales The QOL-PCD adult version consisted of 49 items,19 which were self-completed electronically at home or in the clinic, where possible; ‘pen and paper’ copies were provided to those without access to internet. Development of QOL-PCD followed the procedures and analyses recommended by the FDA guidance on patient-reported outcome measures.15 Previously reported content validity, clinical relevance score and cognitive testing conducted on interim versions supported QOL-PCD concepts, items and scale options.19 Participants were provided with a unique study number and a link to the online survey. No identifiable information was included, and the data were captured on a server of University of Southampton. Most responses used a 4-point Likert scale: ‘not at all true’ to ‘very true’ or ‘never’ to ‘always’. The first time QOL-PCD was completed, participants also completed generic questionnaires: Short-Form 36 Health Survey (SF-36), the shortened St George Respiratory Questionnaire (SGRQ-C) and a measure focusing on rhinosinus symptoms: Sino-Nasal Outcome Test 20 (SNOT-20). Further details on this protocol and these measures can be found in the online supplementary data. 10.1136/thoraxjnl-2016-209356.supp1supplementary data Statistical analysis Statistical analyses were conducted using the SPSS Statistics for Windows, V.21.0 (IBM, Armonk, New York, USA); p<0.05 was considered to be statistically significant.

QOL-PCD scales The QOL-PCD adult version consisted of 49 items,19 which were self-completed electronically at home or in the clinic, where possible; ‘pen and paper’ copies were provided to those without access to internet. Development of QOL-PCD followed the procedures and analyses recommended by the FDA guidance on patient-reported outcome measures.15 Previously reported content validity, clinical relevance score and cognitive testing conducted on interim versions supported QOL-PCD concepts, items and scale options.19 Participants were provided with a unique study number and a link to the online survey. No identifiable information was included, and the data were captured on a server of University of Southampton. Most responses used a 4-point Likert scale: ‘not at all true’ to ‘very true’ or ‘never’ to ‘always’. The first time QOL-PCD was completed, participants also completed generic questionnaires: Short-Form 36 Health Survey (SF-36), the shortened St George Respiratory Questionnaire (SGRQ-C) and a measure focusing on rhinosinus symptoms: Sino-Nasal Outcome Test 20 (SNOT-20). Further details on this protocol and these measures can be found in the online supplementary data. 10.1136/thoraxjnl-2016-209356.supp1supplementary data Statistical analysis Statistical analyses were conducted using the SPSS Statistics for Windows, V.21.0 (IBM, Armonk, New York, USA); p<0.05 was considered to be statistically significant. To estimate the sample size needed to calculate internal consistency, we used equations developed by Bonett22 to identify sample sizes needed to establish Cronbach's α. This formula used parameters for precision, number of items and level of reliability. For an average of five items per scale, a sample size of 59 yielded an α coefficient of 0.70 with 95% CI.

lculate internal consistency, we used equations developed by Bonett22 to identify sample sizes needed to establish Cronbach's α. This formula used parameters for precision, number of items and level of reliability. For an average of five items per scale, a sample size of 59 yielded an α coefficient of 0.70 with 95% CI. We assessed the distribution of responses for each item and each scale to look for floor and ceiling effects. We conducted multitrait analysis to test the fit between items and their hypothesised versus competing scales. This type of analysis was developed for smaller samples for which factor analysis is not appropriate. These analyses assessed the extent to which items correlated with their hypothesised versus competing scales; we required item-to-scale correlations ≥0.40 with the intended scale and lower correlations with competing scales.23–25 We considered floor and ceiling effects, using <15% of participants as the threshold for the highest and lowest scores for a scale.26 Reliability Internal consistency of the QOL-PCD scales were investigated by Cronbach's α values. Cronbach’s α gives a score of between 0 and 1, a value of >0.70 indicating good internal consistency. Items were removed if this led to higher internal consistency (ie, higher Cronbach's α) to increase the parsimony and efficiency of the instrument.27 28 The distributions of responses and multitrait analyses were reviewed in a series of teleconferences to decide which items could be removed, allowing us to shorten QOL-PCD, taking reliability and clinical relevance into consideration.

higher Cronbach's α) to increase the parsimony and efficiency of the instrument.27 28 The distributions of responses and multitrait analyses were reviewed in a series of teleconferences to decide which items could be removed, allowing us to shorten QOL-PCD, taking reliability and clinical relevance into consideration. Test–retest reliability was assessed using intraclass correlation coefficients (ICC) in stable patients who completed the QOL-PCD a second time, 10–14 days after completing the baseline measures. An ICC value of >0.60 provided evidence of good stability and >0.75 excellent stability for each scale.

