The COL5A3 and MMP9 genes interact in eczema susceptibility

Accepted Article PROFESSOR JOCELYNE JUST (Orcid ID : 0000-0002-5646-2429) DR MARIE-HÉLÈNE DIZIER (Orcid ID : 0000-0001-8460-7667) Article type : Original Article-Epidemiology of Allergic Disease The COL5A3 and MMP9 genes interact in eczema susceptibility Patricia Margaritte-Jeannin, PhD1,2*, Marie-Claude Babron, PhD1,2*, Catherine Laprise, PhD3, Nolwenn Lavielle, MSc1,2, Chloé Sarnowski, PhD1,2, Myriam Brossard, PhD1,2, Miriam Moffatt, PhD4, Valérie Gagné-Ouellet, MSc3, Adrien Etcheto, MSc5, Mark Lathrop, PhD6, Jocelyne Just, MD7, William O. Cookson, MD4, Emmanuelle Bouzigon, MD, PhD1,2, Florence Demenais, MD,2, MarieHélène Dizier, PhD1,2 *: equal contribution 1. Inserm, UMR-946, Genetic Variation and Human Diseases unit, F-75010, Paris, France; 2. Univ Paris Diderot, Sorbonne Paris Cité, Institut Universitaire d’Hématologie, F-75010, Paris, France; 3. Université du Québec, Chicoutimi, Canada ; 4. National Heart Lung Institute, Imperial College, London, UK, 5. Paris Descartes University, Rheumatology Department, Cochin Hospital, AP-HP; INSERM U1153, Sorbonne Paris-Cité, Paris, France. 6. Mc Gill University and Genome Quebec's Innovation Centre, Montréal, Canada; 7. Service d’Allergologie Pédiatrique, Centre de l’Asthme et des Allergies. Hôpital d'Enfants ArmandTrousseau - UPMC Paris 06, F-75012, Paris, France ; Corresponding author Marie-Hélène DIZIER UMR946, INSERM / Université Paris-Diderot 27 rue Juliette Dodu This article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article as doi: 10.1111/cea.13064 This article is protected by copyright. All rights reserved. Accepted Article F-75010 Paris, France Phone +33 1 72 63 93 25 Fax +33 1 72 63 93 49 Marie-Helene.Dizier@inserm.fr Running Head: Interaction of COL5A3 and MMP9 genes in eczema Abstract Background: Genetic studies of eczema have identified many genes, which explain only 14% of the heritability. Missing heritability may be partly due to ignored Gene–Gene (G-G) interactions. Objective: Our aim was to detect new interacting genes involved in eczema Methods: The search for G-G interaction in eczema was conducted using a two-step approach, which included as a first step, a biological selection of genes, which are involved either in the skin or epidermis development or in the collagen metabolism, and as a second-step, an interaction analysis of the selected genes. Analyses were carried out at both SNP and gene levels in three asthma-ascertained family samples: the discovery dataset of 388 EGEA (Epidemiological study on the Genetics and Environment of Asthma) families and the two replication datasets of 253 SLSJ (Saguenay-Lac-SaintJean) families and 207 MRCA (Medical Research Council) families. Results: One pair of SNPs, rs2287807 in COL5A3 and rs17576 in MMP9, that were detected in EGEA at P ≤ 10-5 showed significant interaction by meta-analysis of EGEA, SLSJ and MRCA samples (P=1.1x10-8 under the significant threshold of 10-7). Gene-based analysis confirmed strong interaction between COL5A3 and MMP9 (P=4x10-8 under the significant threshold of 4x10-6) by meta-analysis of the three datasets. When stratifying the data on asthma, this interaction remained in both groups of asthmatic and non-asthmatic subjects. This article is protected by copyright. All rights reserved. Accepted Article Conclusion: This study identified significant interaction between two new genes, COL5A3 and MMP9, which may be accounted for by a degradation of COL5A3 by MMP9 influencing eczema susceptibility. Further confirmation of this interaction as well as functional studies are needed to better understand the role of these genes in eczema. Key words (5 words in alphabetical order) Asthma, Eczema, Gene-Gene interaction, Logistic Regression, meta-analysis Introduction Atopic dermatitis (AD), or eczema, is the most common chronic inflammatory skin disorder. Eczema is also an allergic disease associated with asthma and allergic rhinitis. These co-morbidities may share genetic determinants as suggested by their strong associations at both the individual and family levels [1]. However, genes specific to eczema also exist, as first suggested by the observation that parental history of eczema represents a more potent risk factor for eczema than parental history of asthma or allergic rhinitis [1, 2]. A number of susceptibility genes for eczema has been found through genetic studies [3] based on