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. 2024 Dec 19;18(1):101014. doi: 10.1016/j.waojou.2024.101014

Advancing allergic rhinitis research through phenome-wide association studies: Insights from known genetic loci

Xingling Tan b,1, Zhouyouyou Xiao a,1, Yao Wen b, Han Liu b,⁎⁎, Wei Yu b,⁎
PMCID: PMC11728958  PMID: 39807185

Abstract

Background

Allergic rhinitis (AR) is a common chronic respiratory disease that can lead to the development of various other conditions. Although genetic risk loci associated with AR have been reported, the connections between these loci and AR comorbidities or other diseases remain unclear.

Methods

This study conducted a phenome-wide association study (PheWAS) using known AR risk loci to explore the impact of known AR risk variants on a broad spectrum of phenotypes. Subsequently, linkage disequilibrium score regression (LDSC) and bidirectional two-sample mendelian randomization (TSMR) analyses were used to further analyze the genetic correlation and causal relationships between significant and potentially related phenotypes and AR.

Results

The PheWAS analysis indicated significant associations between asthma, eczema, nasal polyps, hypothyroidism, and AR risk variants. Additionally, potential associations were observed with ulcerative colitis, psoriasis, chalazion, pernicious anemia, glaucoma, multiple sclerosis, arthritis, prostate cancer, varicose veins of lower extremities, and heart attack. LDSC analysis showed that only asthma, eczema, and nasal polyps have significant positive genetic correlations with AR. Furthermore, TSMR analysis revealed causal relationships between AR and asthma, eczema, and nasal polyps.

Conclusion

This study highlights the impact of AR risk loci on a variety of diseases. By revealing new associations and shared genetic pathways, our findings provide valuable insights into the pathophysiology of AR and pave the way for more effective targeted interventions to manage AR and its related diseases.

Keywords: Rhinitis, Allergic, Genetic predisposition to disease, Asthma, Dermatitis, Atopic, Nasal polyps

Introduction

Allergic rhinitis (AR) is a global public health, medical, and economic issue caused by an immunoglobulin E (IgE)-mediated response to inhaled allergens, imposing significant healthcare and socioeconomic burdens worldwide.1,2 Epidemiological investigations reveal that AR affects approximately 20% of the global population.3 This condition exerts a significant detrimental impact on the social lives, learning, and work of these patients, substantially reducing their overall quality of life.4, 5, 6 Importantly, AR has been reported to be associated with conditions such as sinusitis, serous otitis media, nasal polyps, sleep disturbances, asthma, atopic dermatitis,7,8 as well as some autoimmune diseases, including rheumatoid arthritis, psoriasis, pernicious anaemia, inflammatory bowel disease, coeliac disease and autoimmune thyroiditis.9,10 However, the causal relationship between AR and these diseases remains unclear.

Genome-wide association studies (GWAS) have significantly advanced our understanding of the etiology of AR. A recent GWAS meta-analysis involving 59,762 cases of allergic rhinitis of European ancestry and 152,358 controls identified 41 risk loci for allergic rhinitis, including 20 new loci not previously associated with AR.11 These findings offer fresh insights into the mechanisms underlying the development of AR and present new targets for its treatment and prevention. Mendelian randomization, based on GWAS, uses single nucleotide polymorphisms (SNPs) as instrumental variables (IVs) to circumvent the issue of unobserved confounders in observational studies, becoming a mature tool for inferring causal relationships.12, 13, 14 However, current Mendelian randomization studies on allergic rhinitis primarily focus on two-sample mendelian randomization (TSMR), where AR is considered either an exposure or an outcome to explore causal links with another phenotype. This approach, which concentrates on the causal association between 2 phenotypes, struggles to demonstrate the extensive impact of genetic variations and misses opportunities to discover unexpected associations between diseases and genetic variations.

Phenome-wide association studies (PheWAS) represent a method for exploring associations between a single genetic variant of interest and multiple diseases or phenotypes.15,16 This approach is capable of uncovering unexpected associations between diseases and genetic variations, revealing the extensive impact of genetic variations, increasing the chances of discovering the pleiotropy of genetic variants, and enhancing the understanding of disease comorbidity.17,18 Therefore, this study conducted a PheWAS analysis based on AR risk loci identified by Johannes et al,11 utilizing pleiotropy between AR and other traits to uncover new AR risk factors. Subsequently, linkage disequilibrium score regression (LDSC) analysis was used to assess genetic correlations between potential risk factors and AR. Finally, TSMR analysis on phenotypes with significant genetic associations was performed to validate causal relationships.

