Salome Scholtens1, Dirkje S Postma2, Miriam F Moffatt3, Sviatlana Panasevich4, Raquel Granell5, A John Henderson5, Erik Melén6, Fredrik Nyberg7, Göran Pershagen4, Deborah Jarvis8, Adaikalavan Ramasamy8, Matthias Wjst9, Cecilie Svanes10, Emmanuelle Bouzigon11, Florence Demenais11, Francine Kauffmann12, Valérie Siroux13, Erika von Mutius14, Markus Johannes Ege14, Charlotte Braun-Fahrländer15, Jon Genuneit16, Bert Brunekreef17, Henriette A Smit18, Alet H Wijga19, Marjan Kerkhof1, Ivan Curjuric15, Medea Imboden15, Gian A Thun15, Nicole Probst-Hensch15, Maxim B Freidin20, Elena Iu Bragina20, I A Deev21, V P Puzyrev22, Denise Daley23, Julie Park23, Allan Becker24, Moira Chan-Yeung25, Anita L Kozyrskyj26, Peter Pare23, Ingo Marenholz27, Susanne Lau28, Thomas Keil29, Young-Ae Lee27, Michael Kabesch30, Cisca Wijmenga31, Lude Franke31, Ilja M Nolte32, Judith Vonk32, Ashish Kumar33, Martin Farrall34, William O C M Cookson3, David P Strachan35, Gerard H Koppelman36, H Marike Boezen37. 1. Department of Epidemiology, University of Groningen, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands; Groningen Research Institute for Asthma and COPD (GRIAC), University of Groningen, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands. 2. Groningen Research Institute for Asthma and COPD (GRIAC), University of Groningen, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands; Department of Pulmonology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands. 3. Division of Respiratory Sciences, Imperial College, London, United Kingdom. 4. Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden. 5. ALSPAC, School of Social and Community Medicine, University of Bristol, Bristol, United Kingdom. 6. Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden; Sachs' Children's Hospital, Stockholm, Sweden. 7. Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden; Global Epidemiology, AstraZeneca R&D, Mölndal, Sweden. 8. Respiratory Epidemiology and Public Health, Imperial College, London, United Kingdom. 9. Institute of Medical Statistics and Epidemiology (IMSE), Klinikum Rechts der Isar, Technical University, Munich, Germany; Comprehensive Pneumology Center (CPC), Institute of Lung Biology and Disease (iLBD), Helmholtz Center Munich, Neuherberg, Germany. 10. Bergen Respiratory Research Group, Institute of Medicine, University of Bergen and Department of Occupational Medicine, Haukeland University Hospital Bergen, Bergen, Norway. 11. Inserm U946, Genetic Variation and Human Diseases Unit, Paris, France; Université Paris Diderot, Sorbonne Paris Cité, Institut Universitaire d'Hématologie, Paris, France; Fondation Jean Dausset, Centre d'Etude du Polymorphisme Humain (CEPH), Paris, France. 12. Inserm U1018, CESP Centre for research in Epidemiology and Population Health, Respiratory and environmental epidemiology Team, Villejuif, France; Université Paris Sud, UMRS 1018, Villejuif, France. 13. Inserm U823, Centre de Recherche Albert Bonniot, La Tronche, France; Université Joseph Fourier, Grenoble, France. 14. LMU Munich, University Children's Hospital, Munich, Germany. 15. Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Basel, Switzerland; University of Basel, Basel, Switzerland. 16. Institute of Epidemiology and Medical Biometry, Ulm University, Ulm, Germany. 17. Institute for Risk Assessment Sciences, Utrecht University, Utrecht, The Netherlands; Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands. 18. Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands; Centre for Prevention and Health Services Research, National Institute of Public Health and the Environment, Bilthoven, The Netherlands. 