Predicting asthma phenotypes: characterization of IL1RL1 in asthma
Dijk, Fokelina Nicole
IMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite from it. Please check the document version below.
Document Version
Publisher's PDF, also known as Version of record
Publication date: 2018
Link to publication in University of Groningen/UMCG research database
Citation for published version (APA):
Dijk, F. N. (2018). Predicting asthma phenotypes: characterization of IL1RL1 in asthma. Rijksuniversiteit Groningen.
Copyright
Other than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons).
Take-down policy
If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.
Downloaded from the University of Groningen/UMCG research database (Pure): http://www.rug.nl/research/portal. For technical reasons the number of authors shown on this cover page is limited to 10 maximum.
IL1RL1 gene variations are
associated with asthma
exacerbations in children
and adolescents using
inhaled corticosteroids
_
Chapter 6
F. Nicole Dijk, Susanne J. Vijverberg, Natalia Hernandez-Pacheco, Katja Repnik, Leila Karimi, Marianna Mitratza, Niloufar Farzan, Martijn C. Nawijn, Esteban G. Burchard, Marjolein Engelkes, Katia M. Verhamme, Uroš Potočnik, María Pino-Yanes, Dirkje S. Postma, Anke-Hilse Maitland-van der Zee*, and Gerard H. Koppelman*
Abstract
Background
Inhaled corticosteroids (ICS) are the cornerstone of asthma treatment, but the clinical response to ICS is variable, which may be explained by genetic factors. To assess the association between Interleukin-1
receptor–like 1 (IL1RL1), gene variants and exacerbations, asthma control, FeNO levels and FEV1 in asthma
patients using ICS.
Methods
The association between IL1RL1 single nucleotide polymorphisms (SNPs) and exacerbations (emergen-cy room (ER) visits/hospitalizations and/or oral corticosteroids (OCS), or the combination of the latter), asthma control, FeNO and FEV1 in children with asthma using ICS was investigated in the Pharmacoge-netics of Asthma Medication in Children: Medication with Anti-Inflammatory effects (PACMAN) cohort (N=820). We replicated significant findings in four studies of the Pharmacogenomics in Childhood Asth-ma consortium and performed a meta-analysis (N= 2412). The association between IL1RL1 variants and the change in FeNO levels (N=40) and FEV1% predicted (N=183) was assessed during 4-6-week ICS use.
Results
IL1RL1 SNPs were significantly associated with ER visits/hospitalizations, OCS use and ‘any exacerbation’
in PACMAN. After replication and meta analysis, two SNPs were associated with ER visits/hospitaliza-tions: (rs13431828; OR=1.32, P=0.005, rs1420101; OR=1.16, P=0.03), whereas one SNP was associated with ‘any exacerbation’ (rs13431828; OR=1.31, P=0.007). No association of IL1RL1 was found with asthma con-trol , change in FeNO levels or FEV1.
Conclusions
Despite the use of ICS, children with IL1RL1 asthma risk alleles experience more exacerbations than chil-dren with the protective genotype. This implicates that the IL1RL1 pathway may constitute a treatment target for add-on asthma treatment in patients on ICS.
Abbreviations
ACT - Asthma Control TestACQ - Asthma Control Questionnaire ATS - American Thoracic Society CI - Confidence interval ER - Emergency room
ERS - European Respiratory Society FeNO - Fraction of exhaled nitric oxide FEV1 - Forced expiratory volume in 1 second
GALA - Genes-Environment and Admixture in Latino Americans GWAS - Genome Wide Association study
ICS - Inhaled corticosteroids IL1RL1 - Interleukin 1 receptor like 1
MARS - Medication Adherence Report Scale OCS - Oral corticosteroids
OR - Odds ratio
PACMAN - Pharmacogenetics of Asthma Medication in Children: Medication with Anti-inflammatory effects
PiCA - Pharmacogenomics in Childhood Asthma
SAGE - Study of African Americans, Asthma, Genes, and Environments SNP - Single nucleotide polymorphism
Introduction
Asthma is among the most common chronic diseases in children caused by interactions between genes and environmental factors.1 Its underlying mechanisms involve ongoing airway inflammation and air-way wall remodeling. The mainstay of treatment is daily use of inhaled corticosteroids (ICS). ICS are the most effective medication to control asthma symptoms and prevent severe exacerbations, reducing both hospitalizations and mortality rates2 and improving asthma control, forced expiratory volume in 1 second (FEV1) levels and fraction of exhaled nitric oxide (FeNO) particularly in asthma patients with eosinophilic, Type 2 airway inflammation.3,4 Despite these beneficial effects, large intra- and inter-individual variabil-ity in treatment response to ICS has been described.5 Some patients using ICS have uncontrolled asthma with asthma exacerbations.6 Exacerbations are one of the most important causes of the major socioeco-nomic burden of asthma,7 since these contribute to the lung function decline8 and increase asthma mor-tality rate.7,9
Recent studies have indicated that the heterogeneous response to ICS might be genetically deter-mined,10–14 although controversy remains.15 The identification of genetic variants associated with reduced treatment response in patients on ICS may provide clues for development of further add-on treatments.16 Genome Wide Association studies (GWAS) have reproducibly found the Interleukin 1 receptor like 1
(IL-1RL1, ST2) gene to be associated with asthma susceptibility.17,18 IL1RL1 is a member of the interleukin 1 re-ceptor family with expression on inflammatory cells located in the lung.19
Alternative splicing of IL1RL1 results in multiple protein isoforms: the soluble IL1RL1-a (sST2, soluble ST2), a transmembrane protein, IL1RL1-b (ST2L) , and two less well-known variants, isoform 3 and IL1RL1-c (ST2V).20 IL1RL1-b is part of the IL-33 receptor complex and involved in signal transduction. Ligation of IL-33 to IL1RL1-b leads to a positive feedback loop of Th2 cytokine production in type 2 cells. The IL-33/
IL1RL1 pathway has been linked to eosinophillic Th2-associated inflammation in asthma patients. 21–24 Furthermore, IL1RL1 single nucleotide polymorphisms (SNPs) and IL1RL1 expression levels are associated with blood eosinopils25–28 and markers of Th2 type inflammation.23,29 However, the role of IL1RL1 on the effect of asthma treatment has not been investigated. If SNPs in IL1RL1 are associated with worse asthma outcome despite ICS, new asthma treatment regimens or add-on treatment is warranted.30
In the current study, we therefore investigated whether IL1RL1 gene variants are associated with asthma exacerbations (based on ER visits/hospitalizations and courses of oral corticosteroid (OCS) use), ques-tionnaire based asthma control and FeNO levels in asthma patients using ICS. Furthermore we aimed to identify whether there is a pharmacogenetic effect of IL1RL1 variants on change in FeNO levels and FEV1%predicted in asthma patients after 4-6 weeks of ICS treatment.