Methods

Acquisition of AR risk loci and control SNPs

From the study by Johannes et al, we obtained information on 41 risk loci for AR, including chromosome positions, single nucleotide polymorphisms (SNPs), and adjacent genes (Table S1). Subsequently, we performed linkage disequilibrium (LD) pruning on these SNPs (kb = 10000, r2 = 0.001), resulting in 34 SNPs suitable for subsequent PheWAS analysis. In addition, we matched these 34 risk loci with control SNPs at a 1:4 ratio, following the criteria described in previous studies.19 Ultimately, a total of 34 AR-related SNPs and 136 control SNPs were included in the PheWAS analysis (Table S2).

PheWAS analyses

The IEU OpenGWAS database (https://gwas.mrcieu.ac.uk) currently contains genetic associations from 14,582 complete GWAS datasets, encompassing 1.26 billion genetic associations across a diverse range of human phenotypes and diseases from different populations. We downloaded and integrated GWAS summary data publicly available from the UK Biobank from the OpenGWAS database. After excluding datasets with binary phenotypes having fewer than 1000 cases and continuous variables with a total sample size of less than 1000, we ultimately included 2502 phenotypes in our analysis. We first queried the trait associations with AR-risk SNPs and control SNPs. As mentioned, nominally significant SNP-trait associations (p < 0.01) were included in trait enrichment analysis.20 We compared the PheWAS results of the risk SNPs across 2502 phenotypes with those of the control SNPs. For traits enriched >1 with AR risk SNPs, we used Fisher's exact test to compare the frequency of individual traits associated with AR SNPs and control SNPs, to determine whether traits were associated with AR risk variants. P-values were corrected using the False Discovery Rate (FDR), considering FDR <0.05 as statistically significant, while FDR >0.05 but with p-values <0.05 were considered as potentially associated.

Genetic correlation analysis

For phenotypes showing statistically significant and potential associations, we conducted LDSC analysis to explore their genetic correlation with AR. GWAS summary statistics were filtered according to HapMap3 reference, excluding non-SNP variants (eg, indels) and variants with ambiguous strand, duplicates, and minor allele frequency (MAF) < 0.01. In the R environment, the "GenomicSEM" package is used to conduct LDSC analysis on 2 traits to assess genetic heritability and genetic correlation. LDSC assesses the relationship between test statistics and LD to quantify contributions from true polygenic signals or biases.21 Furthermore, this method evaluates genetic correlations from GWAS summary statistics without bias from sample overlap.22 It involves multiplying the z-score of each variant for trait 1 by the z-score of each variant for trait 2. Genetic covariance is estimated by regressing this product against the LD score.23 The genetic correlation is represented by the genetic covariance normalized by the SNP-heritability. A p-value <0.0035 (0.05/14, following strict Bonferroni correction) was considered statistically significant.

Mendelian randomization analyses

Further, we conducted TSMR analysis on all phenotypes that exhibited significant or potential associations with AR to evaluate the causal effects between them. The AR GWAS data used as exposure were sourced from the GWAS catalog, including data from 27,415 cases and 457,183 controls. The corresponding outcome datasets were obtained from the IEU OpenGWAS database. Based on selection criteria (p-value <5∗10−8, kb = 10000, r2 = 0.001), 37 SNPs were filtered from the exposure data of AR to serve as IVs. All selected IVs had F-statistics values > 10, indicating a low likelihood of bias due to weak instrumental variables. Indicative associations between AR and related phenotypes were examined across 5 MR methods. We used the inverse variance weighted (IVW) method as the primary method for estimating causality, with additional methods for supplementary validation.24,25 MR-Egger regression test was conducted to assess horizontal pleiotropy, along with Cochran Q test to evaluate heterogeneity among the selected IVs. If heterogeneity was present among IVs, we opted for the random effects IVW method instead of the fixed effects IVW method.26 Similarly, we conducted reverse Mendelian randomization (MR) analysis using these associated phenotypes as exposures and AR as the outcome to investigate whether there is a reverse causal relationship between them. During the selection process for IVs in the reverse MR analysis, under a threshold of p-value <5∗10−8, ulcerative colitis, arthritis, chalazion, and pernicious anemia had ≤2 valid IVs. Consequently, we employed a more lenient threshold of p-value <5∗10−6. Similarly, we applied Bonferroni correction to the P-values (0.05/14).

Enrichment analysis of SNP nearest genes

We conducted gene enrichment analyses, including Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Disease Ontology (DO) analyses, on genes proximal to these risk loci to understand the potential pathogenic pathways of these risk loci. Additionally, a protein-protein interaction (PPI) network for these genes was constructed based on the STRING database (https://string-db.org/). This approach helps to elucidate the biological functions, pathways, and networks potentially involved in the pathogenesis of allergic rhinitis and its associated conditions, providing insights into the molecular mechanisms underlying these diseases.