higher Cronbach's α) to increase the parsimony and efficiency of the instrument.27 28 The distributions of responses and multitrait analyses were reviewed in a series of teleconferences to decide which items could be removed, allowing us to shorten QOL-PCD, taking reliability and clinical relevance into consideration. Test–retest reliability was assessed using intraclass correlation coefficients (ICC) in stable patients who completed the QOL-PCD a second time, 10–14 days after completing the baseline measures. An ICC value of >0.60 provided evidence of good stability and >0.75 excellent stability for each scale. Validity For a questionnaire to be construct valid, all items together should represent the underlying construct (HRQOL). Construct validity can be determined by testing the instrument against hypotheses. We hypothesised a priori that clinical features (age, gender, FEV1 and Pseudomonas status) would correlate with specific scales (eg, FEV1 would correlate with physical functioning and upper respiratory symptoms (construct validity). Cohen's guidelines for the interpretation of correlation coefficients were used; correlations between 0.50 and 1.00 were interpreted as strong, correlations between 0.30 and 0.50 as moderate, correlations between 0.10 and 0.30 as small and correlations <0.1 as weak.29 We also predicted that QOL-PCD scales would have moderate correlations (>0.3), using Spearman's correlation, with generic scales (SF-36, SGRQ-C and SNOT-20) measuring similar constructs (convergent validity). We hypothesised small or weak correlations (<0.3) with scales measuring different constructs (divergent validity). Details of the hypothesised correlations are provided in tables 4 and 5.

), using Spearman's correlation, with generic scales (SF-36, SGRQ-C and SNOT-20) measuring similar constructs (convergent validity). We hypothesised small or weak correlations (<0.3) with scales measuring different constructs (divergent validity). Details of the hypothesised correlations are provided in tables 4 and 5. Distribution-based measures of clinical significance Minimal clinically important difference (MCID) in a HRQOL score is defined as ‘the smallest difference in score in the domain of interest which patients perceive as beneficial and which would mandate, in the absence of troublesome side effects and excessive cost, a change in the patient's management’.30 MCID can be determined using anchor-based and distribution-based methods. Distribution-based methods rely on the statistical distributions of HRQOL scores in a given study. We applied two distribution-based methods in this study. These were: (1) SDs of QOL-PCD scale mean scores were divided by 2 to establish a 1/2 SD change and (2) the SE of measurement (SEM) was generated for all QOL-PCD scores using the following formula: SD√(1−α) (SD=SD of mean baseline QOL-PCD scale score and α=scale reliability).31 Wyrwich et al 32 suggested that a difference of 1 SEM frequently corresponds to a MCID. Ethical approval The study was approved by the National Research Ethics Service, UK (UK 07/Q1702/109), the Research Ethics Board at the Hospital for Sick Children in Toronto, Canada and the Institutional Review Boards at the University of North Carolina, Chapel Hill. Written consent was obtained prior to participation.

Ethical approval The study was approved by the National Research Ethics Service, UK (UK 07/Q1702/109), the Research Ethics Board at the Hospital for Sick Children in Toronto, Canada and the Institutional Review Boards at the University of North Carolina, Chapel Hill. Written consent was obtained prior to participation. Results Participants Between April 2014 and March 2016, 72 adults were recruited, slightly more than that required to establish reliability. Participant characteristics are shown in table 1. The 49-item prototype took a mean time of 8 min (SD=5) to complete. Table 1 Participant characteristics by country of residence

