Genes and Immunity (2014) 15, 1–7 & 2014 Macmillan Publishers Limited All rights reserved 1466-4879/14 www.nature.com/gene

ORIGINAL ARTICLE

Intermediary quantitative traits—an alternative in the identification of disease genes in asthma? M Sargurupremraj, K Pukelsheim, T Hofer and M Wjst Intermediary quantitative traits are a possible alternative for the identification of disease genes. This may be particularly relevant when diagnostic criteria are not very well defined as described for asthma. We analyzed serum samples from 944 individuals of 218 asthma families for 17 cytokines (eotaxin, GM-CSF, IFNg, IL1B, IL1RA, IL-4, IL-5, IL-6, IL-8, IL-10, IL-12(p40), IL-13, IL-17, IL-23, IL-33, TSLP and TNF-a) and determined the heritability. Linked chromosomal regions were identified by a genome-wide analysis using 334 autosomal microsatellite marker and association tested by further 550 SNP marker at genes implicated earlier with immune response. Heritability varied with TNF-a and IL-8 levels having the highest and TSLP having the lowest heritability. Linkage was significantly increased only for IL-12(p40) at D17S949. There were multiple significant single-nucleotide polymorphisms (SNP) associations (Po0.05) as found in the transmission disequilibrium test, whereas only a few replicated in parents or children only. These include SNPs in IL1RN that were associated with IL-33 and TSLP levels, and a SNP in NR3C2 that was associated with eotaxin, IL-13 and IFN-g levels. Circulating level of serum cytokines exhibits genetic associations with asthma traits that are otherwise not detected using clinical diagnosis or when the clinical details are ambiguous. Genes and Immunity (2014) 15, 1–7; doi:10.1038/gene.2013.53; published online 17 October 2013 Keywords: association; linkage; asthma; IL-12(p40); IL1RN; single-nucleotide polymorphisms

INTRODUCTION Asthma is known as a chronic inflammatory disease of the airways. According to the World Health Organization (WHO) it is estimated that B235 million people suffer from this non-communicable disease, being now the most common chronic disease in children. Clinical features like eosinophilic inflammation and airway remodeling characterize asthma. It predominantly coexists with elevated serum IgE levels,1 which emphasize the role of host factors (genetic) and environmental factors in disease onset. One of the most important advancements in respiratory research is the recognition of cytokines as a key factor in regulating inflammatory response. In the inflammatory process the airway wall is infiltrated by T lymphocytes, eosinophils, macrophages and mast cells (MCs) with occasional infiltration by neutrophils.2 Epithelial cells, dendritic cells (DCs) and macrophages are the most widely used models in asthma research.3 Epithelial cells of asthmatic airways exhibits increased expression of stem cell factor4 that activates MCs numbers5, leading to bronchoconstriction. In addition, airway epithelium functions as a source for pro-inflammatory cytokines like thymic stromal lymphopoietin (TSLP), thereby triggering DCs to release type 2 T helper cells (Th2) attracting chemokines.6 Th2 cells in turn regulate the serum IgE levels by releasing IL-4 and IL-13.7 Notably the IL-4/IL-13 pathway is the most studied pathway in the pathogenesis of allergic diseases like asthma. Genetic alterations in these regions have been extensively studied to show that polymorphisms in IL-13 promoter8 and intronic regions of IL-4 are associated with elevated serum IgE levels.9 The effect of serum IgE levels on the increase of mast cell numbers and corresponding release of histamines have been studied earlier10 in asthmatic subjects, which

are confirmed later by in vivo studies.11 Along with IgE, several other cytokines are involved in mast-cell activation and in the release of bronchoconstrictor mediators like histamines and cysteinyl-leukotrienes (Cys-LTs) that leads to bronchoconstriction and airway remodeling. It is now a known fact that allergic disease results from the skewing of Th1/Th2 balance in favor of the Th2 pathway and that this balance is regulated by the cytokines secreted by these cells. From in vivo studies it is shown that the level of cytokine production differs between individuals, and that it generates a difference in their susceptibility to immune disorders.12 Potential variance in cytokine levels and marked difference in the activation of immune system (B cells, T cells, eosinophils, and so on) among individuals show the complexity and heterogeneous nature of asthma. This postulates heavily on the notion that ‘multiple genes contributes phenotypic variability’, and that the variability is greatly influenced by environmental exposure and genetic make-up of the population. Early linkage and association studies identified major histocompatibility complex (MHC II) region in asthma pathology,13 which is re-established by recent studies.14 Genome-wide linkage studies in asthmatic subjects show Chr 2 (IL-1 cluster),15 Chr 5 (IL-13),16 Chr 6 (MHC region),15,17 Chr 12 (IFGN)18 and Chr 1319 as the most consistently recorded loci. Recent technological advancements show genetic variations such as copy number variants that are concentrated in asthmasusceptibility genes20 and rare variants that are contributing to asthma susceptibility in certain population.21 Additional functional studies should be performed to rule out the genetic variants that are in linkage disequilibrium (LD) with the actual causal variants. Among the vast collection of signaling molecules, there are key cytokines that are involved in asthma pathogenesis. The present study focuses on 17 key serum cytokines (Eotaxin, GM-CSF, IFNg,

