
==== Front
Commun Biol
Commun Biol
Communications Biology
2399-3642
Nature Publishing Group UK London

39223263
6795
10.1038/s42003-024-06795-1
Article
Shared genetic architecture between gastro-esophageal reflux disease, asthma, and allergic diseases
http://orcid.org/0000-0002-6887-9432
Gong Tong 1
http://orcid.org/0000-0002-3765-2067
Kuja-Halkola Ralf 1
Harder Arvid 1
Lundholm Cecilia 1
http://orcid.org/0000-0002-4653-6615
Smew Awad I. 1
Lehto Kelli 2
http://orcid.org/0000-0003-0203-7977
Andreasson Anna 3
http://orcid.org/0000-0001-9933-3654
Lu Yi 1
http://orcid.org/0000-0003-2537-3092
Talley Nicholas J. 4
Pasman Joëlle A. 1
http://orcid.org/0000-0002-1045-1898
Almqvist Catarina 15
http://orcid.org/0000-0002-9885-8261
Brew Bronwyn K. Bronwyn.haasdyk.brew@ki.se

16
1 https://ror.org/056d84691 grid.4714.6 0000 0004 1937 0626 Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
2 https://ror.org/03z77qz90 grid.10939.32 0000 0001 0943 7661 Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
3 https://ror.org/05f0yaq80 grid.10548.38 0000 0004 1936 9377 Stress Research Institute, Department of Psychology, Stockholm University, Stockholm, Sweden
4 https://ror.org/00eae9z71 grid.266842.c 0000 0000 8831 109X School of Medicine and Public Health, University of Newcastle, Newcastle, NSW Australia
5 https://ror.org/00m8d6786 grid.24381.3c 0000 0000 9241 5705 Pediatric Allergy and Pulmonology Unit at Astrid Lindgren Children’s Hospital, Karolinska University Hospital, Stockholm, Sweden
6 https://ror.org/03r8z3t63 grid.1005.4 0000 0004 4902 0432 Centre for Big Data Research in Health & School of Clinical Medicine, UNSW, Sydney, NSW Australia
2 9 2024
2 9 2024
2024
7 107713 3 2023
28 8 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by/4.0/ Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
The aim is to investigate the evidence for shared genetic architecture between each of asthma, allergic rhinitis and eczema with gastro-esophageal reflux disease (GERD). Structural equation models (SEM) and polygenic risk score (PRS) analyses are applied to three Swedish twin cohorts (n = 46,582) and reveal a modest genetic correlation between GERD and asthma of 0.18 and bidirectional PRS and phenotypic associations ranging between OR 1.09-1.14 and no correlations for eczema and allergic rhinitis. Linkage disequilibrium score regression is applied to summary statistics of recently published GERD and asthma/allergic disease genome wide association studies and reveals a genetic correlation of 0.48 for asthma and GERD, and Genomic SEM supports a single latent factor. A gene-/gene-set analysis using MAGMA reveals six pleiotropic genes (two at 12q13.2) associated with asthma and GERD. This study provides evidence that there is a common genetic architecture unique to asthma and GERD that may explain comorbidity and requires further investigation.

Structural equation models, polygenic risk score analysis of twin data and linkage disequilibrium score regression of GWAS studies indicate genetic architecture of asthma and GERD. Gene-/gene-set analysis revealed six pleiotropic genes.

Subject terms

Gastro-oesophageal reflux disease
Asthma
Allergy
https://doi.org/10.13039/501100004359 Vetenskapsrådet (Swedish Research Council) 2018-02640 2023-02327 Brew Bronwyn K. https://doi.org/10.13039/501100004047 Karolinska Institutet (Karolinska Institute) 2020-0007 & 2022-02303 Brew Bronwyn K. issue-copyright-statement© Springer Nature Limited 2024
==== Body
pmcIntroduction

Asthma is a common inflammatory respiratory disease causing acute dyspnea and wheezing, affecting 4-9% of the global child and adult population1. The most common non-allergic comorbidity of asthma is gastro-esophageal reflux disease (GERD), characterized by the reflux of gastric acid into the esophagus causing symptoms such as heartburn and regurgitation often leading to esophagitis and complications such as Barrett’s esophagus2. GERD is predominantly an adult disease, although infants and adolescents can also suffer from GERD symptoms at clinically significant rates3. Epidemiological studies report the comorbidity of GERD in patients with asthma between 17% and 53% depending on the country of study (Western countries have a higher prevalence of GERD)3, study population type (patient group or general population), and the detection methods used for asthma and GERD (symptoms alone or lab-based monitoring)2,4,5. Given that asthma affects 4-9% of the world’s adult (18–45 years)1 population and GERD 20–40% of the adult population2, it is estimated that 1–4 in 100 adults around the world live with both asthma and GERD at any one time, varying by country and region.

GERD is well recognized to exacerbate asthma symptoms, particularly in those with severe asthma, and comorbidity has been shown to lead to worse quality of life, anxiety, and depression, compared to only having one of these illnesses6–10. However, despite this evident comorbidity burden, the origins of the co-occurrence of GERD and asthma remain unclear including whether a causal relationship exists7. Studies of adult and child populations for each disease have found evidence for a bidirectional association between the two diseases4,11, and a recent Mendelian randomization (MR) study found evidence for a weak causal association between childhood (but not adult) asthma and GERD (Odds Ratio (OR) 1.003)12. On the other hand, a number of intervention studies aiming to improve asthma symptoms by using anti-reflux (acid-reducing) medication have not been successful13,14.

There is some evidence to suggest that GERD is also associated with other atopic diseases including eczema and allergic rhinitis15–17. Eczema an inflammatory disease of the skin, and allergic rhinitis an inflammatory process of the nasal mucosa, as with asthma, are triggered by allergens and characterized by an IgE response18. Atopic diseases are characteristically childhood diseases beginning very early in life, with 20-25% of cases continuing into adulthood as well as new onset of adult cases particularly with asthma related to chronic smoking and chronic obstructive pulmonary disease (COPD)18. Asthma, GERD, allergic rhinitis, and eczema are complex diseases with reported heritability of 53%, 26%, 55%, and 74%, respectively, based on a large meta-analysis of twin studies19, and single nucleotide polymorphisms (SNP)-based heritability estimates explaining the population variation in GERD, asthma and allergic diseases have landed just above or below 10%20,21.