Ethical approval The study was approved by the National Research Ethics Service, UK (UK 07/Q1702/109), the Research Ethics Board at the Hospital for Sick Children in Toronto, Canada and the Institutional Review Boards at the University of North Carolina, Chapel Hill. Written consent was obtained prior to participation. Results Participants Between April 2014 and March 2016, 72 adults were recruited, slightly more than that required to establish reliability. Participant characteristics are shown in table 1. The 49-item prototype took a mean time of 8 min (SD=5) to complete. Table 1 Participant characteristics by country of residence UK N=34 USA and Canada N=38 Female, n (%) 20 (58.8) 29 (76.3) Age Mean in years (SD) 34.8 (17.3) 31.0 (12.9) Range 18–79 18–65 18–32 years, n (%) 22 (64.7) 24 (63.2) 33–55 years, n (%) 4 (11.8) 12 (31.6) >55 years, n (%) 8 (23.5) 2 (2.6) FEV1% predicted Mean (SD) 72 (26) 66 (19) Range 26–115 33–101 >80%, n (%) 12 (35) 8 (21) Missing, n (%) 1 (3) 1 (3) Past/current growth of Pseudomonas aeruginosa, n (%) Missing, n (%) 11 (32) 23 (61) 2 (6) 1 (3) Education, n (%) Second level or less 11 (32) 8 (21) Some college 4 (12) 7 (19) College graduate/postgraduate 16 (47) 21 (55) Missing 3 (9) 2 (5) Working status, n (%) Part-time or full-time employment 18 (52.9) 7 (18.4) Full-time homemaker 1 (2.9) 13 (34.2) Attending education courses outside the home 7 (20.6) 6 (15.8) Attending education courses inside the home 0 (0) 3 (7.9) Not working due to health 3 (8.8) 2 (5.3) Not working for other reason 3 (8.8) 6 (15.8) Retired 2 (5.9) 1 (2.6) Ethnicity, n (%) White 32 (94.1) 31 (81.6) Black 0 (0) 0 (0) Hispanic 0 (0) 2 (5.3) Asian 0 (0) 2 (5.3) Other 2 (5.9) 1 (2.6) Missing 0 (0) 2 (5.3) Development of scales We used a multitrait analysis to generate 10 hypothesised scales: physical, emotional, role and social functioning, treatment burden, vitality, health perceptions, upper respiratory symptoms, lower respiratory symptoms and ears and hearing symptoms. Examination of the distribution of responses to items and the multitrait analyses enabled us to shorten the questionnaire by removing nine items which were redundant, not strongly endorsed or did not correlate strongly with its designated scale (see online supplementary table E1). The final QOL-PCD comprises 40 items on 10 scales, which were subjected to further psychometric analysis (see online supplementary table E2).

the questionnaire by removing nine items which were redundant, not strongly endorsed or did not correlate strongly with its designated scale (see online supplementary table E1). The final QOL-PCD comprises 40 items on 10 scales, which were subjected to further psychometric analysis (see online supplementary table E2). Analyses of the 40-item QOL-PCD confirmed that all items had strong correlations (≥0.63) with their intended scales. Few floor and ceiling effects were observed with the exception of: (1) floor effects on the social functioning scale (16.7% of respondents had low scores) and (2) ceiling effects were found on the physical functioning and ears and hearing scales, with 23.9% and 18.1% of respondents scoring the highest values, respectively. Reliability: internal consistency and test–retest reliability The QOL-PCD scales had moderate to strong internal consistency (0.74 to 0.94). Thirty-five participants repeated QOL-PCD after 10–14 days, providing evidence of stability across all scales with ICC ranging from 0.73 to 0.96 (table 2). Table 2 Internal consistency of QOL-PCD scales measured by Cronbach's α and test–retest reliability measured by ICC

Reliability: internal consistency and test–retest reliability The QOL-PCD scales had moderate to strong internal consistency (0.74 to 0.94). Thirty-five participants repeated QOL-PCD after 10–14 days, providing evidence of stability across all scales with ICC ranging from 0.73 to 0.96 (table 2). Table 2 Internal consistency of QOL-PCD scales measured by Cronbach's α and test–retest reliability measured by ICC QOL-PCD (adult) scales No. of items Mean (SD) of scales Cronbach's α N=72 ICC (95% CI) N=35 Physical functioning 5 70.51 (30.37) 0.94 0.94 (0.89 to 0.97) Emotional functioning 5 73.68 (19.56) 0.83 0.91 (0.82 to 0.95) Treatment functioning 4 60.80 (23.70) 0.75 0.92 (0.82 to 0.96) Social functioning 3 38.11 (29.47) 0.74 0.73 (0.47 to 0.87) Role functioning 4 64.23 (28.98) 0.86 0.94 (0.88 to 0.97) Health perception 4 51.16 (26.32) 0.83 0.91 (0.82 to 0.95) Vitality 3 53.76 (21.93) 0.79 0.88 (0.75 to 0.94) Upper respiratory symptoms 4 45.83 (26.76) 0.83 0.91 (0.81 to 0.95) Lower respiratory symptoms 6 47.30 (15.49) 0.83 0.92 (0.85 to 0.96) Ear and hearing symptoms 2 61.81 (28.59) 0.79 0.96 (0.87 to 0.97) Cronbach's α >0.7 indicates good internal consistency. ICC >0.6 indicates good stability and >0.75 excellent stability of the scales. ICC, intraclass coefficients; PCD, primary ciliary dyskinesia; QOL, quality of life.

