The Use of Genetic Information to Define Idiopathic Pulmonary Fibrosis in UK Biobank

Abstract

Idiopathic pulmonary fibrosis (IPF) is a rare disease with prevalence of 50 in 100,000 cases in the UK.1The battle for breath - the impact of lung disease in the UK.https://www.blf.org.uk/policy/the-battle-for-breath-2016Date accessed: September 7, 2021Google Scholar Genome-wide association studies have identified 20 independent single nucleotide polymorphisms (SNPs) that are associated with IPF risk to date.2Seibold M.A. Wise A.L. Speer M.C. et al.A common MUC5B promoter polymorphism and pulmonary fibrosis.N Engl J Med. 2011; 364: 1503-1512Crossref PubMed Scopus (837) Google Scholar, 3Dhindsa R.S. Mattsson J. Nag A. et al.Identification of a missense variant in SPDL1 associated with idiopathic pulmonary fibrosis.Commun Biol. 2021; 4: 1-8Crossref PubMed Scopus (19) Google Scholar, 4Allen R.J. Stockwell A. Oldham J.M. et al.Genome-wide association study across five cohorts identifies five novel loci associated with idiopathic pulmonary fibrosis.Thorax. 2022; 77: 829-833Crossref PubMed Scopus (24) Google Scholar A single common SNP in the MUC5B gene promoter region (rs35705950) has a large effect on IPF risk with each copy of the T allele that is associated with a 4- to 5-fold increased risk of IPF.4Allen R.J. Stockwell A. Oldham J.M. et al.Genome-wide association study across five cohorts identifies five novel loci associated with idiopathic pulmonary fibrosis.Thorax. 2022; 77: 829-833Crossref PubMed Scopus (24) Google Scholar,5Zhu Q. Zhang X. Zhang S. et al.Association between the MUC5B promoter polymorphism rs35705950 and idiopathic pulmonary fibrosis: a meta-analysis and trial sequential analysis in Caucasian and Asian populations.Medicine. 2015; 94: e1901Crossref PubMed Scopus (29) Google Scholar Most datasets for genetic studies of IPF were derived from dedicated IPF cohort studies, registries, and clinical trials, which are usually modest in size. Large general population cohorts, such as UK Biobank, represent a valuable resource for increasing IPF case sample sizes for molecular epidemiologic studies. However, observed effect size estimates for rs35705950 on IPF risk in general population cohorts, for which cases are defined with the use of the International Classification of Diseases, revision 10 (ICD-10)6World Health OrganizationICD-10: International statistical classification of diseases and related health problems: tenth revision.2nd ed. World Health Organization, 2004https://apps.who.int/iris/handle/10665/42980Date accessed: September 7, 2021Google Scholar J84.1 code, are smaller than those that are estimated in clinically-derived datasets.7Partanen JJ, Happola P, Zhou W, et al. Leveraging global multiancestry meta-analysis in the study of idiopathic pulmonary fibrosis genetics. medRxiv. 2021.12.29.21268310; doi: https://doi.org/10.1101/2021.12.29.21268310Google Scholar Although this attenuation could be explained by misclassification of IPF cases, the misclassification may be mitigated by the substantial gain in statistical power that can be leveraged from very large biobanks. However, more accurate classification of cases and control subjects in biobanks could provide more accurate effect estimates for use in further analyses. Given this, we proposed that the IPF risk effect size of rs35705950 could be used to evaluate and refine the choice of codes to define IPF cases. We applied this approach in UK Biobank. UK Biobank is a prospective cohort study that contains > 500,000 volunteers who were recruited in the United Kingdom from 2006 to 2010 at ages 49 to 69 years.8Sudlow C. Gallacher J. Allen N. et al.UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age.PLoS Med. 2015; 12e1001779Crossref PubMed Scopus (4604) Google Scholar ICD-10 code J84.1 (“Other interstitial pulmonary diseases with fibrosis”) was used to define IPF from hospital episodes statistics (HES) (2020 release; last admission date: June 30, 2020) and death (May 2020 version; last date of death: May 22, 2020) data, which were available for all UK Biobank participants. Two self-reported pulmonary fibrosis variables were available. At baseline, participants were asked by a trained nurse to self-report any noncancer illnesses (field id 20002), which included “pulmonary fibrosis.” During an online follow-up survey about work environment conducted in 2015, 121,270 participants were asked whether a doctor had ever diagnosed them with IPF (field id: 22135, version July 2017). Primary care data were available for 230,105 participants (last event recorded: August 18, 2019). Eight primary care codes (Read 2 and Read 3) were used to define IPF.9Idiopathic pulmonary fibrosis statistics.https://statistics.blf.org.uk/pulmonary-fibrosisDate accessed: September 7, 2021Google Scholar Control subjects were defined as individuals who had linked primary care data that had not been defined as an IPF case in any of the data sources. We further selected control subjects to be similar to cases for age sex, ever-smoker status. Cases and control subjects were all of genetically determined European ancestry.10Shrine N, Izquierdo AG, Chen J, et al. Multi-ancestry genome-wide association study improves resolution of genes, pathways and pleiotropy for lung function and chronic obstructive pulmonary disease. medRxiv. 