ARG1 Is a Novel Bronchodilator Response Gene: Screening and Replication in Four Asthma Cohorts

0

ARG1 is a novel bronchodilator response gene: screening and replication in four asthma cohorts

Augusto A. Litonjua1,2,3, Jessica Lasky-Su1,3,4, Kady Schneiter5, Kelan G. Tantisira1,2,3, Ross Lazarus1,3, Barbara Klanderman1,3, John J. Lima6, Charles G. Irvin7, Stephen P. Peters8, John P. Hanrahan9, Stephen B. Liggett10, Gregory A. Hawkins8, Deborah A. Meyers8, Eugene R. Bleecker8, Christoph Lange4, Scott T. Weiss1,3

1. Channing Laboratory, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA

2. Pulmonary Division, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA

3. Center for Genomic Medicine, Brigham and Women’s Hospital, Boston, MA4. Harvard School of Public Health, Boston, MA5. Department of Mathematics and Statistics, Utah State University, Logan, Utah6. Nemours Children’s Clinic, Centers for Clinical Pediatric Pharmacology &

Pharmacogenetics, Jacksonville, FL7. Vermont Lung Center, Department of Medicine and Physiology, University of Vermont,

Burlington, VT8. Center for Human Genomics, Section of Pulmonary, Critical Care, Allergy and Immunologic

Diseases, Wake Forest University School of Medicine, Winston Salem, NC9. Pulmonary Clinical Research, Sepracor Inc., Marlborough, MA10. Cardiopulmonary Genomics Program, University of Maryland School of Medicine,

Baltimore, MD

Correspondence: Augusto A. Litonjua, M.D., M.P.H.Channing Laboratory181 Longwood AvenueBoston, MA 02115Phone: (617)525-0997E-mail: [email protected]

Acknowledgement and Support: This work was supported by U01 HL65899: The Pharmacogenetics of Asthma Treatment from the NHLBI. We thank all families for their enthusiastic participation in the Camp Genetics Ancillary Study, supported by the National Heart, Lung, and Blood Institute, NO1-HR-16049. We acknowledge the CAMP investigators and research team, supported by NHLBI, for collection of CAMP Genetic Ancillary Study data. Additional support for this research came from grants N01 HR16044, HR16045, HR16046, HR16047, HR16048, HR16049, HR16050, HR16051, and HR16052 from the National Heart, Lung and Blood Institute. All work on data from the CAMP Genetics Ancillary Study was conducted at the Channing Laboratory of the Brigham and Women’s Hospital under appropriate CAMP policies and human subjects protections. We acknowledge the American Lung Association (ALA) and the ALA’s Asthma Clinical Research Centers investigators and research teams for use of LOCCS and LoDo data, with additional funding from HL071394 and HL074755 from the NHLBI, and Nemours Children's’ Clinic. GlaxoSmithKline supported the

AJRCCM Articles in Press. Published on July 10, 2008 as doi:10.1164/rccm.200709-1363OC

Copyright (C) 2008 by the American Thoracic Society.

1

conduct of the LOCCS Trial by an unrestricted grant to the ALA. We acknowledge Sepracor, Inc. for use of the Asthma Trial data.Short Running Head: ARG1 and bronchodilator response in asthma

Subject Category: 58 (Asthma genetics) and 168 (Genetic epidemiology)

Text Word Count: 3,307

AT A GLANCE COMMENTARY

Scientific Knowledge on the SubjectInvestigations on asthma pharmacogenetics to date have mostly studied only one or a few SNPs in the ß2-adrenergic receptor gene (ADRB2). Since response to inhaled ß agonists in asthma is a complex phenotype, it is likely that other genes are involved.

What This Study Adds to the FieldThis study identifies the arginase1 gene (ARG1) as a potential ß agonist response gene, using a novel family-based method to screen variants in genes in the steroid and ß adrenergic pathways.Findings were replicated in 3 separate asthma populations.

This article has an online data supplement, which is accessible from this issue's table of content online at www.atsjournals.org

1

Abstract

Rationale: Inhaled ß agonists are one of the most widely used classes of drugs for the treatment

of asthma. However, a substantial proportion of asthmatics do not have a favorable response to

these drugs, and identifying genetic determinants of drug response may aid in tailoring treatment

for individual patients. Objective: To screen variants in candidate genes in the steroid and ß

adrenergic pathways for association with response to inhaled ß agonists. Methods: We

genotyped 844 single nucleotide polymorphisms (SNPs) in 111 candidate genes in 209 children

and their parents participating in the Childhood Asthma Management Program. We screened the

association of these SNPs with acute response to inhaled ß agonists (bronchodilator response,

BDR) using a novel algorithm implemented in a family-based association test that ranked SNPs

in order of statistical power. Genes that had SNPs with median power in the highest quartile

were then taken for replication analyses in three other asthma cohorts. Results: We identified 17

genes from the screening algorithm and genotyped 99 SNPs from these genes in a second

population of asthmatics. We then genotyped 63 SNPs from 4 genes with significant associations

with BDR, for replication in a third and fourth population of asthmatics. Evidence for association

from the 4 asthma cohorts was combined, and SNPs from ARG1 were significantly associated

with BDR. SNP rs2781659 survived Bonferroni correction for multiple testing (combined p-

value = 0.00048, adjusted p-value = 0.047). Conclusion: These findings identify ARG1 as a

novel gene for acute BDR in both childhood and adult asthmatics.

Word count: 251

Key Words: Pharmacogenetics; Asthma; Bronchodilator Agents

2

Introduction

Asthma is a complex genetic disorder that currently affects about 300 million people

worldwide(1). Asthma remains the most common chronic disease of childhood in the developed

world(2, 3), and incurs a significant healthcare cost(4). β-agonists form one of the oldest classes

of drugs in medicine(5). They are the most effective medications for the treatment of acute

asthma and remain one of the cornerstones of chronic asthma therapy. However, variability in

the response to inhaled β-agonists exists(6), and it has been estimated that a substantial

proportion of that response is genetic in nature.

To date, asthma pharmacogenetic studies in general(7), and response to inhaled

bronchodilators in particular(8), have been based on one or more polymorphisms in a single

gene. Recent studies from the Asthma Clinical Research Network, for instance, have reported

adverse effects of regular albuterol treatment among asthmatics who were homozygous for the

+49 A allele (Arg16) of the ADRB2 gene(9, 10). However, because asthma is a complex disorder

and response to inhaled beta-agonist drugs is a complex phenotype, it is likely that other genes

also impact on this phenotype. When more than one or a few polymorphisms are tested, the

chances of obtaining false positives increases – the multiple testing issue – and methods to

adjust for the total number of tests need to be applied. Additionally, the ideal design for these

studies would employ samples with large numbers (i.e. in the thousands) of subjects that would

help to offset the more stringent statistical criteria. Unfortunately, most existing datasets

available for asthma pharmacogenetics are of modest size, and thus, screening methods to limit

the number of tests in the first stages of the analysis can help alleviate the multiple testing

problem.

3

We conducted an analysis to screen 844 single nucleotide polymorphisms (SNPs) from

111 candidate genes for association with bronchodilator response (BDR) to inhaled β-agonist in

an asthma clinical trial cohort. Because the issue of multiple testing , we employed an algorithm

in a family-based association testing framework that allowed us to screen SNPs based on power

for replication(11). This screening methodology allows for the identification of the most

promising SNPs for testing without biasing the nominal significance level of the test statistic,

and recently has led to the identification of disease-susceptibility genes(12-14). Additionally, this

algorithm allowed us to screen and test in the same population. After identifying the most

promising SNPs, we then attempted replication in three additional asthma clinical trial cohorts.

Some of the results from these analyses have been previously reported in abstract form(15)

4

Methods

Study Populations.

We utilized DNA samples from four clinical trials. All patients or their legal guardians

consented to each trial study protocol and ancillary genetic testing. The population we used for

the screening algorithm was the Childhood Asthma Management Program (CAMP). Trial

design and methodology have been published(16, 17). A total of 209 Caucasian probands

(randomized to the placebo group) and their parents were included as part of parent-child trios

for the screening analyses. Only subjects randomized to the placebo group were utilized for the

screening analyses to avoid confounding effects of medications (corticosteroids and nedocromil)

other than inhaled β-agonist.

The population we used for the first replication study (hereafter called the Asthma Trial)

was composed of 432 Caucasian subjects with asthma(18, 19) who were part of an asthma

medication trial conducted by Sepracor, Inc. in the United States. Two completed trials

conducted by the American Lung Association Asthma Clinical Research Centers (ALA-ACRC),

the Leukotriene modifier or Corticosteroid or Corticosteroid Salmeterol trial (LOCCS)(20) and

the Effectiveness of Low Dose Theophylline as Add-on Treatment in Asthma (LODO) trial(21),

were used as the second and third replication samples. The 166 Caucasian subjects from the

LOCCS trial and the 155 Caucasian subjects from the LODO trial for whom DNA was available

were used for this analysis. Detailed information on these subjects has been previously published

and is included in the online data supplement.

Selection of Genes and SNPs; Genotyping.

5

We genotyped 844 SNPs in 111 candidate genes: 42 genes involved in beta adrenergic

signaling and regulation; 28 genes involved in innate glucocorticoid synthesis and metabolism,

cellular receptors, and transcriptional regulators; and 41 genes from prior asthma association

studies that had been previously conducted in the CAMP dataset (Table E1 in the online data

supplement). Candidate genes in the beta adrenergic and corticosteroid pathways were selected

based on prior studies in the literature, their known involvement in metabolic pathways(22, 23),

and on expert opinion (SBL and KGT). Corticosteroid pathway candidate genes were included in

this analysis because of the known interactions between beta2 agonists and corticosteroids(24,

25). Finally, we included the candidate genes that our group had previously genotyped and

studied in CAMP, since these were already available and so as to appropriately adjust our current

analyses for all prior tests conducted with these genes. SNPs were primarily selected utilizing

public databases, although resequencing of several core genes was performed. We over-sampled

exonic and promoter regions and attempted coverage of at least one SNP every 10 kb. We

emphasized golden-gate validated and LD tag SNPs, where available.

SNPs were genotyped via an Illumina BeadStation 500G (Illumima Inc., San Diego, CA)

and via a SEQUENOM MassARRAY MALDI-TOF mass spectrometer (Sequenom, San Diego,

CA). Further details are included in the online data supplement. SNPs were also checked for

Mendelian inconsistencies and for Hardy-Weinberg Equilibrium.

