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  • Letter to the Editor
  • Open Access

An association analysis to identify genetic variants linked to asthma and rhino-conjunctivitis in a cohort of Sicilian children

  • 1Email authorView ORCID ID profile,
  • 2,
  • 3,
  • 3,
  • 3,
  • 3,
  • 3, 4,
  • 4,
  • 3, 5 and
  • 2, 3
Contributed equally
Italian Journal of Pediatrics201945:16

https://doi.org/10.1186/s13052-019-0603-4

  • Received: 22 October 2018
  • Accepted: 3 January 2019
  • Published:

Abstract

Asthma and rhino-conjunctivitis are common chronic diseases in childhood. In this cross-sectional study, we performed a gene association analysis with current asthma and rhino-conjunctivitis in a cohort of Sicilian children aged 10–15 years. Overall, our findings reveal the importance of different genetic variants at 4p14, 16p12.1, 17q12, 6p12.2 and 17q21.1, identifying possible candidate genes responsible for susceptibility to asthma and rhino-conjunctivitis.

Keywords

  • Asthma
  • Rhino-conjunctivitis
  • Sicilian children
  • Genetics
  • SNPs

To the Editor,

Asthma and rhino-conjunctivitis (RC) are common diseases worldwide that are frequently associated. Observed differences in prevalence of asthma and RC may be explained by genetic susceptibility, though environmental factors play a relevant role [1]. In order to increase genomic information on Sicilian children, this research has explored some genetic variants to discover possible association with asthma and RC.

A representative sample of 1050 children within the “Palermo Junior High School” (PJHS II) [2] study were investigated through questionnaires, spirometry, and skin prick test (SPT) to quantify the prevalence of asthma and RC, in association with allergic sensitization and respiratory function, and to evaluate the role of environmental and host risk factors for allergic respiratory diseases. The study was approved by the local Institutional Ethical Committee. All parents of the enrolled children signed a written informed consent.

Two different phenotypes were identified: Current Asthma (CA) defined as asthma ever plus at least a wheeze episode in the last 12 months, RC defined as sneezing, or runny, or blocked nose apart from common cold or flu in the last 12 months and nose problem accompanied by itching and/or watering eyes. The concomitant presence of CA and RC was merged into the CA group; children without CA and RC (nAnRC) were used as controls.

A total of 52 Single Nucleotide Polymorphisms (SNPs), involved in the innate immune system pathways were selected for genotyping by Matrix-Assisted Laser Desorption/Ionization (MALDI-TOF-MS). Out of the 52 initially selected SNPs, 7 were complete drop-outs and the remaining SNPs were successfully genotyped. The individuals were genotyped with the Illumina Bead-Chip (Illumina Inc., San Diego, CA, USA); the PLINK v1.07 software was used to perform standard quality control. SNPs were excluded if they had low call rates (proportion of genotyped called < 90%), were not in Hardy-Weinberg equilibrium (HWE, p < 0.001) on the nAnRC subjects, or had a low minor allele frequency (MAF < 1%). A total of 22 SNPs were used for further analyses.

Mean values were compared among children with CA, RC and nAnRC using the analysis of variance (ANOVA). Differences of categorical variables were evaluated using Chi-squared test. Associations between single SNPs and CA and RC were analysed by applying the case/control model of the SNPassoc R package, adjusting for sex, age, body mass index (BMI), SPT+ (at least one positive), exposure to current environmental tobacco smoke and traffic.

The demographic and lung function characteristics of the 1050 subjects are shown in Table 1. The study sample was composed by 523 (49.8%) Female and 527 (50.2%) Male, aged 12.07 ± 0.74 years on average. Subjects were categorized into CA (n = 61), RC (n = 184) and nAnRC (n = 805). Subjects with CA and RC more frequently had SPT+; subjects with CA were younger than nAnRC and RC subjects.
Table 1

Baseline demographic and clinical characteristics of study population

 

nAnRC

n = 805 (76.7%)

CA

n = 61 (5.8%)

RC

n = 184 (17.5%)

p-value

Female, n (%)

390 (48.45)

29 (47.54)

104 (56.52)

0.133

Age, mean (SD)

12.06 (0.74)

11.90 (0.62)

12.18 (0.74)