We recently found evidence for a common origin of asthma and allergic diseases with GERD in a co-twin control study, supporting the hypothesis that a common genetic overlap may exist15. However, no studies have quantified the genetic overlap using diverse post-genome-wide association study (GWAS) analyses. Further investigation is needed harnessing recent summary statistics from GWAS and leveraging the rapid progress in polygenic risk score (PRS) and genomic structural equation modeling (genomic SEM) approaches to identify SNPs with the effects on cross-trait liability to GERD, asthma, and allergic diseases.

The aim of this study was to investigate the evidence for common genetic factors underlying the comorbidity between asthma, allergic rhinitis, and eczema with GERD using a triangulation of methods including both quantitative genetic approach (using classical twin modeling) and molecular genetic approaches (using PRS analysis, linkage disequilibrium score regression (LDSC), genomic SEM, and gene-based association tests).

Our study shows that by using quantitative twin modeling and molecular genetic techniques there is evidence for a modest shared genetic origin for asthma and GERD that may explain high rates of comorbidity. We also identify possible gene targets for further investigation. Little evidence is observed for GERD with eczema or allergic rhinitis.

Results

Demographic and phenotypic information for each twin sub-cohort can be found in Supplementary Table 1. For the cohorts combined, the prevalence of asthma, allergic rhinitis, eczema, and GERD was 8.0%, 11.4%, 7.0%, and 12.0%, respectively. Bivariate phenotypic associations were confirmed for asthma and GERD-adjusted odds ratios (adjOR) 1.61, 95% CI 1.43, 1.80; allergic rhinitis and GERD-adjOR 1.14 (95% CI 1.02, 1.28); eczema and GERD-adjOR 1.14 (95% CI 1.00, 1.30) (Supplementary Table 2).

Quantitative genetic analyses

Univariate results found higher correlations for GERD and all allergic diseases in monozygotic (MZ) twins compared to dizygotic (DZ) twins (Table 1), almost double in all cases suggesting substantial genetic components for each disease. We did not find any systematic pattern in the differences between males and females, except for opposite-sexed twins with the lowest intra-class correlations when compared with female or male DZ twins (Supplementary Table 3). Univariate quantitative genetic modeling found that AE was the best-fitting model with the lowest Akaike information criterion (AIC) for asthma and allergic rhinitis and ADE for eczema and GERD, suggesting a minimal role of shared environmental influences (Table 1). In the AE/ADE models, genetic influences explained 32% of the variance for GERD (95% CI 0.26, 0.39), 63% for asthma (95% CI 0.58, 0.67), 61% for allergic rhinitis (95% CI 0.57, 0.65) and 44% for eczema (95% CI 0.37, 0.51).Table 1 Univariate twin correlations and twin model parameter estimates for GERD, asthma, and allergic diseases in all 3 twin sub-cohorts combined

	N (twin pairs)	Twin correlations (95% CI)	Model	Twin model estimates (95% CI)a	
MZ	DZ	rMZ	rDZ	A	C or D	A + D	E	AIC	
GERD	5141	9374	0.33 (0.28, 0.37)	0.11 (0.07, 0.15)	ACE

ADEb

AE

	0.30 (0.25, 0.35)

0.12 (−0.10, 0.33)

0.30 (0.24, 0.35)

	0.00 (0.00, 0.00)

0.21 (−0.03, 0.45)

-

	-

0.32 (0.26, 0.39)

-

	0.71 (0.65, 0.76)

0.68 (0.61, 0.74)

0.70 (0.65, 0.76)

	21,388.85

21,385.89

21,386.85

	
Asthma	0.64 (0.61, 0.68)	0.29 (0.25, 0.34)	ACE

ADE

AEb

	0.62 (0.53, 0.71)

0.58 (0.34, 0.83)

0.63 (0.58, 0.67)

	0.01 (−0.06, 0.08)

0.05 (−0.21, 0.31)

-

	-

0.63 (0.58, 0.68)

-

	0.37 (0.32, 0.42)

0.37 (0.32, 0.42)

0.37 (0.33, 0.42)

	15,693.01

15,692.73

15,691.01

	
Allergic rhinitis	0.62 (0.59, 0.65)	0.30 (0.26, 0.33)	ACE

ADE

AEb

	0.61 (0.57, 0.65)

0.60 (0.40, 0.81)

0.61 (0.57, 0.65)

	0.00 (0.00, 0.00)

0.01 (−0.21, 0.23)

-

	-

0.61 (0.56, 0.65)

-

	0.39 (0.35, 0.43)

0.39 (0.35, 0.44)

0.39 (0.35, 0.43)

	19,521.58

19,521.57

19,519.58

	
Eczema	0.43 (0.38, 0.49)	0.15 (0.10, 0.20)	ACE

ADb

AE

	0.41 (0.34, 0.47)

0.18 (−0.10, 0.46)

0.41 (0.34, 0.47)

	0.00 (0.00, 0.00)

0.26 (−0.05, 0.56)

-

	-

0.44 (0.37, 0.51)

-

	0.59 (0.53, 0.66)

0.56 (0.49, 0.63)

0.59 (0.53, 0.66)

	14,260.13

14,257.42

14,258.13

	
A additive genetic component, D non-additive/dominant genetic component, A + D broad-sense heritability component, C shared environmental component, E non-shared environmental component (including measurement errors), AIC Akaike information criterion.

aAdjusted for sex and birth year (continuous, standardized).

bBest-fitted models with the lowest AIC.

