Clinica Chimica Acta 436 (2014) 20–26

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Genome-wide association study identifies ALLC polymorphisms correlated with FEV1 change by corticosteroid Tae-Joon Park a,1, Jong-Sook Park b,1, Hyun Sub Cheong c, Byung-Lae Park c, Lyoung Hyo Kim c, Jeong Seok Heo b, Yang Ki Kim d, Ki-Up Kim d, Soo-Taek Uh d, Ho Sung Lee e, Joo-Ock Na e, Ki-Hyun Seo e, Jae-Sung Choi e, Yong Hoon Kim e, Myung-Sin Kim f, Choon-Sik Park b,⁎, Hyoung Doo Shin a,c,⁎⁎ a

Department of Life Science, Sogang University, 35, Baekbeom-ro, Mapo-gu, Seoul 121-742, Republic of Korea Division of Allergy and Respiratory Medicine, Soonchunhyang University Bucheon Hospital, 1174, Jung-dong, Wonmi-gu, Gyeonggi-do 420-020, Republic of Korea c Department of Genetic Epidemiology, SNP Genetics, Inc., 35, Baekbeom-ro, Mapo-gu, Seoul 121-742, Republic of Korea d Division of Allergy and Respiratory Medicine, Soonchunhyang University Seoul Hospital, 59, Daesagwan-ro, Yongsan-gu, Seoul 140-887, Republic of Korea e Division of Allergy and Respiratory Medicine, Soonchunhyang University Cheonan Hospital, 23-20, Byeongmyeong-dong, Dongnam-gu, Cheonan, Chungcheongnam-do 330-721, Republic of Korea f Division of Allergy and Respiratory Medicine, Soonchunhyang University Gumi Hospital, 250, Gongdan-dong, Gumi, Kyungsangbook-do 730-706, Republic of Korea b

a r t i c l e

i n f o

Article history: Received 4 January 2014 Received in revised form 16 April 2014 Accepted 18 April 2014 Available online 2 May 2014 Keywords: Genome-wide association study FEV1 Inhaled corticosteroid ALLC Single-nucleotide polymorphism Haplotype

a b s t r a c t Objectives: Asthma can be suppressed by inhaled corticosteroids (ICS). However, response to ICS shows marked inter-individual variability. This study is aimed to identify the genetic variants associated with the change in the percentage of forced expiratory volume in 1 second (%ΔFEV1) following ICS treatment. Methods: A genome-wide association study was performed in a Korean asthmatic cohort. To further investigate these genetic associations, 11 additional single-nucleotide polymorphisms (SNPs) on the allantoicase (ALLC) gene were selected from the HapMap database and genotyped in the same asthmatic patients in the follow-up study. Results: In a genome-wide study, we identified the lowest P-value in ALLC, but none of the SNPs met the genomewide association criteria (P b 1.0 × 10−8). However, among 25 SNPs on ALLC in the follow-up study, 6 variants showed significant associations with the mean %ΔFEV1 in the study subjects (P b 3.73 × 10−6). Conclusions: Although the associated signals could not overcome the genome-wide multiple correction due to small sample size (n = 189), our results suggest that associated SNPs of ALLC might be genetic predictors of response to ICS, at least with respect to ΔFEV1 in Korean asthmatics. © 2014 Elsevier B.V. All rights reserved.

1. Introduction Inhaled corticosteroids (ICS) are the most widely used medications for asthma control, as they are the most effective anti-inflammatory agents for persistent asthma [1]. Previous clinical studies have reported the effectiveness of ICS in decreasing asthma symptoms, achieving better lung function [2–4], reducing bronchial hyperresponsiveness [4–6], and relieving the exacerbations and mortality of asthma [7]. The responsiveness to ICS treatment such as a change in forced expiratory volume in 1 second (FEV1) varies among individuals, even in patients with indistinguishable clinical phenotypes [8,9]. About 20%–30% of chronic asthma patients do not reveal any recovery in FEV1 or airway

⁎ Corresponding author. Tel.: +82 32 621 5105; fax: +82 32 621 5023. ⁎⁎ Correspondence to: H.D. Shin, Department of Life Science, Sogang University, 1 Shinsu-dong, Mapo-gu, Seoul 121-742, Republic of Korea. Tel.: +82 2 3273 1671; fax: +82 2 3273 1680. E-mail addresses: [email protected] (C.-S. Park), [email protected] (H.D. Shin). 1 These authors contributed equally to this work.

http://dx.doi.org/10.1016/j.cca.2014.04.023 0009-8981/© 2014 Elsevier B.V. All rights reserved.

hyperresponsiveness after ICS treatment [9–11]. There is accumulating evidence of an association between differences in responses to antiasthmatic drugs such as ICS, β2-adenoreceptor agonists, 5-lipoxygenase (5-LOX) inhibitors, cysteine leukotriene receptor 1 (CysLT1) antagonists, muscarinic antagonists and theophylline and individual genetic variants [12]. It has been observed that 60%–80% of this variability among patients results from genetic variation [11]. Specifically, it has been suggested that single-nucleotide polymorphisms (SNPs) of glucocorticoid-induced transcript 1 (GLCCI1), adenylyl cyclase type 9 (ADCY9), neurokinin receptor 2 (NK2R), T-box 21 (TBX21), phosphatase and tensin homolog deleted on chromosome 10 (PTEN), corticotrophin-releasing hormone receptor 1 (CRHR1), and cytotoxic T-lymphocyte-associated protein 4 (CTLA4) show an association with a change of lung function (ΔFEV1) after ICS treatment of asthma [13–18]. In 2011, genetic associations also were reported for response to ICS in the treatment of asthma. Tantisira and colleagues [18] conducted a genome-wide association study (GWAS), and the screening uncovered that a variant in a gene called GLCCI1 was potentially associated with poor ICS response. However, most of the participants in that study

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21

group were of European origin, so the results may not be applicable to all demographics. Here, we conducted an association study in the Korean population to identify new susceptibility loci for the response to ICS. We performed a GWAS with 430,487 SNPs and a follow-up study to evaluate the associations between genetic variants and the effect of ICS treatment on asthmatic patients.

on therapy in the event of an exacerbation (defined as a reduction of at least 12% or greater in FEV1 with aggravation of asthma symptoms). The primary endpoint was a change in %FEV1 (%ΔFEV1) after the course of ICS treatment. The %ΔFEV1 was calculated as [ (final FEV1 − basal FEV1)/basal FEV1] × 100%.