Methods
Study design
Cross-sectional IL1RL1 SNP discovery analysis was performed in ICS treated asthmatic children from the Pharmacogenetics of Asthma Medication in Children: Medication with Anti-inflammatory effects (PAC-MAN) cohort. SNPs were selected for replication when showing a significant (P>0.05) association with one of the outcome variables. Replication was performed in four different cohorts collaborating with-in the scope of the Pharmacogenomics with-in Childhood Asthma (PiCA) consortium31, one Hispanic/Latwith-ino study; Genes-Environment and Admixture in Latino Americans (GALA II) study, one African American population; Study of African Americans, Asthma, Genes, and Environments (SAGE), and two European studies (≥96% European ancestry); the Effectiveness and Safety of Treatment with Asthma Therapy in children (ESTATe) and SLOVENIA. The longitudinal effect of IL1RL1 on FeNO levels and FEV1% predicted upon ICS treatment in asthmatic children and adults was assessed in the SLOVENIA cohort.
PACMAN
The PACMAN cohort includes children (4-12 years) with a reported regular use of any asthma medication recruited through records of community pharmacies in the Netherlands. Details of the study protocol have been described elsewhere.31 Written informed consent was obtained from all participants.32 Data on asthma symptoms, recent exacerbations and current medication use over the preceding 12 months during a study visit in the community pharmacy was obtained using a questionnaire. FeNO was mea-sured in children with reported use of asthma medication in the past 6 months. DNA was extracted from collected saliva samples (Oragene DNA Self Collection kit, DNA Genotek, Inc., ON, Canada). The PACMAN study was approved by the Medical Ethics Committee of the University Medical Centre Utrecht.
Replication cohorts
Detailed information on the study design of GALA II, SAGE, SLOVENIA and ESTATe is provided in the Methods section in this article’s Supporting Information.
Treatment step
Categorization of asthma severity was modified from the British Thoracic Society (BTS) guidelines33 as follows in all studies: step 1: inhaled short-acting b2-agonists (SABA) as needed, step 2: step 1 plus use of inhaled SABA as needed plus regular ICS, step 3: step 2 plus regular long-acting inhaled b2-agonists (LABA) or plus leukotriene receptor antagonists (LTRA), and step 4: step 2 plus regular LABA and LTRA. For this study we included patients on treatment step 2 or higher.
Analyzed variables
In PACMAN, GALA II, SAGE, ESTATe and SLOVENIA data related to asthma exacerbations and question-naire-based asthma control was available. FeNO levels were measured in PACMAN and SLOVENIA. Lon-gitudinal data on FeNO and FEV1 was only available in SLOVENIA, with data being collected before start of ICS and 4-6 weeks after the start of treatment.
Definition of outcomes
Asthma exacerbations
In PACMAN, asthma exacerbations were based on (1) emergency room (ER) visits and (2) courses of OCS use in the 12 months preceding the study visit. In GALA II, SAGE, ESTATe and SLOVENIA exacerbations were based on (1) ER visits and/or hospitalizations and (2) on courses of OCS use in the past 12 months preceding the study visit. These separate exacerbation conditions were combined additionally in one variable: ‘any exacerbation’. In PACMAN ‘any exacerbation’ was defined as ER visit and/or OCS use in the past 12 months. In the studies included in the replication phase ‘any exacerbation’ was defined as ER visits and/or hospitalizations and/or courses of OCS use in the past 12 months.
Asthma control
In PACMAN asthma control was evaluated using the Asthma Control Questionnaire score (ACQ-6)34 in the past week; ACQ<0.75 = well controlled asthma, ACQ 0.75-1.5 = partly controlled asthma and ACQ>1.5 = poor controlled asthma. In GALA II and SAGE asthma control was based on a modified version of the 1978 American Thoracic Society-Division of Lung Diseases (ATS-DLD) Epidemiology Questionnaire on symptoms, nighttime awakening, interferences with normal activities, rescue medication use, and lung function measurements, and defined using a modification of the NHLBI guidelines to fit our question-naire as controlled, partially controlled, or uncontrolled.35 In Slovenia asthma control was measured us-ing the Asthma Control Test (ACT) that contains 5 questions related to the frequency of both asthma symptoms and required rescue medication use during the previous 4 weeks after 6-month ICS treatment. Uncontrolled asthma was defined as ACT <15 points.36
FeNO
In PACMAN, a single-breath on-line measurement of FeNO (ppb) was carried out with a hand-held elec-trochemical analyzer (NIOX Mino; Aerocrine, Solna, Sweden). In Slovenia FeNO levels were measured by a Niox analyzer (Aerocrine Inc., USA), with usage as specified in ERS (European Respiratory Society) and ATS guidelines.
FEV
FEV1 values were adequately measured only in SLOVENIA using a Vitalograph 2150 spirometer (Com-pact,Buckingham, UK) according to ERS/ATS guidelines.
SNP selection and genotyping
We assessed linkage disequilibrium structure of IL1RL1 and selected 6 IL1RL1 SNPs that tag important LD blocks in IL1RL1 (r2 <0.8) with SNPs previously found to be associated with asthma based on an exten-sive literature review as described previously;37 rs13431828, rs1041973, rs1420101, rs1946131, rs1921622 and rs10204137. LD pattern between the IL1RL1 SNPs was assessed based on data from 1000 Genomes Project Phase III38 by means of LDLink.39 Functional SNP annotation was determined with HaploReg v4.1.40 A de-tailed description of the genotyping is provided in the method section of the Supporting Information.
Statistical analysis
Logistic regression models were used to assess the association between IL1RL1 SNPs, exacerbations and asthma control applying an additive genetic model. In PACMAN, SLOVENIA and ESTATe, regression mod-els were adjusted by age, gender and BTS treatment step as described previously, 42 whereas the first two principal components, obtained with EIGENSOFT,43 were also included as covariates for GALA II and SAGE in order to correct the ethnic admixture of these populations. Associations between genetic variants and FeNO levels were evaluated by means of linear regressions using a logistic transformation of the outcome variable. Age and gender were included as covariates in the regression models.
A P-value ≤ 0.05 was considered significant. Longitudinal analyses were performed with linear regression models. The change in FeNO and FEV1% predicted values were calculated by substracting the post ICS treatment value from the pre ICS treatment value.
Sensitivity analyses in PACMAN were performed for Dutch ethnicity, atopy and medication adherence. Atopy was based on the presence of eczema, food allergy or allergic rhinitis, whereas medication adher-ence was assessed by using the Medication Adheradher-ence Report Scale (MARS) comprising five questions on medication use behavior. Patients with a MARS score ≥21 were considered to be highly adherent.44 We performed meta-analyses in METAL45 using a fixed effect model since there was no evidence of hetero-geneity.46 Forest plots were made with the ‘forestplot’ package in R.47 To assess the independent effect of the
IL1RL1 SNPs in the PACMAN cohort we performed conditional analysis using a multivariate model with a
backward step wise regression analysis.