candidate genes, positional cloning approaches and GWAS. Many of these genes are involved in the immune response, and are not specifically associated to eczema, but more generally to allergic diseases including asthma and allergic rhinitis. Among the genes more specifically associated to eczema are genes involved in the skin or epidermis development including SPRR3 [4], ACTL9 [5], OVOL1 [5] and also FLG [6], SPINK5 [7], C11orf30 [8] which are related to epidermal barrier dysfunction and genes involved in collagen metabolism like COL29A1 [9], IL6 [10] and IL6R [11]. However, all the genes associated with eczema explain only a small part of the heritability (1415%)[12, 13]. Mechanisms such as Gene-Gene (G-G) or Gene-Environment interactions may account for this missing heritability. Indeed, most previous genetic studies on eczema focused on single marker analysis. Five small scale G-G interaction studies were conducted for eczema, which involved only on a very small number of genes [14-18]. This article is protected by copyright. All rights reserved. Accepted Article As it has been shown that ignoring interaction may hinder the detection of genetic factors [19], several methods have been developed to identify G-G interactions in association studies. They can be applied to the whole genome or to part of the genome, such as a linkage region. To alleviate the burden of multiple testing, which already exists in single-marker analysis but is even more crucial in two-marker analysis, two-step strategies have been proposed. They typically include a first step of marker selection, followed by a second step of two-marker tests applied to the selected markers [20]. The selection of SNPs at the first step can be based on single-marker association results or on biological information [21]. We showed that, in case of strong interaction with a small marginal effect of each marker, a first-step selection based on association tests was not powerful, as the markers would not be selected at the first step [22]. Moreover, we can assume that genetic variants that do not have a too small marginal effect would have already been detected by single marker association tests, through the numerous GWAS conducted for eczema using large sample sizes [5, 8, 11, 23-26] which recently reached more than 115,000 subjects [13]. The aim of the present study was to detect new interacting genes in eczema. To search for G-G interaction, we applied a two-step approach, with a first step of gene selection based on biological knowledge related to eczema. We selected genes which were, similarly to the genes previously found specifically associated to eczema, involved either in the skin or epidermis development or in the collagen metabolism. In a second step, we performed cross-gene SNP-SNP interaction analyses followed by gene-based interaction analyses. These analyses were conducted in asthma-ascertained family samples from three studies: a discovery dataset, the Epidemiological study on the Genetics and Environment of Asthma (EGEA), and two independent replication datasets, the Saguenay-Lac-SaintJean (SLSJ) and the Medical Research Council (MRCA) studies. This article is protected by copyright. All rights reserved. Accepted Article Material and method Discovery sample The EGEA study has been described in detail previously [27]. The EGEA family sample consisted of 388 French nuclear families, 253 families ascertained through offspring with asthma (one proband in 90% of families and two probands in the remainder) and 135 families ascertained through one parent with asthma. Eczema status was based on the answer to the question: "Have you had eczema during childhood?" (either at the time of data collection and/or at the 10-year follow-up of the EGEA study). The same definition has already been used in previous linkage and association analyses of eczema conducted in EGEA [28, 29]. Ethical approval was obtained from the relevant institutional review board committees (Cochin Port-Royal Hospital and Necker-Enfants Malades Hospital, Paris). Written informed consent was signed by all participants or by kin or guardians of the minors/children. Replication samples The Saguenay–Lac-Saint-Jean Familial Asthma Collection (SLSJ) comprised 253 French-Canadian multigenerational families ascertained through two probands with asthma [30]. Eczema was defined by a self-reported history of eczema on a questionnaire. Positive answers were validated