19. Centre for Prevention and Health Services Research, National Institute of Public Health and the Environment, Bilthoven, The Netherlands. 20. Research Institute of Medical Genetics of the Siberian Branch of Russian Academy of Medical Sciences, Tomsk, Russia. 21. Siberian State Medical University, Tomsk, Russia. 22. Research Institute of Medical Genetics of the Siberian Branch of Russian Academy of Medical Sciences, Tomsk, Russia; Siberian State Medical University, Tomsk, Russia. 23. James Hogg iCAPTURE Center, University of British Columbia, Vancouver, British Columbia, Canada. 24. Pediatrics and Child Health, University of Manitoba, Winnipeg, Manitoba, Canada. 25. Occupational and Environmental Lung Disease Unit, University of British Columbia, Vancouver, British Columbia, Canada. 26. Department of Pediatrics, Faculty of Medicine and Dentistry, University of Alberta and School of Public Health, University of Alberta, Edmonton, Canada. 27. Pediatric Pneumology, Experimental and Clinical Research Center, Charité-Medical University, Berlin, Germany; Max-Delbrück-Center (MDC) for Molecular Medicine, Berlin, Germany. 28. Pediatric Pneumology and Immunology, Charité-Medical University, Berlin, Germany. 29. Institute of Social Medicine, Epidemiology and Health Economics, Charité-Medical University, Berlin, Germany. 30. Department of Paediatric Pneumology and Allergy, University Children's Hospital Regensburg (KUNO), Regensburg, Germany. 31. Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands. 32. Department of Epidemiology, University of Groningen, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands. 33. Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Basel, Switzerland; University of Basel, Basel, Switzerland; Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom. 34. Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom. 35. Division of Population Health Sciences and Education, St George's, University of London, London, United Kingdom. 36. Groningen Research Institute for Asthma and COPD (GRIAC), University of Groningen, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands; Department of Pediatric Pulmonology and Pediatric Allergology, Beatrix Children's Hospital, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands. 37. Department of Epidemiology, University of Groningen, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands; Groningen Research Institute for Asthma and COPD (GRIAC), University of Groningen, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands. Electronic address: h.m.boezen@umcg.nl.
To the Editor:Complex diseases, including asthma, have genetic and environmental origins. Genome-wide association studies have identified multiple genes for the development of asthma, yet they only explain a limited proportion of asthma heritability. Interactions between genetic predisposition and exposure to passive smoking might explain in part the hidden heritability of childhood asthma. However, to date, this approach has not been reported for the discovery of interactions between genes and tobacco smoke exposure.We performed a genome-wide interaction study (GWIS) on childhood asthma to identify genes that interact with 2 well-known environmental risk factors for childhood-onset asthma: in utero and childhood tobacco smoke exposure. We meta-analyzed interaction results from 9 studies participating in the GABRIEL consortium including more than 6,000 subjects of European descent. We replicated our findings in 4 independent studies including more than 13,000 subjects. Childhood-onset asthma was defined as asthma diagnosed by a doctor before the age of 16 years, which is consistent with the definition in the GABRIEL consortium.