Institute of Lung Biology and Health (iLBD), Comprehensive Pneumology Center (CPC), Helmholtz Zentrum Mu¨nchen, Munich-Neuherberg, Germany. Correspondence: Professor M Wjst, Institute of Lung Biology and Health (iLBD), Comprehensive Pneumology Center (CPC), Helmholtz Zentrum Mu¨nchen, Ingolsta¨dter Landstrasse 1, D-85764 Munich-Neuherberg, Germany. E-mail: [email protected] Received 4 June 2013; revised 23 August 2013; accepted 9 September 2013; published online 17 October 2013

Intermediary quantitative traits M Sargurupremraj et al

2 IL1B, IL1RA, IL-4, IL-5, IL-6, IL-8, IL-10, IL-12(p40), IL-13, IL-17, IL-23, IL-33, TSLP, and TNF-a). The following sections briefly cover the role of these cytokines in asthma development. Section A: IL-4 and IL-13 are the key lymphokines that are involved in increased production of IgE and in the differentiation of naive Th cells to Th2 cells. Th2 cells in turn release more of these cytokines; allergen-induced tests demonstrate this influx of IL-4 and IL-13 by T cells in asthmatic airways.22 In animal models of allergic asthma, blockade of IL-4,23 IL-13,24 IL-12(p40)25 and IFNg26 resulted in the reversal of allergen induced airway hyper-reactivity. Similarly, IL-527 and IL-17 levels28 are significantly elevated in severe asthma patients compared with that in healthy controls. Section B: asthmatic airways exhibit increased expression of TSLP29 and IL-33, of which IL-33 is upregulated by other proinflammatory cytokines like TNF-a.30 Eosinophilic and neutrophillic inflammations in the airways are characteristics of asthma, which are mediated by cytokines like IL-23,31 IL1B32 and IL-633, making them as general markers for inflammation. Section C: eotaxin is a member of the cysteine cluster, family of chemokines that are seen in high numbers in the lungs of asthma patients. It induces eosinophil recruitment into the lung tissue through surface receptors like C–C chemokine receptor type 3 (CCR3).34 Similarly, IL-8 is a chemoattractant for neutrophils that functions as an important mediator in neutrophil influx.35 Section D: growth factors like granulocyte macrophage colonystimulating factor (GM-CSF) are abundant in the allergic airways, which might have a regulatory role in Th cell differentiation via dysregulation of DCs.36 IL-10 is an anti-inflammatory cytokine that are inversely correlated with the severity of human disease.37 IL1RA belongs to the IL-1 family that functions as a competitive inhibitor of IL-1. As Th1/Th2 cytokines regulates the balance between IL1B and IL1RA, any dysregulation of their ratio might be important in the inflammation process.38 These 17 serum cytokines are measured as quantitative traits and their genetic relationship is explored. Based on the heritability found, linkage and association studies were performed. Genetic analysis of complex traits is better explained by multiple approaches, especially when the quantitative traits under investigation follow patterns of inheritance within families. Family-based study design allows testing linkage and association for loci influencing asthma and its related quantitative traits. Transmission disequilibrium test (TDT) test was employed39 to conduct a combined linkage and association test40 that determines the identified putative quantitative trait locus (QTL) as the actual disease-causing locus and not merely in disequilibrium with the trait locus.