The phenotypic correlations (within individuals) between GERD and allergic diseases were moderate for GERD and asthma, r = 0.14; and weak for GERD and allergic rhinitis as well as GERD and eczema, r = 0.04 and 0.05, respectively. Cross-twin cross-trait correlations were small, but they were higher for MZ than DZ for GERD and asthma suggesting genetic influences, whereas correlations for GERD with allergic traits and eczema were close to zero (Table 2). Stratification by sex found similar results between males and females (Supplementary Table 3). The best-fitting bivariate model for GERD and asthma was the AE model (for parameter estimates for all models tested see Supplementary Tables 4, 5, and 6). About 56% of the covariance between GERD and asthma was explained by additive genetics and the other 44% was explained by unique environment (Supplementary Table 4). The decomposed genetic correlations were modest and positive between GERD and asthma rA = 0.18, 95% CI 0.08, 0.28, and there was also a weak positive unique environmental correlation, rE = 0.11, 95% CI 0.03, 0.20 (Table 2). The phenotypic and cross-twin cross-trait correlations were too weak for GERD, allergic rhinitis, and eczema to estimate meaningful genetic and environmental correlations.Table 2 Bivariate association and quantitative genetic model estimates for GERD and allergic diseases in all three twin sub-cohorts combined

	GERD
n (%)	Allergic diseases
n (%)	Concordant pairsa
n	Phenotypic correlations (95% CI)	CTCT correlations (95% CI)	Twin model estimates (95% CI)b	
rMZ	rDZ	rA	rE	
GERD & Asthma	3420 (12.0)	2257 (8.0)	319	0.14 (0.11, 0.17)	0.07 (0.02, 0.12)	0.04 (0.00, 0.08)	0.18 (0.08, 0.28)	0.11 (0.03, 0.20)	
GERD & Allergic Rhinitis	3420 (12.0)	3226 (11.4)	403	0.04 (0.01, 0.07)	0.02 (−0.03, 0.07)	0.03 (−0.01, 0.07)	NAc	NAc	
GERD & Eczema	3420 (12.0)	1984 (7.0)	266	0.05 (0.02, 0.09)	0.05 (−0.01, 0.10)	0.02 (−0.02, 0.06)	NAc	NAc	
CTCT cross-twin cross trait, MZ monozygotic, DZ Dizygotic, A additive genetic component, E non-shared environmental component (including measurement errors).

aThe number of pairs where one twin has GERD and the other twin has an allergic disease.

bAdjusted for sex and birth year (continuous, standardized).

cPhenotypic and CTCT correlation coefficients were too low to present twin model estimates, however, completeness estimates are presented in (Tables S4–S6).

Polygenic risk score analyses

Among the 26,895 twins with genotype data, we observed that PRS of asthma had a better prediction power than other traits (area under the receiver operating curve (AUC) range: 0.60–0.66, see Supplementary Table 7 and Supplementary Fig. 2). Associations between PRS and phenotypes for GERD and all allergic diseases are presented in Fig. 1 and Supplementary Table 8. The PRS of GERD was associated with asthma (aOR 1.14, 95% CI 1.08, 1.20 per one standard deviation (SD) increase in GERD-PRS), and similarly, we also observed the PRS of asthma being associated with GERD (aOR 1.09, 95% CI 1.05, 1.14 per one SD increase in asthma-PRS). There were no statistically significant associations observed for GERD with allergic rhinitis or eczema, except for GERD-PRS and allergic rhinitis which had an odds of 1.08 (95% CI 1.03, 1.13).Fig. 1 Associations between phenotypes and polygenic risk scores for GERD with eczema, allergic rhinitis, and asthma in Swedish twins.

The associations being presented in odds ratios (OR) are statistically significant when the horizontal line of the confidence interval (error bars) does not cross the vertical gray line at the value 1.

Linkage disequilibrium score regression

SNP-based heritability results and LDSC genetic correlations are shown in Table 3 and Supplementary Table 9. Genetic correlations between asthma, eczema, and GERD were significant after Bonferroni correction (rg = 0.48 asthma-GERD and rg = 0.20 eczema-GERD, p-values < 0.01) but did not statistically significantly differ from zero for allergic rhinitis and GERD. Correlations were higher for adult asthma with GERD than childhood-onset asthma (COA) with GERD (rg = 0.33, rg = 0.08, respectively).Table 3 Linkage-disequilibrium regression results between GERD and asthma, allergic rhinitis, and eczema

Allergic diseases	N for allergic disease (sample prevalence %,
population prevalence %)a	h2 SNP (allergic disease) (SE)b	Genetic correlation
rg (CI)	p-valuesc	
Asthma	1,800,785 (8.5%, 8%)	0.08 (0.004)	0.48 (0.42, 0.53)	7.83 × 10−75	
Adulthood onset asthma	327,253 (8.1%, 8%)	0.13 (0.01)	0.33 (0.27, 0.39)	8.37 × 10−26	
Childhood-onset asthma	314,633 (4.4%, 5%)	0.30 (0.03)	0.08 (0.03, 0.12)	0.0015	
Allergic rhinitis	38,838 (27.2%, 25%)	0.12 (0.02)	0.15 (0.02, 0.27)	0.0286	
Eczema	796,661 (2.8%, 3%)	0.08 (0.02)	0.20 (0.11, 0.30)	3.49 × 10−5	
aThe population prevalence estimates were based on literature for most traits. However, we assumed a lower population prevalence of childhood-onset asthma to match the reported SNP-based heritability from the original GWAS.

bThe SNP-based heritability for GERD, i.e., h2 SNP (GERD) and SE are 0.13 (0.01), based on the sample and population prevalence at 21.5% and 20% and the sample size of 332,601.

cBonferroni corrected the significance level across 5 tested LD score regressions for the genetic correlation of allergic diseases and GERD at 0.01.

Genomic SEM

A single latent factor fits the genetic covariance structure reasonably well, with comparative fit index (CFI) = 0.93 and standardized root mean square residual (SRMR) = 0.09. All traits loaded significantly on the common factor, with the strongest loading for eczema (β = 0.85, SE = 0.10, p = 1E-33) and the lowest loading for GERD (β = 0.24, SE = 0.03, p = 2E-14; Fig. 2, Supplementary Table 10). Using the combined asthma trait instead of childhood onset and adult onset separately deteriorated fit, with SRMR falling short of the <0.10 criterion (CFI = 0.96, SRMR = 0.15).Fig. 2 Common factor (F1) model for all traits as estimated in Genomic SEM.