QOL-PCD (adult) scales No. of items Mean (SD) of scales Cronbach's α N=72 ICC (95% CI) N=35 Physical functioning 5 70.51 (30.37) 0.94 0.94 (0.89 to 0.97) Emotional functioning 5 73.68 (19.56) 0.83 0.91 (0.82 to 0.95) Treatment functioning 4 60.80 (23.70) 0.75 0.92 (0.82 to 0.96) Social functioning 3 38.11 (29.47) 0.74 0.73 (0.47 to 0.87) Role functioning 4 64.23 (28.98) 0.86 0.94 (0.88 to 0.97) Health perception 4 51.16 (26.32) 0.83 0.91 (0.82 to 0.95) Vitality 3 53.76 (21.93) 0.79 0.88 (0.75 to 0.94) Upper respiratory symptoms 4 45.83 (26.76) 0.83 0.91 (0.81 to 0.95) Lower respiratory symptoms 6 47.30 (15.49) 0.83 0.92 (0.85 to 0.96) Ear and hearing symptoms 2 61.81 (28.59) 0.79 0.96 (0.87 to 0.97) Cronbach's α >0.7 indicates good internal consistency. ICC >0.6 indicates good stability and >0.75 excellent stability of the scales. ICC, intraclass coefficients; PCD, primary ciliary dyskinesia; QOL, quality of life. Validity We had hypothesised that older age groups would report worse physical functioning, poorer vitality and more symptoms (table 3). Mean physical functioning scores were significantly worse in those who were older; average scores for the age group 18–22 years was 88.21 (SD=15.81), decreasing to 49.33 (SD=34.88) in the age group >55 years (p<0.001). Lower respiratory symptoms scores were highest in the youngest (18–22 years; M=58.76 (SD=16.72) and oldest age groups (>55 years (M=55.00 (SD=24.21)) and worst in the age group 37–55 years (M=36.30 SD=19.90). For patients with reported Pseudomonas aeruginosa, lower mean scores were reported for physical functioning and lower and upper respiratory symptoms; however, these did not reach statistical significance. For lung function, physical functioning scores were significantly lower in those with poor lung function, however, no significant associations were found on the upper and lower respiratory symptoms scores.

sical functioning and lower and upper respiratory symptoms; however, these did not reach statistical significance. For lung function, physical functioning scores were significantly lower in those with poor lung function, however, no significant associations were found on the upper and lower respiratory symptoms scores. Table 3 QOL-PCD scales mean scores and SDs for participant characteristics where we had hypothesised an association a priori Physical functioning Social functioning Vitality Treatment burden Upper respiratory symptoms Lower respiratory symptoms Gender Male (n=23) – 43.96 (32.74) – 69.61 (19.75) – 58.70 (15.32) Female (n=49) 35.37 (27.74) 57.62 (24.37) 43.20 (20.36) p=0.284 p=0.052 p=0.004 Age 18–22 years (n=26) 88.21 (15.81) – 58.55 (20.50) – 46.47 (23.35) 58.76 (16.72) 23–36 years (n=21) 69.21 (29.63) 52.91 (20.20) 43.25 (30.80) 40.21 (19.63) 37–55 years (n=15) 56.88 (32.35) 46.66 (20.30) 42.22 (27.72) 36.30 (19.90) >55 years (n=10) 49.33 (34.88) 48.88 (23.54) 55.00 (26.41) 55.00 (24.21) p<0.001 p=0.355 p=0.655 p=0.001 Pseudomonas Yes (n=34) 63.92 (33.43) – – – 43.63 (26.75) 44.12 (20.51) No (n=35) 76.38 (26.78) 48.57 (27.08) 52.22 (21.86) p=0.092 p=0.448 p=0.117 FEV1% predicted <40 (n=11) 41.82 (30.12) – – – 59.09 (24.28) 44.44 (26.99) ≥40 to <60 (n=14) 63.81 (32.94) 45.83 (26.90) 42.86 (25.17) ≥60 to <75 (n=16) 77.33 (29.04) 41.67 (28.87) 48.88 (17.21) ≥75 (n=29) 80.46 (23.70) 43.68 (26.51) 51.92 (19.49) p<0.02 p=0.364 p=0.563 PCD, primary ciliary dyskinesia; QOL, quality of life.

predicted <40 (n=11) 41.82 (30.12) – – – 59.09 (24.28) 44.44 (26.99) ≥40 to <60 (n=14) 63.81 (32.94) 45.83 (26.90) 42.86 (25.17) ≥60 to <75 (n=16) 77.33 (29.04) 41.67 (28.87) 48.88 (17.21) ≥75 (n=29) 80.46 (23.70) 43.68 (26.51) 51.92 (19.49) p<0.02 p=0.364 p=0.563 PCD, primary ciliary dyskinesia; QOL, quality of life. As hypothesised, females reported significantly worse lower respiratory symptoms (p=0.004) (table 3). Although mean treatment burden scores were higher in males, this did not reach statistical significance (p=0.052). There was also no significant difference found between males and females on the social functioning scale (table 3).