2. Materials and methods

2.2. Genome-wide SNP genotyping

2.1. Study subjects

Approximately 200 ng of genomic DNA was used to genotype each sample on the Illumina Human660W BeadChip® (Illumina, San Diego, USA). Samples were processed according to the Illumina Infinium-II assay manual. Briefly, each sample was whole-genome amplified, fragmented, precipitated, and resuspended in an appropriate hybridization buffer. Denatured samples were hybridized on a prepared Human660W BeadChip® for a minimum of 16 hours at 48 °C. Following hybridization, the beadchips were processed for the single-base extension reaction, stained, and imaged on an Illumina Bead Array Reader. Normalized bead intensity data obtained for each sample were loaded into the GenomeStudio® software (Illumina), which converted fluorescence intensities into SNP genotypes. SNP clusters for genotype calling were then examined for all SNPs. The overall call rate for all SNPs was 99.92%. SNPs were excluded if they showed either a call rate lower than 95% in cases or controls, or significant deviation from Hardy– Weinberg equilibrium in the controls (P ≤ 1.0 × 10−6). Samples were subsequently assessed for population stratification using HelixTree® software (Golden Helix, Bozeman, MT, USA).

The prospective controlled trial involved 230 ethnic Korean patients with moderate-to-severe persistent asthma who were recruited from the Asthma Genome Research Center of Soonchunhyang University Seoul, Cheonan and Gumi Hospitals. All subjects were examined by physicians and found to be asthmatics according to the guidelines of the Global Initiative for Asthma [19]. All patients had a history of dyspnea and wheezing during the 12 months before diagnosis. In addition, all patients showed at least one of the following: (1) N 15% increase in FEV1 or N12% increase plus 200 mL following inhalation of a shortacting bronchodilator, or (2) a positive reaction (PC20 methacholine) from b10 mg/mL of a provocative concentration of methacholine. All subjects had less than 80% of the predicted FEV1. Total immunoglobulin E (IgE) levels were measured using the CAP fluorescent enzyme immunoassay (Pharmacia Diagnosis, Uppsala, Sweden). Twenty-four common inhalant allergens were used for a skin prick test [20]. Atopy was defined as a weal with a diameter of 3 mm or greater. The exclusion criteria included an asthma duration of less than 1 year; the occurrence of acute, exacerbated asthma within the previous 4 weeks; a positive skin reaction to pollens; being a smoker or an ex-smoker with a history of at least 10 pack-years; the presence of parenchymal lung disease on chest radiography; a diffusing capacity of less than 80%; use of inhaled or systemic steroids within the previous 4 weeks; and maintenance therapy with theophylline, a leukotriene antagonist, or other add-on asthma therapy. The clinical parameters are summarized in Table 1. All subjects gave informed written consent to participate in the study. The protocols were approved by the ethics committees of the hospital. The subjects were started and maintained on ICS therapy for 4 weeks as described previously [1]. The patients were instructed to selfadminister 1000 μg of fluticasone propionate daily via a multi-dose, dry-powder inhaler (Diskhaler, GlaxoSmithKline, Research Triangle Park, NC, USA), to record their symptom scores, to use short-acting bronchodilators as needed, and to switch to combination ICS or add-

2.3. The follow-up study The gene that includes the SNP showing the most significant association signal in the GWAS, the allantoicase (ALLC) gene, was selected for the follow-up study. Eleven ALLC SNPs, including the nearby upstream region (2 kb), were additionally selected based on their minor allele frequencies (freq. N5%) and linkage disequilibrium (LD) (only one SNP was chosen if there were absolute LDs (r2 = 1)) in an Asian population from the International HapMap Project database (http://hapmap.ncbi.nlm. gov/index.html.en), and these were genotyped using a total of 189 asthmatic patients. The SNPs were scanned using the BeadExpress® system (Illumina).

2.4. Statistics

Table 1 Clinical parameters of the study subjects. Description

Asthmatic patients

Number of subjects (n) Age [year, median (range)] Onset of asthma, age [year, median (range)] Sex (male/female) Current smoker/ex-smoker (%) Positive rate of skin test (%) Basal FVC, predicted (%)⁎ Basal FEV1, predicted (%)⁎ FVC predicted at 4 weeks' treatment (%)⁎ FEV1 predicted at 4 weeks' treatment (%)⁎ Change of FEV1 with treatment (%)⁎⁎ Blood eosinophils (%)⁎ PC20, methacholine (mg/mL)⁎ Total IgE (IU/mL)⁎

189 50.5 (14.8–75.2) 42.7 (1.0–75.2) 80/109 15.9/23.3 54.5 71.6 ± 12.3 66.5 ± 12.6 83.6 ± 14.3 86.3 ± 15.6 28.7 (−34.0 to 182.4) 6.6 ± 5.3 5.1 ± 8.2 362.9 ± 541.3

FVC, forced vital capacity. ⁎ Values are mean ± standard deviation (SD). ⁎⁎ Change (%) of FEV1 (ΔFEV1) with treatment of inhaled corticosteroid for 4 weeks was calculated using the following formula: ΔFEV1 (%) = [ (FEV1 at 4 weeks' treatment − basal FEV1)/basal FEV1] × 100.