F1 is a latent common genetic factor of the genetic components of five GWAS phenotypes, i.e., adult-onset and childhood-onset asthma, allergic rhinitis, eczema, and GERD. The loading of adult-onset asthma was fixed to 1 for model identification purposes. One-headed arrows showed path regression estimates from the independent variable to the dependent variables. Standardized path estimates are given with their standard error in parentheses. The latent u variables with circular arrows reflect the residual variance in the genetic indicators not explained by the common factor.

Bidirectional two-sample MR analyses

There was support for a causal effect of genetic liability to asthma on the increased risk of GERD, which was consistent across different sensitivity analyses (Inverse variance weighted (IVW) OR 1.09, 95% CI 1.05, 1.14, p =  6.55 × 10−5). In the other direction, similar effect estimates of genetic liability to GERD on increased risk of asthma were also detected (OR 1.27, 95% CI 1.12, 1.43, p  =  2.65 × 10−2). The MR-Egger regression intercepts did not significantly deviate from zero (Supplementary Table 11), suggesting no evidence of horizontal pleiotropy. Leave-one-out and Q-heterogeneity analysis showed that the effect estimates were not overly influenced by any one variant (Supplementary Figs. 3 and 4). No support for association between other allergic traits with GERD was observed (Supplementary Table 11, Supplementary Figs. 5–8).

Gene-based association analysis

Using the Multi-marker Analysis of GenoMic Annotation (MAGMA) we identified genes significantly associated with asthma (n = 352), allergic rhinitis (n = 2), eczema (n = 65), and GERD (n = 44) (Supplementary Tables 12–17 (figshare/supplementarytables12-17) and Supplementary Figs. 9–12). After comparison, asthma and GERD were found to share six genes (ERBB3, RBM6, HLA-B, SDK1, RERG, RAB5B), one of which, ERBB3, was also significantly associated with both eczema and GERD. There were no other pleiotropic matches for eczema and GERD and none for allergic rhinitis and GERD (Table 4). Furthermore, we obtained regional association plots close to these six shared genetic loci from FUMA and confirmed that one common locus at 12q13.2 (where RAB5B and ERBB3 are located) is associated with asthma and GERD (Supplementary Figs. 13–17). The independent SNP rs2069408 identified from the regional plot (risk allele G, asthma-GWAS p = 5.397e-10, beta = 0.0313) and the lead SNP rs11171710 (risk allele A, GERD-GWAS p = 4.174e-9, beta = 0.0351) were also positively associated with both asthma and GERD (Supplementary Fig. 13). Based on the GWAS catalog database, this locus is also associated with 269 traits including educational attainment, type 1 diabetes, body mass index, eosinophil and lymphocyte count, smoking, and hypothyroidism (Supplementary Table 16, figshare/supplementarytables12-17).Table 4 The top significantly associated GERD genes identified by gene-based MAGMA analysis, which were also significantly associated with other allergic diseases after multiple-testing correction

Gene location	Gene symbol	Associated allergic diseases	p-value for GERD	p-value for the allergic diseases	Examples of associated traits from GWAS catalog searches	
12:56076799-56103505 (12q13.2)	ERBB3	Asthma

Eczema

	3.87 ×  10−7	1.23  ×  10−11 (asthma)

9.70  ×  10−8 (eczema)

	Type 1 diabetes, BMI, education, math ability, moderate or severe asthma, smoking,	
3:49940007-50100045 (3p21.31)	RBM6	Asthma	1.41 ×  10−7	3.05 ×  10−7	HDL, Body fat distribution, intelligence, BMI, education, insomnia, type 2 diabetes, income	
6:31353872-31367067 (6p21.33)	HLA-B	Asthma	4.08 ×  10−10	1.10 ×  10−14	Hip-waist ratio adjusted for BMI, blood protein levels, psoriasis, HDL, LDL, asthma, autism spectrum disorder or schizophrenia, eczema	
7:3301252-4269000 (7p22.2)	SDK1	Asthma	8.99 ×  10−10	7.30 ×  10−7	Insomnia, math ability, smoking, lymphocyte, and eosinophil count, multisite chronic pain, Alzheimer’s disease or GERD, risk-taking behavior, neuroticism	
12:15107783-15348675 (12p12.3)	RERG	Asthma	6.33 ×  10−8	4.85 ×  10−7	Glomerular filtration rate/creatinine levels, vaginal microbiome measurement, urate levels, education, red blood cell count, Alzheimer’s disease or GERD, lung function (FEV1/FVC)	
12:55973913-55996683 (12q13.2)	RAB5B	Asthma	2.10 ×  10−7	1.61 ×  10−9	Asthma, hypothyroidism, HDL, Peptic ulcer or GERD drug use, FEV1, education, allergic disease (age of onset)	
The Bonferroni-corrected p-value thresholds for asthma, allergic rhinitis, eczema, and GERD are 2.52 × 10−6, 2.57 × 10−6, 2.53 × 10−6, 2.62 × 10−6, respectively.

From all curated gene sets and GO terms obtained from MsigDB, we additionally identified hundreds of significantly enriched asthma-associated gene sets, 18 eczema-associated gene sets, and one GERD-associated gene set which is shared with the asthma-associated gene set: (GO_bp: go_positive_regulation_of_gene_expression, within the GO Biological Process aspect, pGERD = 0.002 and pasthma = 0.0003 after Bonferroni correction, Supplementary Table 17, figshare/supplementarytables12-17).

In the general tissue expression analysis, we found evidence for a small but significant enrichment exclusively in brain tissues for GERD-associated genes; blood, spleen, lung, and small-intestine tissues for asthma-associated genes; and spleen, blood, and small intestine tissues for eczema-associated genes, suggesting different tissues are responsible for GERD-gene and upregulated asthma-/eczema-gene signals, respectively (Supplementary Figs. 18–21).