sed, females reported significantly worse lower respiratory symptoms (p=0.004) (table 3). Although mean treatment burden scores were higher in males, this did not reach statistical significance (p=0.052). There was also no significant difference found between males and females on the social functioning scale (table 3). Convergent validity was tested by examining correlations between scales measuring similar constructs on the QOL-PCD with other validated scales: SNOT-20 (upper airway), the SF-36 (generic health status) and the SGRQ-C (lower respiratory) (table 4). As expected, strong associations were found between the QOL-PCD upper respiratory symptoms and the SNOT-20 total score (r=0.60, p<0.01). Strong correlations were also found between QOL-PCD lower respiratory symptoms and SGRQ-C symptoms (r=0.69, p<0.001). On the SF-36, as hypothesised we found strong correlations between physical functioning and QOL-PCD physical scale (r=0.83, p<0.001), role-physical and QOL-PCD role functioning (r=0.83, p<0.001) and mental health with QOL-PCD emotional functioning scale (r=0.73, p<0.001). In contrast, as hypothesised, much weaker relationships were found between the QOL-PCD scores and generic questionnaires that measured dissimilar constructs (divergent validity). For example, the ear and hearing symptoms on the QOL-PCD correlated weakly with role functioning (r=0.39, p<0.001) on the SF-36 (table 4). Similarly, the QOL-PCD lower respiratory symptoms scale correlated more strongly than the upper respiratory scale, which focused on sinus symptoms, with SGRQ-C symptoms.

validity). For example, the ear and hearing symptoms on the QOL-PCD correlated weakly with role functioning (r=0.39, p<0.001) on the SF-36 (table 4). Similarly, the QOL-PCD lower respiratory symptoms scale correlated more strongly than the upper respiratory scale, which focused on sinus symptoms, with SGRQ-C symptoms. Table 4 Convergent validity testing: Spearman's rank correlation coefficients between scales from QOL-PCD and generic HRQOL measures (SNOT-20, SGRQ-C and SF-36) SNOT-20 SGRQ-C SF-36

validity). For example, the ear and hearing symptoms on the QOL-PCD correlated weakly with role functioning (r=0.39, p<0.001) on the SF-36 (table 4). Similarly, the QOL-PCD lower respiratory symptoms scale correlated more strongly than the upper respiratory scale, which focused on sinus symptoms, with SGRQ-C symptoms. Table 4 Convergent validity testing: Spearman's rank correlation coefficients between scales from QOL-PCD and generic HRQOL measures (SNOT-20, SGRQ-C and SF-36) SNOT-20 SGRQ-C SF-36 Scales of QOL-PCD Total Symptoms Activity Impacts Physical functioning Rolephysical Bodily pain General health Vitality Social functioning Role functioning Mental health Total physical score Total mental score Physical 0.51 −0.83 −0.78 −0.85 −0.74 0.83 0.79 0.54 0.79 0.65 0.62 0.49 0.46 0.84 0.37 Emotional −0.48 −0.52 −0.04 −0.47 −0.54 0.47 0.55 0.60 0.63 0.61 0.67 0.60 0.73 0.57 0.69 Treatment −0.61 −0.67 −0.61 −0.54 −0.65 0.54 0.67 0.78 0.66 0.54 0.50 0.54 0.38 0.71 0.36 Social −0.21 −0.43 −0.40 −0.28 −0.48 0.34 0.35 0.32 0.42 0.46 0.28 0.27 0.34 0.39 0.27 Role −0.60 −0.80 −0.73 −0.67 −0.78 0.73 0.84 0.68 0.79 0.69 0.72 0.44 0.47 0.86 0.43 Health −0.64 −0.84 −0.76 −0.72 −0.84 0.71 0.79 0.76 0.87 0.79 0.76 0.50 0.62 0.87 0.56 Vitality −0.46 −0.63 −0.67 −0.49 −0.59 0.59 0.56 0.60 0.72 0.75 0.52 0.43 0.39 0.69 0.43 Upper respiratory −0.60 −0.39 −0.31 −0.31 −0.39 0.36 0.51 0.54 0.48 0.35 0.42 0.37 0.39 0.48 0.35 Lower respiratory −0.53 −0.72 −0.69 −0.61 −0.69 0.59 0.69 0.58 0.75 0.67 0.59 0.43 0.49 0.69 0.49 Ear and hearing −0.57 −0.47 −0.40 −0.36 −0.49 0.51 0.49 0.46 0.66 0.55 0.51 0.39 0.46 0.56 0.47 We a priori hypothesised scales that would have stronger correlations (closer to 1 or −1); these correlations are shadowed grey.