For genome-wide analysis, associations of genotype distributions among the subjects with %ΔFEV1 were calculated by linear regression analyses, adjusted for initial diagnosis age (continuous value), sex (male = 0, female = 1), smoking status (non-smoker = 0, ex-smoker = 1, current smoker = 2), and atopy (negative = 0, positive = 1) as covariates using HelixTree® software (Golden Helix). The covariates were selected according to previous reports suggesting associations of asthma symptoms with age, sex, smoking status, and atopy [21–24]. Haploview v4.1 software downloaded from the Broad Institute (www.broadinstitute.org/mpg/haploview) was used to determine LD of ALLC [25]. Haplotypes were first estimated using PHASE software [26], and then computed by linear regression analyses using HelixTree® software (Golden Helix). Subjects harboring missing genotypes were omitted in the analysis of individual SNPs and haplotypes. Power calculations for quantitative traits were performed using QUANTO software (v1.2.4, http://hydra.usc.edu/gxe/). Calculations were based on continuous outcome, an independent individuals design, and a gene-only hypothesis. An additive inheritance model was applied for SNPs with MAFs ≥ 0.05. The beta values indicating the effect sizes for differences in mean %ΔFEV1 among the genotypes of each SNP and haplotype were analyzed using the Statistical Analysis System (SAS).

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1 (182.35) 1 (182.35) 3 (91.21) 1 (182.35) 1 (93.18) 4 (93.72) 3 (91.21) 1 (146.51) 1 (20.59) 0 9 (60.99) 8 (64.54) 9 (60.99) 0 0 48 (43.01) 40 (43.85) 54 (41.55) 14 (53.35) 18 (58.59) 37 (41.00) 49 (40.80) 16 (51.65) 33 (52.83) 20 (59.49) 64 (36.83) 62 (36.77) 60 (37.11) 12 (69.09) 12 (69.09) 2 2 2 3 13 13 2 8 12 6 5 5 5 13 13 rs11123610 rs17445240 rs6754459 rs6440972 rs1323555 rs1930336 rs13418767 rs6601306 rs11835157 rs2811670 rs2434364 rs10491400 rs6859083 rs4884502 rs2590931

Genotype distribution of each SNP is presented as the number of subjects (%ΔFEV1 after inhaled corticosteroid treatment, mean). P-values were calculated by linear regression analysis controlling for age, sex, smoking status, and atopy as covariates. C/C, major homozygote; C/R, heterozygote; R/R, minor homozygote; chr, chromosome; MAF, minor allele frequency. Bold values indicate the P -values b 0.00001.

140 (22.72) 148 (23.59) 132 (22.04) 174 (25.85) 170 (25.17) 148 (23.89) 137 (23.02) 172 (25.90) 155 (23.63) 169 (25.07) 116 (21.73) 119 (22.11) 120 (22.10) 177 (25.98) 177 (25.98) 0.132 0.111 0.159 0.042 0.053 0.119 0.146 0.048 0.093 0.053 0.217 0.206 0.206 0.032 0.032 Allantoicase Allantoicase Allantoicase Hypothetical LOC647008 Cysteinyl leukotriene receptor 2 Sprouty homolog 2 Allantoicase Tankyrase, TRF 1-interacting ankyrin-related ADP-ribose polymerase ETS-domain protein Mitogen-activated protein kinase kinase kinase 7 KIAA0825 KIAA0825 KIAA0825 Olfactory receptor, family 7, subfamily E, member 156 pseudogene Olfactory receptor, family 7, subfamily E, member 156 pseudogene

Intron Promoter Intron 5′ flanking 5′ flanking 5′ flanking Promoter 5′ flanking Intron 5′ flanking Intron Intron Intron 5′ flanking 5′ flanking

ANG ANG CNT CNA CNT GNA GNT TNC ANG TNC CNT TNG CNT ANG GNA

C/C

MAF Location (AA change) Description Gene (nearest gene) Chr

The ALLC gene was selected for the follow-up study due to its having the lowest P-values in the GWAS (i.e., four ALLC SNPs, rs11123610, rs17445240, rs6754459, and rs13418767, were P b 0.00001). To discover the causal variants, 11 additional SNPs on the ALLC gene were selected from the HapMap database and genotyped in the same 189 GWAS DNAs. The genetic map and location of ALLC polymorphisms are shown in Fig. 1A. The associations between genotype distributions of each SNP and %ΔFEV1 with treatment of ICS were analyzed using linear regression analyses adjusted for initial diagnosis age, sex, smoking status, and atopic status. Among the 25 total ALLC polymorphisms (14

SNP

3.3. Follow-up study

Table 2 Top 15 SNPs within gene regions with lowest P-value for the %ΔFEV1 after treatment with inhaled corticosteroids in asthmatics (P b 0.00001).

3.2. Genome-wide association analyses

Alleles

Genotype

C/R

R/R

β

The prospective controlled trial initially involved 230 patients with moderate-to-severe persistent asthma. All subjects performed lung function tests at the beginning of the study to determine their basal FEV1 and were instructed to self-administer ICS (1000 μg of fluticasone propionate) daily for 4 weeks. A total of 41 patients were excluded because their ICS therapy compliance rate was less than 80%. Of the 189 subjects remaining in the study, 170 (91.7%) performed a second lung function test after 4 weeks of ICS to determine their final FEV1. The other 19 subjects added LABA or other add-on therapy to the ICS therapy 1 or 2 weeks into the study because they had experienced decreases of at least 12% in FEV1 with symptom aggravation. In this group, the forced vital capacity (FVC) and FEV1 at the time of the medication change were used to determine the final FEV1. The mean overall ICS compliance rate of the 189 subjects was 93.5%. The clinical characteristics of the study subjects are summarized in Table 1. ICS treatment was associated with significant increases in the mean FVC and FEV1 values, but the actual values of %ΔFEV1 ranged widely (from − 34.0% to 182.4%).