In utero tobacco smoke exposure was defined as “exposure to maternal tobacco smoking at any time during pregnancy.” Childhood tobacco smoke exposure was defined as “exposure to passive tobacco smoking at any time from birth until 16 years of age.” Details on the number of subjects, the design of the individual studies, and outcome and exposure definitions are provided in Tables E1 to E4 in this article's Online Repository at www.jacionline.org.The effects of in utero tobacco smoke exposure and childhood tobacco smoke exposure were analyzed separately. All individual studies were analyzed by using a logistic regression model containing the genetic effect, the effect of tobacco smoke exposure, and an interaction term indicating the interaction between the genetic effect and tobacco smoke exposure. Further methodological considerations on GWISs and details on the statistical analyses are described in this article's Online Repository at www.jacionline.org.For in utero tobacco smoke exposure, the discovery genome-wide meta-analysis consisted of 2,654 cases and 3,073 control subjects derived from 7 studies (see Table E1). Overall, in utero tobacco smoke exposure increased the risk of childhood-onset asthma (see Fig E1 in this article's Online Repository at www.jacionline.org). A total of 536,705 single nucleotide polymorphisms (SNPs) were included in the interaction meta-analysis. Fig E2 in this article's Online Repository at www.jacionline.org shows the Manhattan plot. We identified 27 SNPs in the discovery sample with a P value of less than 10−4 based on the fixed effect model (Table I and see Table E5 in this article's Online Repository at www.jacionline.org). Findings did not reach genome-wide significance but were consistent over all studies included, and no significant heterogeneity across studies was present (P value Q-statistic < .05). Four of these SNPs on chromosome 10 were in high linkage disequilibrium with each other in the discovery meta-analysis (r = 0.82-0.96). The most prominent marker was located on chromosome 18 near EPB41L3 (Forest plot, see Fig E3 in this article's Online Repository at www.jacionline.org). Table E6 in this article's Online Repository at www.jacionline.org shows the associations in exposed and nonexposed subjects. EPB41L3 belongs to the protein 4.1 family of membrane-associated proteins, is involved in cell-cell junctions, and might play a role in apoptosis. The literature shows that in utero tobacco smoke exposure affects the expression of genes involved in biological processes, such as cell proliferation and apoptosis, and influences lung development of the child in general. Our data suggest that this effect of in utero smoke exposure might potentially occur through mechanisms involving EPB41L3 (see the additional text in this article's Online Repository).
Table I
Results of the GWIS of in utero tobacco smoke exposure and childhood-onset asthma
Ch
SNP
Closest gene∗
Type
Discovery
Replication
N†
ORint‡
Pf
N†
ORint‡
Pf
1
rs1674877
—
Intergenic
2654
0.51
2.19 × 10−5
201
1.06
.89
2
rs4670230
FAM82A1
Intronic
2654
1.94
2.10 × 10−5
201
0.78
.51
2
rs12624082
GALNT13
Intronic
2654
1.78
3.22 × 10−5
697
1.00
.98
2
rs11684139
GALNT13
Intronic
2654
1.77
7.57 × 10−5
697
0.85
.35
2
rs729454
—
Intergenic
2654
1.67
9.52 × 10−5
697
1.13
.45
3
rs3856848
IL5RA
Intronic
2654
1.96
5.32 × 10−6
201
0.59
.19
4
rs7682603
—
Intergenic
2247
0.54
1.19 × 10−5
562
1.20
.29
5
rs1990977
RNU6ATAC2P
Intergenic
2654
2.12
7.79 × 10−5
697
0.88
.60
5
rs4700239
—
Intergenic
2654
2.15
6.39 × 10−5
562
0.78
.34
6
rs6456433
—
Intergenic
2654
1.99
7.99 × 10−5
562
0.71
.15
6
rs14398
WDR46
Nonsynonymous
2654
0.45
5.44 × 10−5
562
1.77
.01
8
rs360968
—
Intergenic
2654
0.54
5.05 × 10−5
697
0.93
.72
9
rs943856
—
Intergenic
2654
0.59
4.94 × 10−5
697
0.70
.04
10
rs11006296
—
Intergenic
2654
2.01
3.70 × 10−5
562
0.84
.47
10
rs1407696
PDCD4
Intronic
2654
0.57
2.36 × 10−5
66
0.58
.34
10
rs7079511
SHOC2
Intronic
2654
0.58
3.24 × 10−5
697
0.98
.91
10
rs521674
ADRA2A
Upstream
2654
0.57
5.35 × 10−5
562
1.16
.45
10
rs602618
ADRA2A
Downstream
2654
0.57
5.63 × 10−5
562
1.15
.45
11
rs1123991
OR51E2
Synonymous
2654
0.50
6.51 × 10−5
697
0.68
.11
11
rs3898589
CNTN5
Intronic
2654
1.83
6.11 × 10−5
562
1.17
.40
11
rs10892848
CNTN5
Intronic
2654
1.82
5.72 × 10−5
697
1.07
.71
12
rs706793
ACCN2
Intronic
2654
1.66
3.62 × 10−5
697
0.75
.07
13
rs7321384
C13orf35
Intronic
2654
0.58
9.82 × 10−5
697
0.92
.63
16
rs8051325
ANKS4B
Intronic
2654
0.47
8.37 × 10−5
562
0.80
.37
18
rs8094633
EPB41L3
Intergenic
2654
2.13
4.29 × 10−5
201
2.87
.03
21
rs858003
KCNJ6
Intronic
2654
1.81
8.50 × 10−5
697
1.00
1.00
22
rs9613256
CTA-211A9.5
Within noncoding gene
2654
0.59
5.44 × 10−5
562
1.11
.60
Ch, Chromosome; OR, odds ratio interaction; Pf, P value, fixed effect.