Table 1. Clinical characteristics of the study population (n ¼ 923). Descriptors of categorical variables include n (total sample) followed by percentage. Descriptors of continuous variables include mean values followed by standard deviation Parents

Children

Number, n (%) Mean age (in Years)

424 (44.9) 40±5.5

499 (52.8) 11±3.8

Sex (%) Male Female Asthma diagnosis (%)

223 (52.5) 201 (47.4) 93 (21.9)

275 (55.1) 224 (44.8) 443 (88.7)

Skin prick test D.pter skinX3 mm D.far skinX3 mm

129 (30.4) 111 (26.1)

221 (44.2) 189 (37.8)

Serum-specific IgE (CAP) test D.pter D.far FEV1(ml) ln(IgE) (kU/l) ln(eosinophil) (count/mm3)

101 (24.0) 94 (22.3) 3.6±0.896 4.1±1.6 0.5±1.7

237 (48.3) 234 (47.5) 2.592±0.943 5.0±1.9 1.1±1.6

Abbreviations: D.far, Dermatophagoides farinae; D.pter, Dermatophagoides pteronyssinus; FEV1, forced expiratory volume in 1 second. Note: Percentages may not add up to 100 due to rounding. Standard deviation expressed as ±.

Figure 1. Heritability estimates of serum cytokines. Effect sizes ranging from environmental (dotted line) to strong genetic component (straight line).

RESULTS The total sample consisted of 944 individuals from 218 families; after excluding 21 samples with insufficient clinical details 923 individuals remained for further analysis, and serum measurements of 17 cytokines were performed. Assay sensitivities for the cytokines are measured; GM-CSF and IL-12(p40) expressed highest sensitivity for the MILLIPLEX assay with means of minimum detectable concentration in the range of 9.5 and 10.5 (pg ml  1), respectively. The intra-assay sensitivity has a mean coefficient of variance (CV) of 4.5% to 10.4%. Main demographic details of the study participants are summarized in Table 1. Complete genotype and phenotype information were collected for the study. Participants were equally distributed across the two subsets (parents and children) of study sample, with mean age of 40 and 11 years, respectively, and the percentage of individuals diagnosed with asthma is higher in children then in parents (Table 1).

Heritability estimates (h2 ¼ VG/Vt) were estimated for serum concentration of 17 cytokines as shown in Figure 1. If h2 ¼ 0, none of the phenotypic variation is due to genetic variation, and if h2 ¼ 1.0, all of the phenotypic variation is due to genetic variation. After adjusting for the confounding covariates, IL-8 (h2 ¼ 1.0) has the strongest effect size followed by TNF-a (h2 ¼ 0.6), IFN-g (h2 ¼ 0.5) and IL-1B (h2 ¼ 0.5), suggesting that the production of these chemokines and pro-inflammatory cytokines is under tight genetic control, while IL-6 (h2 ¼ 0.4) production exhibited a moderate heritability with substantial shared environmental component. For eotaxin and GM-CSF we obtained more identical estimates including very weak (IL-5) to zero-point or no genetic influence (TSLP, IL-33).

Heritability Heritability shows the weight of phenotypic variation due to genetic variation (VG) in relation to total phenotypic variation (Vt).

Linkage studies Non-parametric linkage (‘model-free’) analysis of innate serum cytokine levels with markers for microsatellites revealed two QTLs

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3 (D6S344; D17S949) with LOD scores that are above the threshold for suggestive genome-wide linkage (Table 2). One peak reached the genome-wide linkage significance level. The values were also adjusted for age and gender using general linear regression analysis. Suggestive evidence of linkage (LOD42) was found on chr.6p25.3 and on chr.17q24.3 for eotaxin (naive P-value ¼ 0.0003) and IL-12(p40) (naive P-value ¼ 7.8  106), respectively. Association studies We selected 550 candidate gene-based single-nucleotide polymorphism (SNPs) from public databases (dbSNP (http:// www.ncbi.nlm.nih.gov/SNP/) and Ensembl (http://www.ensembl.org)), which are analyzed by enzyme-based methods (Primer extension) to generate high-fidelity SNP genotypes. Detection of the SNPs was performed by matrix-assisted laser desorption/ ionization time of flight mass spectroscopy (see Materials and methods). In order to control the population stratification in family-based association (FBA) TDT tests were performed on each SNP; it is a test of linkage in the presence of an association that showed no statistical significance (data not shown). Association between SNP markers and serum cytokine levels was calculated using PLINK tool to test for QTL using QFAM—a FBA test for quantitative traits. Of the 550 SNP markers analyzed, few SNP Table 2.