Statistical Methodology.

The primary outcome measure of the association analyses was acute response to inhaled

bronchodilator (BDR), and calculated as the percent difference between the pre- and post-

bronchodilator FEV1 value (BDR = 100 x [post FEV1 - pre FEV1/pre FEV1]). In all analyses,

6

both screening and replication, BDR was treated as a continuous variable. We initially screened

the genotypic association with BDR in CAMP using a modified version of the screening

algorithm as detailed by Van Steen, et al(11). Further details are included in the online data

supplement. The rationale for using CAMP data as the screening set is because the screening

methodology was designed for family data. No screening methodology has yet been published

for population-based data. We used the 11 repeated measures of BDR over the four years of the

trial in the Placebo group using the FBAT-PC statistic(26) to maximize the heritability of a given

marker and thereby maximize power for the screening stage. Only additive genetic models were

evaluated and all analyses in the screening stage were adjusted for age, sex, height, and baseline

FEV1. We selected the most powerful candidate genes by first ranking the individual SNPs based

upon conditional power and then evaluating the median rank of all of the SNPs within a given

candidate gene. We selected genes whose median SNP ranks for power were within the top 25%

of all SNPs genotyped to be taken forward for genotyping in the Asthma Trial population. For

those selected SNPs, we evaluated the FBAT-PC statistics for directionality of the association

and p-value for each SNP, and this allowed us to conduct 1-sided tests in the replication

analyses. We selected 99 SNPs from 17 genes for replication in the Asthma Trial population.

The analyses of the replicate populations were performed using generalized linear models

as incorporated into PROC GLM of the SAS statistical analysis software (version 9.0, SAS

Institute, Cary, NC), and SNP genotypes were coded for additive models. Only BDR calculated

from spirometric measurements at all baseline visits (prior to randomization) for all the trials

were used. All analyses in the replication populations adjusted for age, sex, height, and baseline

FEV1. Genes with at least one SNP that was at least marginally associated with BDR (one-sided

p-value < 0.05) were then genotyped in the final two replicate populations. For each SNP where

7

the direction of the association was in the same direction in each of the four populations, we then

combined the p-values (2-sided) from the original family-based analysis and the 1-sided p-values

from the replication cohorts using Fisher’s method(27) to increase statistical efficiency(28). No

evidence for population stratification was found in any of the three populations. Further details

on analytic issues are included in the online data supplement.

8

Results

The baseline characteristics of participants of the 4 asthma cohorts are shown in Table 1.

The CAMP subjects on whom we performed the initial screen were composed of children,

whereas the three replication cohorts were primarily adult asthmatics. In the CAMP subjects, we

screened 844 SNPs from 111 candidate genes (Supplementary Table 1 and Fig. 1). In the

screening analysis, we ranked the individual SNPs based on the highest to lowest power

estimates from the FBAT screening analysis. From these, we identified 19 genes whose median

SNP ranks for power were within the top 25% of all SNPs genotyped (Supplementary Table 1).

Because the family-based analysis suggested directionality of the association, we conducted 1-

sided tests in the replication datasets. The first replication analysis was conducted on 432

Caucasian adult asthmatics who had participated in a clinical trial of an asthma medication

(Asthma Trial). We successfully genotyped 99 SNPs (11.7% of all the SNPs that were screened)

from 17 genes in the Asthma Trial. In this first replication analysis, 9 genes contained at least 1

SNP that was at least marginally (1-sided p ≤ 0.05) associated with BDR. We then genotyped 63

SNPs from these 9 genes and tested them in the final two asthma clinical trial populations from

the American Lung Association Asthma Clinical Research Network: the LOCCS trial and the

LODO trial.

Table 2 summarizes the results of the replication analyses. Several SNPs were

individually associated with BDR in the four populations. The p-values from each of these

populations were combined, and four SNPs from ARG1 (rs2781659, rs2781663, rs2781665, and

rs2749935) showed the strongest evidence for association with BDR. After applying Bonferroni

correction for the 99 tests in the initial replication analysis, SNP rs22781659 remained

significantly associated with BDR (combined p-value=0.00048; Bonferroni-corrected p-

9

value=0.047). Evidence for association of SNPs rs2781663 and rs2781665 was borderline

significant after adjustment for multiple testing (Bonferroni-corrected p-values 0.075 and 0.085,

respectively).

We examined the effect of each of these 3 SNPs on the magnitude of BDR in each of the

populations (Table 3). In each case, the presence of the minor allele was associated with lower

adjusted BDR compared with the homozygous major allele, consistent with the initial FBAT-PC

results. Figure 2 compares the LD patterns of the 5 ARG1 SNPs in the 4 asthma populations by

plotting pairwise r2 in physical order. The pairwise r2 values are similar in each of the

populations. The 3 SNPs that were associated with BDR (rs2781659, rs2781663, and rs2781665)

were in strong LD with each other (r2 values ranging from 95% to 100%, depending on the

population). For example, in the CAMP population, r2 between SNPs rs2781659 and rs2781663

was 100%, while the r2 between SNPs rs2781659 and rs2781665 was 99%. In contrast, the other

SNP that was more weakly associated with BDR, rs2749935, was not as tightly linked with the

other 3 SNPs (r2 was 55% with rs2781659, rs2781663, and rs2781665).

10

Discussion

Prior investigations into the pharmacogenetics of asthma have generally been limited to

one or a few SNPs from one gene. We investigated 844 SNPs from 111 candidate genes selected

from asthma β-agonist and corticosteroid pathways, and from our prior candidate gene studies,

and screened these SNPs for association with BDR using a family-based screening algorithm

which allowed us to rank the SNPs based on estimated power for replication. We then genotyped

99 SNPs from 17 genes in a population-based cohort of asthmatics, who participated in an

asthma clinical trial. Finally, we genotyped 83 SNPs in 7 genes in two separate cohorts of

asthmatics. We found SNPs in the ARG1 gene to be associated with BDR in these three asthma

populations, after adjusting for multiple comparisons.

ARG1 has recently been implicated in asthma. Zimmerman et al(29) reported increased

expression of ARG1 and ARG2 in murine lung, and also found increased arginase 1 protein

expression from human asthma bronchoalveolar lavage cells. Variants in ARG1 were associated

with atopy in a cohort of Mexican asthmatics(30). ARG1 maps to chromosome 6q23 and encodes

one isoform of the enzyme arginase, which metabolizes L-arginine. L-Arginine homeostasis is

involved in the regulation of airway function, since the availability of this amino acid to nitric

oxide synthase (NOS) determines the production of the endogenous bronchodilator nitric oxide

(NO)(31). Changes in L-arginine homeostasis may contribute to many of the features of asthma,

such as airway hyperresponsiveness, airway inflammation, and airway remodelling(32).

Intracellular L-arginine levels are regulated by at least 3 distinct mechanisms (reviewed by

Maarsingh et al(32)): (i) cellular uptake by cationic amino acid transporters, (ii) recycling from

L-citrulline, and (iii) metabolism by NOS and arginase. Arginase is postulated to be involved in

11

asthma by depleting stores of L-arginine, a NOS substrate, which leads to decreased production

of NO, a potent bronchial smooth muscle relaxer(33, 34), and it has been shown to inhibit airway

smooth muscle relaxation(35, 36). Finally, RNA interference of arginase1 in the lungs resulted in

complete loss of airway hyperresponsiveness to methacholine due to IL-13 treatment(37). This

correlated with arginase 1 expression, which suggests that the polymorphismsms involved with

the current findings in human asthma may cause a loss of expression or function of arginase 1.

We used a gene-based strategy to select SNPs to take forward for replication. In this

method, after ranking SNPs from 1 (most power) to 844 (least power), we grouped all SNPs for

each gene and calculated the median SNP rank for that gene. Thus, while some genes had one or

two SNPs that were assigned high ranks, these genes may not be taken forward because the

median SNP rank did not meet the predetermined cutoff. We adopted this strategy since we were

not sure that LD patterns across the 4 asthma populations would be similar. It is interesting to

note that ADRB2, a gene that has been widely studied in asthma pharmacogenetics(38), was not

one of the genes that was selected using this strategy, despite including 18 SNPs from this gene

in the screening analysis. It is possible that there was insufficient power in the screening stage

since we only analyzed the 209 trios in the placebo group in CAMP. However, it should be noted

that a prior analysis using all 400 trios also did not find an association with any of the ADRB2

SNPs and BDR(39). Furthermore, the phenotype that we investigated is different from that

reported in other studies reporting on the pharmacogenetic effect of ADRB2(9, 10). We are

currently performing additional genotyping and analyses using a SNP-based strategy for

replication, rather than the gene-based strategy that we used here, to see if we identify important

SNPs in this gene and others for association with BDR.

12

Our analysis used the phenotype of acute response to a short-acting β2-agonist, albuterol,

in part because this was the phenotype that was common to all asthma cohorts. In the screening

algorithm, we used the information from repeated measures of BDR among the 209 Caucasian

children randomized to the placebo group in the CAMP study over the four years of the trial.

This was done to increase the power for the screening method. In contrast, for the replication

cohorts, we only used the information on BDR response on entry into the respective studies, in

order to standardize the phenotype. Thus, our results may not be applicable to asthma patients

who are on regular β2-agonist treatment (either short- or long-acting). We also did not address

interactions with any other class of asthma medication, since baseline medication was different

for all the populations: BDR was performed in both CAMP and the Adult Trial populations after

several weeks of being off all asthma medications; LOCCS subjects were on inhaled

corticosteroids for 4-6 weeks prior to BDR testing; and drug regimen for LODO subjects were

not changed prior to entry into the trial.

We employed a novel method of screening a large number of SNPs for association

analysis(11). This method has been successfully used to identify disease-susceptibility genes(12-

14). Since this method has only been developed for family-based studies and not population-

based studies, we used the CAMP population for screening the original 844 SNPs. The

traditional method would have been to analyze all the SNPs in one population, determine which

SNPs were associated with BDR at a predetermined level of significance, then test these SNPs in

the replication populations. However, if we had employed this usual method for gene finding, we

would then have to adjust our overall results for the 844 SNPs that were originally tested, and it

is likely that no finding would have survived this adjustment for multiple testing, even if the

13

association was real. In our method, because we screened on power and not p-value, we only

needed to adjust for the 99 tests in the first replication step. Thus, this screening method allows

the use of modest sized populations for gene discovery because it limits the number of tests that

are actually being performed.