0.367 0.361 0.359 0.353 0.343 0.346 0.338 0.338 0.324 0.323 0.326 0.323 0.323 0.320 0.320

P-value

3.1. Characteristics of the study subjects

A total of 430,487 SNP genotype assays were tested on the DNA samples of 189 subjects. Principal component analysis (PCA) was used to evaluate the population structure of the samples in comparison to the HapMap individuals. The PCA did not reveal any population stratification or population outliers in our samples (Supplementary Fig. 1). Linear regression analyses were performed to estimate the P-values for association between genotype distributions of each SNP in study subjects and %ΔFEV1 by 4 weeks of ICS treatment. A Manhattan plot of association analyses of all SNPs is presented with the chromosomal positions (X-axis) and negative logarithm of P-values (Y-axis) of each SNP (Supplementary Fig. 2). A quantile–quantile plot (Supplementary Fig. 3) comparing the association P-values with those expected for a null distribution revealed a lambda value of 1.01 and was conservative in nature. The 100 highest-ranked SNPs after screening are listed in Supplementary Table 1. Among them, the top 15 SNPs within the 9 intra- or intergenic regions with the lowest P-values are listed in Table 2. Minor allele frequencies (MAFs), locations in DNA, and mean values of %ΔFEV1 according to genotype distributions are also included. We identified the lowest P-value for association at rs11123610 (P = 3.57 × 10 − 7), which maps to the ALLC gene on 2q35. However, there was no SNP that satisfied the genome-wide association criteria (P = 1.0 × 10− 8). Power calculations yielded powers ranging between 38.1% and 88.3% (at P-value ≤ 0.0002) to detect the observed change per allele, corresponding to a 0.271–0.367 change in beta value of the top 100 SNPs in Supplementary Table 1. Our sample size provided 88.3% power to detect difference among ALLC rs11123610 genotypes in a mean %ΔFEV1 of N20.3 with MAF of 0.132.

3.57E−07 5.01E−007 5.73E−007 7.58E−007 1.94E−006 2.58E−006 2.77E−006 3.68E−006 6.20E−006 6.88E−006 6.89E−006 7.80E−006 7.99E−006 9.44E−006 9.44E−006

3. Results

ALLC ALLC ALLC (FLJ46120) (CYSLTR2) (SPRY2) ALLC (TNKS) ELK3 (MAP3K7) C5orf36 C5orf36 C5orf36 (OR7E156P) (OR7E156P)

22

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23

rs11682163 (0.293)*

rs11680768 (0.291)**

rs6738683 (0.127)*

rs6743352 (0.082)*

rs13426642 (T110I, 0.124)**

rs12618628 (0.108)**

rs2043080 (0.336)*

rs13409287 (0.233)* rs11123610 (0.133)*

rs12105179 (0.312)**

rs7576701 (0.056)* rs13409104 (0.286)**

rs11687349 (0.329)**

rs7558370 (0.114)** rs13395090 (0.325)*

rs17017879 (0.108)** rs10173297 (0.315)**

rs1965732 (0.362)*

rs13384889 (0.206)** rs6754459 (0.147)*

rs17445240 (0.111)*

rs13418767 (0.146)*

rs4849975 (0.193)*

rs11902059 (0.204)**

rs13031619 (0.325)*

A) Physical map of ALLC (allantoicase) gene on chromosome 2q35

+1 Ex1

Ex2

Ex3

B) Haplotypes in ALLC

Ex4 Ex5

Ex6 Ex7

Ex8

Ex10

Ex12

C) LDs among ALLC polymorphisms

rs13384889

rs6754459

rs1965732

rs17017879

rs10173297

rs7558370

G G T G T G G G T G .

C T C C C T T T C T .

C C T C T C C C T C .

A G G A G G G G G G .

G G C G G G G G C G .

C A A C A C A C A A .

A A C A A A A A C A .

C T T C T T C C C T .

0.540 0.146 0.093 0.087 0.032 0.021 0.013 0.011 0.011 0.011 0.037

ht1

T

T C A A A

ht2

G T

T G G A

0.153

ht3

G T

T G G G

0.077

ht4

G C T G A G

0.056

ht5

G T C G A A

0.024

ht6

G T C A A A

0.013

.

0.003

Others

.

.

.

.

Frequency

0.675

rs2043080

rs13426642

rs12618628

rs6743352

rs6738683

rs11680768

ht1

T

C

C

T

0.640

ht1

G

A

T

0.706

ht2

C

C

C

T

0.230

ht2

G

G

C

0.167

0.061

ht3

A

G

C

0.127

C

Frequency

Haplotype

ht3

C

T

G

ht4

C

T

G

T

0.045

ht5

T

T

C

C

0.019

Others

T

C

G

T

0.006

rs11682163

Block 4

Block 3 Haplotype

.

rs11123610

rs13418767

A A G A A A A A G A .

rs13409287

rs17445240

A G A A A G G G A G .

rs12105179

rs11902059

G A G G G A A A G G .

Haplotype

rs7576701

rs4849975

G A G A A A A A G G .