wer respiratory −0.53 −0.72 −0.69 −0.61 −0.69 0.59 0.69 0.58 0.75 0.67 0.59 0.43 0.49 0.69 0.49 Ear and hearing −0.57 −0.47 −0.40 −0.36 −0.49 0.51 0.49 0.46 0.66 0.55 0.51 0.39 0.46 0.56 0.47 We a priori hypothesised scales that would have stronger correlations (closer to 1 or −1); these correlations are shadowed grey. HRQOL, health-related quality of life; PCD, primary ciliary dyskinesia; QOL, quality of life; SF-36, Medical Outcome Survey Short Form-36 (higher score indicates better health status); SGRQ-C, St George Respiratory Questionnaire (higher score indicates worse HRQOL). Summary of distribution-based measures The MCID was estimated from distribution analyses (table 5). Using 0.5 SD of baseline scores, MCID estimates ranged from 3.2 point (lower respiratory symptoms) to 15.2 points (physical functioning). Using SEM values derived from baseline scores, values ranged from 6.4 (lower respiratory symptoms) to 15.0 points (social functioning). Table 5 Summary of distribution-based criteria to determine a minimum clinical significant change using 0.5 SD of the mean and SE of the measure (SEM) for each of the QOL-PCD scales QOL-PCD (adult) scales 0.5 SD SEM Physical 15.2 7.4 Emotional 4.1 8.1 Treatment 6.0 11.9 Social 7.5 15.0 Role 5.4 10.8 Health perception 5.5 10.9 Vitality 3.8 7.6 Upper respiratory 5.5 11.0 Lower respiratory 3.2 6.4 Ears and hearing 6.6 13.1 PCD, primary ciliary dyskinesia; QOL, quality of life.

Table 5 Summary of distribution-based criteria to determine a minimum clinical significant change using 0.5 SD of the mean and SE of the measure (SEM) for each of the QOL-PCD scales QOL-PCD (adult) scales 0.5 SD SEM Physical 15.2 7.4 Emotional 4.1 8.1 Treatment 6.0 11.9 Social 7.5 15.0 Role 5.4 10.8 Health perception 5.5 10.9 Vitality 3.8 7.6 Upper respiratory 5.5 11.0 Lower respiratory 3.2 6.4 Ears and hearing 6.6 13.1 PCD, primary ciliary dyskinesia; QOL, quality of life. Discussion We have shown that QOL-PCD (adult version) is a valid instrument to measure HRQOL in patients with PCD. Psychometric testing confirmed our measure to be robust, reliable and valid. QOL-PCD was developed in the UK and North America to ensure cross-cultural equivalence in English-speaking countries.19 The QOL-PCD has already been translated into Danish, Dutch, German (developed and linguistically validated for Germany and Switzerland), Greek and French; translations are progressing well in Turkish, Arabic, Spanish (European), Italian and Spanish (Latin America). This international approach is important for rare diseases since multinational clinical trials are needed to recruit sufficient patients.

inguistically validated for Germany and Switzerland), Greek and French; translations are progressing well in Turkish, Arabic, Spanish (European), Italian and Spanish (Latin America). This international approach is important for rare diseases since multinational clinical trials are needed to recruit sufficient patients. Our study involved the recruitment of 72 patients with PCD from centres in North America (n=38) and the UK (n=34). All age groups were represented in the study, and participants had a range of disease severity (FEV1% predicted: 26%–115%). This collaborative effort allowed us to recruit sufficient participants with this rare disease. However, given the modest size of the study population, we conducted a multitrait analysis to develop the scales rather than an exploratory factor analysis (which would require >200 patients), or more complex analysis such as Rasche analysis (which would require >100 patients).33 The multitrait analysis supported the conceptual foundations of 10 scales with 40 items and has been recommended for use in the development of HRQOL measures, which are expected to have correlations across domains (eg, increased respiratory symptoms are likely to lead to decreased physical functioning). QOL-PCD had excellent item-to-total correlations and strong internal consistency across all scales (Cronbach's α 0.74 to 0.94). More complex analysis including factor analysis and Rasche analysis may be an option in future studies if we are able to recruit larger sample sizes.

o lead to decreased physical functioning). QOL-PCD had excellent item-to-total correlations and strong internal consistency across all scales (Cronbach's α 0.74 to 0.94). More complex analysis including factor analysis and Rasche analysis may be an option in future studies if we are able to recruit larger sample sizes. All scales were stable in an analysis of test–retest reliability over 10–14 days. QOL-PCD correlated with generic HRQOL measures (SF-36, SGRQ-C, SNOT-20). As expected, there were stronger relationships between scales assessing similar rather than dissimilar constructs. As hypothesised, physical functioning scores were highest for those with FEV1>75%. However, FEV1 did not correlate with lower respiratory symptoms scales. FEV1 may be a poor measure for disease severity in patients with PCD as demonstrated in studies showing normal spirometry in individuals with PCD with substantial structural lung changes on chest CT.3 We recommend investigation into the association between QOL-PCD and other outcome measures assessing disease severity in PCD, such as chest CT,3 7 MRI8 and lung clearance index9–12 in the future.