Statistical analyses were carried out using IBM SPSS 23.0 for Windows (SPSS, Inc., Chicago, IL, USA). In GALAII and SAGE, logistic regressions were performed by means of the binary Wald test implemented in EPACTS 3.2.6.48
Results
Characteristics of the study population
The PACMAN cohort comprised 820 children with available phenotypic and genetic data, from which 720 individuals on BTS treatment step 2 or higher were selected for inclusion in the association testing. In the replication cohorts, data were available for 876 children in GALA II, 525 children in SAGE, 197 chil-dren in SLOVENIA, and 104 chilchil-dren in ESTATe. Gender distribution was similar between studies with a male proportion ranging from 54 to 62%. Different ethnicities were represented among the cohorts with PACMAN, ESTATe and SLOVENIA consisting of over 91% children of European ancestry and GALA II and
SAGE consisting exclusively of Hispanics/Latinos and African Americans, respectively (Table 1). Children in PACMAN showed decreased asthma exacerbations rates based on ER visits (6.3%), OCS use (6.5%), and presence of ‘any exacerbation’ (10.7%) compared to the replication cohorts (Table 1). However, the proportion of children with uncontrolled asthma was of 40-45% in GALA II and SAGE, and was remark-ably low in PACMAN (19.4%) and SLOVENIA (1%). Mean FeNO levels were significantly lower in PACMAN (23.02 ± 29.5) compared to SLOVENIA (35.25 ± 25.3).
Although allele frequency of the SNPs tested was similar among studies, slight differences were found, with A allele of rs10204137 ranging from 0.32 (GALA II) to 0.82 (ESTATe) (Table 2).
Table 1. Clinical characteristics of the study population.
SD, standard deviation; ER, emergency room; OCS, oral corticosteroid; ICS, inhalation corticosteroid; SABA, short-act-ing beta agonists; LABA, long-actshort-act-ing beta agonists; LTRA, leukotriene receptor antagonists.
*In PACMAN ER visits in the past 12 months were measured. In PAGE II, SAGE, SLOVENIA and ESTATe ER visits or hos-pitalizations in the past 12 months were measured.
†Courses of OCS use in the past 12 months preceding the study visit was measured.
‡In PACMAN any exacerbation was defined as ER visit and/or OCS use in the past 12 months. In PAGE II, SAGE, SLOVENIA and ESTATe any exacerbation was defined as ER visits and/or hospitalizations and/or OCS use in the past 12 months. §In PACMAN asthma control was measured using the Asthma Control Questionnaire score (ACQ-6) in the past week. Poor asthma control was defined as ACQ>1.5. In GALA II and SAGE asthma control was based on a modified version of the 1978 American Thoracic Society-Division of Lung Diseases Epidemiology Questionnaire on symptoms, nighttime awakening, interferences with normal activities, rescue medication use, and lung function measurements. Asthma controlled and not well-controlled patients were considered as controls and very poorly asthmatic patients were con-sidered as cases. In Slovenia Asthma control was measured using the ACT questionnaire which contains 5 questions that are related to the frequency of both asthma symptoms and required rescue medication use during the previous 4 week after 6 months of ICS treatment. Uncontrolled asthma was defined as ACT <15 points.
¶Medication adherence was measured using the Medication Adherence Rate Scale (MARS),≥ 21: highly adherent. **Treatment steps were based on the British Thoracic Society (BTS) guidelines.
Table 2. Allele frequency of the investigated SNPs per study.
Alelle frequency is displayed for the effect allele. *R = reference allele, E = effect allele.
†rs13431828 was not present in ESTATe, rs3771180 was used as a surrogate marker (LD r2 = 1).
‡rs10204137 was not present in ESTATe, rs4988956 was used as a surrogate marker (LD r2 = 1).
Effect of IL1RL1 SNPs on asthma exacerbations, asthma control and FeNO
All 6 selected IL1RL1 SNPs passed quality control. LD between these SNPs is presented in Table S1 in the Supporting Information. In PACMAN, we found a significant association between 4 of these 6 SNPs with ER visits, OCS use and ‘any exacerbation’ (Table 3A-C), and these 4 were selected for the replication study. The C allele of rs13431828 was associated with ER visits (OR=2.78, 95%CI=1.11-6.94, P=0.02), OCS use (OR=2.70, 95%CI=1.08-6.79, P=0.03) and ‘any exacerbation’ (OR=2.63, 95%CI=1.33-5.18, P=0.007). The A allele of rs1420101 was associated with ER visits (OR= 1.61, 95%CI=1.05-2.47, P=0.02) and ‘any exacerba-tion’ (OR=1.52, 95%CI=1.08-2.13, P=0.01). Similar results were found for the A allele of rs1921622 (ER visit; OR=1.89, 95%CI=1.018-3.03, P=0.01, ‘any exacerbation’; OR=1.45, 95%CI=1.03-2.04, P=0.03). The A allele of rs10204137 was associated with OCS use (OR=1.69, 95%CI=1.05-2.73, P=0.03) and ‘any exacerbation’ (OR=1.52, 95%CI=1.05-2.18, P=0.02). Sensitivity analyses on Dutch ethnicity, atopy and medication ad-herence did not change these results (results not shown). We did not observe a significant association of
IL1RL1 SNPs with questionnaire based asthma control and FeNO measurements (Table S2A-B).
Replication results
In GALA II (N=876) we replicated our findings with significant results for rs13431828, rs1420101, rs1921622 on ER visits/hospitalizations and ‘any exacerbation’ with the same direction of effect. Rs10204137 showed also a significant association with ‘any exacerbation’ (Table 3A-C). In SAGE (N=525) rs1921622 was asso-ciated with ER visits/hospitalizations and ‘any exacerbation’ but the direction of the effect was different when compared to PACMAN. In SAGE, the A allele of rs10204137 was associated with better asthma con-trol based on the ATS-DLD questionnaire. No IL1RL1 SNPs were significantly associated with OCS use in GALA II and SAGE, but direction of effect was similar for all risk alleles in GALA II. In the smaller SLOVE-NIA (N= 97) and (N=104) ESTATe studies no significant associations were found.
A meta-analysis of the 4 replicated IL1RL1 SNPs across the 5 studies showed statistically significant results for rs13431828 and rs1420101. The C allele of rs13431828 (OR=1.32, 95%CI=1.08-1.62, P=0.005) and the A allele of rs1420101 (OR=1.16, 95%CI=1.01-1.34, P=0.03) were associated with ER visits/hospitalisations. Additionally, rs13431828 was also associated with increased risk of ‘any exacerbations’ (OR for C allele:1.31, CI=1.07-1.59, P=0.007) (Table 3A-C, Figure 1). No evidence of heterogeneity was found (Q=3.6, P=0.33) .
Table 3. Results of associations of IL1RL1 SNPs with ER visits/hospitalizations, OCS use and ‘any exacerbation per study and meta-analysis.
SNP, single nucleotide polymorphism; OR, odss ratio; CI, confidence interval; ER. Emergency room; OCS, oral corticosteroid.
In PACMAN, SLOVENIA and ESTATe all results were corrected for age, gender and BTS treatment step. In SAGE and GALA II results were corrected for age, gender, BTS treatment step and principal components 1 and 2.
Bold faced results are significant results (P<<0.05). Missing values means the SNP was not present in the study. *R = reference allele, E = effect allele.