0.025

Height, mean (SD)

152.58 (7.87)

151.52 (7.81)

153.51 (7.41)

0.168

Weight, mean (SD)

49.05 (12.07)

50.11 (12.65)

50.28 (13.52)

0.417

BMI (kg/m2), mean (SD)

20.92 (4.28)

21.62 (4.15)

21.16 (4.78)

0.426

Skin Prick Test +, n (%) (#)

274 (34.16)

43 (70.49)

76 (41.30)

< 0.001

Environmental exposure current

 Tobacco smoke, n (%)

440 (54.93)

27 (44.26)

107 (58.15)

0.167

 Traffic in the zone of residence, n (%)

627 (77.99)

41 (67.21)

134 (72.83)

0.071

 Mould/dampness, n (%)

108 (13.47)

7 (11.67)

31 (17.03)

0.396

Spirometric values*

 FEV1%predicted, mean (SD)

100.39 (11.80)

96.96 (12.68)

98.82 (11.34)

0.035

 FEV1 Z, mean (SD)

0.04 (1.02)

−0.26 (1.09)

−0.09 (0.98)

0.037

 FVC %predicted, mean (SD)

97.10 (13.29)

97.60 (13.63)

95.51 (12.44)

0.304

 FVC Z, mean (SD)

−0.27 (1.14)

−0.22 (1.17)

− 0.40 (1.07)

0.314

 FEV1/FVC % predicted, mean (SD)

103.28 (7.57)

99.17 (8.11)

103.33 (7.89)

< 0.001

 FEV1/FVC Z, mean (SD)

0.60 (1.21)

−0.04 (1.20)

0.60 (1.25)

< 0.001

 FEF25–75%predicted, mean (SD)

102.10 (22.19)

89.17 (21.67)

101.76 (23.98)

< 0.001

 FEF25–75 Z, mean (SD)

0.05 (0.98)

−0.55 (1.02)

0.02 (1.05)

< 0.001

CA current asthma, RC rhino-conjunctivitis, nAnRC not asthma and not rhino-connjunctivitis

# according to Allergy diagnostic testing: an updated practice parameter (2008); allergic sensitization was defined as at least one positive skin prick test (SPT)

*according to ATS/ERS guidelines and normalized in accordance with the Global Lungs Initiative 2012

p-values come from Pearson’s test for categorical variable or ANOVA test for mean comparison; bold values indicate significance (p-values < 0.05)

Chromosome, gene, SNP name, quality control tag, alleles coding (Major/minor), minor allele frequency (MAF), test for Hardy-Weinberg Equilibrium, percentage of missing values (%) and genotyping distribution are reported in Table 2.
Table 2

Characteristics of the 52 Single Nucleotide Polymorphisms (SNPs)

Chr

Gene

SNP name

QC

Alleles (M/m)

MAF

HWE p values

Missing (%)

GENO (AA/Aa/aa)