Closest gene within range of 500 kb of the position of the SNP.
Number of studies and cases included in meta-analysis.
Additive genetic model.
For childhood tobacco smoke exposure, the discovery genome-wide meta-analysis consisted of 3,048 cases and 3,509 control subjects derived from 9 studies (see Table E1). Overall, childhood tobacco smoke exposure increased the risk of childhood-onset asthma (see Fig E1). A total of 538,233 SNPs were included in the interaction meta-analysis. Fig E4 in this article's Online Repository at www.jacionline.org shows the Manhattan plot. We identified 35 SNPs in the discovery sample with a P value of less than 10−4 based on the fixed effect model. Four of these SNPs were excluded because they showed heterogeneity, and the P value of the random effect was greater than 10−4. Findings did not reach genome-wide significance. Table II and Table E7 (see this article's Online Repository at www.jacionline.org) the results for the top SNPs. Seven SNPs on chromosome 5 (except rs2312164) were in high linkage disequilibrium with each other in the discovery studies (r = 0.83-1.00).
Table II
Results of the GWIS on childhood tobacco smoke exposure and childhood-onset asthma
Ch
SNP
Closest gene∗
Type
Discovery
Replication
N†
ORint‡
Pf
N†
ORint‡
Pf
1
rs2026604
S100A7L2
Downstream
3048
1.44
7.49 × 10−5
1003
0.83
.17
2
rs10184453
—
Intergenic
3048
1.53
8.85 × 10−5
1003
1.18
.30
2
rs895565
—
Intergenic
3048
1.53
7.26 × 10−5
1003
1.18
.30
2
rs11126185
—
Intergenic
3048
0.67
6.81 × 10−6
868
1.22
.16
3
rs4234677
CTD-2230D16.1
Within noncoding gene
3048
0.65
6.57 × 10−5
261
1.58
.22
3
rs264096
MAGI1
Intronic
3048
0.62
6.93 × 10−6
396
0.89
.63
3
rs17239426
KCNAB1
Intronic
3048
0.58
7.28 × 10−5
1003
1.18
.39
4
rs1425551
IRF2
Intronic
3048
1.40
6.92 × 10−5
1003
0.98
.86
5
rs162036
MTRR
Nonsynonymous
3048
0.60
8.26 × 10−5
1003
1.14
.45
5
rs7719963
—
Intergenic
3048
0.56
3.06 × 10−5
868
0.89
.59
5
rs7447231
—
Intergenic
3048
1.55
8.36 × 10−6
868
0.85
.31
5
rs10155635
—
Intergenic
3048
1.56
7.55 × 10−6
261
0.62
.11
5
rs10038850
—
Intergenic
3048
1.53
1.51 × 10−5
261
0.41
.04
5
rs10479335
—
Intergenic
3048
1.52
5.98 × 10−5
868
0.85
.29
5
rs2312164
—
Intergenic
3048
1.42
7.18 × 10−5
1003
0.82
.14
5
rs13357477
—
Intergenic
3048
1.58
3.59 × 10−6
868
0.83
.24
5
rs12719549
—
Intergenic
3048
1.57
4.61 × 10−6
868
0.82
.21
5
rs4607330
—
Intergenic
3048
1.59
2.70 × 10−6
868
0.85
.31
6
rs441463
LYRM4
Intronic
3048
1.41
4.91 × 10−5
1003
0.90
.42
6
rs1575472
PACRG
Intronic
3048
1.78
1.37 × 10−5
1003
1.51
.06
7
rs17544971
GRB10
Intronic
3048
1.70
8.12 × 10−5
868
1.26
.28
9
rs4977750
MTAP
Nonsense-mediated decay transcript
3048
0.61
1.91 × 10−5
1003
0.93
.66
13
rs4769148
—
Intergenic
2445
0.67
4.45 × 10−5
261
0.72
.21
13
rs12874184
—
Intergenic
3048
1.98
8.75 × 10−5
868
1.18
.50
13
rs16972472
—
Intergenic
3048
1.79
9.59 × 10−5
868
0.79
.32
14
rs10141836
OR11G2
Upstream
3048
0.70
8.89 × 10−5
1003
0.90
.41
15
rs2602923
C15orf41
Intronic
3048
1.61
4.52 × 10−5