The population to which we applied our screening algorithm was a cohort of childhood

asthmatics, whereas the three asthma replication cohorts were composed predominantly of adult

asthmatics. As we stated previously, the rationale for this is that the screening method was

developed for the setting of family-based studies and not for population-based studies. There is

no similar screening method that has yet been developed for population-based studies. Because

our replication populations were of small to modest sizes, we applied the screening method as a

means of minimizing the number of tests. While there were only 209 parent-child trios included

in the screening analysis, we maximized the power in the screening stage by utilizing the 11

repeated measures of BDR over the 4 years of the trial. Additionally, there were differences in

the asthma severity and in the magnitude of the BDR between the populations as shown in Table

1. Despite these differences, we were able to detect associations between SNPs in ARG1 and

BDR in each of the three replication populations. While the association between these SNPs and

BDR in CAMP were not statistically significant, the effect sized of each SNP were of sufficient

magnitude for them to be selected based on power in the screening analysis. We can only

surmise at this point, that there may be age-related effects associated with the SNPs in this gene.

We believe, therefore, that the results of the association between ARG1 polymorphisms and BDR

are robust and applicable to both childhood and adult asthmatics in a variety of settings.

14

The three ARG1 SNPs that were associated with BDR were all in the promoter region of

the gene and were in tight LD with each other. Genotyping of all known SNPs in the gene or re-

sequencing of the gene will need to be performed to determine if these three promoter SNPs are

in LD with the functional mutation. There is mounting evidence for “crosstalk” between

pathways involved with the relaxation and constriction of airway smooth muscle(40, 41). It is

thus not entirely unexpected that a gene involved with airway hyperresponsiveness in mouse

studies is associated with a bronchodilator response in human asthma. However, arginase 1 has

not been previously identified as one of the proteins involved in such crosstalk. This unexpected

finding shows the potential value of whole-genome coverage to study drug response may be

necessary to uncover novel genetic determinants. The screening method that we used for this

analysis would be easily applicable to the case of whole-genome association.

In summary, we have identified SNPs in ARG1 as novel BDR determinants. Further

studies will need to identify the functional SNP or SNPs in this gene. Other pharmacogenetic

studies using long-acting β2-agonists, either alone or in conjunction with corticosteroids, and

investigating other phenotypes (e.g. FEV1, peak flow, etc.) are needed to clarify the effects of

variants in this gene. Our analysis shows the utility of a family-based algorithm to effectively

screen SNPs for replication in other cohorts. This method is easily applicable to the case of

whole-genome association.

15

References1. Masoli M, Fabian D, Holt S, Beasley R. Global burden of asthma report. Global initiative for asthma. 2004 April 14, 2006].2. Mannino DM, Homa DM, Akinbami LJ, Moorman JE, Gwynn C, Redd SC. Surveillance for asthma--united states, 1980-1999. MMWR Surveill Summ 2002;51:1-13.3. Gergen PJ, Mullally DI, Evans R, 3rd. National survey of prevalence of asthma among children in the united states, 1976 to 1980. Pediatrics 1988;81:1-7.4. Smith DH, Malone DC, Lawson KA, Okamoto LJ, Battista C, Saunders WB. A national estimate of the economic costs of asthma. Am J Respir Crit Care Med 1997;156:787-793.5. Sears MR, Lotvall J. Past, present and future--beta2-adrenoceptor agonists in asthma management. Respir Med 2005;99:152-170.6. Drazen JM, Silverman EK, Lee TH. Heterogeneity of therapeutic responses in asthma. Br Med Bull 2000;56:1054-1070.7. Weiss ST, Litonjua AA, Lange C, Lazarus R, Liggett SB, Bleecker ER, Tantisira KG. Overview of the pharmacogenetics of asthma treatment. Pharmacogenomics J 2006;6:311-326.8. Litonjua AA. The significance of beta2-adrenergic receptor polymorphisms in asthma. Curr Opin Pulm Med 2006;12:12-17.9. Israel E, Chinchilli VM, Ford JG, Boushey HA, Cherniack R, Craig TJ, Deykin A, Fagan JK, Fahy JV, Fish J, et al. Use of regularly scheduled albuterol treatment in asthma: Genotype-stratified, randomised, placebo-controlled cross-over trial. Lancet 2004;364:1505-1512.10. Israel E, Drazen JM, Liggett SB, Boushey HA, Cherniack RM, Chinchilli VM, Cooper DM, Fahy JV, Fish JE, Ford JG, et al. The effect of polymorphisms of the beta(2)-adrenergic receptor on the response to regular use of albuterol in asthma. Am J Respir Crit Care Med 2000;162:75-80.11. Van Steen K, McQueen MB, Herbert A, Raby B, Lyon H, Demeo DL, Murphy A, Su J, Datta S, Rosenow C, et al. Genomic screening and replication using the same data set in family-based association testing. Nat Genet 2005;37:683-691.12. Herbert A, Gerry NP, McQueen MB, Heid IM, Pfeufer A, Illig T, Wichmann HE,Meitinger T, Hunter D, Hu FB, et al. A common genetic variant is associated with adult and childhood obesity. Science 2006;312:279-283.13. Lyon HN, Emilsson V, Hinney A, Heid IM, Lasky-Su J, Zhu X, Thorleifsson G, Gunnarsdottir S, Walters GB, Thorsteinsdottir U, et al. The association of a snp upstream of insig2 with body mass index is reproduced in several but not all cohorts. PLoS Genet 2007;3:e61.14. Lasky-Su J, Lyon HN, Emilsson V, Heid IM, Molony C, Raby BA, Lazarus R, Klanderman B, Soto-Quiros ME, Avila L, et al. On the replication of genetic associations: Timing can be everything! Am J Hum Genet 2008;82:849-858.15. Litonjua AA, Tantisira KG, Su JA, Lazarus R, Klanderman B, Lange C, Weiss ST. Polymorphisms in arg1 are determinants of bronchodilator response: Screening and replication in 4 asthma cohorts. Am J Resp Crit Care Med 2007;175:A968.16. The childhood asthma management program (camp): Design, rationale, and methods. Childhood asthma management program research group. Control Clin Trials 1999;20:91-120.17. Long-term effects of budesonide or nedocromil in children with asthma. The childhood asthma management program research group. N Engl J Med 2000;343:1054-1063.

16

18. Baron RM, Palmer LJ, Tantisira K, Gabriel S, Sonna LA, Le L, Hallock A, Libermann TA, Drazen JM, Weiss ST, et al. DNA sequence variants in epithelium-specific ets-2 and ets-3 are not associated with asthma. Am J Respir Crit Care Med 2002;166:927-932.19. Silverman ES, Palmer LJ, Subramaniam V, Hallock A, Mathew S, Vallone J, Faffe DS, Shikanai T, Raby BA, Weiss ST, et al. Transforming growth factor-beta1 promoter polymorphism c-509t is associated with asthma. Am J Respir Crit Care Med 2004;169:214-219.20. Peters SP, Anthonisen N, Castro M, Holbrook JT, Irvin CG, Smith LJ, Wise RA. Randomized comparison of strategies for reducing treatment in mild persistent asthma. N Engl J Med 2007;356:2027-2039.21. American lung association asthma clinical research centers. Clinical trial of low-dose theophylline and montelukast in patients with poorly controlled asthma. Am J Respir Crit Care Med 2007;175:235-242.22. Litonjua AA, Thorn CF, Liggett SB. Β-agonist and β-blocker pathway. 2004 August 1, 2007 [cited 2008 May 11]. Available from: http://www.pharmgkb.org/do/serve?objId=PA2024&objCls=Pathway.23. Weiss ST, Litonjua AA, Tantisira KG, Wong M-L, Thorn CF, Licinio J. Glucocorticoid and inflammatory genes pathway. 2003 August 1, 2007 [cited 2008 May 11]. Available from: http://www.pharmgkb.org/do/serve?objId=PA2026&objCls=Pathway.24. Barnes PJ. Scientific rationale for using a single inhaler for asthma control. Eur Respir J 2007;29:587-595.25. Johnson M. Interactions between corticosteroids and beta2-agonists in asthma and chronic obstructive pulmonary disease. Proc Am Thorac Soc 2004;1:200-206.26. Lange C, van Steen K, Andrew T, Lyon H, DeMeo DL, Raby B, Murphy A, Silverman EK, MacGregor A, Weiss ST, et al. A family-based association test for repeatedly measured quantitative traits adjusting for unknown environmental and/or polygenic effects. Stat Appl Genet Mol Biol 2004;3:Article17.27. Fisher RA. Statistical methods for research workers. New York: Hafner; 1950.28. Skol AD, Scott LJ, Abecasis GR, Boehnke M. Joint analysis is more efficient than replication-based analysis for two-stage genome-wide association studies. Nat Genet 2006;38:209-213.29. Zimmermann N, King NE, Laporte J, Yang M, Mishra A, Pope SM, Muntel EE, Witte DP, Pegg AA, Foster PS, et al. Dissection of experimental asthma with DNA microarray analysis identifies arginase in asthma pathogenesis. J Clin Invest 2003;111:1863-1874.30. Li H, Romieu I, Sienra-Monge JJ, Ramirez-Aguilar M, Estela Del Rio-Navarro B, Kistner EO, Gjessing HK, Lara-Sanchez Idel C, Chiu GY, London SJ. Genetic polymorphisms in arginase i and ii and childhood asthma and atopy. J Allergy Clin Immunol 2006;117:119-126.31. Ricciardolo FL, Sterk PJ, Gaston B, Folkerts G. Nitric oxide in health and disease of the respiratory system. Physiol Rev 2004;84:731-765.32. Maarsingh H, Zaagsma J, Meurs H. Arginine homeostasis in allergic asthma. Eur J Pharmacol 2008;585:375-384.33. Zimmermann N, Rothenberg ME. The arginine-arginase balance in asthma and lung inflammation. Eur J Pharmacol 2006;533:253-262.34. Meurs H, Maarsingh H, Zaagsma J. Arginase and asthma: Novel insights into nitric oxide homeostasis and airway hyperresponsiveness. Trends Pharmacol Sci 2003;24:450-455.