by physicians or through clinical records. The SLSJ local ethics committee approved the study, and all subjects gave written informed consent. The Medical Research Council (MRC) UK National family collection included 207 nuclear families, recruited through at least one proband with asthma (MRCA sample)[31]. Eczema definition was based on answers to questions on the presence of symptoms. The MRCA local ethics committee approved the study, and all subjects gave written informed consent. This article is protected by copyright. All rights reserved. Accepted Article Genetic data The EGEA and the SLSJ samples were genotyped using the Illumina 610-Quad , as part of the European Gabriel consortium asthma GWAS [32] while the MRCA sample was genotyped using the smaller Illumina Sentrix HumanHap300 BeadChip [31]. To get a number of SNPs in the MRCA dataset as large as in the EGEA and SLSJ datasets, SNP imputation was performed using MACH v1.00 software (http://www.sph.umich.edu/csg/abecasis/MACH/) and HapMap2 CEU haplotypes as reference panel. Imputed SNPs with imputation quality score greater than or equal to 0.9 were retained. Stringent quality criteria (QC) [32] were used to select both individuals and SNPs. After genotyping, an ancestry analysis was carried out in the three European descent samples (EGEA, SLSJ and MRCA) [32] and putative non-European samples were eliminated from subsequent analyses. Informative principal components for within-Europe diversity were included as covariates in analyses. Statistical analysis Two step strategy in the EGEA discovery sample The first step consisted in the selection of genes based on biological knowledge contained in the Gene Ontology (GO) database (http://amigo.geneontology.org/). From the GO biological process categories, we defined two groups. The first group included GO categories containing the term "collagen" i.e; "collagen metabolic process" (GO:0032963), "collagen-activated signaling pathway" (GO:0038065), "regulation of collagen binding" (GO:0033341). The second group included GO categories containing the terms "epidermis" or "skin" i.e. "skin development” (GO:0043588) and "epidermis development” (GO:0008544) after exclusion of sub-categories related to hair follicle, limb and nipple. After selection of the genes belonging to each group, all SNPs lying within the gene boundaries (Build 37.3) and present in our three datasets were retained for analysis. The first group, called the "collagen" group, comprised 121 genes (97 genes after excluding those having no SNP genotyped and 1,942 SNPs after QC checking), while the second group, the "epidermis and skin" group, totaled 251 genes (176 genes after excluding those having no SNP genotyped and 2,189 SNPs after QC checking) This article is protected by copyright. All rights reserved. Accepted Article (Supplementary Table I). Only SNPs located within the genes were considered to limit the number of tests. The second step consisted in conducting cross-gene SNP-SNP interaction analyses within each gene group, i.e. between SNPs located in two different genes belonging to the same group. We performed logistic regression using Stata® V14.1 (Stata Corporation, College Station, Texas) assuming an additive model for SNP main effect and interaction and taking familial dependencies into account through the cluster and robust options of the logit function. We used the same coding scheme as usually proposed for SNP-SNP interaction modelling [21]. We modelled the additive effect of a SNP by coding the genotypes of homozygotes for the minor allele, heterozygotes and homozygotes for the major allele as 1, 0, and -1; the interaction term between two SNPs was obtained by multiplication of these genotypic values for the two SNPs. Test of interaction was performed by comparing the full model with SNP main effect and interaction to the restricted model without interaction, using a likelihood-ratio test which follows a Chi-square distribution with one degree of freedom. SNP pairs in which the expected number of joint genotypes in cases or in controls was less than 5 were excluded. The total number of tested SNP pairs was 1.2x106. To take into account the dependence between tests due to the Linkage Disequilibrium (LD) between SNPs within the same gene, we calculated the effective number of independent SNP pairs. We first calculated the LD matrix of each gene, i.e. the correlation between the SNPs of that gene. Then, as