Drug effects on cytokine levels Multiple regression analysis was performed to study the effect of drug intake on serum cytokine levels. Significant effect was found

Non-parametric linkage analysis (n ¼ 923)

Phenotypes Eotaxin IL-12(p40) IL-4 IL-5 TNF-a

markers replicated a case–control analysis of parents or children only. Association of serum cytokines was found to be highly significant with SNP markers rs439154 (2q13), rs2070948 (4q31.23), rs3894194 (17q21.1), rs3135459 (4p16.3), rs3782905 (12q13.11) residing in IL1RN, NR3C2, GSDM1, CARD15 and VDR gene respectively (Table 3). In IL1RN, serum level of IL-12(p40) is significantly associated with the polymorphism; levels were less in patients with AA genotype than in those with the AG genotype or the AG/GG genotype combined (Table 3). On the contrary, IL-23, TSLP and IL-33 have shown statistical significance with the polymorphism and are higher for the homozygous allele (AA), whereas IL-33 was rather uniform across all the genotypes. In NR3C2, serum levels of IL-13 and eotaxin have shown statistically significant association with the polymorphism and are greater for the patients with homozygous allele (TT) when compared with IFN-g, which is heterozygous for the variant allele (AT). Patients with AA genotype and those with GG genotype did not significantly differ in serum IL-23 or IL-33 levels, which might be due, at least in part, to the small subject number.

Marker

Chromosome

cM

LOD score

P-value

Candidate genes (distance from the marker in Mba)

D6S344 D6S470 D9S288 D9S286 D17S949 D6S344 D9S288 D12S85

6p25.3 6p24.3 9p24.2 9p24.1 17q24.3 6p25.3 9p24.2 12q13.11

1.4 18.22 9.83 18.06 93.27 1.4 9.83 61.34

2.56 2.14 2.13 2.09 4.05 1.51 1.57 2.02

0.0003 0.0008 0.0008 0.0009 7.8  10  6 0.004 0.003 0.001

FOXF2 (- 0.2), SERPINB1 (1.2) GCNT2 (0.4), EDN1 (2.2) RFX3 (-0.4), JAK2 (1.0) IL-33 (-1.7), PTPRD (0.2) MAP2K6 (-0.9), KCNJ2 (-0.2), SSTR2 (2.6) FOXF2 (- 0.2), SERPINB1 (1.2) RFX3 (-0.4), JAK2 (1.0) WNT1 (2.0), KCNH3 (2.5), GPD1 (3.1)

Abbreviations: EDN1, endothelin 1; FOXF2, forkhead box F2; GCNT2, glucosaminyl (N-acetyl) transferase 2; GPD1, glycerol-3-phosphate dehydrogenase 1; JAK2, janus kinase 2; KCNH3, potassium voltage-gated channel H3; IL-33, interleukin -33; MAP2K6, mitogen-activated protein kinase kinase 6; PTPRD, protein tyrosine phosphatase receptor type DKCNJ2, potassium inwardly rectifying channel J2; RFX3, regulatory factor X 3; SERPINB1, Serpin peptidase inhibitor, clade B1; SSTR2, Somatostatin receptor 2; WNT1, wingless-type MMTV integration site 1. aDistance separating the marker and the candidate genes are taken from UCSC genome bioinformatics site (http://genome.ucsc.edu/). Negative and positive values represent the position of the gene located downstream or upstream from the marker, respectively.

Table 3.

SNP association with cytokine expression profile

Gene

rs Numbera

Chromosome

MAF

SNP position

IL1RN

rs439154

113884401

0.47

NR3C2

rs2070948

149358729

0.33

P-value

Base change

Cytokine

Intron

A/G

IL-4 IL-5 IL-12(p40) IL-23 IL-33 TSLP

AA vs AG 0.032 0.021 0.003 0.042 0.467 0.019

AG vs GG 0.529 0.145 0.036 6  10-4 0.028 0.004

Intron

A/T Eotaxin IFN-g IL-13

AA vs AT 0.021 0.043 0.026

AT vs TT 0.036 0.136 0.004

GSDM1

rs3894194

38121993

0.42

Missense

C/T

IL-33

CC vs CT 0.015

CT vs TT 0.001

CARD15

rs3135499

50766127

0.37

3’ UTR

A/C

GM-CSF

AA vs AC 0.029

AC vs CC 0.031

VDR

rs3782905

48266167

0.24

Intron

C/G

IL-13

CC vs CG 0.013

CG vs GG 0.659

a

dbSNP, the SNP database.

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4 Table 4.