Frequency

rs11687349

rs13031619

ht1 ht2 ht3 ht4 ht5 ht6 ht7 ht8 ht9 ht10 Others

rs13395090

Haplotype

rs13409104

Block 2

Block 1

Frequency

Fig. 1. Physical map, haplotypes and LD of ALLC. (A) Polymorphisms of ALLC investigated in this study. Coding exons are marked by black blocks, 5′ and 3′ untranslated region (UTR) by white blocks. Numbers in parentheses are minor allele frequencies of each SNP. *SNPs that have been analyzed in the first stage of the genome-wide association study (GWAS). **SNPs newly selected from the HapMap database (http://hapmap.ncbi.nlm.nih.gov/index.html.en) for the second stage of the follow-up study. (B) Haplotypes of ALLC in Korean asthmatics. (C) LD blocks among ALLC polymorphisms.

GWAS and 11 follow-up SNPs), 2 promoter SNPs (rs17445240 and rs13418767) and 4 intronic SNPs (rs6754459, rs17017879, rs7558370, and rs11123610) were significantly associated with the %ΔFEV1 with treatment of inhaled corticosteroids (P b 1.0 × 10−5, Table 3). The associations of two intronic SNPs (rs17017879 and rs7558370) were newly identified in our follow-up data from HapMap. The most significant association was detected at rs11123610. The beta values of the ALLC SNPs ranged from 0.025 to 0.353, with rs11123610 yielding the highest beta value accompanied by differences in mean %ΔFEV1 N 20.3 among its genotypes (Table 3). A regional association plot of the ALLC locus is also presented in Supplementary Fig. 4. The regional plot shows that three SNPs (rs17445240, rs17017879, and rs7558370) are in relatively high LD with rs11123610 (r2 N 0.6). Haplotypes were constructed with the 25 SNPs using PHASE software, resulting in four haplotype blocks (Fig. 1B and 1C). Pair-wise comparisons among SNPs of the ALLC gene showed tight LDs in each haplotype block (Fig. 1C). After haplotypes were inferred using the PHASE software, results from linear regression analyses for associations between haplotypes and %ΔFEV1 after ICS treatment showed that Bl1-ht3 in haplotype block 1 was significantly associated (P = 4.43 × 10−6, Table 4). The beta values of the haplotypes showed a range from 0.013 to 0.329, with Bl1-ht3 in haplotype block 1 revealing the highest beta value with differences in %ΔFEV1 N 20.3 among its genotypes (Table 4).

4. Discussion In our genome-wide study of a Korean asthmatic population, we identified top SNPs located in gene regions for %ΔFEV1 after the administration of ICS. Linear regression analyses identified polymorphisms of the ALLC gene, with the lowest P-value at the intronic SNP rs11123610. However, no SNP was observed that met the genome-wide association criteria (P b 1.0 × 10−8). ICS therapy has been the basis of treatment of asthma due to its ability to suppress airway inflammation, improve lung function, and prevent exacerbation. There are many SNPs located in various genes that are significantly associated with the therapeutic effect following the treatment of ICS [13–18]. In addition, different expression levels of genes derived from peripheral blood mononuclear cells have been observed between the ICS responder and ICS non-responder groups of asthmatics [27]. In previous research, Tantisira and colleagues [18] conducted a GWAS, and the screening uncovered that a variant in a gene called GLCCI1 was potentially associated with poor ICS response. The rs37972 SNP on the promoter region of GLCCI1 was found to be associated with the change in FEV1. The mean increase in FEV1 after treatment with ICS was about one third of that seen in patients with the wild type (3.2% vs. 9.4%, P = 0.0007). In this European-origin population, the minor (T) rs37972 allele frequency was 0.40, which was similar to

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Table 3 Association analysis of ALLC variants with the %ΔFEV1 following inhaled corticosteroid treatment. SNP

rs13031619† rs4849975† rs11902059 rs17445240† rs13418767† rs13384889 rs6754459† rs1965732† rs17017879 rs10173297 rs7558370 rs13395090† rs11687349 rs7576701† rs13409104 rs12105179 rs13409287† rs11123610† rs2043080† rs13426642 rs12618628 rs6743352† rs6738683† rs11680768 rs11682163†

Alleles

GNA GNA ANG ANG GNT CNT CNT ANG GNC CNA ANC CNT TNG TNC CNT ANG ANG ANG TNC CNT CNG TNC GNA ANG TNC

Location (AA change)

MAF

Promoter Promoter Promoter Promoter Promoter Intron 1 Intron 1 Intron 1 Intron 1 Intron 1 Intron 1 Intron 1 Intron 1 Intron 1 Exon 2 (5′UTR) Intron 3 Intron 3 Intron 3 Intron 4 Exon 6 (T110I) Intron 6 Intron 7 Intron 7 Intron 11 Intron 11

0.323 0.193 0.204 0.111 0.146 0.206 0.147 0.362 0.108 0.315 0.114 0.325 0.329 0.056 0.286 0.312 0.233 0.133 0.336 0.124 0.108 0.082 0.127 0.291 0.293

HWE⁎

Genotype

0.819 0.982 0.706 0.326 0.558 0.642 0.571 0.228 0.357 0.357 0.297 0.505 0.685 0.419 0.611 0.631 0.474 0.141 0.082 0.199 0.357 0.486 0.975 0.967 0.749

C/C

C/R

R/R

86 (30.68) 123 (31.19) 119 (31.8) 148 (23.59) 137 (23.02) 118 (31.52) 133 (22.08) 73 (20.5) 149 (23.79) 86 (22.34) 147 (23.7) 84 (22.09) 83 (19.76) 168 (26.28) 95 (20.92) 88 (19.86) 113 (24.7) 140 (22.72) 78 (24.07) 143 (29.28) 149 (29.79) 159 (28.9) 144 (27.21) 94 (25.16) 95 (25.69)