proposed by Emily [33], the correlation matrix of a gene pair is set to the Kronecker product of the LD matrices of the two genes. It is then possible to estimate the number of independent tests, Keff, for a given gene pair, using the procedure of Moskvina and Schmidt [34]. Summing over all pairs of genes provides a total Keff equal to 4.6 x105, thus giving a significance threshold of 10-7 after Bonferroni correction for multiple testing. Pairwise interactions at the gene level within each group of biological categories were also tested using the AGGrEGATOr method [33]. Briefly, this method aggregates the SNP-SNP interaction Pvalues to estimate a gene-based interaction P-value, based on the minimum-P-value, accounting for This article is protected by copyright. All rights reserved. Accepted Article the number of SNP pairs and dependency between these SNP pairs as proposed by Connely and Boenhnke [35] . As 14,682 pairs of genes were tested, the Bonferroni significance threshold was equal to 3.4x10-6. Replication in SLSJ and MRCA The SNP pairs exhibiting P SNP-SNP ≤ 10-5 in EGEA were tested for replication in SLSJ and MRCA. For the MRCA imputed SNPs, logistic regression was carried out with allele dosage to take into account the uncertainty of imputation. The threshold of 10-5 was chosen to obtain both a strong indication of interaction and a reasonable number of SNP pairs (here 7, see results) to be tested in the replication datasets. After Bonferroni correction for this number of SNP pairs), the threshold for significant replication was thus equal to 7x10-3. The gene-based interaction test was also applied to each replication sample for all pairs of genes with PG-G ≤10-4 in EGEA. Three gene pairs were retained, leading to a Bonferroni threshold for significance of 0.017. We then conducted a fixed-effects meta-analysis of SNP-SNP interactions in EGEA, SLSJ and MRCA for the SNP pairs detected in EGEA at P SNP-SNP ≤ 10-5 and for the SNP pairs belonging to the gene pairs detected in EGEA at P G-G ≤ 10-4. Gene x gene analyses were performed using AGGrEGATOr with P values of meta-analysis of SNPxSNP interaction test (Pmeta-SNP-SNP) in EGEA, SLSJ and MRCA. The thresholds to declare significance were identical to those indicated above for analyses in the discovery sample (10-7 for SNP-SNP interactions and 3.4x10-6 for G-G interactions respectively). Finally, to investigate whether the evidence for interaction was related to the presence of asthma in these asthma-ascertained samples, we performed a sensitivity analysis according to asthma status. In this sake, the SNP-SNP interaction tests were performed in subjects with and without asthma separately in the pooled EGEA, SLSJ and MRCA samples to have a sufficient sample size in each group of subjects. Analyses were conducted in the pooled samples while adjusting for study and for principal components within study. Homogeneity of interaction between the two groups was tested. Gene-based interaction analyses were also repeated in subjects with and without asthma. This article is protected by copyright. All rights reserved. Accepted Article Results Descriptive statistics Table I shows the number of genotyped subjects and their phenotypic distribution according to eczema status in the EGEA, SLSJ and MRCA samples. The total number of subjects was 1,493 in EGEA, 1,153 in SLSJ and 254 in MRCA. The proportion of subjects with eczema was similar in the three samples, ranging between 27 and 31%. The proportion of males was higher in subjects with eczema than without eczema in MRCA, while it was smaller in SLSJ and similar in EGEA. The reason may be that in MRCA, all subjects are children and most subjects with eczema are also asthmatic (89%) and it is known that the risk of asthma in children is higher among boys than among girls. The mean age was lower in subjects with eczema than in those without eczema in EGEA and SLSJ while it was similar in these two groups in MRCA. The variation of age across studies, with younger subjects in MRCA and older subjects in SLSJ, is at least partly due to the ascertainment of the samples, only children in MRCA and the inclusion of fifty percent of three-generation pedigrees in SLSJ. As expected, the proportion of subjects with asthma and atopy (defined by a positive skin prick test response to at least one aeroallergen) was much higher among individuals having eczema than among unaffected subjects. Note that in