Discussion

This study explored the potential shared genetic origin between GERD with asthma, allergic rhinitis, and eczema using quantitative genetic and molecular genetic approaches applied to large twin datasets. We found consistent evidence of a modest genetic overlap between GERD and asthma across all methods, however, the analyses for allergic rhinitis and eczema with GERD revealed small to negligible genetic overlap.

We found that the phenotypic correlation for GERD and asthma was moderate at 0.14 and just over half of this was attributed to a shared genetic architecture. PRS and phenotypic associations were observed falling in the range of OR 1.09–1.14 per one SD increase, which indicates modest genetic overlap. The SNP-based genetic correlation was moderate at 0.48. The Genomic SEM results show that the common genetic liability to GERD, asthma, allergic rhinitis, and eczema can be summarized by a single underlying common factor. These findings confirm our earlier co-twin control and population-based analyses for GERD and asthma suggesting a common genetic origin15, as well as the evidence for genetic correlation between asthma with GERD (rg = 0.40) from a recent large-scale GWAS using global biobank meta-analysis initiative data20. In addition, there was a moderate unique environment correlation. Stronger correlations for adult rather than child onset asthma may help to point towards shared biological mechanisms of interest especially given that the genetic profiles for adult and child onset asthma have some differences22. However, it should be noted that a common etiology only accounts for a part of the comorbidity, which means direct pathways previously proposed may be important including microaspiration, neuroinflammation, or vagal reflex responses23. For example, clues may be found in recent studies using in vivo, ex vivo, and proteomic methods showing GERD to be associated with weakened bronchial epithelial barriers and increased inflammatory factors such as IL-33 upregulation8,9.

Allergic rhinitis and GERD were weakly associated (phenotypic correlation = 0.04), and a genetic association between GERD-PRS and allergic rhinitis phenotype was supported by the PRS analysis. However, the lack of consistency in the direction of effects measured by the cross-twin cross-trait correlations, LDSC regression, and bidirectional MR analysis, suggests no evidence of a causal relationship between allergic rhinitis and GERD. Similarly, eczema and GERD estimates were null for bidirectional MR analyses, not supporting a causal connection either. Meanwhile, the LDSC regression revealed a possible signal for genetic overlap between eczema and GERD using summary data, which we could not replicate with individual-level data. Our previous study applying a co-twin control design also revealed only a weak association between self-reported GERD with allergic rhinitis and eczema15. Given that asthma, allergic rhinitis, and eczema have shared genetic architecture24, we would expect to find some shared genetic nature between the other two allergic diseases with GERD as we did for asthma-GERD but were not able to pick up the weak signals. This could possibly be due to the lack of power of the twin modeling and published allergic rhinitis/eczema GWAS. For example, the available summary data for allergic rhinitis from Waage et al was based on 38,838 individuals25, including 10,563 cases, which was relatively smaller than the 153,763 asthma cases or 71,552 GERD cases (seen also in Supplementary Fig. 2 and Table 5). Further, differences in case recruitment (e.g., low prevalence in the UKB sample and high prevalence in the FinnGene sample) in the eczema GWAS meta-analyses may also have contributed to a diluted SNP-based heritability26. Therefore, although the triangulation of genetic methods in our study does not support a genetic explanation for GERD-eczema and GERD-allergic rhinitis comorbidity, future research harnessing larger, more accurately defined cohorts will provide further clarification.Table 5 Detailed information of published GWAS summary data of asthma, allergic diseases, and GERD used to calculate the polygenic risk scores and used in the LD score regression and genomic SEM analyses

Source	PMID	Phenotype		Sample size	Reference panel used	Statistical method used	SNP-based heritability reported	Summary data download URL	
Asthma	
 Ferreira MA et al.22	30929738	Childhood-onset (COA) and adulthood-onset asthma (AOA)		447,628 (however, the UKB and QSKIN data is based on 327253 and 314633 individuals)	1000 Genome Project	Linear mixed model +  Logistic regression + inverse-variance-weighted fixed-effects meta-analysis	h2SNP (COA) = 25.6%, h2SNP (AOA) = 10.6%	https://genepi.qimr.edu.au/staff/manuelF/gwas_results/CHILD_ONSET_ASTHMA.20180501.allchr.assoc.GC.gz

https://genepi.qimr.edu.au/staff/manuelF/gwas_results/ADULT1_ADULT2_ONSET_ASTHMA.20180716.allchr.assoc.GC.gz

	
 Zhou W et al.40	Cell Genomics (in press)	Asthma		153,763 cases/1,647,022 controls	1000 Genome Project + Human Genome Diversity Project	SAIGE or REGENIE by cohort and inverse-variance weighted fixed effect model for meta-analyses	h2SNP = 8.7%	https://github.com/globalbiobank	
Allergic rhinitis	
 Waage J et al.25	30013184	Allergic rhinitis (symptoms/ diagnosis) ever		59,762 cases/152,358 controls. However, the downloaded summary data included 38,838 individuals	UK10K	Inverse-variance weighted fixed effect model for meta-analyses	h2SNP = 7.8%	https://hmgubox.helmholtz-muenchen.de/d/b55da086360c40118ae8/files/?p=/2018-05-11_EAGLE_AR.txt.gz	
Eczema	
 Sliz E et al.26	34454985	Diagnosis of atopic dermatitis		22,474 cases/774,187 controls	Finnish population-specific SISu v3 + Estonian-specific reference panel +  1000 Genomes phase 3	Inverse-variance weighted fixed effect model for meta-analyses	h2SNP = 5.4%	http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST90027001-GCST90028000/GCST90027161/harmonized/34454985-GCST90027161-EFO_0000274.h.tsv.gz	
GERD	
 An J et al.21	31346403	GERD: Self-reported or diagnosis or medication		71,522 cases /261,079 controls (excl 23andMe)	1000 Genomes Phase 3	Fixed effect model for meta-analyses	h2SNP = 11.3%	https://figshare.com/articles/dataset/GERD_GWAS_summary/8986589	

The MAGMA analysis identified a distinct signal at locus 12q13.2 that is associated with both asthma and GERD and also corresponds to two pleiotropic genes (i.e., RAB5B and ERBB3) identified in the gene-based MAGMA results21,27–29. Locus 12q13.2 has also been reported to be associated with other obesity-related and autoimmune-related traits, although a precise biological pathway cannot be pinpointed by current gene- and gene-set-based results.