2022.05.11.22274314; doi: https://doi.org/10.1101/2022.05.11.22274314Google Scholar Association of rs35705950 with IPF risk was tested with the use of logistic regression that was adjusted for the first ten genetic principal components. We compared the effect size (OR) of the association using each IPF definition with that reported by the largest genome-wide association study with the use of clinically defined IPF cases4Allen R.J. Stockwell A. Oldham J.M. et al.Genome-wide association study across five cohorts identifies five novel loci associated with idiopathic pulmonary fibrosis.Thorax. 2022; 77: 829-833Crossref PubMed Scopus (24) Google Scholar and a meta-analysis of published rs35705950 studies.5Zhu Q. Zhang X. Zhang S. et al.Association between the MUC5B promoter polymorphism rs35705950 and idiopathic pulmonary fibrosis: a meta-analysis and trial sequential analysis in Caucasian and Asian populations.Medicine. 2015; 94: e1901Crossref PubMed Scopus (29) Google Scholar We considered these previously reported rs35705950 IPF susceptibility effect sizes as the “gold standard” against which to evaluate codes for IPF in UK Biobank. Using only the ICD-10 (HES and death) defined dataset, we evaluated the effect of excluding participants with cooccurring ICD-10 codes (in HES or death) that might indicate misclassification. We then repeated the association by testing for the MUC5B SNP and compared the effect size to the gold standard. Specifically, we excluded (1) secondary or other causes of pulmonary fibrosis (previously collated by Bellou et al11Bellou V. Belbasis L. Evangelou E. Tobacco smoking and risk for pulmonary fibrosis: a prospective cohort study from the UK Biobank.Chest. 2021; 160: 983-993Abstract Full Text Full Text PDF PubMed Scopus (32) Google Scholar) (non-IPF pulmonary fibrosis), and (2) J84.1 ICD-10 code occurrence before the year that the most recent clinical guidelines for diagnosis of IPF12Raghu G. Remy-Jardin M. Myers J.L. et al.Diagnosis of idiopathic pulmonary fibrosis: an official ATS/ERS/JRS/ALAT clinical practice guideline. Am J Respir.Crit Care Med. 2018; 198: e44-68Crossref Scopus (2286) Google Scholar that were published in 2018. Of 453,587 European-ancestry participants in UK Biobank, there were 2,535 individuals with one or more codes indicative of IPF; 50,924 individuals were selected as control subjects (Fig 1). SNP rs35705950 was genome-wide significantly associated with IPF risk (P < 5 × 10-8) for all but self-reported pulmonary fibrosis (P = 1.00 × 10-6) (Fig 2A ). For all definitions, the observed ORs were lower than those previously reported.4Allen R.J. Stockwell A. Oldham J.M. et al.Genome-wide association study across five cohorts identifies five novel loci associated with idiopathic pulmonary fibrosis.Thorax. 2022; 77: 829-833Crossref PubMed Scopus (24) Google Scholar,5Zhu Q. Zhang X. Zhang S. et al.Association between the MUC5B promoter polymorphism rs35705950 and idiopathic pulmonary fibrosis: a meta-analysis and trial sequential analysis in Caucasian and Asian populations.Medicine. 2015; 94: e1901Crossref PubMed Scopus (29) Google Scholar Self-reported IPF cases gave an OR closest to previously published estimates. Defining IPF with the use of the J84.1 ICD-10 code in HES data or the self-reported pulmonary fibrosis gave the OR furthest away from previously reported estimates.Figure 2A and B, Effect size estimates of rs35705950 T allele association with IPF risk. Each line shows the effect size estimate and CI for the association between rs35705950 and idiopathic pulmonary fibrosis risk with the use of the different methods for defining idiopathic pulmonary fibrosis in UK Biobank. Estimates in grey are the reference effect size estimates taken from Allen et al4Allen R.J. Stockwell A. Oldham J.M. et al.Genome-wide association study across five cohorts identifies five novel loci associated with idiopathic pulmonary fibrosis.Thorax. 2022; 77: 829-833Crossref PubMed Scopus (24) Google Scholar(2022) and Zhu et al5Zhu Q. Zhang X. Zhang S. et al.Association between the MUC5B promoter polymorphism rs35705950 and idiopathic pulmonary fibrosis: a meta-analysis and trial sequential analysis in Caucasian and Asian populations.Medicine. 