†In PACMAN ER visits in the past 12 months preceding the study visit were measured. In PAGE II, SAGE, SLOVENIA and ESTATe ER visits or hospitalizations in the past 12 months preceding the study visit were measured.
‡rs13431828 was not present in ESTATe, rs3771180 was used as a surrogate marker (LD r2 = 1).
§rs10204137 was not present in ESTATe, rs4988956 was used as a surrogate marker (LD r2 = 1).
¶Courses of OCS use in the past 12 months preceding the study visit were measured.
**In PACMAN ‘any exacerbation’ was defined as ER visit and/or OCS use in the 12 months preceding the study visit. In PAGE II, SAGE, SLOVENIA and ESTATe ‘any exacerbation’ was defined as ER visits and/or hospitalizations and/or OCS use in the past 12 months preceding the study visit.
Figure 1. Forest plot showing the meta-analysis result of the association between the IL1RL1 SNP rs13431828 (C) and ‘any exacerbation’ (P=0.007). Included cohorts are PACMAN, GALA II, SAGE, SLOVENIA and ESTATe. Odds ratio (OR) and 95% confidence intervals (CI) are shown for the effect alleles (additive model). ‘Any exacerbation’ was defined as ER visits/hospitalizations and/or OCS use.
LD pattern and conditional analysis
LD r2 between rs13431828 and rs1041973 was 0.52. All other r2 values between the SNPs were below 0.4 (Table S1). Conditional analysis in PACMAN on rs13431828, rs142010, rs1921622 and rs10204137 for ‘any exacerbation’ indicated that only rs13431828 remained independently significantly associated (Table S3).
IL1RL1 SNPS and their effect on ICS treatment response
In SLOVENIA, no pharmacogenetic effect of the IL1RL1 SNPs on changes in FeNO (P>0.09) and FEV1% predicted values (P>0.29) was observed after 4-6 weeks of ICS use (Table S4).
Discussion
In our study, IL1RL1 SNPs were significantly associated with the occurrence of asthma exacerbations, de-fined by ER visits/hospitalizations and/or use of OCS courses, despite ICS use by children and adolescent with asthma. Remarkably, we replicated these findings in independent populations of Europeanand Hispanic/Latino ancestry, but not with a population of African American ancestry. After performing a meta-analysis of the results obtained from five populations, two SNPs (rs1343828 and rs1420101) were significantly associated with ER visits/hospitalizations and one SNP (rs1343828) with ‘any exacerbation’. The association of IL1RL1 SNPs with asthma exacerbations strengthens previously published data that SNPs in IL1RL1 are important in different asthma phenotypes, such as (childhood-onset) asthma17, specific childhood wheezing phenotypes26, atopy22,49 and eosinophilia.25,50 The C allele of rs13431828, the A allele of rs1420101, the A allele of rs1921622 and the A allele of rs10204137 have been associated with a higher risk (OR 1.13-2.20) of developing asthma37,50,51, with the effect of rs13431828 and rs10204137 being more prominent in childhood-onset asthma, as is the case in all our populations investigated, than in adult onset asthma.51 The direction of effect of the allelic association was the same: we observed that children with asthma carrying an asthma risk allele had more ER visits/hospitalizations and more frequent use of OCS courses. Rs1420101 has been especially linked to the type 2-high asthma phenotype 29, as well as to increased eosinophil num-bers in peripheral blood.25 Blood eosinophils and FeNO levels are used as biomarkers for eosinophilic airway inflammation, with both showing associations with asthma exacerbations.52,53 The risk allele for rs13431828 and rs1420101 was associated with moderately higher FeNO levels in PACMAN, but this finding was not statistically significant.
Different mechanisms may explain our findings First, IL1RL1 SNPs may modify the asthma phenotype into a more severe phenotype, with more severe exacerbations, which are insufficiently treated with the ICS dosages prescribed to the children in this study. In this scenario, IL1RL1 risk alleles may enhance eo-sinophilic inflammation in association with increased exacerbations. GTEx consortium data showed that rs13431828, rs1420101, rs1921622 and rs10204137 were all observed as eQTLs of the IL1RL1 locus in lung tissue. In addition the risk alleles described in our study for rs13431828 (C), rs1420101 (T), rs1921622 (A) and rs10204137 (A) were previously associated with lower IL1RL1 blood methylation levels28 and lower serum IL1RL1-a levels.28,50,54 This indicates that the associated SNPs are important for regulation of IL1RL1 gene expression. Since IL1RL1-a functions as a decoy receptor to dampen IL-33 induced signaling, genet-ically determined low levels of IL1RL1-a may predispose to enhanced IL-33 induced inflammation.55,56 Rs10204137 is a missense mutation and has been associated with increased IL1RL1-a expression by in-ducing IL-33 expression and enhancing IL-33 responsiveness. This indicates that the activity of the IL1RL1/ IL-33 pathway might be controlled by a negative feedback loop whereby IL-33 signaling leads to increased
IL1RL1 expression.57 Moreover, rs10204137 tags an LD block that contains 5 nonsynonymous coding SNPs that result in change of four amino acids in the intracellular part of IL1RL1-b . These coding changes af-fect the TIR domain of the intracellular part of the IL1RL1 protein, which plays an important role in IL-33 induced signal transduction by IL1RL1, which triggers a signaling cascade that eventually results in the activation of downstream mitogen-activated protein kinases and transcription factors, such as nuclear factor kB (NF-kB) and activator protein-1.37 Through this pathway, asthmatic children carrying the risk allele of rs10204137 are more sensitive to IL-33. As IL1RL1 is expressed on effector cells of the type-2 im-mune response such as mast cells, eosinophils, basophils, Th2 cells and ILC2 cells58, an increased sensitivi-ty to IL33 will contribute to an exaggerated sensitivi-type-2 inflammatory response after viral or allergen exposure. This may explain why asthmatic children with this risk allele might experience more exacerbations even when using ICS.
Second, IL1RL1 may have a direct pharmacogenetic interaction with steroids resulting in reduced efficacy of the steroids. A recent study, which investigated ulcerative colitis patients, found that dexamethasone upregulated soluble IL1RL1 transcription via interaction with the glucocorticoid-responsive element in patients carrying the IL1RL1 SNP rs6543116 (A), as present in the distal promotor.59 Although the SNPs we report on here are not in LD with rs6543116, this finding suggests that IL1RL1 polymorphisms could affect the efficacy of ICS in inducing high levels of IL1RL1-a, which can contribute to suppression of IL-33 induced type-2 inflammation.