We confirmed the bidirectional causal association of asthma and GERD reported in previous studies11,12. The association between other allergic traits and GERD has seldom been studied, except for Ahn and colleagues who report a causal association between genetic liability to GERD with eczema using the same summary statistics30. Our null finding on GERD and eczema could be due to the stricter exclusion of any ambiguous SNPs being IVs (18 vs. 21 variants). The downstream evaluation by MR results (including one pleiotropic SNP at 12q13) did not show evidence for horizontal pleiotropy, suggesting that the putatively causal association between asthma and GERD was not biased by the two pleiotropic genes mentioned above, but it could still be mediated by them through possible inflammatory pathways (vertical pleiotropy). Further research is needed to tease out the specific role of 12q13.2 in GERD and asthma and the possible involvement of obesity, autoimmunity, and inflammation. In addition, our MAGMA analyses implicated another four pleiotropic genes—RERG, RBM6, SDK1, and HLA-B as potential targets for further investigation into asthma and GERD.

Using the MAGMA tissue enrichment analysis to detect differentially expressed gene sets for each disease we were expecting to find an overlapping tissue type for GERD and asthma or allergic rhinitis which may provide indication of a candidate gene set to explain genetic overlap. However, the results pointed to different tissue types, asthma gene sets were expressed in blood, spleen, small intestine, and as expected, in lung tissues. GERD genes were most expressed in brain tissues. Rather than shared expression in a shared tissue type, it may be that shared genes for asthma and GERD behave differently in each tissue for each disease, i.e., that gene in blood and spleen lead to inflammatory processes causing asthma, and that in the brain they play a sensory role in reflux pain. The brain is known to play an important role in behavioral traits such as smoking and food consumption choices which are important factors for GERD development31. Alternatively, expressed genes may be working through other pathways such as internalizing psychopathology disorders which are known comorbidities for both asthma32,33 and GERD34. Indeed earlier work by this team and others looking at asthma and GERD comorbidity found that affective traits (depression, anxiety, and neuroticism) were important confounders of GERD and allergic disease associations15.

The strengths of this study were firstly the size and scope of the study population and secondly, the triangulation of evidence from different approaches. We had access to three cohorts of the Swedish Twin Registry data covering a range of ages including children and adults over almost a whole century of birth dates. This improves the validity and generalizability of the study. Using a number of different methods harnessing both phenotypic and genotypic data from the twin cohorts allowed for triangulation of evidence independent of each other. The consistent findings provide strong evidence for a genetic overlap at least in part for GERD and asthma. Finally, due to recent industrious efforts by other research groups to produce summary statistics from large GWAS studies and create gene repositories such as MAGMA, we were able to harness these to carry out polygenic risk score, LDSC regression, and gene-based association analysis to confirm and extend the quantitative gene analyses.

This study also has some limitations. Firstly, the allergic rhinitis phenotype could be under-estimated in this study because the definition was based on a self-report of a doctor’s diagnosis. Since allergic rhinitis often presents as a mild disease it may not be given a doctor’s diagnosis and may be self-managed or with over-the-counter medications. Diagnosis of GERD was based on symptoms which are diagnostic but while the analyses would have been strengthened by also having objective test evidence of GERD (endoscopy and/or esophageal pH testing) this was not available. Secondly, the exploration of reported pleiotropic genes is based on very stringent p-value thresholds, which means we may have missed other potential target genes. Future studies should make use of other new and evolving analytical methods to investigate the strength of the pleiotropic genetic effects on both asthma and GERD at specific risk loci. Finally, there may be misclassification of phenotypes used in current GWAS due to the need to restrict time windows to boost sample size, thus diluting accuracy. As a consequence, in the context of heterogeneous and highly prevalent diseases like allergic rhinitis, eczema, and GERD, GWAS-identified common variants only explained a small part of the heritability compared to the moderate-to-high heritability found in twin studies.

In conclusion, using quantitative twin modeling and molecular genetic techniques we were able to show evidence for a modest shared genetic origin for asthma and GERD that may explain high rates of comorbidity. We also identified possible gene targets for further investigation. Little evidence was observed for GERD with eczema or allergic rhinitis.

Methods

Study participants

The study was based on phenotypic and genotyped data of participants from the Swedish Twin Registry (STR). The STR has collected extensive data on twins born 1886–2015 to study the genetic and environmental aspects of a broad spectrum of traits, phenotypes, and disorders35,36.

In this study, all twins born in Sweden between 1911 and 1985 were invited to participate. Sub-cohorts included: the Study of Twin Adults Genes and Environment (STAGE)—born 1959-1985, response rate 59.6%, n = 25,387; TwinGene-born 1911–1958, response rate 46%, n = 14,590; and Screening Across the Lifespan of Twins—Young (SALTY)—born 1939–1958, response rate 65%, n = 6605. Information on zygosity was retrieved from either the collected DNA sample or the answers to five questions on twin similarity throughout questionnaires/interviews35. We excluded twin pairs if any twin’s asthma, allergic rhinitis, eczema, or GERD information was missing or if only one twin participated in the study. In total, complete phenotypic data was available for 28,394 (61%) twins in 14,197 twin pairs.

DNA for polygenic risk score analysis was obtained at the study enrollment from saliva samples for STAGE and SALTY and from blood samples for TwinGene37. Individuals with DNA samples were genotyped using the Illumina Global Screening Array BeadChip, Illumina PsychArray BeadChip, and Illumina OmniExpress bead chip. Genotype imputation was performed using 1000 Genome data (Phase 3 Version 5) as the reference panel. Phasing was performed using Shapeit2 on each chromosome, and imputation was performed using Minimac3 on 5 Mb chromosomal chunks (with a window of 1 Mb on either side). After imputation, ~47 M markers were available, and over 7 M common variants (MAF ≥ 1%) have high imputation quality (imputation R2 ≥ 0.8).