Results

Enrichment analysis of risk loci

Based on the PheWAS analysis, we examined the enrichment of these AR SNPs as well as control SNPs across 2502 phenotypes. The results (Fig. 1, Table S3) indicate that, in comparison to control SNPs, asthma, allergic rhinitis, nasal polyps, eczema/dermatitis, and hypothyroidism/myxoedema are significantly associated with AR SNPs (FDR <0.05). Meanwhile, ulcerative colitis, psoriasis, chalazion, pernicious anemia, glaucoma, multiple sclerosis, arthritis, prostate cancer, varicose veins of lower extremities (VVLE), and heart attack have potential associations with AR SNPs (P < 0.05). These results indicate that AR might be potentially linked to the occurrence of these conditions.

Fig. 1.

Fig. 1

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Manhattan plot of trait enrichment analysis based on PheWAS for AR SNPs and control SNPs

LDSC analysis

Further, we examined the genetic correlation (rg) between AR and these associated phenotypes. The results (Fig. 2) showed significant positive genetic correlations between AR and asthma (rg = 0.62, P = 5.41∗10−14), nasal polyps (rg = 0.51, P = 2.64∗10−14), and eczema/dermatitis (rg = 0.43, P = 1.21∗10−8). No significant genetic correlations were found between AR and the other phenotypes.

Fig. 2.

Fig. 2

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Circular heatmap of genetic correlation analysis between significant and potentially related phenotypes and allergic rhinitis. rg represents genetic correlation, with positive and negative values indicating positive and negative genetic correlations, respectively; SE represents the standard error of the correlation. VVLE represents Varicose Veins of Lower Extremities

Bidirectional Mendelian randomization analysis

Next, using TSMR, we examined the causal relationships between AR and related phenotypes. The results (Table 1) indicated significant causal associations between AR and asthma (OR = 5.12, 95% CI = 1.11–1.29, P = 3.83∗10−6), nasal polyps (OR = 1.09, 95% CI = 1.05–1.12, P = 3.68∗10−7), and eczema/dermatitis (OR = 1.20, 95% CI = 3.95–6.64, P = 5.78∗10−35). No significant causal relationships were found with the other related phenotypes. Conducting reverse MR analysis with associated phenotypes as exposures and AR as the outcome revealed a bidirectional causal relationship between AR and asthma. However, no significant causal links were found between AR and the other examined phenotypes (Table S4).

Table 1.

Mendelian Randomization (MR) analysis with allergic rhinitis as the exposure and related phenotypes as the outcomes

outcome OR 95%CI P-value Pleiotropy test Cochran Q
Chalazion 0.98 0.963–1.007 0.168 0.13 P < 0.05
VVLE 0.986 0.952–1.022 0.468 0.54 0.08
Heart attack 0.972 0.930–1.016 0.207 0.78 P < 0.05
Prostate cancer 0.996 0.979–1.014 0.674 0.14 0.7
Arthritis 0.997 0.976–1.018 0.742 0.12 0.14
Multiple sclerosis 0.991 0.961–1.023 0.589 0.6 P < 0.05
Glaucoma 0.975 0.899–1.056 0.535 0.46 0.22
Ulcerative colitis 1.002 0.983–1.022 0.810 0.01 0.09
Psoriasis 1.03 0.943–1.130 0.493 0.52 P < 0.05
Eczema/dermatitis 1.198 1.109–1.293 3.83∗10−6 0.88 P < 0.05
Nasal polyps 1.089 1.054–1.125 3.68∗10−7 0.99 P < 0.05
Asthma 5.124 3.952–6.643 5.78∗10−35 0.75 P < 0.05
Pernicious anaemia 1.001 0.979–1.023 0.939 0.82 P < 0.05
Hypothyroidism 0.981 0.856–1.124 0.781 0.46 P < 0.05
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Abbreviations: VVLE stands for Varicose Veins of Lower Extremities; OR represents Odds Ratio; P-value refers to the p-value from the Inverse Variance Weighted (IVW) model; Pleiotropy testing represents the p-value for the MR-Egger regression test, with P > 0.05 indicating no evidence of horizontal pleiotropy; Cochran Q represents the heterogeneity test, where P > 0.05 indicates no heterogeneity and the fixed-effect IVW model is used, otherwise, the random-effects IVW model is selected.