17

35. Maarsingh H, Leusink J, Bos IS, Zaagsma J, Meurs H. Arginase strongly impairs neuronal nitric oxide-mediated airway smooth muscle relaxation in allergic asthma. Respir Res 2006;7:6.36. Maarsingh H, Tio MA, Zaagsma J, Meurs H. Arginase attenuates inhibitory nonadrenergic noncholinergic nerve-induced nitric oxide generation and airway smooth muscle relaxation. Respir Res 2005;6:23.37. Yang M, Rangasamy D, Matthaei KI, Frew AJ, Zimmmermann N, Mahalingam S, Webb DC, Tremethick DJ, Thompson PJ, Hogan SP, et al. Inhibition of arginase i activity by rna interference attenuates il-13-induced airways hyperresponsiveness. J Immunol 2006;177:5595-5603.38. Liggett SB, Hall IP. Beta2-adrenergic receptor polymorphisms and asthmatic phenotypes. In: Postma DS, Weiss ST, editors. Genetics of asthma and chronic obstructive pulmonary disease. New York, NY: Informa Healthcare USA, Inc.; 2007. p. 299-316.39. Silverman EK, Kwiatkowski DJ, Sylvia JS, Lazarus R, Drazen JM, Lange C, Laird NM, Weiss ST. Family-based association analysis of beta2-adrenergic receptor polymorphisms in the childhood asthma management program. J Allergy Clin Immunol 2003;112:870-876.40. McGraw DW, Almoosa KF, Paul RJ, Kobilka BK, Liggett SB. Antithetic regulation by beta-adrenergic receptors of gq receptor signaling via phospholipase c underlies the airway beta-agonist paradox. J Clin Invest 2003;112:619-626.41. McGraw DW, Elwing JM, Fogel KM, Wang WC, Glinka CB, Mihlbachler KA, Rothenberg ME, Liggett SB. Crosstalk between gi and gq/gs pathways in airway smooth muscle regulates bronchial contractility and relaxation. J Clin Invest 2007;117:1391-1398.

Figure Legends.

Figure1. Overall Strategy of Screening and Replication

Figure 2. Linkage Disequilibrium (LD) patterns of ARG1 SNPs in the 4 asthma cohorts. Numbers in the individual blocks represent r2 values for each pair of SNPs (blank values=100%), with the colors corresponding to the r2 values. Plots were created using the program Haploview (http://www.broad.mit.edu/mpg/haploview/).

Table 1. Baseline Characteristics of Participants in the 4 Asthma Trials.CAMP Asthma Trial LOCCS LODOn=209 n=432 n=166 n=155

Age, mean (sd) 8.8 (2.1) 32.5 (13.7) 34.5 (15.3) 42.9 (14.7) range 5.2 - 13.2 12 - 80 7 - 71 15 - 76Gender, n(%) male 125 (59.8) 216 (50.0) 58 (34.9) 39 (25.2) female 84 (40.2) 216 (50.0) 108 (65.1) 116 (74.8)

Percent Predicted FEV1, mean (sd) 95.0 (13.1) 61.4 (6.9) 90.8 (9.7) 78.2 (16.5)BDR, mean (sd) 10.4 (9.4) 39.9 (20.4) 6.4 (6.1) 9.7 (11.1)

Table 2. Summary of results of testing and replication in the four asthma clinical trials*.

Gene Name rs#CAMP

p-valueAsthma Trial

p-valueLODO

p-valueLOCCS p-value

Combined p-value

ARG1 rs2781659 0.310 0.030 0.029 0.003 0.00048

ARG1 rs2781663 0.428 0.022 0.060 0.003 0.00076

ARG1 rs2781665 0.559 0.031 0.036 0.003 0.00086

ARG1 rs2749935 0.918 0.072 0.011 0.029 0.00596

CRHR2 rs1003929 0.159 0.003 0.484 0.222 0.01244

CRHR2 rs2190242 0.022 0.071 0.176 0.281 0.01509

CPM rs1144961 0.401 0.265 0.140 0.009 0.02189

CRHR2 rs2240403 0.053 0.270 0.040 0.342 0.02917

CRHR2 rs2284220 0.209 0.016 0.263 0.315 0.03747

CRHR2 rs917195 0.097 0.106 0.107 0.270 0.03906

CRHR2 rs2284217 0.472 0.068 0.154 0.084 0.04869

CREBL2 rs4555 0.860 0.077 0.280 0.023 0.05003

CREM rs10827492 0.827 0.009 0.465 0.136 0.05331

CREM rs4934736 0.884 0.009 0.260 0.242 0.05694

CREM rs1148247 0.610 0.016 0.397 0.140 0.05758

CRHR2 rs929377 0.652 0.180 0.076 0.066 0.06155

CRHR2 rs7793837 0.873 0.096 0.015 0.480 0.06336

CRHR2 rs2267716 0.342 0.366 0.026 0.244 0.07444

CREM rs7077242 0.835 0.024 0.232 0.174 0.07590

CREM rs10827493 0.956 0.010 0.322 0.273 0.07867

* Only the top 20 SNPs with the smallest p-values are included in the table. All analyses investigated additive genetic models. All analyses were adjusted for age, gender, height, and baseline FEV1. p-values for CAMP were obtained from FBAT (2-sided p-values); p-values for the replication analyses were obtained from linear regression models (1-sided p-values) based on the direction of association obtained from the FBAT analysis. SNPs are ranked based on the smallest to the largest combined p-value.

Table 3. Effects of ARG1 SNPs on BDR*Asthma Trial LOCCS LODO

ARG1_RS2781665Genotype N (%) BDR SE N (%) BDR SE N (%) BDR SEAA 189 (46.55) 41.08 1.33 71 (44.38) 7.53 0.69 70 (47.30) 11.53 1.17AT 169 (41.63) 39.44 1.41 68 (42.50) 6.42 0.72 62 (41.89) 7.95 1.25TT 48 (11.82) 35.33 2.65 21 (13.13) 3.14 1.29 16 (10.81) 8.59 2.44

ARG1_RS2781663Genotype N (%) BDR SE N (%) BDR SE N (%) BDR SETT 190 (44.92) 41.71 1.32 71 (44.38) 7.43 0.68 67 (46.85) 10.78 1.12AT 184 (43.50) 39.14 1.34 67 (41.88) 6.68 0.70 61 (42.66) 7.85 1.18AA 49 (11.58) 36.28 2.62 22 (13.75) 2.99 1.22 15 (10.49) 8.40 2.38

ARG1_RS2781659Genotype N (%) BDR SE N (%) BDR SE N (%) BDR SEAA 193 (45.43) 41.39 1.31 74 (47.13) 7.35 0.69 71 (47.65) 11.53 1.15AG 183 (42.86) 39.50 1.35 62 (33.49) 6.39 0.76 61 (40.94) 7.51 1.24GG 50 (11.71) 35.93 2.59 21 (13.38) 2.90 1.30 17 (11.41) 8.73 2.35* BDR is expressed as the mean for each genotype category. BDR means are obtained from multiple linear regression models adjusted for age, height, gender and baseline FEV1, using the lsmeans option in the GLM Procedure in SAS.

Figure 1

Figure 2

Online Data Supplement

ARG1 gene is a novel bronchodilator response gene: screening and replication in four asthma cohorts

Augusto A. LitonjuaJessica Lasky-SuKady SchneiterKelan G. TantisiraRoss LazarusBarbara KlandermanJohn J. LimaCharles G. IrvinStephen P. PetersJohn P. HanrahanStephen B. LiggettGregory A. HawkinsDeborah A. MeyersEugene R. BleeckerChristoph LangeScott T. Weiss

E1

Methods

Study Populations.

We utilized DNA samples from four clinical trials. All patients or their legal guardians

consented to each trial study protocol and ancillary genetic testing. The population we used for

the screening algorithm was the Childhood Asthma Management Program (CAMP), a

multicenter, randomized, double-blinded clinical trial testing the safety and efficacy of inhaled

budesonide vs. nedocromil vs. placebo over a mean of 4.3 years. Trial design and methodology

have been published(E1, E2). A total of 418 children were randomized to the placebo group,

from which we included the 209 Caucasian probands and their parents as part of a parent-child

trio for these analyses. These trios formed the basis of our family-based screening cohort for

bronchodilator response (BDR). Only subjects randomized to the placebo group were utilized

for the screening analyses to avoid confounding effects of medications (corticosteroids and

nedocromil) other than inhaled β-agonist.

The population we used for the first replication study (hereafter called the Asthma Trial)

was composed of 432 Caucasian subjects with asthma(E3, E4) who were part of an asthma

medication trial conducted by Sepracor, Inc. in the United States. To qualify for inclusion,

patients had to be nonsmokers, have no significant comorbid medical conditions, and to have

diagnostic findings consistent with moderate to severe asthma according to American Thoracic

Society (ATS) criteria(E5). The only medications used by the patients were inhaled beta agonists

as needed. Patients were required to have a forced expiratory volume in 1 second (FEV1) of 40-

85% of predicted normal values after at least eight hours without inhaled short-acting β-agonists;

oral or inhaled corticosteroids were excluded for six weeks before the study. Reversibility of

E2

airflow obstruction by beta agonists (15% change required) or methacholine sensitivity testing

was employed to confirm asthma diagnosis.

Two completed trials conducted by the American Lung Association Asthma Clinical

Research Centers (ALA-ACRC), the Leukotriene modifier or Corticosteroid or Corticosteroid

Salmeterol trial (LOCCS)(E6) and the Effectiveness of Low Dose Theophylline as Add-on

Treatment in Asthma (LODO) trial(E7), were used as the second and third replication samples.

The LOCCS cohort comprised 500 subjects ≥ 6 yrs old who successfully completed a 4-6 week

run-in period of inhaled fluticasone propionate. Subjects were required to have a pre-

bronchodilator FEV1 of ≥ 80% of predicted value, 12% or higher bronchodilator reversibility or

PC20 of 8 mg/ml or less within the past two years, and well controlled asthma (Juniper Asthma

Control Questionnaire score < 1.5) after the run-in period. The data for BDR for this analysis

was determined from spirometry performed after completion of the run-in period. The 166

Caucasian subjects for whom DNA was available were used for this analysis. The LODO cohort

comprised 489 participants 15 years old with poorly controlled asthma (regardless of baseline

treatment regimen) as measured by a score of 1.5 on the Juniper asthma control questionnaire.

Participants who had smoked within the last 6 months or who had a > 20 pack-year smoking

history were excluded. At baseline, asthma severity was rated according to symptoms by the

Asthma Symptom Utility Index (ASUI)(E8) and according to lung function by peak flow and

spirometry (before and after bronchodilator). The 155 Caucasian subjects for whom DNA was

available were used for this analysis.