Multiple regression analysis – effect of drug on serum cytokine levels (Significantly correlated 8 cytokines from the total of 17 cytokines) Regression Model

Glucocorticoid Leukotriene antagonist b2-Sympathomimetic Aminophylline Cromoglycate Anticholinergic Antihistamine

Eotaxin

GM-CSF

IL-4

IL-5

IL-8

TNFa

IL1B

IL1RA

 0.071 0.0048**  0.014 0.9035  0.053 0.0582 0.018 0.7315 0.023 0.3962 0.069 0.088  0.03 0.4733

 0.079 0.309  0.105 0.764  0.018 0.837 0.173 0.281  0.006 0.939 0.15 0.237  0.317 0.016*

0.2 0.0427*  0.453 0.3079 0.025 0.8212  0.314 0.1202 0.259 0.0152* 0.123 0.4407 0.026 0.8774

0.19 0.013* 0.006 0.986 0.003 0.976  0.221 0.156 0.172 0.037*  0.046 0.709 0 0.998

 0.038 0.5654 0.018 0.95172 0.157 0.03440*  0.118 0.38702  0.238 0.00102**  0.165 0.12741 0.009 0.93582

 0.004 0.9007  0.111 0.4089 0.037 0.2674  0.004 0.9427  0.028 0.3794 0.061 0.2061  0.13 0.0099**

0 0.9963  0.739 0.0231* 0.081 0.3179  0.193 0.1925  0.211 0.0074** 0.056 0.6364  0.197 0.1095

 0.021 0.685  0.434 0.057 0.111 0.052  0.109 0.295  0.057 0.299 0.029 0.726  0.164 0.058

Depicted are regression coefficients followed by P-values. *Po0.05 (Significant); **Po0.01 (highly significant).

in cromoglycate, glucocorticoid and antihistamine drug groups than in any other groups (Po0.05) (Table 4). The coefficient of cromoglycate is highly significant and negatively related to IL-8 and IL1B. As the model is log-linear, any increase seen in cromoglycate is associated with decrease in IL-8 and IL1B values. Eotaxin and TNF-a decrease with glucocorticoid and antihistamine, respectively, whereas leukotriene antagonist (IL1B) and b2-sympathomimetic (IL-8) correlate negatively and positively (respectively) to a lesser extent. Lastly, the presence of glucocorticoid is positively associated with IL-4 and IL-5. However, multivariate analysis of variance that studies the overall effect of drugs on serum cytokines shows that glucocorticoid contributes to the model more than other groups (multivariate analysis of variance, P ¼ 0.0006) (Supplementary Table 1).

DISCUSSION AND CONCLUSION Our results from a family-based study shows that genetic factors regulating the intermediary quantitative traits could function as an alternative approach in discerning the disease genes in asthmatic individuals. By determining the genetic factors that regulate the circulating cytokine level, the relationship between cytokines and asthma traits could be studied when other clinical details may be ambiguous or missing. Heritability, linkage and association studies were performed to measure its relationship with the variability in serum cytokine level; unfortunately, the results did not match. We observed marked difference in the results of these genetic studies with moderate (TNF-a) to high heritability (IL-8), peaks of suggestive linkage for IL-12(p40) and eotaxin levels (LOD 4.05 and 2.56, respectively) and association of serum cytokines levels (IL-23, IL-33) with SNP markers residing in IL-1 receptor antagonist and Gasdermin family gene. The large number of parents and children (with asthma diagnosis: 21.9%, and 88.7% respectively) in this study can be seen as case-control sets, as the SNP association found in TDT test is replicated in few parents or children only, in the association results (Supplementary Figure 1a and b). The results of this study highlight the genetic heterogeneity of asthma, likely related to the environmental modifiers. The strength of our analysis is with the inclusion of individuals from families with two affected children (affected sib pair, ASP), which are controlled for any correlation across the family members. Though the study design exhibits a bias towards the samples with strong familiar (genetic) components, such extreme cases could provide more insight into their genetic anomalies. Genes and Immunity (2014) 1 – 7