Construction of PPI network and gene enrichment analysis

We have built a PPI network based on the STRING database for these neighboring genes to explore their interrelationships (Fig. 3A). Enrichment analyses, including GO and KEGG, showed that these genes are significantly enriched in immune-related pathways, including the differentiation and activation of immune cells, highlighting the pivotal role of immune cells in the progression of AR (Fig. 3B and C). These genes may influence the course of AR by affecting the differentiation and activation of immune cells, as well as mediating immune responses. DO analysis (Fig. 3D) indicated that these genes are enriched in various allergic diseases, autoimmune diseases, and nose diseases, suggesting potential connections between these conditions and AR, as well as a shared comorbidity basis.

Fig. 3.

Fig. 3

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Protein interaction network and functional enrichment analysis of genes adjacent to AR SNPs. (A) Protein interaction network of adjacent genes; (B) Bar plot for KEGG enrichment analysis; (C) Bar plot for GO enrichment analysis; (D) Bar plot for DO analysis

Discussion

In this study, we utilized risk loci for AR reported from previous large-scale GWAS studies to perform a PheWAS, aiming to reveal the extensive impact of genetic variations in AR and to promote understanding of comorbidities associated with the disease. In the trait-enriched PheWAS analysis, we found significant associations between asthma, nasal polyps, eczema, and hypothyroidism with AR risk loci. Potential associations were observed with ulcerative colitis, psoriasis, chalazion, pernicious anemia, glaucoma, multiple sclerosis, arthritis, prostate cancer, varicose veins of lower extremities, and heart attack. However, only asthma, nasal polyps, and eczema showed significant genetic correlations with AR in the LDSC analysis. Further validation using MR analysis indicated that AR has a causal relationship with asthma, nasal polyps, and eczema. These findings highlight specific comorbidities that may share common genetic pathways with AR and underscore the importance of considering these relationships in the clinical management of AR.

Indeed, the connection between AR and asthma has been well-documented, with epidemiological surveys indicating that up to 78% of patients with asthma also suffer from allergic rhinitis, while 38% of individuals with allergic rhinitis develop asthma.27 These statistics are in line with our findings, which demonstrate a significant genetic correlation and a bidirectional causal relationship between the 2 conditions. Allergic rhinitis and asthma share common pathogenic mechanisms, with both conditions associated with Th2 mediated inflammatory responses characterized by increased eosinophilic tissue infiltration.28 These diseases also share features such as increased bronchial hyperreactivity and enhanced responsiveness to a variety of stimuli.29 Furthermore, the unified airway theory suggests an intrinsic link between upper respiratory tract (rhinitis) and lower respiratory tract (asthma) diseases, as evidenced by studies like Braunstahl et al., which revealed that nasal irritation and allergic rhinitis can lead to systemic airway inflammation through upregulation of adhesion molecules.30,31 In our study, enrichment analysis of genes near AR risk loci also found these genes to be largely associated with immune cell differentiation, particularly Th1 and Th2, and immune cell-mediated inflammatory responses, deepening our understanding of the comorbid mechanisms of these conditions. Eczema is also strongly linked to AR and asthma, often occurring together as part of the atopic triad disorders.32,33 These conditions share a common pathophysiological basis, and our research, based on GWAS data, confirms the association between them from another perspective. Nasal polyps and are also closely linked to AR, with several studies finding that individuals with allergic rhinitis have a higher incidence of nasal polyps compared to the general population,34, 35, 36 although the causal relationship between the 2 remains unclear. Our study found that in trait enrichment analysis, AR SNPs were significantly enriched in nasal polyps compared to control SNPs, suggesting a potential association between AR and nasal polyps. LDSC analysis further confirmed a significant genetic correlation between the 2 conditions, while MR analysis established a causal link between them. Since these diseases may share similar genetic and immune-inflammatory pathways, this suggests that patients diagnosed with allergic rhinitis may be more susceptible to asthma, nasal polyps, and eczema in clinical practice. Doctors should remain vigilant, actively screening and assessing these comorbidities during diagnosis and follow-up to facilitate early intervention and management. Additionally, the development of integrated treatment strategies aimed at the shared pathomechanisms among comorbidities can concurrently ameliorate symptoms of several disorders, thus enhancing the overall quality of life and therapeutic results for patients.