Genotyping.

E3

We genotyped 844 SNPs in 111 candidate genes: 42 genes involved in beta adrenergic

signaling and regulation; 28 genes involved in innate glucocorticoid synthesis and metabolism,

cellular receptors, and transcriptional regulators; and 41 genes from prior asthma association

studies that had been previously conducted in the CAMP dataset (Supplementary Table 1).

Candidate genes in the beta adrenergic and corticosteroid pathways were selected based on prior

studies in the literature, their known involvement in metabolic pathways(E9, E10), and on expert

opinion (SBL and KGT). Corticosteroid pathway candidate genes were included in this analysis

because of the known interactions between beta2 agonists and corticosteroids(E11, E12). Finally,

we included the candidate genes that our group had previously genotyped and studied in CAMP,

since these were already available and so as to appropriately adjust our current analyses for all

prior tests conducted with these genes. SNPs were primarily selected utilizing public databases,

although resequencing of several core genes was performed. We over-sampled exonic and

promoter regions and attempted coverage of at least one SNP every 10 kb. We emphasized

golden-gate validated and LD tag SNPs, where available. Replicate genotyping was performed

in the candidate genes powered in CAMP as below.

SNPs were genotyped via an Illumina BeadStation 500G (Illumima Inc., San Diego, CA)

and via a SEQUENOM MassARRAY MALDI-TOF mass spectrometer (Sequenom, San Diego,

CA). For the Illumina system, primers were created upon the submission of SNPs of interest.

For each gene of interest, all SNPs available in the public database (dbSNP) were pulled. The

flanking sequences for these SNPs were evaluated with the Illumina Assay Design Tool by the

technical support group at Illumina (San Diego, CA). A file was returned that contained, for each

SNP, a design score, design rank, and minor allele frequency in all available populations,

E4

functional status, chromosome and position, and validation status (single submitter, multi-

submitter, or pre-validated on Illumina platform). Design scores (scale of 0 to 1) indicates the

likelihood of the assay design converting to a successful genotyping assay, with higher scores

correlating with greater success. The design score is based on an algorithm that takes into

account nrepetitive sequence, other SNPs that may interfere with primer placement, and GC

content. SNPs with scores above 0.6 were selected for inclusion in the GoldenGate multiplex,

with preference given to score above 0.8. Preference was also given to multi-hit or pre-validated

assays, and markers with minor allele frequency > 0.5. A semiautomated primer design program

(SpectroDESIGNER, Sequenom) was used for Sequenom. Each genotype was checked for

percent completion rates and replicate genotyping was performed on a subset of genotypes for

quality control. SNPs were also checked for Mendelian inconsistencies and for Hardy-Weinberg

Equilibrium.

Statistical Methodology.

The primary outcome measure of the association analyses was acute response to inhaled

bronchodilator (BDR), and calculated as the percent difference between the pre- and post-

bronchodilator FEV1 value (BDR = 100 x [post FEV1 - pre FEV1/pre FEV1]). In all analyses,

both screening and replication, BDR was treated as a continuous variable. We initially screened

the genotypic association with BDR in CAMP using a modified version of the screening

algorithm applied to genome-wide association studies as detailed by Van Steen, et al(E13). Our

algorithm differed in that we screened to maximize power at the candidate gene level instead of

the SNP marker level. This was both to provide a basis for replication at both the SNP and at the

locus level, since differences in the LD patterns within our 4 populations were unknown prior to

E5

genotyping. Moreover, we wanted to ascertain a consistent signal across a gene in order to

augment our efforts in functional modeling. We used repeated measures of BDR over the four

years of the trial in the Placebo group using the FBAT-PC statistic to maximize the heritability of

a given marker and thereby maximize power for the screening stage. Only additive genetic

models were evaluated for this analysis. We selected the most powerful candidate genes by first

ranking the individual SNPs based upon conditional power and then evaluating the median rank

of all of the SNPs within a given candidate gene. Conditional power was calculated based on

first assigning expected probabilities of genotypes for each proband, based on parental genotypes

(i.e. the screening method is blinded to the actual genotypes of the probands), then estimating the

effect size for each of the expected proband genotypes. We selected genes whose median SNP

ranks for power were within the top 25% of all SNPs genotyped to be taken forward for

genotyping in the Asthma Trial population. We evaluated the FBAT-PC statistics for the SNPs

for replication for directionality of the association for each SNP, and this allowed us to conduct

1-sided tests in the replication analyses. We selected 99 SNPs from 14 genes for replication in

this population.

The analyses of the replicate populations were performed using generalized linear models

as incorporated into PROC GLM of the SAS statistical analysis software (version 9.0, SAS

Institute, Cary, NC). For all replicate analyses, SNP genotypes were coded for additive models.

Only BDR calculated from the pre- and post-bronchodilator spirometric measurements at all

baseline visits (prior to randomization) for all the trials were used. All models adjusted for age,

gender, height, and baseline FEV1. Genes with at least one SNP that was at least marginally

associated with BDR (one-sided p-value < 0.05) were then genotyped in the final two replicate

E6

populations (these were genotyped together, but analyzed sequentially). We then took the p-

values (2-sided) from the original family-based analysis and the 1-sided p-values from the

replication cohorts and combined them using a standard Fischer’s method to increase statistical

efficiency(E14). Prior evaluations of the Asthma Trial cohort has revealed no evidence of

population stratification(3, 4). In a separate analysis of a random panel of 160 SNPs across the

genome in both the LOCCS and the LODO populations, we found no evidence of population

stratification. FBAT testing in CAMP is robust to potential population admixture(E15).

E7

Table E1. 844 SNPs from 111 genes screened in CAMP.Gene Chromosome Role* rs# Allele† PBAT Power‡