During the quality control procedures of the genotype data, low-quality markers were removed, e.g., with call rates < 98%, that deviate grossly from the Hardy–Weinberg Equilibrium (p-value < 1e-6), large allele frequency differences from the 1000 Genome European reference samples, and low-quality score, i.e., mean GenCall scores < 0.5. In total, we removed ~2% of samples with a sample calling rate < 98%; unusual heterozygosity; possible sample contamination; sex violation, or non-European ancestral outliers. After removing low-quality samples and imputing the genotypes of MZ twins from their paired genotyped twins, there were 26895 unique samples (9589 twins from STAGE, 6 398 twins from SALTY, and 10,908 twins from TwinGene).

All participants provided informed consent before participation, and the data was pseudonymized for management and analyses. Ethical approval was provided by the Swedish Ethical Review Authority. All ethical regulations relevant to human research participants were followed.

GERD

Data were collected from questionnaire/interview data which included GERD-specific symptom questions38,39. GERD was defined as reporting 1) heartburn more than once per week (“heartburn symptoms”), OR 2) pain behind the sternum more than once per week, and the pain was relieved by antacid or acid-suppressing medicine (“reflux-like chest pain”).

Asthma, allergic rhinitis, and eczema

Data were collected from questionnaire/interview data which included questions about asthma, allergic rhinitis, and eczema. A case was defined as reporting a current disease and having received a doctor’s diagnosis24.

Polygenic risk scores (PRS)

Individual PRS for GERD, asthma, allergic rhinitis, and eczema were generated in each sub-cohort using summary statistic data from the largest to-date and publicly available GWAS on the relevant diseases (see Table 5 for information on discovery sets).

Searching the GWAS Catalog, Pubmed, MedRxiv, and UK Biobank’s website revealed several available summary statistic data for allergic traits and GERD. The analyzed association results were used for each SNP from the discovery sets with the largest sample sizes. First, the largest genome-wide association study of asthma to date (153,763 cases and 1,647,022 controls) was identified via meta-analysis across 18 biobanks spanning multiple countries and ancestries. Specifically, the European ancestry-based summary statistics for asthma from 14 biobanks, i.e., BioMe, BioVU, CCPM, DECODE, ESTBB, FinnGen, GS, HUNT, Lifelines, MGB, MGI, QSKIN, UCLA, UKBB were used as discovery samples to estimate the PRS40. However, most of the GWAS on asthma phenotypes are under-powered. For example, Nick Shrine and others have published one study on moderate-to-severe asthma using 5135 cases and 25675 controls from the UK41. The eosinophilic asthma phenotype was under-powered in the UK Biobank sample with 2302 cases and 358892 controls. No GWAS reported on allergic asthma. We could only use one available powerful GWAS (based on a UK Biobank sample) on childhood-onset asthma (COA) and adult-onset asthma (AOA) (COA cases: 13,962, AOA cases: 26,582, common set of controls: 300,671)22.

For allergic rhinitis, the largest genome-wide meta-analysis available and published was chosen (59,762 cases and 152,358 controls) to be the discovery sample25. Non-allergic rhinitis with 2028 cases and 9606 controls was not used due to the small sample size. The authors combined data from children 6 years and above as well as adult participants. Allergic rhinitis was defined as individuals having either a diagnosis or symptoms of allergic rhinitis depending on cohort-specific data availability (23andMe, UKBB, deCODE being the largest three studies together with 22 researcher-led cohorts).

Regarding eczema, the summary statistics data from the largest genome-wide meta-analysis on eczema using participants from the FinnGen, Estonian Biobank, and the UK Biobank with European ancestry (22,474 cases and 774,187 controls) was chosen26. Phenotypic definition for eczema was based on diagnostic records with relevant ICD-codes (ICD-10 L20; ICD-9 6918; ICD-8: 691) among adults.

Regarding GERD, the largest genome-wide meta-analysis of GERD (80,265 cases and 305,011 controls) using population-based samples from the UK, the USA, and Australia was chosen to be the discovery set21. Slightly different definitions of GERD phenotype were used in the UK Biobank (diagnosis of GERD by ICD-10 codes, self-reported GERD condition, or use of GERD medication which in total consists of 68,535 cases and 250,910 controls), 23andMe (self-reported doctor diagnosis with heartburn, acid reflux or acid reflux disease, or treated with medicines for acid reflux/heartburn, which in total consists of 8743 GERD cases and 43,932 controls), and QSkin samples (self-reported heartburn or dispensed reflux medications identified from the PBS database, which in total consists of 2987 cases and 10,169 controls).

There was no sample overlap between the twin sub-cohorts and the summary statistic data from discovery sets. We used SBayesR to generate individual PRS for each trait, which has better prediction accuracy compared to other conventional PRS approaches including clumping and thresholding methods42. Due to the complex and long-ranging linkage disequilibrium (LD), it is recommended to exclude the major histocompatibility complex (MHC) region when applying SbayesR. However, several of our traits harbor variants of large effects in the MHC region. We therefore first extracted the most significant variant in the MHC region for each trait, applied SbayesR as normal (which excludes the MHC region), and added back in the most significant variant, using the raw effect size from the original GWAS. After obtaining the SBayesR estimates of SNP effects, we used plink2’s—score command to generate polygenic risk scores and standardized the scores.

Statistics and reproducibility

Bivariate associations between allergic diseases and GERD

To confirm the phenotypic associations between each allergic disease including asthma with GERD for each twin sub-cohort, we applied generalized estimating equations (GEE) with logit link function and corrected for twin clustering. Models were adjusted for birth year and sex.