Our study has several strengths and limitations. We performed our IL1RL1 association analyses in over 2,400 children from five populations of different ancestries and were therefore able to investigate less prevalent phenotypes, such as hospitalizations and ER visits. Moreover, we observed highly replicable as-sociations of the same IL1RL1 risk alleles in the European ancestry (PACMAN) and Hispanic/Latino (GALA II) population, but not in the African American study population (SAGE). However, the direction of effect of rs1921622 on exacerbations was different in SAGE when compared to PACMAN and GALA II. This could be due to differences in ethnicity between study groups and LD patterns in this gene. The SNPs were selected based on an extensive literature review and tag the relevant LD blocks in this gene, but the latter results were mostly derived from white European ancestry populations, thereby disregarding possibly more complex LD structure of the IL1LR1 gene in African Americans. This is a plausible explanation since we observed differences in allele frequency between the cohorts (see Table 2). Previous analysis of IL1RL1 in relation to asthma susceptibility in the EVE consortium also showed that significant findings were driven by the association in the European American and Hispanic/Latino population, but not in African Americans, questioning the role of IL1RL1 in asthma in African Americans. 18
Although we observed replicable associations with asthma exacerbations, IL1RL1 SNPs showed no signif-icant association with asthma control measured using questionnaires that pertains to control in the last 1-4 weeks. However, measuring asthma control by a questionnaire over a short period of time may not be satisfactorily accurate, for instance due to substantial seasonal variation; moreover cut-off points should be used with caution in research.34,60
The fact that we did not observe a pharmacogenetic effect on FEV1 change could be explained by the lack of sufficient power of these analyses, as they were only performed in a subgroup of the population. In addition, a previously reported ICS pharmacogenetic study which used change in FEV1 as an outcome variable did also not report a strong effect of IL1RL1 gene variants on ICS treatment response. However, in that study asthma exacerbation as an outcome was not reported. More studies are needed to replicate our findings. .Since our study is an observational study, future studies with a prospective genotype strat-ified design with inclusion of functional data are warranted.
Our data provides new evidence that despite the use of ICS, children and adolescents with the IL1RL1 risk alleles are prone to more exacerbations than children with the protective genotypes. This observation may have important implications for the application of patient based novel asthma treatments, 57,61,62 such as anti-IL-33 and soluble IL1RL1-a that are currently being developed targeting the IL-33/IL1RL1 path-way. The inclusion of a pharmacogenetic profile in validation analyses of these new therapies is warrant-ed to distinguish between an overall effect and an effect present in a subgroup with a specific genetic profile. We conclude that IL1RL1 is an important gene in the manifestation of asthma exacerbations. The fact that its effect is still present in asthmatic patients using ICS makes this gene and the biological path-way in which it is involved important for new asthma treatment regimens.
Acknowledgments
We would like to thank the participants and their parents of the studied cohorts for their participation. We also would like to acknowledge the field workers, data managers and scientific collaborators dedicat-ed to these cohorts.
The authors acknowledge the GALA II and SAGE investigators (Kelley Meade, Harold J. Farber, Pedro C. Avila, Denise Serebrisky, Shannon M. Thyne, Emerita Brigino-Buenaventura, William Rodriguez-Cintron, Saunak Sen, Rajesh Kumar, Michael Lenoir, Luisa N. Borrell, and Jose R. Rodriguez-Santana), the recruit-ers, participants and the study coordinator Sandra Salazar. We thank as well the ESTATe investigators (Pharmo: Ron Herings, Annemarie Janse, Jettie Overbeek, Josine Kuiper. IPCI: Katia Verhamme, Hettie Janssens, Johan de Jongste, Miriam Sturkenboom).
Clinical implications:
Children and adolescents with asthma using ICS are still susceptible to more exacerbations when having IL1RL1 risk alleles than children with the protective genotypes. This pinpoints the
References
1. Masoli M, Fabian D, Holt S, Beasley R, Program GI for A (GINA). The global burden of asthma: executive summary of the GINA Dissemination Committee report. Allergy. 2004;59:469–78.
2. Global Initiative for Asthma. Global Strategy for Asthma Management and Prevention, 2018. Available from: www.ginasthma.org.
3. Cowan DC, Cowan JO, Palmay R, Williamson A, Taylor DR. Effects of steroid therapy on
inflammatory cell subtypes in asthma. 2010;384–90.
4. Woodruff PG, Modrek B, Choy DF, Jia G, Abbas AR, Ellwanger A, et al. T-helper Type 2 – driven Inflammation Defines Major Subphenotypes of Asthma. Am J Respir CritCare Med. 2009;180:388-95.
5. Martin RJ, Szefler SJ, King TS, Kraft M, Boushey HA, Chinchilli VM, et al. The Predicting Response to Inhaled Corticosteroid Efficacy (PRICE) trial. J Allergy Clin Immunol. 2007;119:73–80.
6. Zhang J, Yu C, Holgate ST, Reiss TF. Variability and lack of predictive ability of asthma end-points in clinical trials. Eur Respir J. 2002;20:1102–9.
7. Jackson DJ, Sykes A, Mallia P. Asthma : Current status and future directions Asthma exacerbations : Origin , effect , and prevention. J Allergy Clin Immunol. 128:1165–74.
8. Calhoun WJ, Haselkorn T, Miller DP, Omachi TA. Asthma exacerbations and lung function in patients with severe or difficult-to-treat asthma. J Allergy Clin Immunol. 201;136:1125-7.
9. Rennard SI, Farmer SG. Exacerbations and Progression of Disease in Asthma and Chronic Obstructive Pulmonary Disease. Proc Am Thorac Soc. 2004;1:88–92.
10. Park HW, Dahlin A, Tse S, Duan QL, Schuemann B, Martinez FD, et al. Genetic predictors associated with improvement of asthma symptoms in response to inhaled corticosteroids. J Allergy Clin Immunol. 2014;133:664–69.
11. Vijverberg SJH, Tavendale R, Leusink M, Koenderman L, Raaijmakers JAM, Postma DS, et al. Pharmacogenetic analysis of GLCCI1 in three north European pediatric asthma populations with a reported use of inhaled corticosteroids. Pharmacogenomics. 2014;15:799–806.
12. Keskin O, Uluca Ü, Birben E, Coşkun Y, Ozkars MY, Keskin M, et al. Genetic associations of the response to inhaled corticosteroids in children during an asthma exacerbation. Pediatr Allergy Immunol. 2016;27:507–13.
13. Leusink M, Vijverberg SJH, Koenderman L, Raaijmakers JAM, De Jongste JC, Sterk PJ, et al. Genetic variation in uncontrolled childhood asthma despite ICS treatment. Pharmacogenomics. 2016;16:158–63.
14. Kersten ETG, Koppelman GH. Pharmacogenetics of asthma : toward precision medicine. Curr Opin Pulm Med. 2017;23:12–20.
15. Mosteller M, Hosking L, Murphy K, Shen J, Song K, Nelson M, et al. No evidence of large genetic effects on steroid response in asthma patients. J Allergy Clin Immunol. 2017;139:797–803
16. Woodruff PG, Boushey HA, Dolganov GM, Barker
CS, Yang YH, Donnelly S, et al. Genome-wide profiling identifies epithelial cell genes associated with asthma and with treatment response to corticosteroids. Proc Natl Acad Sci U S A. 2007;104:15858-63.
17. Moffatt MF, Gut IG, Demenais F, Strachan DP, Bouzigon E, Heath S, et al. A large-scale, consortium-based genomewide association study of asthma. N Engl J Med. 2010;363:1211–21.