1003
1.19
.33
16
rs13331814
ZP2
Intronic
3048
0.62
8.38 × 10−5
868
0.85
.34
19
rs11085080
PLIN5
Intronic
3048
0.51
6.30 × 10−5
1003
1.12
.63
20
rs6077755
PSMF1
Upstream
3048
1.52
6.65 × 10−6
1003
1.11
.47
X
rs6641609
PRKX
Intronic
1939
0.49
3.43 × 10−5
261
1.90
.15
Ch, Chromosome; OR, odds ratio interaction; Pf, P value, fixed effect.
Closest gene within range of 500 kb of the position of the SNP.
Number of cases and control subjects included in the meta-analysis.
Additive genetic model.
The most prominent marker was located on chromosome 6 in PACRG (parkin coregulated gene; Forest plot, see Fig E5 in this article's Online Repository at www.jacionline.org). Table E8 in this article's Online Repository at www.jacionline.org shows the associations in exposed and nonexposed subjects. PACRG is located next to and has an overlapping promoter region with parkin 2 (PARK2). The gene has been associated with leprosy and parkinsonian diseases and has an important role in motile cilia function and cilia morphogenesis.
PACRG is relatively highly expressed in the trachea and nasal mucosa. Ciliary dysfunction might impair mucus clearance from the airways and has been shown to affect asthma severity. Our data suggest that changes in ciliary function particularly affect the development of asthma in children exposed to passive tobacco smoke.The genes that have been reported previously to interact with tobacco smoke exposure with respect to asthma development (ie, TNF,
GSTP1, and ADAM33) were not among our most significant hits. This can be explained by the fact that the genetic variants in these candidate gene studies have a strong main effect on asthma development. Bouzigon et al showed a more pronounced effect of the 17q21 region on the development of early-onset asthma in children with early-life tobacco smoke exposure than in those without. The genetic effect of these markers in our GWIS showed a similar direction, but the interaction was not significant.This study on childhood asthma is the first hypothesis-free GWIS specifically aiming to identify SNPs that interact with tobacco smoke exposure in disease development. We found suggestive evidence for an interaction between rs8094633 on chromosome 18 near EPB41L3 and in utero tobacco smoke exposure and an interaction between rs1575472 on chromosome 6 in PACRG and childhood tobacco smoke exposure. The SNPs found have not been identified previously in general genome-wide association studies on childhood asthma. Interestingly, the SNPs interacting with in utero and childhood tobacco smoke exposure were different and were not involved in the same pathway (see Fig E6 in this article's Online Repository at www.jacionline.org). Interactions between these SNPs and tobacco smoke exposure in utero and in childhood might explain part of the missing heritability of asthma. Future research needs to confirm these findings and further unravel the biological pathways.
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