nstrated in studies showing normal spirometry in individuals with PCD with substantial structural lung changes on chest CT.3 We recommend investigation into the association between QOL-PCD and other outcome measures assessing disease severity in PCD, such as chest CT,3 7 MRI8 and lung clearance index9–12 in the future. Worsening QOL with age has previously been described in a PCD study using generic measures34 and decreases with age were also found in this study for both physical functioning and lower respiratory symptoms. Interestingly, the lowest QOL-PCD score in the upper and lower respiratory symptom scales were in our age group 37–55 years, with scores increasing again in the age group >55 years. Furthermore, the age group >55 years had significantly lower FEV1 compared with the younger age group combined (p=0.019). This may reflect specific challenges for this younger age group in comparison to those who are older, for example, lack of time to fit in treatments due to careers and families, or perhaps a survivor effect that is, those who have survived to 55 years may have milder symptoms. This highlights the kind of nuanced information that can be derived from HRQOL measures, reflecting the impact of disease management (ie, adherence to prescribed treatments) and progression of disease. Caution should be exercised in the interpretation of these findings where there is a limited sample size in each age group.

highlights the kind of nuanced information that can be derived from HRQOL measures, reflecting the impact of disease management (ie, adherence to prescribed treatments) and progression of disease. Caution should be exercised in the interpretation of these findings where there is a limited sample size in each age group. By facilitating discussion on issues that are of importance to patients, clinicians can focus attention on their patient's perceptions of their illness, facilitating collaborative care and shared medical decision-making. Moreover, patient's perceptions of improved functioning that are not reflected in other physiological outcomes may be important factors in promoting adherence to treatments. QOL-PCD has also been developed for children (aged 6–12 years), adolescents (aged 13–17 years) and for parents (proxy) of children aged 6–12 years. QOL-PCD has been translated into six conceptually and linguistically equivalent language versions; a number of further translations are in progress, each following a protocol-led process of forward and back translation followed by cognitive testing. Validation of the remaining age-specific tools and different languages is underway.

een translated into six conceptually and linguistically equivalent language versions; a number of further translations are in progress, each following a protocol-led process of forward and back translation followed by cognitive testing. Validation of the remaining age-specific tools and different languages is underway. We had planned to measure responsiveness in this study. Participants who contacted the study team towards the start of a respiratory exacerbation were asked to complete the questionnaire during a respiratory exacerbation. However, only 10 participants reported an exacerbation within the relatively short study period, and time periods from the well to the ill states varied considerable between patients (median of 54 days, range 3–152 days). Despite the small numbers of patients who completed QOL-PCD during an exacerbation (n=10), most scales evidenced worse mean scores in comparison to these patients' stable state. Scores for three subscales did not change during exacerbations (ears and hearing symptoms, social and emotional functioning) (figure 1) perhaps suggesting that chest exacerbations have minimal effects on these domains of functioning. Statistically significant results were also found (see online supplementary table E3); however, these were difficult to interpret given the small numbers. QOL-PCD is now being used in a randomised controlled study of azithromycin prophylaxis in PCD;35 we aim to assess the responsiveness to treatment in this study.

ing. Statistically significant results were also found (see online supplementary table E3); however, these were difficult to interpret given the small numbers. QOL-PCD is now being used in a randomised controlled study of azithromycin prophylaxis in PCD;35 we aim to assess the responsiveness to treatment in this study. Figure 1 Difference in quality of life-primary ciliary dyskinesia between a stable baseline and a day during an exacerbation. (A) Lower respiratory symptoms scores. (B) Physical functioning (n=10). The adult version of QOL-PCD is ready for use in clinical trials to assess the benefits of medications or non-pharmacological interventions. It can also be used to understand the natural course and progression of the disease in terms of its effects on physical, emotional, role and social functioning. We also propose QOL-PCD as a tool to be used at annual assessments, providing a broad assessment of well-being, as perceived by the patient. Before QOL-PCD can effectively be used to assess intervention studies, it is important to determine the MCID score to allow for interpretation of changes in scores. We conducted preliminary analysis to estimate the MCID through distribution methods. This method is considered only supportive of an MCID with an intervention required to produce an accurate determination of change. QOL-PCD is currently being included in the first randomised clinical trial for PCD. This will facilitate the MCID for each scale to be determined.