MRCA, whichever the eczema affection status, the proportion of asthmatics was higher than in the other two samples, while the proportion of atopics was smaller. Analysis of offspring only in MRCA and of subjects belonging to parental and offspring generation in the other two samples might partly explain the higher proportion of asthmatics in MRCA families ascertained through asthmatic offspring. The smaller proportion of atopics in MRCA may be due to the smaller number of aeroallergens tested in MRCA (five allergens) than in the other two samples (ten or more allergens) (see table I). SNP-SNP and G-G interaction analyses EGEA discovery dataset In the SNP-SNP interaction analysis, seven SNP pairs, three within genes of the "collagen" group and four within genes of the "epidermis and skin" group, had PSNP-SNP ≤ 10-5 in EGEA (Table II and more details, supplementary Table II). No pair reached the significance threshold of 10-7. In the gene-based This article is protected by copyright. All rights reserved. Accepted Article interaction analysis, three gene pairs (COL5A3 and MMP9, COL1A2 and COL6A1, CTSB and COL5A3) had P G-G ≤ 10-4 (Table III). None reached the significance threshold of 3.4 x10-6. As expected, these three pairs of genes included the SNP pairs with the smallest P SNP-SNP value. SLSJ and MRCA replication datasets Only one SNP pair (rs2287807 in COL5A3 and rs17576 in MMP9) among the pairs detected in EGEA was significantly replicated in SLSJ (PSNP-SNP =1.24x10-3, under the replication threshold of 7x10-3) but not in MRCA (PSNP-SNP =0.13). However, meta-analysis of the EGEA, SLSJ and MRCA results for this SNP pair showed strong evidence for interaction (Pmeta-SNP-SNP =1.11x10-8 , below the significance threshold of 10-7) and strong improvement with respect to the interaction signal obtained in the EGEA discovery dataset (PSNP-SNP =6.70x10-6) (Table II). This SNP pair was also the only one showing the same direction of interaction term in the three datasets. This SNP pair showed a pattern of interaction in which OR associated CC (or TT) genotype at rs2287807 (COL5A3) showed an opposite effect depending on the genotype AA (or GG) at rs17576 (MMP9) (Figure 1 and supplementary Table III). More precisely, for subjects homozygous for allele A at rs17576, T allele at rs2287807 will increase the risk of eczema while C allele will be protective. For the other subjects, heterozygous or homozygous for allele G, the effect of the alleles at rs2287807 will be inversed: the T allele will be protective and the C allele at risk. In the gene-based interaction analysis, only one pair of genes (COL5A3 and MMP9) among the gene pairs detected in EGEA was significantly replicated in SLSJ (PG-G =8.79x10-3, below the replication threshold of 1.5x10-2) but not in MRCA (PG-G =0.52). Moreover, this pair was also highly significant by the meta-analysis of the three datasets (Pmeta-G-G = 4.04x10-8, well under the significance threshold of 3.4x10-6). The other two gene pairs did not reach significance: Pmeta-G-G =7.18x10-2 for COL1A2 and COL6A1 pair, and Pmeta-G-G =9.86x10-3 for CTSB and COL5A3 pair (Table III). Finally, after stratification of the pooled sample according to asthma status, the SNP pair (rs2287807, rs17576) showed similar evidence for interaction in 1423 asthmatic (P SNP-SNP =7.54x10-5) and in 1477 non-asthmatic subjects (PSNP-SNP =1.55x10-5). There was no heterogeneity between the two groups This article is protected by copyright. All rights reserved. Accepted Article (Phomogeneity=0.16), clearly indicating this interaction is not related to the presence of asthma. Likewise, in the gene-based interaction analyses, the COL5A3-MMP9 gene pair showed similar results in asthmatic (P G-G =3.09x10-4) and non-asthmatic subjects (P G-G =6.28x10-5). Discussion This study identified the interactive effect of genetic variants in the COL5A3 and MMP9 genes on eczema risk in families ascertained through asthmatic subjects. Evidence for this interaction rests on the results obtained in the EGEA discovery sample and on the highly significant results obtained when the EGEA dataset was combined with the two SLSJ and MRCA replication datasets. This result is further strengthened by the fact that interaction was significant in both SNP-SNP and gene-based interaction analyses. Moreover, the interaction between these two genes in eczema susceptibility appears independently whichever the subject asthma status. The