Risk locus enrichment analysis reveals that autoimmune diseases (including ulcerative colitis, psoriasis, multiple sclerosis, and arthritis), cardiovascular diseases (such as heart attack and VVLE), ocular diseases (glaucoma and chalazion), pernicious anemia, and prostate cancer are potentially associated with AR SNPs. The pathophysiological link between AR and these diseases may involve the immune system and inflammatory responses, suggesting a broader implication of immune dysfunction across these conditions. AR represents an IgE-mediated Type I hypersensitivity reaction, characterized by dysfunction in immune cells such as type 2 innate lymphoid cells (ILC2s), T helper (Th2) cells, follicular helper T cells, follicular regulatory T cells, regulatory T cells, B cells, dendritic cells, and epithelial cells, as well as abnormal secretion of inflammatory factors (IL1, IL2, IL4, IL5, IL6, IL9, IL13, IL33, TNF-alpha).37,38 These immune irregularities are similar to those observed in autoimmune diseases, which also typically involve an imbalance in immune responses and aberrant release of inflammatory factors, particularly IL1, IL6, and TNF-alpha.39, 40, 41, 42 Thus, shared immune pathways may contribute to the co-occurrence of these conditions. Furthermore, the elevation of systemic inflammatory mediators (such as interleukins and tumor necrosis factors) caused by AR may promote arteriosclerosis and endothelial dysfunction, thereby increasing the risk of heart attacks.43,44 Similarly, these inflammatory mediators may affect the structure and function of venous walls, contributing to the occurrence of VVLE.45 Additionally, patients with AR often exhibit ocular symptoms, such as allergic conjunctivitis,46 which, when combined with chronic inflammation, may lead to increased intraocular pressure and impaired aqueous humor outflow, thereby elevating the risk of glaucoma.47 In a similar manner, the occurrence of chalazion may also be associated with localized immune responses and inflammation.48 Although no definitive connection has been established between AR, pernicious anemia, and prostate cancer, it is evident that inflammatory factors play a significant role in the progression of these diseases.49,50 However, it is important to note that genetic correlation analysis and subsequent TSMR analyses did not reveal a significant association between these diseases and AR, underscoring the importance of considering environmental and lifestyle factors that may contribute to these conditions alongside genetic predispositions. Taken together, our findings highlight the need for further research to understand the multifactorial nature of AR and its comorbidities, incorporating both genetic and non-genetic data to provide a more comprehensive understanding of disease mechanisms.

Our study has limitations. Firstly, a relatively lenient threshold of p < 0.01 was used in the SNP-trait association enrichment analysis, based on thresholds determined from previous studies aimed at balancing study power and the risk of false positives. This threshold was only used to bring traits into subsequent enrichment analyses, with associations in these comparisons adjusted using FDR correction to enhance the robustness of the enrichment analysis. Secondly, our study relied on GWAS summary data from the UK Biobank included in the IEU openGWAS database, potentially missing important phenotypes related to the pathophysiology of AR. Furthermore, our study, based on published AR risk loci, may overlook new, less-validated alleles. As research on AR progresses, integrating newly identified risk alleles could refine our understanding. However, due to the dynamic nature of genetic insights into AR, this may also complicate the specificity of our findings.

Conclusion

Our study reveals significant genetic links between AR and conditions such as asthma, nasal polyps, and eczema, establishing causal relationships between AR and these conditions. These insights emphasize the importance of considering AR within a broader genetic context, offering new avenues for research and potential therapeutic strategies.

Abbreviations

AR, Allergic rhinitis; PheWAS, phenome-wide association study; LDSC, Linkage Disequilibrium Score Regression; TSMR, two-sample mendelian randomization; IgE, immunoglobulin E; GWAS, Genome-wide association studies; SNPs, single nucleotide Polymorphisms; IVs, instrumental variables; LD, linkage disequilibrium; FDR, False Discovery Rate; MAF, minor allele frequency; IVW, inverse variance weighted; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; DO, Disease Ontology; PPI, protein-protein interaction; VVLE, varicose veins of lower extremities; rg, genetic correlation.

Funding

Not applicable.

Data sharing statement

In this study, the publicly available GWAS summary data were sourced from the IEU openGWAS database ((https://gwas.mrcieu.ac.uk) and the GWAS Catalog (ID = GCST90038664; www.ebi.ac.uk/gwas).

Contributors

WY and HL conceived and designed the study. ZX and XT performed data analysis as well as wrote the manuscript. XT completed manuscript corrections. WY and ZX collected the data. HL, XT and YW corrected code. All authors read and approved the final manuscript.

Ethics

As the study was conducted using publicly available data, there was no requirement for informed consent or ethics committee approval.

Authors’ consent for publication

All the authors reviewed the final draft and provided consent for publication.

Declaration of competing interest

The authors affirm that there were no financial or commercial relationships that might be viewed as having a potential conflict of interest.

Acknowledgment

We extend our thanks to the researchers who generously provided the GWAS data, which played a pivotal role in enabling our study.