ate the MCID through distribution methods. This method is considered only supportive of an MCID with an intervention required to produce an accurate determination of change. QOL-PCD is currently being included in the first randomised clinical trial for PCD. This will facilitate the MCID for each scale to be determined. In summary, we have developed19 and validated the first HRQOL instrument specific for PCD. QOL-PCD is valid and reliable; it is short and easy for patients to complete and provides a promising outcome measure for use in clinical trials and clinical practice. We appreciate the ongoing collaboration with other Site Principal Investigators and Lead Study Coordinators in the Genetic Disorders of Mucociliary Clearance who recruited adults with primary ciliary dyskinesia (PCD) for this study, including Michael R Knowles, MD and Kelli M Sullivan at the University of North Carolina in Chapel Hill, NC; Thomas W Ferkol, MD and Jane Quante at Washington University School of Medicine, St Louis, MO; and Melody Miki at The Hospital for Sick Children, University of Toronto, Toronto, Ontario, Canada. We are grateful to the participants in this study. Members of the PCD Foundation, North America (Director, Michele Manion) and PCD Support Group, UK (Chair, Fiona Copeland) contributed to all aspects of study conduct.

O; and Melody Miki at The Hospital for Sick Children, University of Toronto, Toronto, Ontario, Canada. We are grateful to the participants in this study. Members of the PCD Foundation, North America (Director, Michele Manion) and PCD Support Group, UK (Chair, Fiona Copeland) contributed to all aspects of study conduct. Contributors: MWL, SDD, ALQ and JSL had the concept for this study. All authors contributed to the study design. JSL and LB managed the conduct of the study and analysed the data. LB, MWL, SDD, ALQ and JSL discussed the analysed data and agreed the resultant 40-item QOL-PCD. LB drafted the manuscript. All authors contributed to iterations and approved the final document. JSL is accountable for the accuracy and integrity of the data.

and LB managed the conduct of the study and analysed the data. LB, MWL, SDD, ALQ and JSL discussed the analysed data and agreed the resultant 40-item QOL-PCD. LB drafted the manuscript. All authors contributed to iterations and approved the final document. JSL is accountable for the accuracy and integrity of the data. Funding: This research was funded by grant support to JSL, ALQ and MWL by funding from the European Union's Seventh Framework Programme under EC-GA No. 305404 BESTCILIA; by grant support to SDD and MWL: U54HL096458 from the National Institutes of Health (NIH) through the Genetic Disorders of Mucociliary Clearance Consortium, an initiative of the NIH Office of Rare Diseases Research at the National Center for Advancing Translational Science and the National Heart, Lung and Blood Institute; by grant support to SDD by Maya's March, The Hospital for Sick Children Foundation, Toronto, Ontario, Canada; ALQ was supported from an investigator-initiated grant, Gilead Sciences; The National PCD Centre in Southampton is commissioned and funded by NHS England. Research in Southampton is supported by NIHR Southampton Respiratory Biomedical Research Unit, NIHR Wellcome Trust Clinical Research Facility and The AAIR Charity (Reg. No. 1129698). JSL, MWL and LB are members of ERS Task Force for PCD Diagnostics (ERS TF-2014-04) and EU-funded COST Action BEAT-PCD (BM1407). Competing interests: None declared. Patient consent: Obtained.

Funding: This research was funded by grant support to JSL, ALQ and MWL by funding from the European Union's Seventh Framework Programme under EC-GA No. 305404 BESTCILIA; by grant support to SDD and MWL: U54HL096458 from the National Institutes of Health (NIH) through the Genetic Disorders of Mucociliary Clearance Consortium, an initiative of the NIH Office of Rare Diseases Research at the National Center for Advancing Translational Science and the National Heart, Lung and Blood Institute; by grant support to SDD by Maya's March, The Hospital for Sick Children Foundation, Toronto, Ontario, Canada; ALQ was supported from an investigator-initiated grant, Gilead Sciences; The National PCD Centre in Southampton is commissioned and funded by NHS England. Research in Southampton is supported by NIHR Southampton Respiratory Biomedical Research Unit, NIHR Wellcome Trust Clinical Research Facility and The AAIR Charity (Reg. No. 1129698). JSL, MWL and LB are members of ERS Task Force for PCD Diagnostics (ERS TF-2014-04) and EU-funded COST Action BEAT-PCD (BM1407). Competing interests: None declared. Patient consent: Obtained. Ethics approval: The study was approved by the National Research Ethics Service, UK (UK 07/Q1702/109), the Research Ethics Board at the Hospital for Sick Children in Toronto, Canada and the Institutional Review Boards at the University of North Carolina, Chapel Hill. Provenance and peer review: Not commissioned; externally peer reviewed.