EGEA results were significantly replicated in the French Canadian SLSJ dataset both at the SNP and gene level, but not in the MRCA sample. This might be partly explained by the shared French genetic background of EGEA and SLSJ families, but also by other phenotypic characteristics, shared by EGEA and SLSJ, but different in MRCA. In particular, age at onset is known to be a factor of genetic heterogeneity. Here, mean age was similar in EGA and SLSJ but much younger in MRCA, indirectly suggesting earlier onset of eczema and asthma in MRCA. The proportion of asthmatic and atopic subjects was similar in EGEA and SLSJ but different in MRCA. Other phenotypic differences not measured here such as severity of eczema or asthma could also have influenced the results. Another explanation might be the reduced power in MRCA because of smaller sample size. Nevertheless, meta-analysis of EGEA, SLSJ and MRCA led to improvement of the SNP-SNP interaction signal (P =1.11x10-8) compared to the one obtained in the meta-analysis of EGEA and SLSJ (P =2.55x10-8). Our study illustrates the advantage of SNP-SNP interaction analysis since the significant pair of SNPs was not detectable by single SNP association test (P = 0.81 for rs2287807 and 0.03 for rs17576 in EGEA). Similarly, COL5A3 and MMP9 would not have been detected by gene-based association analysis (P=0.12 for COL5A3 and 0.03 for MMP9 in EGEA). This article is protected by copyright. All rights reserved. Accepted Article Our findings also show the interest of a two-step strategy with selection of genes based on biological knowledge, prior to performing interaction analysis. Indeed, if the first step had been based on singleSNP association analysis, neither the genes nor the SNP pairs showing significant interaction would have been retained, because of the weak association signals observed in the discovery sample. Likewise, the SNP pair would not have been detected by a one-step interaction strategy as the correction for multiple testing would have been too stringent (threshold for significance estimated to 10-13 for a genome-wide interaction study of a 500K chip [36]). We did not consider interaction across different GO classes because the hypothesis of a statistical interaction between genes belonging to the same biological process is more plausible. Similarly, we did not extend our search for interaction to other groups of genes, such as those involved in allergy and immunity, which have already been extensively investigated in atopy and related phenotypes such as asthma and eczema. Tested groups of genes included genes involved in regulatory T-cell function [37], toll-like receptor (TLR)-related pathway [38] and TH2 differentiation pathway (IL13, IL4, IL33, IL1RL1 and STAT6) [15, 39, 40]. Here, we focused on genes specifically related to eczema, a field poorly investigated until now, in order to reduce the multiple testing burden and to gain power. This strategy allowed detecting a significant interaction between the COL5A3 and MMP9 genes. Note that the interaction detected here between two SNPs each with a very small or no marginal effect, showed an inversion of the effect of genotypes at one variant depending on genotypes at the other variant. Such situations correspond to flip-flop models. The biological plausibility of flip-flop models may be discussed, however several human diseases, including Alzheimer’s disease [41], breast cancer [42], melanoma [43] have been reported to be influenced by interaction of loci with no significant main effect at either locus and showing a flip-flop type of interaction. The present study is the first one to investigate G-G interaction in the genetic susceptibility to eczema at a large-scale level. Neither MMP9 nor COL5A3 were detected in the three previous G-G interaction studies [14-18]. We also verified in the GWAS-Catalog of Published Genome-Wide This article is protected by copyright. All rights reserved. Accepted Article Association Studies (http://www.ebi.ac.uk/gwas/), that no significant association of SNPs in COL5A3 nor in MMP9 had been reported with eczema and asthma and any other allergy related phenotypes, such IgE sensitization, IgE levels. However, MMP9 has been shown to be associated with either atopic [44] or non-atopic asthma [45] in candidate gene studies. To our knowledge, no association study of this gene with eczema has been conducted. However, it is of note that MMP9 was shown to