ACVRL1 12 candidate rs706812 A 0.001

ADAM33 20 candidate rs2280090 G 0.051858291

rs2280093 C 0.011184686

rs2485700 T 0.380180012

rs3918395 G 0.070842636

rs487377 G 0.255535487

rs597980 C 0.152426886

rs615436 A 0.001

rs630712 A 0.150491812

ADCY7 16 adrenergic rs1064448 A 0.052175717

rs1540624 A 0.145272311

rs1872691 C 0.105425872

rs2302715 T 0.135980929

rs4785211 A 0.072756291

rs7184802 G 0.066152354

ADCY9 16 adrenergic rs1967309 A 0.073440134

rs2072341 C 0.130456255

rs2072342 G 0.051637616

rs2072346 A 0.145486577

rs2230739 A 0.073652384

rs2230742 G 0.201548436

rs2238436 G 0.169181456

rs2239313 C 0.197311756

rs2240735 A 0.094816275

rs2256156 G 0.149891269

rs2531977 G 0.170671781

rs2531990 G 0.103440628

rs2531992 C 0.250254879

rs2532001 C 0.022339062

rs2601790 C 0.001

rs2601828 G 0.061165768

rs3730097 G 0.003236582

rs3730129 G 0.006321889

rs432166 G 0.05984892

rs7204987 G 0.149227494

rs879620 A 0.162428383

ADCYAP1 18 adrenergic rs2231187 C 0.313085988

rs928978 G 0.029118168

ADCYAP1R1 7 adrenergic rs1006622 T 0.256716935

rs1468687 A 0.205544836

rs2267732 A 0.025170219

rs741051 T 0.309872353

rs741052 T 0.332164828

rs887703 C 0.331598901

ADRB2 5 adrenergic rs17287460 (C-709A) C 0.001

rs1036173 T 0.17642073

rs1036174 G 0.281146346

rs1042713 G 0.106026779

E8

rs1042714 G 0.132673182

rs1042718 C 0.444037754

rs11168070 G 0.306374331

rs12654778 G 0.055803514

rs1368277 T 0.044732351

rs1432626 C 0.004744992

rs1432628 C 0.454057244

rs1432630 G 0.02102983

rs1432631 G 0.030869075

rs1800888 C 0.001639273

rs1801704 C 0.186666654

rs2053044 A 0.160962853

rs2116717 T 0.191105085

rs754357 T 0.001278948

AQP5 12 candidate rs2242357 C 0.224773232

rs296753 G 0.001

ARG1 6 candidate rs2749935 T 0.272300521

rs2781659 A 0.374627581

rs2781663 A 0.320638168

rs2781665 A 0.317997759

rs3756780 T 0.024807942

ARRB1 11 adrenergic rs1676887 C 0.104197828

rs1789682 A 0.046618146

rs472112 A 0.330691653

rs494146 T 0.119123115

rs508435 C 0.320030629

rs512797 G 0.208760552

rs520563 C 0.062436537

rs529513 C 0.053073291

rs555031 C 0.051917593

rs567807 C 0.040410941

rs576014 G 0.075495613

rs647630 G 0.040782851

rs745373 G 0.069142359

rs746168 T 0.024732639

rs7929974 G 0.059686764

rs877711 C 0.034859633

rs899115 C 0.048020841

ARRB2 17 adrenergic rs2271167 A 0.093592315

rs7208257 A 0.164955548

rs9905578 A 0.166412107

rs9913156 C 0.066431276

ATP2A2 12 adrenergic rs1860561 G 0.325541263

rs3026434 A 0.001

rs3026446 G 0.003627286

rs9540 C 0.001

BDKRB1 14 adrenergic rs10143977 T 0.057200211

rs10147171 A 0.146053489

rs11625494 G 0.006929342

rs12050217 T 0.094172892

E9

rs2071084 G 0.095907181

rs885845 G 0.077292011

rs10220336 A 0.359491693

rs1046248 G 0.02837315

rs11627456 C 0.018955238

rs11847625 C 0.204448735

rs1800515 C 0.080336103

rs1959053 A 0.094355506

rs2069571 C 0.040627941

rs2069585 C 0.001

rs2242964 C 0.02075041

rs4144132 T 0.047567097

rs4900312 G 0.511285078

rs4900318 G 0.480240155

rs4905461 C 0.15200328

rs4905470 G 0.049482257

rs5224 C 0.143017742

rs5225 A 0.119499924

rs6575577 C 0.132257345

rs7150828 C 0.276278372

rs8008168 A 0.309711224

rs8012552 A 0.514990824

rs8016905 G 0.108916199

C5 9 candidate rs17611 T 0.028782159

rs2300931 T 0.069428559

CCL11 17 candidate rs4795896 T 0.066454772

rs1019109 G 0.339696022

rs17809012 G 0.359312003

rs1860184 A 0.371290751

rs3744508 G 0.351959656

rs3815341 G 0.09543791

rs4795898 T 0.084612964

rs714910 A 0.299029085

rs16969415 C 0.085174995

CD3E 11 steroid rs1945765 A 0.218305625

CD4 12 steroid rs1045261 T 0.037378656

rs1055141 T 0.059973478

rs3829972 G 0.060567409

CHRM2 7 adrenergic rs6962027 A 0.296539977

rs6963577 A 0.357410007

rs6967953 A 0.291108642

rs8191992 A 0.284930132

CHRM3 1 adrenergic rs10495449 A 0.053451229

rs12021598 G 0.061688339

rs12036109 A 0.094461392

rs2067481 C 0.004729275

rs4072234 C 0.170240983

rs4659554 A 0.042674494

rs6669810 C 0.252467227

rs6682184 C 0.186652392

E10

rs7520974 C 0.261688857

COL2A1 12 candidate rs1635536 G 0.521197672

rs1635544 G 0.647647593

rs1635546 G 0.592369015

rs1793931 T 0.534527587

rs1793959 C 0.262988133

rs1814231 G 0.001

rs2071357 G 0.342896609

rs2276454 A 0.41754818

rs2276455 A 0.442515171

rs2276458 G 0.265763276

rs6823 C 0.613177739

rs915920 G 0.001

rs917055 G 0.625841941

CPM 12 candidate rs1144960 G 0.645973616

rs1144961 G 0.557243239

rs1144963 C 0.297726401

rs1908669 A 0.001178004

rs2172988 C 0.134677726

rs2293637 G 0.277678205

CREB1 2 adrenergic rs2254137 A 0.148364189

rs2551640 T 0.132673716

rs2551919 C 0.076401059

rs2551921 A 0.13956091

rs2709356 C 0.101787864

rs2709387 G 0.113624623

rs7369949 T 0.117943003

rs889895 A 0.111307489

CREB3L2 7 adrenergic rs273957 C 0.029345962

CREB5 7 adrenergic rs1008048 A 0.059072291

rs150607 C 0.280659862

rs150610 A 0.063282285

rs160335 G 0.190920131

rs160337 G 0.143431341

rs160356 C 0.102925587

rs160357 A 0.102998686

rs160369 A 0.063970786

rs160375 T 0.089178384

rs177584 G 0.064901588

rs177590 T 0.053333543

rs1859020 T 0.191069275

rs1964240 C 0.120555404

rs1976489 G 0.095210921

rs2073537 T 0.086494582

rs216708 G 0.213137149

rs216715 T 0.057639682

rs216730 C 0.025783676

rs216737 G 0.22533519

rs216750 T 0.105041981

rs217515 G 0.280710392

E11

rs217519 C 0.682008068

rs2237351 A 0.44483769

rs2237353 T 0.137142339

rs2237361 A 0.336778121

rs2299110 A 0.274981427

rs2391666 A 0.288084815

rs2391668 G 0.317802703

rs310353 A 0.158906021

rs3757677 G 0.245584528

rs41304 C 0.129943642

rs41305 C 0.114963991

rs41327 A 0.274120017

rs41333 T 0.055454117

rs41334 A 0.033857681

rs41348 C 0.090567667

rs41351 C 0.385552688

rs42322 T 0.092975291

rs4719934 C 0.089746812

rs4719945 A 0.072538914

rs4722804 C 0.040961632

rs4722834 C 0.483247739

rs6462085 G 0.172519991

rs6462088 A 0.389057781

rs6462100 A 0.217025021

rs6949786 T 0.386471967

rs6972081 G 0.25586762

rs6976396 C 0.068462399

rs740315 A 0.09456921

rs757980 A 0.314286632

rs886816 G 0.049193248

rs989438 T 0.164802879

CREBBP 16 adrenergic rs129974 G 0.001

rs2230140 A 0.001

CREBL2 12 adrenergic rs4555 A 0.254068879

CREM 10 adrenergic rs1057108 A 0.244079047

rs10827491 C 0.254284554

rs10827492 C 0.22569427

rs10827493 C 0.283984827

rs1148247 C 0.14977395

rs11592037 C 0.22461565

rs11592356 T 0.336869625

rs11597746 C 0.331648519

rs1213392 C 0.280511624

rs12761675 T 0.258910646

rs1545757 A 0.176594992

rs2295415 T 0.077870955

rs4934535 C 0.146205678

rs4934540 T 0.258305323

rs4934734 A 0.205964064

rs4934736 G 0.244330188

E12

rs6481941 G 0.248354291

rs7077242 C 0.250529114

rs7913615 A 0.249474673

CRH 8 steroid rs10105164 C 0.079318914

rs11997416 C 0.329387244

rs3176921 A 0.302891618

rs5030875 T 0.074488853

rs6472257 C 0.279434243

rs7835214 T 0.008998522

CRHBP 5 steroid rs10473984 A 0.040949508

rs10514082 T 0.09011322

rs1053989 A 0.010312854

rs1700676 T 0.088683628

rs247742 G 0.090868966

rs3811939 C 0.240640207

rs7718461 C 0.012410772

CRHR1 17 steroid rs12150390 A 0.032133224

rs12950522 T 0.452051696

rs1396862 T 0.240161625

rs171440 T 0.513482771

rs171441 C 0.041119528

rs173365 A 0.118844745

rs1876827 A 0.224428224

rs1876828 A 0.20945287

rs1876829 A 0.20991795

rs1876831 G 0.160918441

rs242925 C 0.366328304

rs242938 C 0.026690213

rs242939 A 0.055168716

rs242941 G 0.322119891

rs242942 C 0.069707281

rs242947 C 0.125320551

rs242949 C 0.301854272

rs242950 G 0.068102785

rs4792886 G 0.031563512

rs4792888 A 0.034166317

rs7209436 G 0.142123271

rs739645 G 0.1932213

CRHR2 7 steroid rs1003929 C 0.287211163

rs155100 T 0.510546554

rs2014663 T 0.390533615

rs2190242 A 0.097135319

rs2240403 C 0.071882927

rs2251002 C 0.526386461

rs2267712 C 0.227704046

rs2267713 G 0.489918981

rs2267715 A 0.556974612

rs2267716 A 0.107541034

rs2270007 G 0.206928498

rs2270008 C 0.382708948

E13

rs2284216 G 0.191034943

rs2284217 G 0.43015793

rs2284219 C 0.295477331

rs2284220 A 0.233274816

rs255102 A 0.091497673

rs3735430 C 0.002271765

rs3779250 A 0.449884297

rs4723000 G 0.323052951

rs4723002 A 0.055463187

rs733453 A 0.503049917

rs7793837 T 0.120348962

rs8192496 A 0.422070013

rs917195 C 0.079008212

rs929377 T 0.489698913

rs973022 A 0.191183389

rs975537 A 0.561840719

CSK 15 adrenergic rs12439525 C 0.017997931

rs1378942 A 0.267661343

rs2168518 A 0.259889193

rs2301249 G 0.271375766

rs7085 C 0.302556681

CTSS 1 candidate rs1136774 G 0.282855819

CX3CR1 3 adrenergic rs2669849 A 0.15010471

rs3732378 C 0.150384071

rs3732379 G 0.214402095

DEFB1 8 candidate rs2738182 A 0.710758688

rs2741136 C 0.287021919

rs5743402 A 0.361865511

DPP10 2 candidate rs10208402 C 0.106011773

rs6737251 T 0.169604702

rs982213 G 0.783015238

F2R 5 adrenergic rs153311 C 0.064490777