Footnotes

Full list of author information is available at the end of the article

Appendix A

Supplementary data to this article can be found online at https://doi.org/10.1016/j.waojou.2024.101014.

Contributor Information

Han Liu, Email: liuhan2020123@163.com.

Wei Yu, Email: drjackyu2019@163.com.

Appendix A. Supplementary data

The following is the Supplementary data to this article.

Multimedia component 1
mmc1.docx (56.3KB, docx)

References

  • 1.Zhang Y., Lan F., Zhang L. Advances and highlights in allergic rhinitis. Allergy. 2021;76:3383–3389. doi: 10.1111/all.15044. [DOI] [PubMed] [Google Scholar]
  • 2.Bousquet J., Anto J., Bachert C., et al. Allergic rhinitis. Nat Rev Dis Prim. 2020;6:95. doi: 10.1038/s41572-020-00227-0. [DOI] [PubMed] [Google Scholar]
  • 3.Matsushita K., Kato Y., Akasaki S., Yoshimoto T. Proallergic cytokines and group 2 innate lymphoid cells in allergic nasal diseases. Allergol Int : official journal of the Japanese Society of Allergology. 2015;64:235–240. doi: 10.1016/j.alit.2014.12.008. [DOI] [PubMed] [Google Scholar]
  • 4.Siddiqui Z., Walker A., Pirwani M., Tahiri M., Syed I. Allergic rhinitis: diagnosis and management. Br J Hosp Med. 2022;83:1–9. doi: 10.12968/hmed.2021.0570. [DOI] [PubMed] [Google Scholar]
  • 5.Bernstein D., Schwartz G., Bernstein J. Allergic rhinitis: mechanisms and treatment. Immunol Allergy Clin. 2016;36:261–278. doi: 10.1016/j.iac.2015.12.004. [DOI] [PubMed] [Google Scholar]
  • 6.Ponda P., Carr T., Rank M., Bousquet J. Nonallergic rhinitis, allergic rhinitis, and immunotherapy: advances in the last decade. J Allergy Clin Immunol Pract. 2023;11:35–42. doi: 10.1016/j.jaip.2022.09.010. [DOI] [PubMed] [Google Scholar]
  • 7.Iordache A., Balica N., Horhat I., et al. A review regarding the connections between allergic rhinitis and asthma - epidemiology, diagnosis and treatment. Current health sciences journal. 2023;49:5–18. doi: 10.12865/CHSJ.49.01.5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Wise S., Lin S., Toskala E., et al. International consensus statement on allergy and rhinology: allergic rhinitis. International forum of allergy & rhinology. 2018;8:108–352. doi: 10.1002/alr.22070. [DOI] [PubMed] [Google Scholar]
  • 9.Charoenngam N., Ponvilawan B., Rittiphairoj T., et al. The association between allergic rhinitis and risk of rheumatoid arthritis: a systematic review and meta-analysis. J Evidence-based Med. 2021;14:27–39. doi: 10.1111/jebm.12393. [DOI] [PubMed] [Google Scholar]
  • 10.Krishna M., Subramanian A., Adderley N., Zemedikun D., Gkoutos G., Nirantharakumar K. Allergic diseases and long-term risk of autoimmune disorders: longitudinal cohort study and cluster analysis. Eur Respir J. 2019;54 doi: 10.1183/13993003.00476-2019. [DOI] [PubMed] [Google Scholar]
  • 11.Waage J., Standl M., Curtin J., et al. Genome-wide association and HLA fine-mapping studies identify risk loci and genetic pathways underlying allergic rhinitis. Nat Genet. 2018;50:1072–1080. doi: 10.1038/s41588-018-0157-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Yu X., Lophatananon A., Mekli K., Muir K., Guo H. Exploring the causal role of the immune response to varicella-zoster virus on multiple traits: a phenome-wide Mendelian randomization study. BMC Med. 2023;21:143. doi: 10.1186/s12916-023-02843-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Sekula P., Del Greco M.F., Pattaro C., Köttgen A. Mendelian randomization as an approach to assess causality using observational data. J Am Soc Nephrol : JASN (J Am Soc Nephrol) 2016;27:3253–3265. doi: 10.1681/ASN.2016010098. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Birney E. Mendelian randomization. Cold Spring Harbor perspectives in medicine. 2022;12 doi: 10.1101/cshperspect.a041302. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Wang L., Zhang X., Meng X., et al. Methodology in phenome-wide association studies: a systematic review. J Med Genet. 2021;58:720–728. doi: 10.1136/jmedgenet-2021-107696. [DOI] [PubMed] [Google Scholar]