ASP analysis (concordant) is an effective study design in studying allele sharing and in identifying the marker linked to the disease locus. This study was done in two stages with each stage representing a set of collected samples, whereas only for the chemoattractant cytokine IL-8 higher values were observed in the second part of the study (details in Materials and methods section). IL-8 levels are strongly correlated with elevated neutrophil levels in airway secretions of acute asthma patients.35 In accordance, we show that the heritability estimates of IL-8 (h2 ¼ 1.0) is higher than with any other cytokine. Hull et al.41 first reported IL-8 in virus-inflicted respiratory illness while later studies associated IL-8 with asthma42 and concluded its role in genetic predisposition to asthma.43 In addition, genome-wide linkage scan of cytokines identified a QTL for IL-12(p40) (LOD ¼ 4.05) levels on chr.17q24.3 with the microsatellite marker D17S949 that maps closely to an unknown gene (BP14) and the type 2 somatostatin receptor gene (SSTR2). Somatostatin activates MCs to secrete histamine, thereby effecting allergic reactions like bronchial asthma.44 Finally, from FBA studies we found a new association of serum cytokines level (IL-12(p40), IL-23, TSLP, IL-33, eotaxin and IL-13) with rs439154 SNP (2q13) and rs2070948 SNP (4q31.23). These SNPs are located in the intron region of IL1RN and NR3C2, respectively, of which IL1RN codes for an anti-inflammatory cytokine that has a vital role in pro- and anti-inflammatory balance. Thus, any genetic variants in this region might contribute heavily to asthma pathology.45 Asthma is characterized by genetic heterogeneity, in which many disease loci contribute in smaller fractions to the overall disease phenotype. Selection of diseased subjects affects the power to identify disease locus in such complex genetics. Familybased study designs avoid problems with population stratification and other heterogeneity on the basis of shared genetic and environmental background. In this study, testing for association and linkage was performed using affected-sib–parent study design that tolerates the effects of nonallelic (locus) heterogeneity. ASP linkage analysis has shown evidence of IL-12(p40) levels linked within a region of Chr.17 (D17S949) at 93.27 cM (LOD43), which is B6 cM from the SSTR2 gene—a subtype of somatostatin receptor (SST). This region also harbors KCNJ2 gene belonging to the family of genes that produce potassium channels and MAP2K6 gene (protein kinase family) that functions as mitogen-activated protein kinase (MAPK). Potential therapeutic application of potassium channel openers/modulators has already been discussed in the treatment of complex lung disease.46 MAP2K6 has a key role in signal transduction by phosphorylating and activating & 2014 Macmillan Publishers Limited

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5 p38 MAPK in response to inflammatory cytokines and other environmental stress. Recent studies47 emphasize the effect of inhibiting p38 MAPK and preventing allergen-induced complications in the airways. Furthermore, steroid insensitivity in the treatment of severe asthma cases could be related to the activation of p38 in lung barrier epithelial cells. Thus, by blocking p38 MAPK corticosteroid insensitivity in severe asthma may be improved.48 Another strong positional candidate gene for the IL-12(p40) linkage signal is SSTR2. Activation of SSTR2 results in increased transcription of somatostatin gene, which in turn triggers an antiproliferative autocrine feedback loop.49 The differential expression of SST in human immune cells suggests their importance in human immune system and in the maintenance of immune homeostasis.50 In-vitro studies has shown significant transcriptional activity of SST in human bronchial epithelial cell line (BEAS-2B)51 and increased expression in epithelial bronchial cells of fibrotic mouse lung.52 Inappropriate proliferation of bronchial cells has a vital role in chronic asthma, accordingly SST localized in human bronchial epithelial cells could control this proliferation through anti-proliferative property of somatostatin.53 Similarly, microsatellite marker D6S344 in chr.6p25.3 showed suggestive evidence of linkage (LOD ¼ 2.2) with eotaxin levels in the serum. D6S344 are located closely with the serine protease inhibitor genes (serpins). Serpins are involved in the inflammation and protection from autolysis by granule proteinases54 and are primarily found in placenta, lung and lymphocytes. It was previously shown that D6S344 marker exhibits microsatellite instability (MSI) in COPD (chronic obstructive pulmonary disease) patients55 and in a representative sample of asthmatic patients.56 Microsatellites are repeat sequences that can change to highly unstable structures (MSI)57 by processes like polymerase slippage and defective mismatch repair pathway. In addition, transposable elements (TEs) (mobile genetics elements) that are attributed in human disease etiology58 have a crucial role in the genesis of these structural variations.59 Though there are reports on genomic instability events in complex disorders like COPD and asthma,55,60 the importance of those events and its association with activity of unstable TEs still remains obscure. FBA design bridges the gap between traditional linkage and case–control association studies. It overcomes errors due to population heterogeneity and detects common alleles of moderate effects. As the outcome of such studies rely on prerequisites such as tenable candidate gene with SNPs functioning as markers, we implicated genes that are previously shown to be related in asthma along with their SNPs. IL-1RN is one such asthma candidate gene that codes for anti-inflammatory cytokine and regulates the balance between inflammatory and anti-inflammatory cytokines. Association between SNP markers IL1RN and IL-33, TSLP, IL-12(p40) and IL-23 shows that genotypes in IL1RN affect the serum cytokine concentration, of which IL-33 and TSLP have a critical role in signaling events towards Th2-driven inflammation. Apart from the signature Th2-favouring events TSLP initiates allergic airway inflammation with increased serum IgE levels.61 Our previous studies on IL1RN allele showed its association with frequency of asthma in German and to a lesser extent in Italian population.45,62 In addition, it shows that SNPs residing in NR3C2 are indeed associated with asthma-related traits (serum levels of eotaxin, IFN-g and IL-13). Eotaxin levels are implicated as a potent stimulus in eosinophil activation and its accumulation in the lung.63 Activated eosinophils in the airways are characteristics of allergic asthma. It is shown that CCR3 regulates eosinophil and mast cell accumulation into the lungs,64 and is expressed on Th2 lymphocytes. Furthermore, CCR3 inhibition is a promising Th2 cell target that reduces innate and adaptive allergic inflammation. Thus, it is possible that SNPs residing in these DNA motifs can function as the causative variants and that the alleles might have variable effects on serum cytokine levels. & 2014 Macmillan Publishers Limited