1

SELE

rs5361

T/G

9.9

0.005

5.4

814/161/18

2

ORMDL1

rs5742940

G/A

1.8

0.028

1.0

1004/33/2

3

CACNA2D2

rs12488468

LCR

G/T

48.2

0.014

25.9

187/432/159

3

DOCK3

rs76699816

HWEd

G/A

11.6

< 0.001

8.7

779/138/42

4

TLR1

rs17616434

T/C

47.1

0.020

5.6

295/459/237

4

TLR1

rs2101521

HWEd/LCR

G/A

34.4

< 0.001

15.6

444/274/168

4

TLR1

rs4833095

HWEd

T/C

48.4

< 0.001

7.1

292/422/261

4

TLR1

rs5743595

HWEd

A/G

30.5

< 0.001

5.8

506/363/120

4

TLR10

rs10004195

HWEd/LCR

T/A

46.2

< 0.001

11.0

309/387/238

4

TLR10

rs4274855

LCR

C/T

29.4

1.000

22.5

408/334/72

4

TLR2

rs11736691

GF

100.0

4

TLR6

rs1039560

T/C

33.4

0.126

3.3

455/442/118

4

TLR6

rs5743789

HWEd/LCR

A/T

29.5

< 0.001

19.8

460/268/114

5

IL13

rs1800925

C/T

18.4

0.721

4.6

668/300/34

5

IL13

rs1881457

A/C

18.4

0.720

2.8

681/305/35

5

IL13

rs20541

HWEd

G/A

14.5

< 0.001

7.1

734/200/41

6

IL17

rs7741835

C/T

19.4

0.371

5.0

659/291/48

9

DMRT1

rs3812523

A/G

15.6

0.074

3.4

733/245/36

9

IL33

rs1342326

A/C

21.8

0.174

3.5

627/331/55

9

IL33

rs928413

A/G

30.8

0.397

4.3

486/418/101

11

ANO9

rs7482596

HWEd

G/T

13.3

< 0.001

4.2

770/205/31

11

ANO9

rs7484182

HWEd

T/C

14.1

< 0.001

4.1

758/215/34

11

DHCR7

rs1044482

GF

100.0

11

GST-P1

rs1695

HWEd

A/G

29.9

< 0.001

6.2

521/338/126

11

NADSYN1

rs2186777

A/C

26.7

0.014

4.5

553/365/85

11

SIGIRR

rs4074794

HWEd

G/A

19.5

< 0.001

6.3

659/266/59

12

IRAK3

rs1152918

C/T

6.9

0.295

2.1

893/128/7

12

IRAK3

rs2701652

G/C

22.0

0.256

4.1

620/330/57

12

ORMDL2

rs7954619

GF

100.0

16

IL4R

rs1801275

A/G

16.0

0.001

4.7

722/237/42

16

IL4R

rs1805012

HWEd

T/C

5.8

< 0.001

2.6

916/96/11

16

IL4R

rs3024548

HWEd/LCR

C/G

46.4

< 0.001

13.0

314/351/249

17

ERBB2

rs1058808

G/C

30.6

0.009

6.8

495/369/115

17

ERBB2

rs1136201

HWEd

A/G

13.8

< 0.001

4.1

767/203/37

17

ERBB2

rs2934971

GF

100.0

17

ERBB2

rs2952155

GF

100.0

17

ERBB2

rs4252665

C/T

1.7

0.137

1.6

999/33/1

17

GSDMA

rs3859192

C/T

37.9

0.028

6.5

398/423/161

17

GSDMA

rs3894194

G/A

41.6

0.005

5.3

359/442/193

17

GSDMA

rs7212938

T/G

44.2

0.039

4.8

326/463/211

17

GSDMB

rs2305479

C/T

42.0

0.166

1.9

355/485/190

17

GSDMB

rs2305480

G/A

40.2

0.088

3.8

371/466/173

17

GSDMB

rs7216389

HWEd

T/C

42.2

< 0.001

6.6

359/416/206

17

LRRC3C

rs8065126

HWEd/LCR

C/T

38.3

< 0.001

12.5

386/362/171

17

LRRC3C

rs8079416

T/C

45.2

0.013

4.5

315/469/219

17

MAP2K3

rs10468608

HWEd/LCR

C/T

30.2

< 0.001

19.8

462/251/129

17

MAP2K3

rs2363226

GF

100.0

17

MAP2K4

rs3760201

HWEd/LCR

A/G

32.9

< 0.001

34.6

343/236/108

17

ORMDL3

rs8076131

HWEd/LCR

A/G

40.7

< 0.001

15.9

333/382/168

17

PGAP3

rs1495102

HWEd/LCR

C/T

14.0

< 0.001

17.7

697/92/75

17

ZPBP2

rs11557467

GF

100.0

X

IRAK1

rs1059703

HWEd

A/G

25.5

< 0.001

4.2

679/140/187

Chr chromosome, MAF minor allele frequency, GF genotyping failing, LCR low call rate, HWEd deviation from the Hardy-Weinberg equilibrium; A: major allele; a: minor allele; bold values indicate significance (p-values < 0.001)