rs2227744 A 0.495744636

rs2227754 A 0.001

rs2227795 A 0.001

rs2227817 G 0.001

rs27135 A 0.337375028

rs37242 A 0.089913645

F2RL1 5 adrenergic rs2243010 C 0.100281246

rs2243072 C 0.003552027

rs631465 C 0.01209055

rs6453253 C 0.370624492

F2RL3 19 adrenergic rs2227349 C 0.060802825

rs706765 A 0.041701571

rs7245967 C 0.06717012

rs773901 A 0.125468343

FBN2 5 candidate rs154001 T 0.315396916

rs154003 G 0.213905086

rs1801167 G 0.001

rs2042327 T 0.218425441

E14

rs2307110 A 0.069754958

rs27855 A 0.18599896

rs28114 G 0.19433764

rs32209 T 0.175743397

rs32216 T 0.277987385

rs331079 G 0.025259402

rs3805635 T 0.02299758

rs3805651 G 0.104419937

rs3805652 A 0.038489389

rs468026 T 0.133940325

rs764371 A 0.105462185

FCER2 19 candidate rs10415518 G 0.033648218

rs12610479 G 0.202843348

rs12611038 A 0.262149529

rs12980031 A 0.155320675

rs1990975 C 0.124802901

rs2277989 T 0.284483267

rs2277991 T 0.112755888

rs2287867 T 0.162565659

rs2287868 T 0.2403883

rs3760687 C 0.010240238

rs4804221 A 0.21978839

rs4804773 A 0.342302546

rs4996974 T 0.383907392

rs7249320 G 0.296417825

rs753733 G 0.06318342

rs8110128 G 0.120893747

rs889182 A 0.114907445

GAL 11 adrenergic rs1042577 T 0.215461261

rs1546309 A 0.143852595

rs2513304 A 0.332273463

rs3136538 A 0.206974022

rs3136541 C 0.243709464

rs3136543 A 0.21228777

GATA3 10 candidate rs10905277 G 0.142840763

rs1149901 G 0.031664313

rs1399180 C 0.083082828

rs2229359 G 0.037732034

rs2229360 C 0.001

rs2275806 A 0.178831595

rs406103 C 0.447198351

rs485411 G 0.036024832

GRK4 4 adrenergic rs1024323 C 0.134313057

rs1056094 G 0.227540918

rs1801058 C 0.456058127

rs2471322 A 0.203160852

rs2471327 C 0.110343217

rs2471337 A 0.21970918

rs2471350 A 0.138994039

rs2488806 T 0.114126711

E15

rs2488815 G 0.167594142

rs2960306 G 0.231936649

rs3021140 C 0.265565148

GRK5 10 adrenergic rs10886430 A 0.2014091

rs11198907 C 0.076649799

rs1268947 G 0.120716409

rs1473799 C 0.026173123

rs1475753 C 0.21993736

rs1537576 C 0.137367549

rs2085185 G 0.07060072

rs2230345 T 0.003549978

rs2230349 C 0.148552534

rs4752266 A 0.326206526

rs506657 A 0.190829298

rs915394 T 0.238084115

rs928670 A 0.328653439

GRK7 3 adrenergic rs11921607 T 0.117783275

rs2138789 A 0.137705936

rs4234469 G 0.285880035

rs4337623 A 0.226718476

rs4683625 C 0.249005544

rs6806847 A 0.225880364

HAT1 2 steroid rs10165126 C 0.097389995

rs10439296 A 0.221910295

rs10930498 C 0.18045929

rs11692418 G 0.279534718

rs1443700 G 0.313639258

rs1982288 T 0.210954077

rs3791342 A 0.001

rs6758494 T 0.280138762

HDAC1 1 steroid rs6697130 A 0.001

HDAC2 6 steroid rs10499079 C 0.049945546

rs10499080 C 0.063810156

rs2499618 C 0.049536925

rs3757016 C 0.157203653

rs6568819 C 0.180301098

rs9481408 C 0.400636654

HDAC3 5 steroid rs187515 C 0.234420322

rs32956 C 0.284339831

HDAC5 17 steroid rs228757 C 0.350353677

rs375171 A 0.17927437

HDAC7A 12 steroid rs2240106 C 0.107589108

rs2240108 C 0.070172838

rs2301783 A 0.125542086

rs3782908 A 0.408269818

HSPCA 14 steroid rs2277465 G 0.00129919

rs2298877 G 0.045349473

rs4906179 G 0.066133861

rs8004640 G 0.084642789

IFNG 12 candidate rs1861494 A 0.054581684

E16

rs2069727 G 0.087332538

IKBKAP 9 candidate rs10759326 A 0.454160801

rs11791783 G 0.210236129

IL10 1 candidate rs1800871 C 0.180983287

rs1800872 C 0.091547065

rs1800896 G 0.344973432

rs3024492 A 0.360132946

rs3024496 C 0.323327241

rs3024509 T 0.132081736

IL12B 5 candidate rs1368439 A 0.229968469

rs2569253 A 0.084247734

rs3181216 T 0.283829525

rs3212219 G 0.120777336

IL13 . candidate G+2044A G 0.179815332

IL18BP 11 candidate G9772A6 C 0.99999043

rs1541304 C 0.032313321

rs1573503 C 0.039641094

rs1892919 A 0.238324822

rs2298455 A 0.106359307

rs3814721 T 0.23974763

rs949323 G 0.080435917

IL22 12 candidate rs2227485 C 0.313672938

rs2227513 A 0.068633788

IRAK3 12 candidate rs1152888 G 0.140872862

rs1152909 C 0.234872683

rs1168774 G 0.194520772

rs1732887 A 0.024966293

rs1732888 T 0.055183921

rs2701653 C 0.080496836

ITPR1 3 adrenergic rs13079522 C 0.217679473

rs1389162 A 0.100471669

rs1866999 A 0.099271793

rs1994500 A 0.0636528

rs2054871 C 0.051328803

rs2119802 A 0.21032721

rs2291859 C 0.143692665

rs2291862 C 0.540808322

rs2306874 C 0.003371338

rs2306875 A 0.35805047

rs2306877 G 0.099303681

rs2306881 G 0.067273526

rs304011 A 0.050931716

rs304028 A 0.198055501

rs304039 G 0.210788702

rs3792494 A 0.014774918

rs3792511 A 0.06762596

rs3804984 C 0.0635552

rs3804999 C 0.057726513

rs3805018 A 0.161115835

rs4510365 T 0.122114757

E17

rs4685785 A 0.137231681

rs4685798 C 0.062373573

rs6442887 A 0.083843588

rs6442905 G 0.026355986

rs6768493 G 0.164543294

rs6786081 G 0.090528733

rs6786487 G 0.061493027

rs6802929 G 0.044653673

rs731915 C 0.188481036

rs7630009 T 0.153166816

rs7631664 A 0.072296952

rs873768 C 0.371844771

rs901856 G 0.127328929

rs9815192 A 0.123414069

rs9876432 T 0.05408341

ITPR2 12 adrenergic rs1002835 T 0.060631463

rs1007938 A 0.095725126

rs1031301 A 0.139870524

rs10743585 T 0.198398847

rs10743591 C 0.060719775

rs10743592 T 0.144525396

rs10771277 C 0.30996399

rs10771301 A 0.123089168

rs10842720 A 0.104445099

rs10842774 A 0.250039638

rs10842796 T 0.014244758

rs10842797 T 0.022274484

rs10842798 A 0.016915129

rs11048588 A 0.043933404

rs12313993 G 0.049222491

rs12582043 C 0.258806764

rs1386810 G 0.172520984

rs1393413 C 0.180434599

rs1463589 G 0.228349258

rs1463590 A 0.065708547

rs1482979 G 0.044832041

rs1484881 A 0.051299017

rs1532720 A 0.121026817

rs1565175 G 0.134385925

rs1875579 G 0.144365502

rs2035440 C 0.127456389

rs2036434 G 0.113219525

rs2062165 C 0.097108232

rs2170980 A 0.144964016

rs2171520 A 0.082148521

rs2230377 C 0.028316173

rs2270960 T 0.216454459

rs2306549 G 0.266559406

rs2880877 G 0.219108756

rs3782290 T 0.192903948

E18

rs3782294 C 0.184991764

rs3782295 C 0.160719066

rs3782309 C 0.0462586

rs3816834 C 0.233113001

rs4963984 C 0.088346425

rs4963993 C 0.044787919

rs4964005 A 0.118411327

rs7137796 A 0.269343162

rs728009 A 0.1064405

rs7297828 A 0.320455494

rs7306427 C 0.037722993

rs7309048 C 0.180760705

rs732149 G 0.230458618

rs7960020 T 0.175712771

rs901840 T 0.186409558

rs984972 C 0.164592686

ITPR3 6 adrenergic rs10947415 C 0.152634178

rs10947426 A 0.237369259

rs10947428 T 0.391017807

rs1536036 A 0.320025431

rs1570760 C 0.240397925

rs2274197 C 0.163313669

rs2281917 G 0.480273183

rs2296329 T 0.075356078

rs2296343 A 0.297529781

rs2296742 A 0.186150328

rs3736893 C 0.322833447

rs3804550 C 0.001

rs3818526 A 0.499118874

rs4259245 A 0.383043505

rs4711332 C 0.121885216

rs4711336 A 0.137297801

rs594223 A 0.461252998

rs6457738 C 0.242242455

rs6901411 C 0.343627381

rs6903502 C 0.100210682

rs6913517 C 0.018516954

rs6921825 C 0.119025282

rs9366826 G 0.191381481

rs9368771 T 0.456901906

rs942643 C 0.130252086

rs9469537 C 0.355046527

rs999943 T 0.196986462

KITLG 12 candidate ASP210TYR G 0.001

rs1472899 T 0.100130794

rs995030 G 0.039512694

LOX 5 candidate rs840466 G 0.171182609

rs840467 G 0.20303067

MAPK1 22 adrenergic rs13058 A 0.069775933

rs2266967 C 0.040706842

E19

rs2266969 G 0.02475453

rs2283793 A 0.022048352

rs2283794 A 0.071027449

rs2298432 A 0.029941269

rs2298434 G 0.061895455

rs4821402 C 0.06623561

rs9610375 C 0.027662652

MAPK3 16 adrenergic rs12922100 G 0.116702363

MDM2 12 candidate rs769412 T 0.160677711

MFNG 22 candidate rs2284052 C 0.021113473

MMP12 11 candidate rs476391 G 0.110766879

rs632009 T 0.403426572

rs651159 A 0.158437354

rs652438 A 0.097685139

rs660727 C 0.131074354

MMP19 12 candidate rs1056784 C 0.001

rs1056785 T 0.01074801

rs2242295 A 0.566233124

MS4A2 11 steroid rs12576889 A 0.035178676

rs2583476 C 0.126168047

rs2847655 A 0.152003302

rs2847668 A 0.128095498

rs502581 A 0.110095958

rs512555 G 0.006245678

rs556917 A 0.048878797

rs569108 A 0.006262096

rs574700 C 0.012490966

NCOA1 2 steroid rs2119117 C 0.264825034

NCOA2 8 steroid rs3088092 T 0.056501951

rs4738070 G 0.021877729

NDFIP1 5 candidate rs2338820 G 0.108277629

rs249637 C 0.094152393

rs249680 A 0.119679119

rs8378 C 0.178410832

NOS3 7 candidate rs1799983 G 0.328489109

NR0B2 1 steroid rs6659176 C 0.098322665

NR1I2 3 steroid rs12721607 C 0.035764236

rs3732360 T 0.062483024

rs3814055 C 0.064615829

rs3814058 T 0.105523248

NR3C1 5 steroid rs10041520 A 0.174877626

rs10052957 C 0.18220497

rs10482616 C 0.296096104

rs10482633 T 0.235748881

rs10482655 A 0.103998829

rs1438732 C 0.198131591

rs2918418 C 0.206929396

rs2918419 A 0.212746666

rs2963154 A 0.200220679

rs33389 C 0.175956395

E20

rs4986593 A 0.077038757

rs6188 C 0.157787634

rs6189 G 0.001

rs6191 A 0.247702562

rs6196 T 0.219828574

rs6198 T 0.232338472