84 (26.91) 59 (24.27) 63 (23.57) 40 (43.85) 49 (40.8) 64 (24.21) 48 (41.07) 95 (34.01) 39 (43.59) 87 (33.48) 41 (42.93) 87 (32.95) 85 (35.07) 21 (48.17) 80 (35.59) 84 (34.86) 64 (34.12) 48 (43.01) 95 (30.59) 45 (27.09) 39 (24.84) 27 (27.06) 42 (33.76) 77 (28.31) 76 (28.41)

19 (27.79) 7 (22.63) 7 (22.63) 1 (182.35) 3 (91.21) 7 (22.63) 3 (91.21) 21 (33.31) 1 (182.35) 16 (37.05) 1 (182.35) 18 (39.16) 19 (40.68) 0 (0) 14 (42.31) 17 (44.22) 12 (37.7) 1 (182.35) 16 (40.19) 1 (20.34) 1 (20.34) 2 (51.7) 3 (30.24) 16 (48.51) 17 (47.44)

β

P-value

0.052 0.114 0.127 0.346 0.324 0.113 0.351 0.169 0.337 0.169 0.332 0.185 0.243 0.207 0.235 0.257 0.147 0.353 0.143 0.025 0.061 0.030 0.067 0.159 0.150

0.5 0.13 0.09 5.01E−07 2.77E−06 0.12 5.73E−07 0.02 2.49E−06 0.02 3.73E−06 0.01 0.0009 0.003 0.001 0.0004 0.05 3.57E−07 0.07 0.74 0.41 0.8 0.37 0.03 0.05

Genotype distribution of each SNP is presented as the number of subjects (%ΔFEV1 after inhaled corticosteroid treatment, mean). P-values were calculated by linear regression analysis controlling for age, sex, smoking status, and atopy as covariates. C/C, major homozygote; C/R, heterozygote; R/R, minor homozygote; AA, amino acid; MAF, minor allele frequency. ⁎ P-values for deviation from Hardy–Weinberg equilibrium. † SNPs that had been used in the first stage of the genome-wide association study (GWAS). Bold values indicate the P-values b 0.05.

that found in the Korean population in our study (0.418). However, the significant association of rs37972 was not replicated in our GWAS data. Taken together, it is possible that this contrast was due to geographical difference. The geographical specificity may in turn be due to varying interactions in different populations with environmental, genetic, or epigenetic factors. ALLC is a type of uricolysis enzyme. The function of ALLC in the uric acid degradation pathway was lost during vertebrate evolution. Despite the absence of this activity, the ALLC gene and its transcripts have been found in mice and in humans [28,29]. Previously, the human ALLC gene was cloned from a fetal cDNA library and an adult kidney expressed

sequence tag (EST) clone [28]. Due to its lack of functional activity, to our knowledge, no previous study has suggested an association between the ALLC gene and human illnesses or treatment for disease, including asthma and the effect of ICS treatment. However, it has been suggested that uric acid levels are significantly higher in allergeninduced asthmatic patients who were not treated with ICS than in ICS-treated patients [21]. Thus, the role of ICS in affecting uric acid level in asthmatic patients might be seen as supportive information for our data. Moreover, a recent genome-wide study revealed the association of genes located in chromosome 2q35, the same location as the ALLC gene. The SNP rs2571445, in the tensin 1 (TNS1) gene, was the

Table 4 Association analysis of ALLC haplotypes with %ΔFEV1 following inhaled corticosteroid treatment. Block

Haplotype

MAF

HWE⁎

Genotype −/−

−/ht

ht/ht

Block1

Bl1-ht1 Bl1-ht2 Bl1-ht3 Bl1-ht4 Bl2-ht1 Bl2-ht2 Bl2-ht3 Bl2-ht4 Bl3-ht1 Bl3-ht2 Bl3-ht3 Bl4-ht1 Bl4-ht2 Bl4-ht3

0.46 0.146 0.093 0.087 0.325 0.153 0.077 0.056 0.36 0.23 0.061 0.294 0.167 0.127

0.759 0.559 0.591 0.688 0.737 0.153 0.908 0.419 0.866 0.678 0.702 0.805 0.694 0.975

54 (18.93) 139 (29.38) 155 (24.46) 157 (28.82) 85 (19.69) 138 (28.83) 161 (26.04) 168 (26.28) 78 (24.07) 111 (23.52) 167 (29.16) 95 (25.69) 132 (27.02) 144 (27.21)

96 (32.69) 45 (27.76) 33 (44.03) 31 (29.24) 85 (35.07) 44 (29.41) 27 (39) 21 (48.17) 86 (29.3) 69 (35.1) 21 (25.55) 77 (28.31) 51 (29.95) 42 (33.76)

39 (32.47) 5 (18.86) 1 (182.35) 1 (−4.11) 19 (40.68) 7 (22.05) 1 (182.35) 0 (0) 25 (41.2) 9 (43.76) 1 (20.34) 17 (47.44) 6 (55.53) 3 (30.24)

Block2

Block3

Block4

Genotype distribution of each haplotype is presented as the number of subjects (%ΔFEV1 after inhaled corticosteroid treatment, mean). P-values were calculated by linear regression analysis controlling for age, sex, smoking status, and atopy as covariates. MAF, minor allele frequency. ⁎ P-values for deviation from Hardy–Weinberg equilibrium. Bold values indicate the P-values b 0.05.