Despite the significance of IL-33 and TSLP observed in the heritable traits of asthma pathogenesis, its heritability results contradict with lowest observations. Reported high heritability of IL-8 and TNF-a supports the argument that SNPs in these regions may affect allergen-induced cytokine production. Nevertheless, it is also possible that high heritability of IL-8 may be caused by unapparent infection running in the families. IL-33 along with few other cytokines exhibited strong association with SNP marker located in IL-1 cluster (IL1RN). Therefore, it is dubious that a gene flanking the IL1RN gene is responsible for the association results. It is to be mentioned that in this study three cytokines (IL-4, IL-5 and IL-8) were positively correlated with drug intake, of which IL-8 could be of relevance, as it is possible that the higher values of IL-8 seen in the second part of the study could be the effect of samples that are under sympathomimetic drug. However, significant negative correlation of IL-8 was seen in the cromoglycate group. Earlier studies show that asthmatic subjects are responsive to cromoglycate,65 and topical application of cromoglycate along with anti-allergic drug decreases IL-8 levels in atomic dermatitis66 but its methodology in asthma is unclear. Some other drug groups that negatively correlate are antihistamines (TNF-a) and glucocorticoids (eotaxin), and it is already shown to have a vital role in asthma treatment.67,68 Though there are few drug interactions on cytokine levels considering the overall effect of drug intake, implications of the current study seem to be intact and a follow-up study should provide more insight into its interaction pattern. Taken together, the present study shows that serum cytokines exhibit an association with asthma-related cytokines that are otherwise not detected by using a clinical diagnosis alone. Thus intermediary quantitative traits may be an alternative for the identification of disease genes in asthma. Further functional studies are needed for a better understanding of the molecular mechanism that drives the phenotype through such hotspots. MATERIALS AND METHODS Probands As reported in Pukelsheim69 218 core families were selected during two study stages (with each stage representing a set of collected samples).15,70,71 In the first stage, 97 families consisting of at least two children with confirmed clinical asthma were selected for an initial genome-wide linkage scan15, followed with the second stage of the study containing 121 asthma sib pair families. At least two children with confirmed clinical asthma were required, and prematurity or low birth weight of the children was excluded, along with any severe pulmonary disease other than asthma. In the second study stage, another set of asthma sib pair families was selected. In addition to a clinical asthma diagnosis, all affected children over age 3 had a history of at least 3 years of recurrent wheezing and with no other airway diseases diagnosed. The clinical procedures contained detailed interviews of every family member, skin prick tests (SPT) of frequent allergens, blood samples (for IgE and allergen-specific IgE (RAST) measurements among several other functional tests. Written consent was given by all study participants. The parent/ guardians of all participants under 18 gave informed consent too. All study methods were approved in 1995 by the Ethics Commission of ‘NordrheinWestfalen’ and again in 2001 by ‘Bayerische Landesa¨rztekammer Mu¨nchen’.