be overexpressed in the skin of patients with eczema[46] and differently expressed in lung tissue between pre and post corticosteroids[47]. It was also found that allergen exposure induces the release of MMP9 into airway of allergic subjects [48]. SNP rs2287807 is located in intron 27 of the COL5A3 gene. Examination of functional annotations using the HaploReg tool (http://compbio.mit.edu/HaploReg) shows that this SNP maps to promoter and enhancer histone marks in skin (fibroblast, melanocyte and keratinocyte cells) and is also DNAse hypersensitive in skin cells. SNP rs17576 is located in exon 6 of the MMP9 gene and corresponds to a missense substitution (Gln>Arg) at position 279 of the protein. This SNP also maps to promoter and enhancer histone marks in skin (fibroblast and keratinocyte cells). The COL5A3 gene, located in 19p13.2 region, encodes an alpha chain for one of the low abundance fibrillar collagens. Type V collagen is found in tissues containing type I collagen, the collagen the most present in skin, and appears to regulate the assembly of heterotypic fibers composed of both type I and type V collagen. This gene belongs to both "collagen" and "epidermis and skin" groups defined here, and more precisely to the 3 GO biological process sub-categories, "collagen catabolic process", "collagen fibril organization" and "skin development". Interestingly, the gene MMP9 also belongs to the biological process sub-category "collagen catabolic process". MMP9, located on chromosome 20q13.12, is involved in the breakdown of extracellular matrix in normal physiological processes, such as tissue remodeling and collagen deposition. It also plays a significant role in inflammation by facilitating cellular traffic, including neutrophils and eosinophils. MMP9 also codes for an enzyme which degrades type IV and V collagens. Studies showed experimental evidence of interaction (or binding) between MMP9 and type IV and V collagens, e.g. COL4A1, COL4A2 and COL5A1, while COL5A3 and COL5A1 [49] have also been This article is protected by copyright. All rights reserved. Accepted Article experimentally shown to interact (to be in the same protein complex). Moreover, MMP9 and COL5A3 were both showed experimentally to interact with the same protein COL1A2 [49, 50], which also belongs to the category of collagen catabolic process. All these observations support our finding of a statistical interaction effect between COL5A3 and MMP9, which may be accounted for by a degradation of COL5A3 by MMP9 influencing eczema susceptibility. In conclusion, the present study detected two new genes having an interactive effect in eczema susceptibility. It highlights that G-G interaction analyses, beyond single SNP association analysis, can greatly contribute to the identification of new genes. Further confirmation of the interaction of MMP9 and COL5A3 as well as functional studies are needed to provide a full understanding of the role of these genes in eczema. Conflict of interest: none Sources of support: This work was supported by the GWIS-AM grant (ANR-11-BSV1-027, ANR-USPC-2013EDAGWAS). The Canada Research Chair in Environment and Genetics of Respiratory Diseases and Allergy held by C. Laprise since 2005 allows the maintenance of the French Canadian study. Acknowledgments We thank the EGEA cooperative group: This article is protected by copyright. All rights reserved. Accepted Article EGEA cooperative group Coordination: V Siroux (epidemiology, PI since 2013); F Demenais (genetics); I Pin (clinical aspects); R Nadif (biology); F Kauffmann (PI 1992-2012). Respiratory epidemiology: Inserm U 700, Paris: M Korobaeff (Egea1), F Neukirch (Egea1); Inserm U 707, Paris: I Annesi-Maesano (Egea1-2) ; Inserm CESP/U 1018, Villejuif: F Kauffmann, N Le Moual, R Nadif, MP Oryszczyn (Egea1-2), R Varraso ; Inserm U 823, Grenoble: V Siroux. Genetics: Inserm U 393, Paris: J Feingold ; Inserm U 946, Paris: E Bouzigon, F Demenais, MH Dizier ; CNG, Evry: I Gut (now CNAG, Barcelona, Spain), M Lathrop (now Univ McGill, Montreal, Canada). Clinical centers: Grenoble: I Pin, C Pison; Lyon: D Ecochard (Egea1), F Gormand, Y Pacheco ; Marseille: D Charpin (Egea1), D Vervloet (Egea1-2) ; Montpellier: J Bousquet ; Paris Cochin: A Lockhart (Egea1), R Matran (now in Lille) ; Paris Necker: E Paty (Egea1-2), P Scheinmann (Egea1-2) ; Paris-Trousseau: A Grimfeld (Egea1-2), J Just. Data and quality management: Inserm ex-U155 (Egea1): J Hochez ; Inserm