β

P-value

0.159 0.058 0.329 0.013 0.245 0.022 0.253 0.207 0.166 0.197 0.034 0.149 0.120 0.067

0.03 0.44 4.43E−06 0.87 0.0007 0.77 0.0005 0.005 0.02 0.008 0.65 0.04 0.1 0.37

T.-J. Park et al. / Clinica Chimica Acta 436 (2014) 20–26

most significantly associated with FEV1 among the polymorphisms in chromosome 2q35 [30]. In addition, a genome-wide screen for lung function in a Dutch population yielded distinct evidence of involvement of chromosome 2q in the ratio of FEV1 to vital capacity [31]. Also, the cytosolic T lymphocyte-associated 4 (CTLA4) polymorphisms in chromosome 2q33 showed an association with parameters of asthma diagnosis such as bronchial hyperresponsiveness and total IgE level [32]. In summary, chromosome 2q, including the ALLC gene, may be an important region when it comes to genetic influences on the reversibility of lung function or allergic responses in asthmatic patients. We employed in silico analyses to predict potential functions of the significantly associated rs17445240 and rs13418767 in the promoter; rs6754459, rs17017879, rs7558370, and rs11123610 in the intron; and rs13409104 in the 5′ untranslated region (5′UTR). We could not find any functional evidence for the association of rs11123610 with %ΔFEV1, the parameter of response to ICS. However, rs13418767, rs6754459, and rs13409104 in strong LD with the most significantly associated SNP, rs11123610, were shown to have a correlation with gene function in our in silico analysis. The prediction of transcription factors, using the TFSEARCH program (www.cbrc.jp/research/db/TFSEARCH. html), revealed that rs13418767 is the binding site for stimulating protein 1 (Sp1). Glucocorticoids enhance the binding of Sp1 in the ubiquitin promoter [33] and the expression of the monoamine oxidase A (MAO-A) gene via the indirect interaction between the glucocorticoid receptor (GR) and Sp1 [34]. A possible splicing site of the intronic rs6754459 was detected using the Netgene2 server (www.cbs.dtu.dk/services/ Netgene2/) (Supplementary Table 2). Despite difficulty in assessing the functions of SNPs that are not positioned at the exon and promoter regions, previous studies have suggested that intronic SNPs may play important roles in gene transcription rate and abnormal splicing events such as exon skipping, activation of cryptic splice sites, and production of alternatively spliced isoforms in human disease phenotypes [35,36]. Furthermore, it also has been reported that alternative splicing in the human GR gene produces various GR isoforms that take part in the glucocorticoid-sensitive or -insensitive pathways [37]. In addition, different mRNA second structures of 5′UTR including rs13409104C N T were predicted according to its alleles using the MFOLD program (http://mfold.rna.albany.edu/?q=mfold) in this study. The mRNA folding structure of the rs13409104T genotype was more stable than that of the rs13409104C genotype (ΔG = −136.33 vs. −133.80), suggesting that this SNP may affect the transcription rate of ALLC (Supplementary Fig. 5). Our study has its limitations. We specifically focused on ALLC SNPs, and thus the vast majority of SNPs in the GWAS were not tested in a follow-up study. Although our in silico results would suggest that SNPs in the ALLC gene have a functional role, other potentially functional variants may be present within the ALLC gene and/or nearby loci. The small number of subjects studied is a major limitation of the study and suggests the need for caution in the interpretation of our results. Insufficient statistical power because of the small number of subjects might explain some of the negative results seen. In conclusion, this study confers an association between ALLC and the change in %FEV1 after ICS treatment. Although a larger number of study subjects and further functional evaluations are needed, to our knowledge, the current report is the first to investigate the association of ALLC polymorphisms and haplotypes with %ΔFEV1 after ICS treatment. Our preliminary findings from this study could provide new insight into the genetic factors associated with the effect of ICS in asthmatic patients. Conflict of interests The authors declare no conflict of interests. Acknowledgments DNA samples were generously provided by the Soonchunhyang University Bucheon Hospital Biobank, a member of the National Biobank of