18. Torgerson DG, Ampleford EJ, Chiu GY, Gauderman WJ, Gignoux CR, Graves PE, et al. Meta-analysis of genome-wide association studies of asthma in ethnically diverse North American populations. Nat Genet. 2011;43:887–92.
19. Coyle AJ, Lloyd C, Tian J, Nguyen T, Erikkson C, Wang L, et al. Crucial role of the interleukin 1 receptor family member T1/ST2 in T helper cell type 2-mediated lung mucosal immune responses. J Exp Med. 1999;190:895–902.
20. Li H, Tago K, Io K, Kuroiwa K, Arai T, Iwahana H, et al. The cloning and nucleotide sequence of human ST2L cDNA. Genomics. 2000;67:284–90.
21. Cherry WB, Yoon J, Bartemes KR, Iijima K, Kita H. A novel IL-1 family cytokine, IL-33, potently activates human eosinophils. J Allergy Clin Immunol. 2008;121:1484–90.
22. Kurokawa M, Matsukura S, Kawaguchi M, Ieki K, Suzuki S, Odaka M, et al. Expression and Effects of IL-33 and ST2 in Allergic Bronchial Asthma : IL-33 Induces Eotaxin Production in Lung Fibroblasts. Int Arch Allergy Immunol. 2011;155:12–20.
23. Traister RS, Uvalle CE, Hawkins GA, Meyers DA, Bleecker ER, Wenzel SE. Phenotypic and genotypic association of epithelial IL1RL1 to human T2-like asthma. J Allergy Clin Immunol. 2015 Jan;135:92-9.
24. Ketelaar ME, van de Kant KD, Dijk FN, Klaassen EM, Grotenboer NS, Nawijn MC, et al. Predictive value of serum sST2 in preschool wheezers for development of asthma with high FeNO. Allergy Eur J Allergy Clin Immunol. 2017;72:1811–5.
25. Gudbjartsson DF, Bjornsdottir US, Halapi E, Helgadottir A, Sulem P, Jonsdottir GM, et al. Sequence variants affecting eosinophil numbers associate with asthma and myocardial infarction. Nat Genet. 2009;41:342–7.
26. Savenije OE, Mahachie John JM, Granell R, Kerkhof M, Dijk FN, De Jongste JC, et al. Association of IL33-IL-1 receptor-like 1 (IL1RL1) pathway polymorphisms with wheezing phenotypes and asthma in childhood. J Allergy Clin Immunol. 2014;134:170–7.
27. Ali M, Zhang G, Thomas WR, McLean CJ, Bizzintino JA, Laing IA, et al. Investigations into the role of ST2 in acute asthma in children. Tissue Antigens. 2009;73:206–12.
28. Dijk FN, Xu C, Melén E, Carsin A-E, Kumar A, Nolte IM, et al. Genetic regulation of IL1RL1 methylation and IL1RL1-a protein levels in asthma. Eur Respir J. 2018;51:1701377.
29. Gordon ED, Palandra J, Wesolowska-Andersen A, Ringel L, Rios CL, Lachowicz-Scroggins ME, et al. IL1RL1 asthma risk variants regulate airway type 2 inflammation. JCI Insight. 2016;1:e87871.
30. Liu KYP, Hu S, Chan BCL, Wat ECL, Lau CBS, Hon KL, et al. Anti-inflammatory and anti-allergic activities
of pentaherb formula, moutan cortex (Danpi) and gallic acid. Molecules. 2013;18:2483–500.
31. Farzan N, Vijverberg SJ, Andiappan AK, Arianto L, Berce V, Blanca-López N, et al. Rationale and design of the multiethnic Pharmacogenomics in Childhood Asthmaconsortium. Pharmacogenomics. 2017;18:931–43.
32. Koster ES, Raaijmakers JAM, Koppelman GH, Postma DS, van der Ent CK, Koenderman L, et al. Pharmacogenetics of anti-inflammatory treatment in children with asthma: rationale and design of the PACMAN cohort. Pharmacogenomics. 2009;10:1351–61.
33. Levy ML, Thomas M, Small I, Pearce L, Pinnock H, Stephenson P. Summary of the 2008 BTS/SIGN British Guideline on the management of asthma. Prim Care Respir J. 2009;18:Suppl 1:S1–16
34. Juniper EF, Bousquet J, Abetz L, Bateman ED; GOAL Committee. Identifying ‘wellcontrolled’ and ‘not well-controlled’ asthma using the Asthma Control Questionnaire. Respir Med. 2006;100:616–21.
35. Borrell LN, Nguyen EA, Roth LA, Oh SS,
Tcheurekdjian H, Sen S, et al. Childhood Obesity and Asthma Control in the GALA II and SAGE II Studies. Am J Respir Crit Care Med. 2013;187:697–702.
36. Schatz M, Sorkness CA, Li JT, Marcus P, Murray JJ, Nathan RA, et al. Asthma Control Test: Reliability, validity, and responsiveness in patients not previously followed by asthma specialists. J Allergy Clin Immunol. 2006;117:549–56.
37. Grotenboer NS, Ketelaar ME, Koppelman GH, Nawijn MC. Decoding asthma: translating genetic variation in IL33 and IL1RL1 into disease pathophysiology. J Allergy Clin Immunol. 2013;131:856–65.
38. 1000 Genomes Project Consortium, Auton A, Abecasis GR, Altshuler DM, Durbin RM, Abecasis GR, Bentley DR, et al. A global reference for human genetic variation. Nature. 2015;526:68–74.
39. Machiela MJ, Chanock SJ. LDassoc: an online tool for interactively exploring genome wide association study results and prioritizing variants for functional investigation. Bioinformatics. 2018;34:887–9.
40. Ward LD, Kellis M. HaploReg: a resource for exploring chromatin states, conservation, and regulatory motif alterations within sets of genetically linked variants. Nucleic Acids Res. 2012;40:D930-4.
41. GTEx Consortium. Genetic effects on gene expression across human tissues. Nature 2017; 550: 204–213.
42. Koster ES, Maitland-Van Der Zee AH, Tavendale R, Mukhopadhyay S, Vijverberg SJH, Raaijmakers JAM, et al. FCER2 T2206C variant associated with chronic symptoms and exacerbations in steroid-treated asthmatic children. Allergy. 2011;66:1546–52.
43. Price AL, Patterson NJ, Plenge RM, Weinblatt ME, Shadick NA, Reich D. Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet. 2006;38:904–9.
44. Menckeberg TT. Adherence to Inhaled Corticosteroids and Patient Perception: towards a better understanding and individualised care. Utrecht University, 2008. (Thesis).
45. Willer CJ, Li Y, Abecasis GR. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics. 2010;26:2190–1.
46. Higgins JPT, Thompson SG, Deeks JJ, Altman DG.
Measuring inconsistency in meta-analyses. BMJ Br Med J. 2003;327:557–60.
47. (2015) RCT. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL http://www.R-project.org/ [Internet]. Vienna, Austria; Available from: https://www.r-project.org
48. HM K. EPACTS (Efficient and Parallelizable Association Container Toolbox).http://genome.sph. umich.edu/wiki/EPACTS (2016).