Serum measurements Concentration levels of serum eotaxin, GM-CSF, IFNg, IL1B, IL1RA, IL-4, IL-5, IL-6, IL-8, IL-10, IL-12(p40), IL-13, IL-17, IL-23, IL-33, TSLP and TNF-a were quantified using custom-made MILLIPLEX MAP (multiple analyte profile) Human Cytokine/Chemokine Panels (Millipore, Schwalbach/Ts, Germany). All assays were performed according to the recommendations of the manufacturer using an eight-point standard curve for every cytokine. Samples were analyzed on a Luminex 100 device (BioRad, Mu¨nchen, Germany) and the data were evaluated using the Bio-Plex Manager software (BioRad). Standards and internal controls were reported as means of duplicate measurements. Samples were analyzed in single Genes and Immunity (2014) 1 – 7

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6 measurements (with details in Supplementary Table S1 in the study by Pukelsheim et al.69). Values below the detection limit were set to the half of the detection limit and to 3000 pg ml  1 if exceeding the maximum concentration of 3000 pg ml  1.

DISCLAIMER: The funders had no role in study design, data collection and

DNA preparation and genotype analysis

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DNA was isolated from peripheral white blood cells using the salt method,72 and in some cases also with the Qiamp blood kit (Qiagen, Hilden, Germany) according to the manufacturer‘s recommendation. For genotyping we used almost the same microsatellite marker as in the first scan.15 Genotyping was based on an own microsatellite mapping panel, based on microsatellites from the Ge´ne´thon reference including also 18 markers on the X chromosome. The microsatellite panel has been described in an initial description of the German Asthma Family Study15 while the data set has been released for the Genetic Analysis Workshop 12.73 Highly polymorphic markers were selected for equal distances on the genome with markers always at telomeric positions. PCR pools were separated on ABI 377 automatic sequencers and genotypes were scored using GENE-SCAN and GENOTYPER (ABI) software.

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SNP selection and genotyping SNP genotyping was done over a 5-year period. To resolve the genomic region, SNP selection was based on previous reports of asthma genes including a total of 550 SNPs. In addition, public database (dbSNP (http:// www.ncbi.nlm.nih.gov/SNP/) and Ensembl (http://www.ensembl.org)) were used in the selection and validated by using MALDI-TOF mass spectrometry of allele-specific primer extension products generated from amplified DNA sequences (MassARRAY, SEQUENOM Inc., San Diego, CA, USA). A few SNPs were also genotyped at Illumina (San Diego, CA, USA) by use of the Sentrix bead arrays.

Statistical analysis Statistical analysis was performed using R 2.12 software (RStudio: Integrated-development environment for R, 0.93, Boston, MA, USA). Principal component analysis was performed to explore relations among samples and for diagnostics before a formal statistical test. Following a descriptive analysis using cross-tabulation and distribution of single variables, linear and logistic regression analyses were performed for effect estimates (altogether 500 tests has been done, exploratory analysis). Cytokine levels were first normalized to bring all cytokine values within range. Cytokine levels were generalized with clinical characteristics and with other atopic diseases (allergic rhinitis, eczema), medications and hospitalization in a previous paper.69 Clinical covariates in the model included age and sex and were modeled on the thresholds. Merlin 1.1.2 was used to calculate heritability, single and multipoint non-parametric linkage, QTL and variance component analysis as well as affected pair linkage analyses using some earlier microsatellite data.15 The narrow sense heritability or additive genetic heritability (h2) was estimated and adjusted for covariates. LD among the microsatellite markers affects the assumption of results in multipoint linkage approaches. With merlin-LD such false positive evidence can be avoided using a pre-specified value of correlation coefficient of the frequencies (r2). Association between SNP marker and cytokine levels was calculated using PLINK 1.07 after testing for missing data, paternity errors and Hardy–Weinberg equilibrium. Association analysis was performed using the non-parametric w2 and Mann–Whitney U test. QTL association was tested using the QFAM module that uses permutation to account for the dependence between related individuals. Rather than fitting a maximum likelihood variance components model, PLINK performs a simple linear regression of phenotype on genotype, but then uses a special permutation procedure to correct for family structure.

CONFLICT OF INTEREST The authors declare no conflict of interest.

ACKNOWLEDGEMENTS We thank BMBF, Deutsche Forschungsgemeinschaft and National Genome Network. MS and MW are funded by HMGU (Helmholtz Center Munich), and the EU Grant Europrevall. We also thank all the participating families for their help and the members of the clinical centers for their work.

Genes and Immunity (2014) 1 – 7

analysis, decision to publish or preparation of the manuscript.

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