By applying the case/control model, no SNP reached the Bonferroni corrected significance threshold (P value < 0.002, i.e., 0.05/#tests), and only one SNP reached the Bonferroni corrected suggested significance threshold (P value < 0.005, i.e., 0.10/#tests). However, we also included those SNPs reaching the nominal significance threshold (P value < 0.05) just to highlight modest associations with the two studied phenotypes. CA was strongly associated only with rs4252665 and modestly with rs1801275 and rs17616434; RC was modestly associated with rs7741835, rs8079416, rs3859192, rs3894194 and rs7212938. Table 3 shows the genotypic frequencies of the associated SNPs in CA/RC and nAnRC groups, and the adjusted OR and 95% CI from the logistic regression model for the only SNP strongly associated. The SNP rs4252665 showed significantly different genotypic frequencies between the two groups, i.e., the CA subjects had a high frequency of CT heterozygote genotype compared with nAnRC, which were mostly homozygous. Indeed, using the overdominant genetic model, in which the baseline is the homozygous genotype (CC/TT), the CT genotype of rs4252665 showed a large increased risk of CA (ORC/T = 5.75; 95% CI = 2.03–16.29). The SNPs modestly associated with CA showed a high frequency of the major allele homozygote genotype compared with nAnRC, in which the genotypes were mostly characterized by the presence of the minor allele. Furthermore, with respect to nAnRC, SNPs modestly associated with RC showed small variations in the genotypic frequencies. In particular, the SNPs rs8079416, rs7741835 and rs3894194 showed high frequencies of heterozygote genotypes compared with nAnRC, which are frequently homozygous, whilst the SNPs rs7212938 and rs3859192 had high frequencies of the minor allele homozygote genotypes compared with nAnRC, in which genotypes are characterized by the presence of the major allele.
Table 3

Genotypic frequencies of the associated SNPs in CA/RC and nAnRC groups

Gene

Region

SNP name

Alleles

Group

AA (%)

Aa (%)

aa (%)

OR (95% CI)

TLR1

4p14

rs17616434

T/C

nAnRC

29.4

45.6

25.0

CA

42.4

44.1

13.5

IL4R

16p12.1

rs1801275

A/G

nAnRC

71.0

24.5

4.5

CA

80.0

20.0

0.0

ERBB2

17q12

rs4252665

C/T

nAnRC

97.4

2.5

0.1

5.75 (2.03–16.29)

CA

90.2

9.8

0.0

IL17

6p12.2

rs7741835

C/T

nAnRC

64.3

31.2

4.5

RC

69.1

25.5

5.4

LRRC3C

17q21.1

rs8079416

T/C

nAnRC

32.8

45.0

22.2

RC

33.9

42.4

23.7

GSDMA

17q21.1

rs7212938

T/G

nAnRC

34.1

45.4

20.5

RC

32.8

41.4

25.8

GSDMA

17q21.1

rs3859192

C/T

nAnRC

42.2

42.6

15.2

RC

40.7

40.7

18.6

GSDMA

17q21.1

rs3894194

G/A

nAnRC

37.9

43.3

18.8

RC

39.0

37.3

23.7

A: major allele; a: minor allele

Adjusted odds ratios (OR) and 95% confidence interval (CI) of the logistic regression models

The detected association signals for CA were located within the Toll-like receptor (TLR1) on chromosome 4, the interleukin 4 receptor (IL4R) on chromosome 16 and the Erb-b2 receptor tyrosine kinase 2 (ERBB2) on chromosome 17. It is known that Toll-like receptors (TLRs) represent a major group of receptors for the specific recognition of pathogen-associated molecular patterns of microbes capable of activating innate and adaptive immunity that reduce the risk for asthma [3]. The IL4R gene is known to encode a protein that regulates IgE production and it has been shown that allelic variations in this gene are associated with atopy, allergic rhinitis and asthma [4]. Recently, some loci of ERBB2, which belong to the encoding region 17q12, have been reported to be in linkage disequilibrium with loci in the region 17q21 encoding gasdermin A (GSDMA) gene, previously associated with childhood asthma [57].

With regard to the RC, the modestly associated genes were interleukin 17 (IL17) on chromosome 6, leucine rich repeat containing 3C (LRRC3C), and GSDMA on chromosome 17. IL17 is a pro-inflammatory cytokine that targets epithelial cells48 and its expression in the nasal mucosa has been associated with allergic rhinitis and its degree of severity [8, 9]. To our knowledge, no functional studies have been published on LRRC3C, although, within the human genome, the gene LRRC32 has been associated with eczema and allergic rhinitis [10], and probably some similarities between the two proteins encoded by LRRC3C and LRRC32 exist. Finally, GSDMA gene has been associated with childhood asthma and allergic disease in many populations. In particular, region 17q21 has been originally identified in the first GWAS on childhood asthma [6], and GSDMA variants were suggested to be strong risk factors for asthma and airway inflammation [7].