49. Traister RS, Uvalle CE, Hawkins GA, Meyers DA, Bleecker ER, Wenzel SE. Phenotypic and genotypic association of epithelial IL1RL1 to human TH2-like asthma. J Allergy Clin Immunol. 2015;135:92–9.
50. Savenije OE, Kerkhof M, Reijmerink NE, Brunekreef B, de Jongste JC, Smit HA, et al. Interleukin-1 receptor-like 1 polymorphisms are associated with serum IL1RL1-a, eosinophils, and asthma in childhood. J Allergy Clin Immunol. 2011;127:750–5.
51. Moffatt MF. A large-scale, consortium-based genomewide association study of asthma. N Engl J Med. 2010;363:1211–21.
52. Van Der Valk RJP, Baraldi E, Stern G, Frey U, De Jongste JC. Daily exhaled nitric oxide measurements and asthma exacerbations in children. Allergy Eur J Allergy Clin Immunol. 2012;67:265–71.
53. Malinovschi A, Fonseca JA, Jacinto T, Alving K, Janson C. Exhaled nitric oxide levels and blood eosinophil counts independently associate with wheeze and asthma events in National Health and Nutrition Examination Survey subjects. J Allergy Clin Immunol. 2013;132:821-7.
54. Ho JE, Chen WY, Chen MH, Larson MG, McCabe EL, Cheng S, et al. Common genetic variation at the IL1RL1 locus regulates IL-33 / ST2 signaling. J Clin Invest. 2013;123:4208–18.
55. Hayakawa H, Hayakawa M, Kume A, Tominaga SI. Soluble ST2 blocks interleukin-33 signaling in allergic airway inflammation. J Biol Chem. 2007;282:26369–80.
56. Lee HY, Rhee CK, Kang JY, Byun JH, Choi JY, Kim SJ, et al. Blockade of IL-33/ST2 ameliorates airway inflammation in a murine model of allergic asthma. Exp Lung Res. 2014;40:66–76.
57. Brusselle GG, Maes T, Bracke KR. Eosinophils in the Spotlight: Eosinophilic airway inflammation in nonallergic asthma. Nat Med. 2013;19:977–9.
58. Saluja R, Ketelaar ME, Hawro T, Church MK, Maurer M, Nawijn MC. The role of the IL-33/IL-1RL1 axis in mast cell and basophil activation in allergic disorders. Mol Immunol. 2015;63:80–5.
59. Díaz-Jiménez D, Núñez L, De La Fuente M, Dubois-Camacho K, Sepúlveda H, Montecino M, et al. A functional IL1RL1 variant regulates corticosteroid-induced sST2 expression in ulcerative colitis. Sci Rep. 2017;7:1–14.
60. Koster ES, Raaijmakers JAM, Vijverberg SJH, Koenderman L, Postma DS, Koppelman GH, et al. Limited agreement between current and long-term asthma control in children: The PACMAN cohort study. Pediatric Allergy and Immunology. 201;22:776-83.
61. Portelli M, Sayers I. Genetic basis for personalized medicine in asthma. Expert Rev Respir Med. 2012;6:223-36.
62. Mitchell PD, O’Byrne PM. Epithelial-Derived Cytokines in Asthma. Chest. 2017;151:1338–44.
IL1RL1 gene variations are associated
with asthma exacerbations in children and
adolescents using inhaled corticosteroids
-Chapter 6
Supporting InformationSupporting Methods
Study design
GALA II
The Genes-environments & Admixture in Latino Americans (GALA II) study is a case-control study of asthma in Latinos recruited in five centers throughout the United States (Chicago, Illinois; Bronx, New York; Houston, Texas; San Francisco Bay Area, California; and San Juan, Puerto Rico). Participants were eligible if they were between 8 to 21 years old and self-identified all four grandparents as Latino. Sub-jects were excluded if they reported any of the following: (1) history of sickle cell disease, cystic fibrosis, sarcoidosis, cerebral palsy, or history of heart or chest surgery; (2) 10 or more pack-years of smoking; (3) any smoking within one year of recruitment date; or (4) pregnancy in the third trimester. Only children with a physician-diagnosis of asthma that reported active symptoms and use of medication during the 12 months preceding the study enrollment were analyzed in the current study. All local institutional review boards approved the study and all subjects/parents provided written assent/consent, respectively.1–3
SAGE
The Study of African Americans, Asthma, Genes & Environments (SAGE) is an ongoing study of asthma in children and young adults coordinated from the University of California San Francisco. Recruitment protocols were similar to GALA II with the only differences being that the recruitment was restricted to San Francisco Bay Area and participants self-identified all four grandparents as African American.4
SLOVENIA
In the SLOVENIA cohort5, 224 children aged 5–18 years were selected. Asthma was diagnosed accord-ing to American Thoracic Society (ATS) criteria. Children were included with mild or moderate persistent asthma newly detected in this period. Data on asthma symptoms, exacerbations and clinical were ob-tained during the physician visit over the preceding 12 months. The study was carried out in accordance with the Helsinki declaration of the World Medical Association and approved by the Slovenian National Medical Ethics Committee (KME 31/12/06). Parents signed informed consent for children under 15 years, whereas older children gave informed consent by themselves.
ESTATe
The Effectiveness and Safety of Treatment with Asthma Therapy in children (ESTATe) is a case-control study that includes children and young adults (4-19 years) with a physician diagnosis of asthma recruited from primary care practices in the Netherlands. Patients were selected from either IPCI (Interdisciplinary Processing of Clinical Information) database or the PHARMO Database Network. Both databases contain the electronic medical records of more than one million patients throughout the Netherlands with detailed information on patient diagnosis, patient prescription (IPCI) or patient dispensing (PHARMO). During the study period (2000 -2012) all children with asthma, aged 5 years and older and treated with asthma control-ler therapy were selected. Within this cohort, we selected cases with a asthma exacerbation (use of systemic corticosteroids, ER visit or hospitalization because of asthma) and matched each case to 4 controls on age, sex, general practice (GP) and type of asthma controller therapy. Next, all potential cases and controls were invited to participate via their respective GP. If patients agreed to participate, they provided written consent, completed a research questionnaire, and provided a saliva sample (for DNA extraction).
Genotyping data
PACMAN
In PACMAN, genotyping was performed at LGC Genomics (UK). Quality control (QC) was performed by visual inspection of the genotypes, exclusion of individuals with missing genotype call rate >0.50. SNPs were excluded with a missing genotype rate >0.01, a Hardy-Weinberg equilibrium P-value <0.001 and a MAF <0.01.
Significant SNPs (p < 0.05) in PACMAN were selected for replication analysis. All genetic data were aligned to assembly GRCh37/hg19.