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Korea, supported by the Ministry of Health, Welfare and Family Affairs, Republic of Korea. This work was supported by a grant from the Korea Health 21 R&D Project (A090548), a grant (M1-0302-00-0073) from the Korea Science and Engineering Foundation (KOSEF) funded by the Korean government (MEST) (2009-0080157), and partly supported by a research grant of Soonchunhyang University. Appendix A. Supplementary data Supplementary data to this article can be found online at http://dx. doi.org/10.1016/j.cca.2014.04.023. References [1] Jang AS, Lee JH, Park SW, et al. Factors influencing the responsiveness to inhaled glucocorticoids of patients with moderate-to-severe asthma. Chest 2005;128:1140–5. [2] O'Connell EJ. Efficacy of budesonide in moderate to severe asthma. Clin Ther 2002;24:887–905 [discussion 837]. [3] Barnes PJ, Pedersen S, Busse WW. Efficacy and safety of inhaled corticosteroids. New developments. Am J Respir Crit Care Med 1998;157:S1–S53. [4] Juniper EF, Kline PA, Vanzieleghem MA, et al. Effect of long-term treatment with an inhaled corticosteroid (budesonide) on airway hyperresponsiveness and clinical asthma in nonsteroid-dependent asthmatics. Am Rev Respir Dis 1990;142:832–6. [5] Gyllfors P, Dahlen SE, Kumlin M, Larsson K, Dahlen B. Bronchial responsiveness to leukotriene D4 is resistant to inhaled fluticasone propionate. J Allergy Clin Immunol 2006;118:78–83. [6] Kanniess F, Richter K, Bohme S, Jorres RA, Magnussen H. Effect of inhaled ciclesonide on airway responsiveness to inhaled AMP, the composition of induced sputum and exhaled nitric oxide in patients with mild asthma. Pulm Pharmacol Ther 2001;14:141–7. [7] Suissa S, Ernst P. Inhaled corticosteroids: impact on asthma morbidity and mortality. J Allergy Clin Immunol 2001;107:937–44. [8] Drazen JM, Silverman EK, Lee TH. Heterogeneity of therapeutic responses in asthma. Br Med Bull 2000;56:1054–70. [9] Szefler SJ, Martin RJ, King TS, et al. Significant variability in response to inhaled corticosteroids for persistent asthma. J Allergy Clin Immunol 2002;109:410–8. [10] Malmstrom K, Rodriguez-Gomez G, Guerra J, et al. Oral montelukast, inhaled beclomethasone, and placebo for chronic asthma. A randomized, controlled trial. Montelukast/Beclomethasone Study Group. Ann Intern Med 1999;130:487–95. [11] Tamesis GP, Krawiec ME. Heterogeneity in response to asthma medications. Curr Opin Allergy Clin Immunol 2007;7:185–9. [12] Fenech A, Hall IP. Pharmacogenetics of asthma. Br J Clin Pharmacol 2002;53:3–15. [13] Kim SH, Ye YM, Lee HY, Sin HJ, Park HS. Combined pharmacogenetic effect of ADCY9 and ADRB2 gene polymorphisms on the bronchodilator response to inhaled combination therapy. J Clin Pharm Ther 2011;36:399–405. [14] Ye YM, Lee HY, Kim SH, et al. Pharmacogenetic study of the effects of NK2R G231E G N A and TBX21 H33Q C N G polymorphisms on asthma control with inhaled corticosteroid treatment. J Clin Pharm Ther 2009;34:693–701. [15] Ni Z, Tang J, Cai Z, et al. A new pathway of glucocorticoid action for asthma treatment through the regulation of PTEN expression. Respir Res 2011;12:47. [16] Kim WJ, Sheen SS, Kim TH, et al. Association between CRHR1 polymorphism and improved lung function in response to inhaled corticosteroid in patients with COPD. Respirology 2009;14:260–3. [17] Berce V, Potocnik U. Functional polymorphism in CTLA4 gene influences the response to therapy with inhaled corticosteroids in Slovenian children with atopic asthma. Biomarkers 2010;15:158–66. [18] Tantisira KG, Lasky-Su J, Harada M, et al. Genomewide association between GLCCI1 and response to glucocorticoid therapy in asthma. N Engl J Med 2011;365:1173–83. [19] GINA. National Asthma Education and Prevention Program. Expert panel report: guidelines for the diagnosis and management of asthma update on selected topics—2002. J Allergy Clin Immunol 2002;110:S141–219. [20] Kim TH, Chang HS, Park SM, et al. Association of angiotensin I-converting enzyme gene polymorphisms with aspirin intolerance in asthmatics. Clin Exp Allergy 2008;38:1727–37. [21] Kim TH, An SH, Cha JY, et al. Association of 5-hydroxytryptamine (serotonin) receptor 4 (5-HTR4) gene polymorphisms with asthma. Respirology 2011;16:630–8. [22] Matsunaga K, Akamatsu K, Miyatake A, Ichinose M. Natural history and risk factors of obstructive changes over a 10-year period in severe asthma. Respir Med 2013;10711:355–60. [23] Tomita K, Sano H, Iwanaga T, et al. Association between episodes of upper respiratory infection and exacerbations in adult patients with asthma. J Asthma 2012;49:253–9. [24] To T, Daly C, Feldman R, McLimont S. Results from a community-based program evaluating the effect of changing smoking status on asthma symptom control. BMC Public Health 2012;12:293. [25] Barrett JC, Fry B, Maller J, Daly MJ. Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics 2005;21:263–5. [26] Stephens M, Smith NJ, Donnelly P. A new statistical method for haplotype reconstruction from population data. Am J Hum Genet 2001;68:978–89. [27] Hakonarson H, Bjornsdottir US, Halapi E, et al. Profiling of genes expressed in peripheral blood mononuclear cells predicts glucocorticoid sensitivity in asthma patients. Proc Natl Acad Sci U S A 2005;102:14789–94.

26

T.-J. Park et al. / Clinica Chimica Acta 436 (2014) 20–26

[28] Vigetti D, Monetti C, Acquati F, Taramelli R, Bernardini G. Human allantoicase gene: cDNA cloning, genomic organization and chromosome localization. Gene 2000;256:253–60. [29] Vigetti D, Monetti C, Prati M, Gornati R, Bernardini G. Genomic organization and chromosome localization of the murine and human allantoicase gene. Gene 2002;289:13–7. [30] Repapi E, Sayers I, Wain LV, et al. Genome-wide association study identifies five loci associated with lung function. Nat Genet 2010;42:36–44. [31] Postma DS, Meyers DA, Jongepier H, et al. Genomewide screen for pulmonary function in 200 families ascertained for asthma. Am J Respir Crit Care Med 2005;172:446–52. [32] Howard TD, Postma DS, Hawkins GA, et al. Fine mapping of an IgE-controlling gene on chromosome 2q: analysis of CTLA4 and CD28. J Allergy Clin Immunol 2002;110:743–51.

[33] Mao X, Zhu X, Hurren R, Ezzat S, Schimmer AD. Dexamethasone increases ubiquitin transcription through an SP-1 dependent mechanism in multiple myeloma cells. Leuk Res 2008;32:1480–2. [34] Ou XM, Chen K, Shih JC. Glucocorticoid and androgen activation of monoamine oxidase A is regulated differently by R1 and Sp1. J Biol Chem 2006;281:21512–25. [35] Brinster RL, Allen JM, Behringer RR, Gelinas RE, Palmiter RD. Introns increase transcriptional efficiency in transgenic mice. Proc Natl Acad Sci U S A 1988;85:836–40. [36] Pagani F, Baralle FE. Genomic variants in exons and introns: identifying the splicing spoilers. Nat Rev Genet 2004;5:389–96. [37] Oakley RH, Cidlowski JA. Cellular processing of the glucocorticoid receptor gene and protein: new mechanisms for generating tissue-specific actions of glucocorticoids. J Biol Chem 2011;286:3177–84.