CESP/U 1018, Villejuif: N Le Moual ; Inserm ex-U780: C Ravault (Egea1-2) ; Inserm ex-U794: N Chateigner (Egea1-2) ; Grenoble: J Quentin-Ferran (Egea1-2). SLSJ: The Canada Research Chair in Environment and Genetics of Respiratory Diseases and Allergy held by C Laprise and the funding supports from Canadian Institutes of Health Research (CIHR) enabled the maintenance and continuation of the SLSJ asthma study. C. Laprise is the director of the Asthma Strategic Group of the Respiratory Health Network of the Fonds de la recherche en santé du Québec (FRSQ) and director of the SLSJ cohort. She is also member of Allergen network. This article is protected by copyright. All rights reserved. Accepted Article References 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. Dold S, Wjst M, von Mutius E, Reitmeir P, Stiepel E, Genetic risk for asthma, allergic rhinitis, and atopic dermatitis. Archives of disease in childhood 1992;67: 1018-22. Wadonda-Kabondo N, Sterne JA, Golding J, Kennedy CT, Archer CB, Dunnill MG, Association of parental eczema, hayfever, and asthma with atopic dermatitis in infancy: birth cohort study. 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Study Phenotypes No. (%) Male sex (%) Mean age (SD) Asthma (%) Atopy (%) EGEA All 1493 (100) 768 (51) 30.3 (0.44) 690 (46) 788 (53) ECZ- 1082 (72) 553 (51) 32.9 (0.51) 419 (39) 510 (47) ECZ+ 411 (28) 215 (52) 23.3 (0.75) 271 (66) 278 (68) All 1153 (100) 527 (46) 38.2 (0.64) 557 (48) 639 (56) ECZ- 844 (73) 421 (50) 39.9 (0.76) 366 (43) 424 (51) ECZ+ 309 (27) 106 (34) 33.4 (1.17) 191 (62) 215 (70) All 254 (100) 144 (57) 11.2 (0.30) 176 (69) 104 (41) ECZ- 174 (69) 96 (55) 11.0 (0.37) 105 (60) 55 (32) ECZ+ 80 (31) 48 (60) 11.5 (0.49) 71 (89) 49 (61) SLSJ MRCA ECZ+, with eczema; ECZ-, without eczema; SD, standard deviation; Atopy: positive response of skin prick test to at least one allergen (house dust mite, cockroach, cat, timothy grass, olive, birch, ragweed, pellitory, Alternaria, Aspergillus and Cladosporium were tested in EGEA; house dust mite, dust, cat, herbs, trees, Alternaria, Aspergillus, Cladosporium, Hormodendrum and Penicillium in SLSJ; dust mite, cat, timothy grass, Aspergillus and Cladosporium in MRCA). This article is protected by copyright. All rights reserved. Accepted Article Table I: Phenotypic characteristics of genotyped individuals in the three asthma-ascertained samples Collagen gene EGEA SLSJ MRCA All samples* PSNP-SNP PSNP-SNP PSNP-SNP Pmeta-SNP-SNP SNP1 gene SNP2 rs1293288 CTSB rs3745596 COL5A3 3.56x10-6 8.65x10-1 5.65x10-1 1.20x10-3 rs2521205 COL1A2 rs1053312 COL6A1 6.70x10-6 4.36x10-1 8.61x10-3 2.88x10-2 rs2287807 COL5A3 rs17576 MMP9 6.70x10-6 1.24x10-3 1.33x10-1 1.11x10-8 SNP2 gene PSNP-SNP Epidermis or Skin SNP1 gene rs12854617 ATP8A2 rs6106524 TGM3 PSNP-SNP PSNP-SNP 6.35x10-6 4.18x10-1 6.60x10-1 Pmeta-SNP-SNP 4.69x10-4 ITGA2 6.73x10-6 1.79x10-1 3.37x10-1 5.74x10-3 GAB1 rs6026561 GNAS 8.60x10-6 3.75x10-1 8.73x10-1 1.37x10-4 GAB1 rs6026561 GNAS 9.25x10-6 3.66x10-1 8.78x10-1 1.34x10-4 rs7586353 ABCA12 rs246406 rs6537155 rs3805251 * : meta-analysis of interactive effects using fixed effect model This article is protected by copyright. All rights reserved. Accepted Article Table II : SNPxSNP interaction test results in EGEA, SLSJ and MRCA for SNP pairs selected in EGEA (PSNP-SNP≤10-5) EGEA SLSJ MRCA Meta-analysis PG-G * PG-G * Pmeta-G-G ** gene1 gene2 No of PG-G * SNP pairs COL1A2 COL6A1 7 2.14x10-5 2.87x10-1 2.90x10-2 7.18x10-2 COL5A3 MMP9 8 4.49x10-5 8.79x10-3 5.23x10-1 4.04x10-8 COL5A3 CTSB 47 1.48x10-4 2.61x10-2 7.11x10-1 9.86x10-3 * : gene x gene analysis using AGGrEGATOr **: gene x gene meta-analysis using AGGrEGATOr with Pvalues of meta-analysis of SNPxSNP interaction test in EGEA, SLSJ and MRCA Table III: Gene x Gene interaction test results in EGEA, SLSJ and MRCA for SNP pairs selected in EGEA (PG-G ≤ 10-4) Figure legend Figure 1. Two-locus Odds-ratio (ORs) and 95% confidence intervals for eczema estimated for the genotypic combinations of the two SNPs, rs2287807 (COL5A3) and rs17576 (MMP9). The ORs were computing using the estimates of SNP main effect and interaction obtained from the pooled analysis of EGEA, SLSJ and MRCA. This article is protected by copyright. All rights reserved. Accepted Article Figure 1 : Two locus Odds-ratio (ORs) and 95% confidence intervals for eczema for the genotypic combinations of COL5A3 and MMP9 SNPS. This article is protected by copyright. All rights reserved.