  • 16.Roden D. Phenome-wide association studies: a new method for functional genomics in humans. J Physiol. 2017;595:4109–4115. doi: 10.1113/JP273122. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Hebbring S. The challenges, advantages and future of phenome-wide association studies. Immunology. 2014;141:157–165. doi: 10.1111/imm.12195. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Byun J., Han Y., Ostrom Q., et al. The shared genetic architectures between lung cancer and multiple polygenic phenotypes in genome-wide association studies. Cancer Epidemiol Biomarkers Prev : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology. 2021;30:1156–1164. doi: 10.1158/1055-9965.EPI-20-1635. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Khaire A., Wimberly C., Semmes E., Hurst J., Walsh K. An integrated genome and phenome-wide association study approach to understanding Alzheimer's disease predisposition. Neurobiol Aging. 2022;118:117–123. doi: 10.1016/j.neurobiolaging.2022.05.011. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Semmes E., Vijayakrishnan J., Zhang C., Hurst J., Houlston R., Walsh K. Leveraging genome and phenome-wide association studies to investigate genetic risk of acute Lymphoblastic Leukemia. Cancer Epidemiol Biomarkers Prev : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology. 2020;29:1606–1614. doi: 10.1158/1055-9965.EPI-20-0113. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Bulik-Sullivan B., Loh P., Finucane H., et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat Genet. 2015;47:291–295. doi: 10.1038/ng.3211. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Bulik-Sullivan B., Finucane H., Anttila V., et al. An atlas of genetic correlations across human diseases and traits. Nat Genet. 2015;47:1236–1241. doi: 10.1038/ng.3406. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Wielscher M., Amaral A., van der Plaat D., et al. Genetic correlation and causal relationships between cardio-metabolic traits and lung function impairment. Genome Med. 2021;13:104. doi: 10.1186/s13073-021-00914-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Sun J., Xu J., Liu C., et al. The association between human papillomavirus and bladder cancer: evidence from meta-analysis and two-sample mendelian randomization. J Med Virol. 2023;95 doi: 10.1002/jmv.28208. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Larsson S., Butterworth A., Burgess S. Mendelian randomization for cardiovascular diseases: principles and applications. Eur Heart J. 2023;44:4913–4924. doi: 10.1093/eurheartj/ehad736. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Papadimitriou N., Dimou N., Tsilidis K., et al. Physical activity and risks of breast and colorectal cancer: a Mendelian randomisation analysis. Nat Commun. 2020;11:597. doi: 10.1038/s41467-020-14389-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Patel K., Mims J., Clinger J. Otolaryngologic clinics of North America; 2023. The Burden of Asthma and Allergic Rhinitis: Epidemiology and Health Care Costs. [DOI] [PubMed] [Google Scholar]
  • 28.Kariyawasam H., Rotiroti G. Allergic rhinitis, chronic rhinosinusitis and asthma: unravelling a complex relationship. Curr Opin Otolaryngol Head Neck Surg. 2013;21:79–86. doi: 10.1097/MOO.0b013e32835ac640. [DOI] [PubMed] [Google Scholar]
  • 29.Khan D. Allergic rhinitis and asthma: epidemiology and common pathophysiology. Allergy Asthma Proc. 2014;35:357–361. doi: 10.2500/aap.2014.35.3794. [DOI] [PubMed] [Google Scholar]
  • 30.Braunstahl G., Kleinjan A., Overbeek S., Prins J., Hoogsteden H., Fokkens W. Segmental bronchial provocation induces nasal inflammation in allergic rhinitis patients. Am J Respir Crit Care Med. 2000;161:2051–2057. doi: 10.1164/ajrccm.161.6.9906121. [DOI] [PubMed] [Google Scholar]
  • 31.Braunstahl G., Overbeek S., Kleinjan A., Prins J., Hoogsteden H., Fokkens W. Nasal allergen provocation induces adhesion molecule expression and tissue eosinophilia in upper and lower airways. J Allergy Clin Immunol. 2001;107:469–476. doi: 10.1067/mai.2001.113046. [DOI] [PubMed] [Google Scholar]