2015; 94: e1901Crossref PubMed Scopus (29) Google Scholar(2015). A, The use of different idiopathic pulmonary fibrosis case definitions in UK Biobank. Hospital episodes statistics and idiopathic pulmonary fibrosis death defined by J84.1 ICD-10 code. Primary care idiopathic pulmonary fibrosis defined by the following Read 2/Read 3 codes: H563./XE0Yb, H563./X102v, H563./XE0Yb, H563./XE0Yb, H5631/H5631, H5633/X102v, H563z/H563z, H5632/X102u (8). Self-reported idiopathic pulmonary fibrosis defined by UK Biobank field 22135. Self-reported pulmonary fibrosis defined by UK Biobank field 20002. B, With the use of International Classification of Diseases-10 codes and after exclusion of cases with a cooccurring code indicative of being non-idiopathic pulmonary fibrosis pulmonary fibrosis or removing cases defined by the occurrence of a J84.1 code before January 2018. All International Classification of Diseases-10 defined idiopathic pulmonary fibrosis (cases defined using hospital episodes statistics and mortality data only). Non-idiopathic pulmonary fibrosis pulmonary fibrosis code list defined by Bellou et al.11Bellou V. Belbasis L. Evangelou E. Tobacco smoking and risk for pulmonary fibrosis: a prospective cohort study from the UK Biobank.Chest. 2021; 160: 983-993Abstract Full Text Full Text PDF PubMed Scopus (32) Google Scholar HES = hospital episodes statistics; IPF = idiopathic pulmonary fibrosis.View Large Image Figure ViewerDownload Hi-res image Download (PPT) Removal of the cases with a cooccurring code that is suggestive of non-IPF pulmonary fibrosis or removal of the cases that are defined by the occurrence of a J84.1 code before January 2018 led to slightly closer effect estimates to those previously reported, but with substantially reduced sample sizes (Fig 2B). We used association of rs35705950 with IPF risk to evaluate code-based definitions of IPF in UK Biobank. We show that none of the available IPF code definitions, either individually or in combination, replicate the association effect size that is obtained with the use of clinically defined IPF cohorts. We observed that self-reported IPF in UK Biobank provided an effect estimate closest to those previously reported. We hypothesized that applying code-based exclusions to reduce misclassification among the cases would improve the effect estimates. Although this led to some increase in the effect sizes, they were still < 95% CI of the estimates from IPF studies that used tertiary care diagnoses to recruit participants. Excluding J84.1 ICD-10 code entries that occurred prior to January 2018 was more effective at increasing the OR on its own than removing cases with cooccurring medical conditions that can cause pulmonary fibrosis. The combined definitions of IPF in UK Biobank gave a prevalence of 559 of 100,000 cases, which is 10-fold higher than population estimates. Because IPF is a rare disease and we used a large control sample, the effect estimate attenuation that we observed for rs35705950 suggests that there is over-estimation of cases in UK Biobank because of low specificity of the definitions that are used. In conclusion, large biobanks offer an excellent resource for the study of less prevalent common diseases. However, we show that commonly used codes fail to define an IPF case sample that is able to replicate previously reported association effect sizes. Furthermore, pragmatic attempts to refine the phenotype with the use of further code exclusions were unable to improve the estimates. Researchers who use biobanks to study IPF should take these findings into consideration when designing future studies. L. V. W. holds a GSK/Asthma+Lung UK Chair in Respiratory Research (C17-1). L. V. W. and R. G. J. are supported by MRC Programme grant MR/V00235X/1. R. J. A. is an Action for Pulmonary Fibrosis Research Fellow. R. G. J. is supported by a National Institute for Health Research (NIHR) Research Professorship (NIHR reference RP-2017-08-ST2-014). L. M. K. was funded by a Medical Research Council (MRC) PhD studentship (MR/N013913/1). M. D. T. is supported by a Wellcome Trust Investigator Award (WT202849/Z/16/Z). A CC BY or equivalent license is applied to the Author Accepted Manuscript arising from this submission, in accordance with the grant’s open access conditions. The research was partially supported by the NIHR Leicester Biomedical Research Centre; This research has been conducted using the UK Biobank Resource under application 77050. This research used the SPECTRE High Performance Computing Facility at the University of Leicester.

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