GALA II and SAGE
Both GALA II and SAGE samples were genotyped with the Axiom® LAT1 array (Affymetrix Inc.), and qual-ity control of the data was carried out as described elsewhere.3,4 Imputation was performed with Mini-mac6 by means of the Michigan Imputation Server7, using as reference panel the second release of the Haplotype Reference Consortium (HRC) (r1.1 2016).8 For that, a prior step of haplotype reconstruction from genotype data was performed with SHAPEIT.9
SLOVENIA
DNA samples were obtained from peripheral blood. First, peripheral blood lymphocytes (PBMCs) were isolated using FicollPaque PLUS (GE Healthcare, Uppsala, Sweden) and then DNA was isolated from PBMCs using TRI reagent (Sigma, Steinheim, Germany) according to manufacturer instructions. SNPs rs13431828, rs1921622, rs1420101 and rs10204137 were genotyped using high resolution melting analysis (HRMA) on LightCycler 480 instrument (Roche, Germany) using LC480 HRM Master Mix (Roche, Germa-ny). Following primers were used for amplification:
rs13431828 (1) AAGAGTATCACCAACTGCCTCA rs13431828 (2) GCAAGTATTGGTAACTCGTTGTTG rs1921622 (1) TGCCACTTCTTAATTCTGTCCA rs1921622 (2) GTACTTTTAAGGTATTTCAGCTAGTGC rs1420101 (1) CTCGACAACATTTATGTACACCAT rs1420101 (2) TTGGTGTCAGAGTTTCTGCAA rs10204137 (1) CTCTGAGCGAGCTGGACAT rs10204137 (2) ATGTGGTCCTCCCTCCACTT
Conditions were as follow: initial denaturation at 95°C for 10min, followed by 45 cycles of 95°C for 10s, 60°C for 15s and 72°C for 10s, followed by HRM step of 95°C for 1min, 40°C for 1min and 60-90°C at 0,02°C/s. Genotypes were determined using gene scanning analysis software.
ESTATe
Genotyping of ESTATe was performed with the Illumina Infinium CoreExome-24 BeadChip (Illumina). SNPs and samples with a call rate < 99%, SNPs with Hardy-Weinberg p-value <10−4, and samples with ex-cess autosomal heterozygosity were excluded. The genetic data were aligned to assembly GRCh37/hg19.
Supporting Tables
Table S1. LD pattern of the six selected IL1RL1 SNPS.
LD, linkage disequillibrum; BP, base pair; SNP, single nucleotide polymorphism.
Table S2. Results of associations of IL1RL1 SNPs with asthma control and FeNO levels per study and meta-analysis. SNP, single nucleotide polymorphism; OR, odds ratio; CI, confidence interval.
For asthma control, in PACMAN and SLOVENIA all results were corrected for age, gender and BTS treatment step. In SAGE and GALA II results were corrected for age, gender, BTS treatment step and principal components 1 and 2. For FeNO all results were corrected for age and gender.
Bold faced results are significant results (P<0.05). Missing values means the SNP was not present in the study. *R = referece allele, E = effect allele.
†In PACMAN asthma control was measured using the Asthma Control Questionnaire score (ACQ-6) in the past week. Poor asthma control was defined as ACQ>1.5. In GALA II and SAGE asthma control was based on a modified version of the 1978 American Thoracic Society-Division of Lung Diseases Epidemiology Questionnaire on symptoms, nighttime awakening, interferences with normal activities, rescue medication use, and lung function measurements. Asthma controlled and not well-controlled patients were considered as controls and very poorly asthmatic patients were con-sidered as cases. In Slovenia Asthma control was measured using the ACT questionnaire which contains 5 questions that are related to the frequency of both asthma symptoms and required rescue medication use during the previous 4 week after 6 months of ICS treatment. Uncontrolled asthma was defined as ACT <15 points.
‡Exhaled nitric oxide (eNO) was measured in ppb. Before analyses values were log transformed. In PACMAN FeNo was measured in children with reported use of asthma medication in the past 6 months. In SLOVENIA FeNO levels were mea-sured 4-6 weeks after start of ICS treatment.
Table S3. Conditional analysis in PACMAN on rs13431828, rs142010, rs1921622 and rs10204137 for ‘any exacerbation’.
SNP, single nucleotide polymorphism; OR, odds ratio; CI, confidence interval. Results were corrected for age and gender.
Bold faced results are significant results (P<0.05). *R = reference allele, E = effect allele.
†Any exacerbation was defined as ER visit and/or OCS use in the 12 months preceding the study visit. Table S4. Pharmacogenetic effect of the IL1RL1 SNPs on changes in FeNO and FEV1% predicted values.
SNP, single nucleotide polymorphism; CI, confidence interval; AF, allele frequency. Missing values means the SNP was not present in the study.
*R = reference allele, E = effect allele.
†Delta FeNO values were calculated by substracting the post inhaled corticosteroid (ICS) treatment (4-6 weeks after start of treatment) value from the pretreatment ICS value.
‡Delta FEV1% predicted values were calculated by substracting the post ICS treatment (4-6 weeks after start of treat-ment) value from the pretreatment ICS value.
Supporting References
1. Nishimura KK, Galanter JM, Roth LA, Oh SS, Thakur N, Nguyen EA, et al. Early-Life air pollution and asthma risk in minority children the GALA II and SAGE II studies. Am J Respir Crit Care Med. 2013;188:309–18.
2. Thakur N, Oh SS, Nguyen EA, Martin M, Roth LA, Galanter J, et al. Socioeconomic status and childhood asthma in urban minority youths: The GALA II and SAGE II studies. Am J Respir Crit Care Med. 2013;188:1202–9.
3. Pino-Yanes M, Thakur N, Gignoux CR, Galanter JM, Roth LA, Eng C, et al. Genetic ancestry influences asthma susceptibility and lung function among Latinos. J Allergy Clin Immunol.2015;135:228–35.
4. White MJ, Risse-Adams O, Goddard P, Contreras MG, Adams J, Hu D, et al. Novel genetic risk factors for asthma in African American children: Precision Medicine and the SAGE II Study. Immunogenetics. 2016;68:391–400.
5. Berce V, Kozmus CEP, Potočnik U. Association among ORMDL3 gene expression, 17q21 polymorphism and response to treatment with inhaled corticosteroids in children with asthma. Pharmacogenomics J. 2013;13:523–9.
6. Fuchsberger C, Abecasis GR, Hinds DA. Minimac2: Faster genotype imputation. Bioinformatics. 2015;31:782–4.
7. Das S, Forer L, Schönherr S, Sidore C, Locke AE, Kwong A, et al. Next-generation genotype imputation service and methods. Nat Genet. 2016;48:1284–7.
8. McCarthy S, Das S, Kretzschmar W, Delaneau O, Wood AR, Teumer A, et al. A reference panel of 64,976 haplotypes for genotype imputation. Nat Genet. 2016;48:1279–83.
9. Delaneau O, Coulonges C, Zagury JF. Shape-IT: New rapid and accurate algorithm for haplotype inference. BMC Bioinformatics. 2008;9:1–14.
10. Auton A, Abecasis GR, Altshuler DM, Durbin RM, Abecasis GR, Bentley DR, et al. A global reference for human genetic variation. Nature. 2015:526;68–74.