  • 32.Willis-Owen S., Morar N., Willis-Owen C. Atopic dermatitis: insights from linkage overlap and disease co-morbidity. Expet Rev Mol Med. 2007;9:1–13. doi: 10.1017/S1462399407000282. [DOI] [PubMed] [Google Scholar]
  • 33.Kapoor R., Menon C., Hoffstad O., Bilker W., Leclerc P., Margolis D. The prevalence of atopic triad in children with physician-confirmed atopic dermatitis. J Am Acad Dermatol. 2008;58:68–73. doi: 10.1016/j.jaad.2007.06.041. [DOI] [PubMed] [Google Scholar]
  • 34.Slavin R. Nasal polyps and sinusitis. JAMA. 1997;278:1849–1854. [PubMed] [Google Scholar]
  • 35.Drake-Lee A., Lowe D., Swanston A., Grace A. Clinical profile and recurrence of nasal polyps. J Laryngol Otol. 1984;98:783–793. doi: 10.1017/s0022215100147462. [DOI] [PubMed] [Google Scholar]
  • 36.Fritz S., Terrell J., Conner E., Kukowska-Latallo J., Baker J. Nasal mucosal gene expression in patients with allergic rhinitis with and without nasal polyps. J Allergy Clin Immunol. 2003;112:1057–1063. doi: 10.1016/j.jaci.2003.09.042. [DOI] [PubMed] [Google Scholar]
  • 37.Zhang Y., Lan F., Zhang L. Update on pathomechanisms and treatments in allergic rhinitis. Allergy. 2022;77:3309–3319. doi: 10.1111/all.15454. [DOI] [PubMed] [Google Scholar]
  • 38.Fireman P. Cytokines and allergic rhinitis. Allergy Asthma Proc. 1996;17:175–178. doi: 10.2500/108854196778996886. [DOI] [PubMed] [Google Scholar]
  • 39.Tatiya-Aphiradee N., Chatuphonprasert W., Jarukamjorn K. Immune response and inflammatory pathway of ulcerative colitis. J Basic Clin Physiol Pharmacol. 2018;30:1–10. doi: 10.1515/jbcpp-2018-0036. [DOI] [PubMed] [Google Scholar]
  • 40.Koji K., Megumi K., Junichi S., Mayumi K., Mamitaro O. Risk factors for the development of psoriasis. Int J Mol Sci. 2019;20 doi: 10.3390/ijms20184347. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41.Kexin W., Feng S., Alejandro F.-E., Gang L., Jun-Hui W., Yu S. The properties of cytokines in multiple sclerosis: pros and cons. Am J Med Sci. 2018;356 doi: 10.1016/j.amjms.2018.08.018. [DOI] [PubMed] [Google Scholar]
  • 42.Federico D.-G., María V.H.-H. Rheumatoid arthritis. Med Clin. 2023;161 [Google Scholar]
  • 43.Ying S., Zhongjian C., Xinyuan L., Valeria C., Hong W., Xiao-feng Y. Immunosuppressive/anti-inflammatory cytokines directly and indirectly inhibit endothelial dysfunction--a novel mechanism for maintaining vascular function. J Hematol Oncol. 2014;7 doi: 10.1186/s13045-014-0080-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.Santhosh J.K., Rohit R.A. The utility of anti-inflammatory agents in cardiovascular disease: a novel perspective on the treatment of atherosclerosis. J Cardiovasc Pharmacol Therapeut. 2018;23 doi: 10.1177/1074248418778548. [DOI] [PubMed] [Google Scholar]
  • 45.Satyendra K.T., Anoop K., Shiv Prakash M., Puneet K., Ajay K.K. Study of association of varicose veins and inflammation by inflammatory markers. Phlebology. 2020;35 doi: 10.1177/0268355520932410. [DOI] [PubMed] [Google Scholar]
  • 46.Andreea I., Mădălina B., Ovidiu M., Daniel Alexandru J., Călin Petru T., Gabriela Cornelia M. Relationship between allergic rhinitis and allergic conjunctivitis (allergic rhinoconjunctivitis) - review. Rom J Ophthalmol. 2022;66 doi: 10.22336/rjo.2022.3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47.Jami M.G., Michael H.E. The role of Caveolin-1 in Retinal inflammation. Adv Exp Med Biol. 2019;1185 doi: 10.1007/978-3-030-27378-1_28. [DOI] [PubMed] [Google Scholar]
  • 48.Geraldine C., Bernard Y.P.C., Susan K., Paul M. Beware the empty curette. Orbit. 2002;21 [Google Scholar]
  • 49.Martin P., Angelo D.M., William I., et al. Inflammation, Microbiota, and prostate cancer. Eur Urol Focus. 2017;2 doi: 10.1016/j.euf.2016.08.010. [DOI] [PubMed] [Google Scholar]
  • 50.Chiara D.B., Antonio A., Maria Piera P., et al. Elevated IL-19 Serum Levels in patients with pernicious anemia and autoimmune Gastritis. Front Immunol. 2022;13 doi: 10.3389/fimmu.2022.887256. [DOI] [PMC free article] [PubMed] [Google Scholar]

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