Overall, our findings reveal the importance of different genetic variants at 4p14, 16p12.1 17q12, 6p12.2 and 17q21.1, identifying possible candidate genes responsible for CA and RC in the Sicilian child population. These results are a preliminary step in understanding the pathophysiology of asthma and rhino-conjunctivitis in a paediatric population in the Mediterranean area and need to be verified by further studies using more advanced technologies. Furthermore, novel methodologies combining genome-wide association study (GWAS; [11]) and expression quantitative trait locus (eQTL [12]) such as summary-data based Mendelian randomization (SMR; [13]), PrediXcan [14], MetaXcan [15], would be useful in discovering new genetic variants linked to these allergic respiratory diseases in this geographic area. Unlike traditional single-variant tests, these innovative approaches based on SNP-gene linkage will provide valuable insights on disease causality. Noteworthy, the integrative analysis of GWAS and eQTL studies, by identifying gene-trait-associated changes in the expression, would mitigate some tasks associated with a GWAS approach, allowing the discover of genetic variants which can affect gene expression [16]. Moreover, since some gene functions are often pleiotropic, this combined approach would allow a better comprehension of the pathways through which pleiotropy can affect clinical phenotypes.

In conclusion, the present study could facilitate the application of novel therapeutics and preventive strategies arising from the genomics era of precision medicine.

Notes

Abbreviations

BMI: 

Body mass index

CA: 

Current Asthma

eQTL: 

Expression quantitative trait locus

ERBB2: 

Human epidermal growth factor receptor 2

GSDMA: 

Gasdermin A

GWAS: 

Genome-wide association study

HWE: 

Hardy-Weinberg equilibrium

IL17: 

Interleukin 17

IL4R: 

Interleukin 4 receptor

LRRC3C: 

Leucine rich repeat containing 3C

MAF: 

Minor allele frequency

nAnRC: 

Not asthma and not rhino-conjunctivitis

RC: 

Rhino-conjunctivitis

SMR: 

Summary-data based Mendelian randomization

SNP: 

Single Nucleotide Polymorphism

SPT: 

Skin prick tests

TLR: 

Toll-like receptor

Declarations

Acknowledgements

We are grateful to Dr. Maximillian Schieck and to Prof. M. Kabesch for supporting gene analysis. We also thank all the school staff, children, and parents who made the study possible.

Funding

The present research was fully supported by a grant from the Regional Agency for Environment Protection (ARPA Sicilia) [DDG No. 303/2005].

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Authors’ contributions

FC and SLG designed the study. Gianluca Sottile, GC, GF and SLG wrote the initial draft and had final responsibility for the decision to submit for publication. Gianluca Sottile, GC and SF conducted the statistical analyses. RA and Gregorio Seidita contributed to the collection of samples. MT performed genotyping of the DNA samples. GV, FC and SLG performed a critical revision of the manuscript and offered precious technical advice on how the study might be improved. All authors provided substantial contributions to the conception or design of the work, or the acquisition, analysis, or interpretation of data for the paper, revised the manuscript for important intellectual content, approved the final version, and agreed to be accountable for all aspects of the work.

Ethics approval and consent to participate

The study was approved by the local ethics committee (A.O.U.P. “Paolo Giaccone”, Palermo, Italy), and written informed consent was provided by parents of all participants.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Department of Economics, Business and Statistical Science, University of Palermo, Viale delle Scienze, Building 13, 90128 Palermo, Italy
(2)
Dipartimento di Promozione della Salute, Materno-Infantile, di Medicina Interna e Specialistica di Eccellenza “G. D’Alessandro”, University of Palermo, Palermo, Italy
(3)
National Research Council of Italy, Institute of Biomedicine and Molecular Immunology, Palermo, Italy
(4)
Dipartimento di Biomedicina, Neuroscienze e Diagnostica avanzata, University of Palermo, Palermo, Italy
(5)
National Research Council of Italy, Institute of Clinical Physiology, Pisa, Italy

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Copyright

© The Author(s). 2019

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