rs6877893 C 0.279053342

rs852975 T 0.201812136

rs852977 A 0.049155217

rs852978 T 0.265915264

rs860457 T 0.055273806

rs9324918 T 0.085377481

P11 12 candidate rs1233046 G 0.185016658

rs1235153 C 0.055646268

rs2074531 G 0.06511118

rs2074532 C 0.212888695

rs2285830 T 0.119080516

rs2285831 G 0.19971427

rs886431 A 0.182912334

rs929269 C 0.082260394

PCAF 3 steroid rs10510499 T 0.049835763

rs3021408 G 0.08578828

PCDH12 5 candidate rs10323 A 0.007339409

rs108042 T 0.134686589

rs164073 A 0.232223517

rs164074 G 0.408058635

rs164079 A 0.344053867

rs164083 A 0.650156113

rs164515 T 0.100193843

rs2434322 C 0.331412767

rs252108 G 0.128569467

rs252109 C 0.001

rs3747717 C 0.173821098

PLCB1 20 adrenergic rs1033399 A 0.184665341

rs1033566 A 0.071511799

rs1040496 C 0.142445409

rs1047383 C 0.075577025

rs1237071 A 0.041854043

rs1883503 C 0.193514331

rs2076413 G 0.092775988

rs2142669 A 0.189444068

rs2223837 A 0.10570851

rs227130 C 0.122818168

rs2294259 C 0.023462477

rs4399790 G 0.231344469

rs6055578 A 0.199399714

rs6077332 C 0.083152291

rs6077414 G 0.184529019

rs6086473 G 0.131163538

rs708933 C 0.138629763

E21

rs737532 T 0.099069903

rs926506 A 0.086557515

PLCB2 15 adrenergic rs1869901 T 0.088680831

rs3784399 C 0.232263337

rs936212 T 0.001

PLCB3 11 adrenergic rs2244625 C 0.057438336

rs915987 C 0.021711042

PLCB4 20 adrenergic rs1474670 C 0.038934871

rs2076392 T 0.060691673

rs6077510 A 0.162075261

rs742279 G 0.084620533

PLN 6 adrenergic rs3752581 C 0.484757023

rs9481825 C 0.133746848

rs9489435 T 0.034575018

POMC 2 steroid rs1866146 C 0.3137269

rs3769671 A 0.028209774

rs6713532 A 0.098847758

rs6719226 G 0.026025807

rs7565877 A 0.153140763

rs934778 A 0.372722772

PPARG 3 candidate rs1175542 G 0.252898259

rs1801282 C 0.130633148

rs709150 C 0.210913768

rs709157 A 0.110146665

PTAFR 1 adrenergic rs313149 A 0.001

rs313151 G 0.137212086

PTGIR 19 adrenergic rs1126510 T 0.260201234

rs11668478 G 0.136345104

RAC2 22 steroid rs1064498 A 0.116266983

rs2284038 A 0.119115571

rs6572 C 0.209685045

rs739043 C 0.264098091

RAPGEF3 12 adrenergic rs2072115 A 0.091779575

rs2074534 G 0.314593596

rs2238143 G 0.052944401

rs2238144 C 0.085735385

rs2239189 C 0.061382451

rs2240079 G 0.297860091

rs2240080 T 0.4455054

rs757282 T 0.27168553

RASGRP4 19 candidate rs1541919 C 0.184577519

rs3745962 G 0.060735084

rs3745963 T 0.036044274

rs892055 G 0.080856315

rs919781 C 0.066259284

RSG4_G165R G 0.317627516

RGS12 4 adrenergic rs2269497 A 0.013533329

rs2281470 C 0.105801661

RGS16 1 adrenergic rs1144566 C 0.00460986

rs680431 C 0.02370444

E22

SerpinA6 14 steroid rs1042394 G 0.285276117

rs2228542 C 0.26024923

rs3748320 G 0.194064581

SMARCB1 22 steroid rs11705032 C 0.014800745

rs11912715 T 0.0618335

rs2073387 C 0.062541882

rs2186364 T 0.058995189

rs2186370 C 0.039988487

rs2267034 T 0.04211419

rs3177244 G 0.030186482

rs3788362 C 0.042758657

rs5751738 G 0.062541882

rs5751745 G 0.037424064

rs5760045 A 0.041032919

rs5996620 G 0.05034238

rs6003904 A 0.025388595

rs6003909 C 0.038879695

rs738799 G 0.024756998

rs8135303 T 0.018951439

rs9608196 A 0.021897865

rs9612484 G 0.13489727

SMARCE1 17 steroid rs1029791 T 0.108284631

rs1048573 A 0.524267757

rs1526603 G 0.082081955

rs2014704 T 0.600549569

rs3752026 A 0.504680701

rs757412 G 0.065755506

SPINK5 5 candidate rs2303067 G 0.189982287

STAT3 17 steroid rs1053023 A 0.138895101

rs2230097 T 0.013879767

rs2293152 G 0.066120383

rs2306581 T 0.069795483

rs3198502 A 0.145107281

rs3744483 A 0.136037536

rs8075442 G 0.003659108

rs957971 C 0.037501564

STAT6 12 steroid rs1057290 G 0.018418786

rs3024983 C 0.01392359

rs324015 C 0.09864506

TACR1 2 adrenergic rs10208860 G 0.166958504

rs1861457 A 0.052594279

rs2024512 A 0.038361768

rs2111378 C 0.092728194

rs3755456 A 0.079191191

rs3771809 A 0.063706908

rs3771827 A 0.138950289

rs3771836 G 0.1871268

rs3771859 C 0.097449364

rs3821318 G 0.279001823

rs4439987 T 0.147709418

E23

rs6546951 G 0.038594166

rs6715729 G 0.028491763

TBX21 17 candidate CLSNP2_050902 C 0.199320383

CLSNP3_050902 G 0.171529157

PRO485PRO G 0.010442218

rs11650354 C 0.060939643

rs1808192 C 0.254876802

rs2013383 A 0.350882897

rs2074190 G 0.83533594

rs2158079 T 0.564666617

rs2240017 C 0.149997997

rs2325717 A 0.10240464

rs4794067 C 0.757533384

rs7502875 A 0.625420146

rs9910408 A 0.091129323

WITBET_10_082902 G 0.037833858

WITBET_18_082902 C 0.186519107

WITBET_6 T 0.07041005

WITBET_8_082902 G 0.06419135

TGFB1 19 candidate rs1800472 G 0.001499019

TLR10 4 candidate rs10776482 T 0.278132178

rs10776483 T 0.353563755

rs10856839 A 0.082238223

rs11096955 A 0.189568999

rs11096956 G 0.310491838

rs11096957 A 0.194000983

rs11466617 A 0.103266402

rs11466657 T 0.029004888

rs4129009 A 0.105701756

rs4274855 G 0.111563084

TLR4 9 candidate rs10759932 T 0.122278817

rs1927914 G 0.347081488

rs4986790 A 0.107193733

rs4986791 C 0.025657056

rs7866214 C 0.015756465

TNF 6 candidate rs1800610 G 0.058017131

rs1800629 G 0.171502513

UCN3 10 steroid rs10795269 A 0.437990192

rs7088971 C 0.601152296

rs7478136 C 0.481779856

VDR 12 candidate rs10735810 T 0.079955732

rs1540339 C 0.278656778

rs2239179 C 0.089538484

rs3782905 C 0.034802494

rs731236 G 0.176428429

rs7975232 A 0.665085079* Defined as whether the gene was chosen as part of the adrenergic pathway, steroid pathway, or a prior asthma candidate gene. Bolded entries are the genes whose median SNP ranks were in the top quartile of ranks of the 844 SNPs screened.† The associated allele in an additive genetic model.‡ Estimated power for replication obtained from the PBAT screening analysis.

E24

References: E1. 1999. The Childhood Asthma Management Program (CAMP); design, rationale, and methods. Childhood Asthma Management Program Research Group. Control Clin Trials 20:91-120.E2. 2000. Long-term effects of budesonide or nedocromil in children with asthma. Childhood Asthma Management Program Research Group. N Engl J Med 343:1054-1063.E3. Baron, R. M., L. J. Palmer, K. Tantisira, S. Gabriel, L. A. Sonna, L. Le, A. Hallock, T. A. Libermann, J. M. Drazen, S. T. Weiss, and E. S. Silverman. 2002. DNA sequence variants in epithelium-specific ETS-2 and ETS-3 are not associated with asthma. Am J Respir Crit Care Med 166(7):927-32.E4. Silverman, E. S., L. J. Palmer, V. Subramaniam, A. Hallock, S. Mathew, J. Vallone, D. S. Faffe, T. Shikanai, B. A. Raby, S. T. Weiss, and S. A. Shore. 2004. Transforming growth factor-beta1 promoter polymorphism C-509T is associated with asthma. Am J Respir Crit Care Med169(2):214-9.E5. 1995. ATS Statement: Standards for the diagnosis and care of patients with chronic obstructive pulmonary disease. Am J Respir Crit Care Med 152:s77-s-120.E6. Peters, S. P., N. Anthonisen, M. Castro, J. T. Holbrook, C. G. Irvin, L. J. Smith, and R. A. Wise. 2007. Randomized comparison of strategies for reducing treatment in mild persistent asthma. N Engl J Med 356(20):2027-39.E7. 2007. American Lung Association Asthma Clinical Research Centers. Clinical trial of low-dose theophylline and montelukast in patients with poorly controlled asthma. Am J Respir Crit Care Med 175(3):235-42.E8. Revicki, D. A., N. K. Leidy, F. Brennan-Diemer, S. Sorensen, and A. Togias. 1998. Integrating patient preferences into health outcomes assessment: the multiattribute Asthma Symptom Utility Index. Chest 114(4):998-1007.E9. Litonjua AA, Thorn CF, Liggett SB. Β-agonist and β-blocker pathway. 2004 August 1, 2007 [cited 2008 May 11]. Available from: http://www.pharmgkb.org/do/serve?objId=PA2024&objCls=Pathway.E10. Weiss ST, Litonjua AA, Tantisira KG, Wong M-L, Thorn CF, Licinio J. Glucocorticoid and inflammatory genes pathway. 2003 August 1, 2007 [cited 2008 May 11]. Available from: http://www.pharmgkb.org/do/serve?objId=PA2026&objCls=Pathway.E11. Barnes, P. J. 2007. Scientific rationale for using a single inhaler for asthma control. Eur Respir J 29(3):587-95.E12. Johnson, M. 2004. Interactions between corticosteroids and beta2-agonists in asthma and chronic obstructive pulmonary disease. Proc Am Thorac Soc 1(3):200-6.E13. Van Steen, K., M. B. McQueen, A. Herbert, B. Raby, H. Lyon, D. L. Demeo, A. Murphy, J. Su, S. Datta, C. Rosenow, M. Christman, E. K. Silverman, N. M. Laird, S. T. Weiss, and C. Lange. 2005. Genomic screening and replication using the same data set in family-based association testing. Nat Genet 37(7):683-91.E14. Skol, A. D., L. J. Scott, G. R. Abecasis, and M. Boehnke. 2006. Joint analysis is more efficient than replication-based analysis for two-stage genome-wide association studies. Nat Genet 38(2):209-13.E15. Lange, C., D. DeMeo, E. K. Silverman, S. T. Weiss, and N. M. Laird. 2004. PBAT: tools for family-based association studies. Am J Hum Genet 74(2):367-9.