Quantitative genetic analyses

Individual cross-trait (i.e., phenotypic) correlation, cross-twin within-trait, and cross-twin cross-trait correlations were estimated as tetrachoric correlations for each allergic disease including asthma and GERD. Different univariate and bivariate structural equation models were then tested to quantify the proportion of variation in liability to allergic diseases and GERD that was due to genetic and environmental components43. Estimations using classic twin methodology were calculated for (1) additive genetic deviations (noted as A, assuming MZ twins share 100% and DZ twins share 50% of genetic variance); (2a) non-additive/dominant genetic deviations (noted as D, assuming MZ twins share 100% and DZ twins share 25% of the dominance deviation variance); or (2b) shared environmental effects (noted as C, assuming MZ and DZ twins share 100% environmental influences), and (3) unique environmental effects (noted as E)44. Univariate ACE (including A, C, and E-sources of variance and covariance), ADE, and AE models were fitted, with adjustments for sex and birth year. For a simplified interpretation of bivariate ADE models, the broad-sense heritability A + D was also calculated. The likelihood ratio test and the AIC were used to select the best-fitting model. Bivariate models were fitted with a 4 × 4 predicted variance-covariance matrix for MZ and DZ pairs to decompose the variation within diseases and covariation between diseases into A, C/D, and E components. Additionally, rA/rC/rD/rE was used to indicate the strength/correlation efficiency of A/C/D/E explaining the phenotypic correlation between diseases. The Wald method was used to calculate 95% CIs for all parameter estimates.

Polygenic risk score analyses

First, correlations between the PRS of each trait in each sub-cohort and the relevant phenotypic definitions were estimated to validate the PRS. The PRS prediction accuracy and performance were assessed by using the AUC value, OR by decile (i.e., checking the sign of the logistic regression coefficient in the expected direction), and Nagelkerke Pseudo-R2. Second, GEE with logit link function was used to assess the association between PRS for each allergic disease with GERD phenotype, and the PRS for GERD with each allergic disease phenotype among all twins with available genotype data. The GEE quasi-likelihood approach modeled the correlated data by specifying an exchangeable working correlation matrix to account for the correlation due to clustering within twin pairs. OR and 95% confidence intervals (CI) were presented without or with adjustment for birth year, sex, and the interaction between the top 5 principal components (population stratifications) with sub-cohort (i.e., STAGE, TwinGene, SALTY). The analyses were performed in SAS 9.4.

Linkage disequilibrium score regression (LDSC)

The genetic correlation between allergic diseases and GERD was estimated in LDSC using published GWAS summary statistics (see Table 5). Association test statistics were regressed on their LD scores, a measure of each SNP’s relationship with other variants. SNP-based heritability (h2SNP converted to liability scale with population-based prevalence from literature and sample prevalence data from summary statistics) was estimated as well as the genetic correlation (rg) for each allergic disease with GERD using LDSC. Statistical significance was assessed using Bonferroni correction45. Analysis was performed using the LDSC command line tool (Python 2.7.5).

Genomic structural equation model

Using Genomic Structural Equation Modeling46 (Genomic SEM) we aimed to assess if a single underlying latent factor could explain the overlap between GERD, asthma, and allergic diseases. Genomic SEM uses GWAS summary statistics to identify factor structures in the genetic correlation pattern between traits. Because of the conceptual and factual genetic overlap (see Supplementary Fig. 1) between asthma and the asthma sub-types (childhood and adult-onset) we fit separate models containing either all-onset asthma or the sub-types. Following common practice, cut-off values for acceptable model fit were set at CFI >0.90 and SRMR <0.10. The loading of the first indicator was fixed at 1. Asthma was a Heywood case in the model containing the all-onset asthma trait, and its variance was forced to be positive (>0.001). The analyses were performed in R version 4.2.3.

Bidirectional two-sample Mendelian randomization (MR)

Bi-directional two-sample MR analysis was performed to strengthen the causal inference of the results using the TwoSample MR R package (R version 4.2.3)47. The SNPs associated with each trait at the genome-wide significance level (p < 5 × 10−8) with clumping window > 10,000 kb and the LD level (r2 < 0.001) were selected as instrumental variables (IV) from published GWAS summary statistics (see Table 5). The instruments’ strength in the final IV set was detected with F-statistics after the exclusion of palindromic variants. Due to the very high sample overlap with GERD summary statistics, we did not use the asthma subtype summary statistics for MR analyses. We used the multiplicative random effects IVW model as the primary MR method to estimate the associations of genetically determined allergic traits with the risk of GERD, and vice versa. Sensitivity analyses with unweighted and weighted mode-based estimations, weighted median, and MR-Egger methods were performed to examine the robustness of the results and identify horizontal pleiotropy. Leave-one-out analysis was performed to assess whether there was a significant effect on the results after the removal of a single SNP instrument.

Gene-based association analysis

To further understand the potential biological mechanisms underlying the genetic associations, published GWAS summary statistics were utilized to run gene-based and/or gene-set-based analysis with MAGMA48 v1.6 (a dynamic repository for trait-associated gene discoveries) on the FUMA platform49. From all the protein-coding genes included in the Ensembl build 85 and tested “Curated gene sets” and “Gene Ontology (GO) terms” included in the Molecular Signatures Database (MSigDB v7.0), the relevant genome-wide significant genes/gene sets for each disease were identified (after Bonferroni correction). Then these results were compared between each allergic disease and GERD to identify possible shared genes/gene-sets for comorbidities. Enrichment of differentially expressed gene (DEG) sets in a specific tissue type compared to the average gene expression by all general tissue types was also presented from MAGMA tissue enrichment analysis.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Supplementary information

Peer review file

Supplementary Information

Reporting summary

Supplementary information

The online version contains supplementary material available at 10.1038/s42003-024-06795-1.

Acknowledgements

We acknowledge the Swedish Twin Registry for access to data, Camilla Palm, and Robert Karlsson for the work on quality control and quality assurance in all the individual phenotype and genotype data. The Swedish Twin Registry is managed by Karolinska Institutet and receives funding through the Swedish Research Council under grant no. 2017-00641. We wish to thank the Biobank at Karolinska Institutet for professional biobank service. Financial support was provided by the Swedish Research Council (grant no. 2018-02640 and 2023-02327), the Swedish Heart-Lung Foundation (grant no. 20180512 and 20210416), and Karolinska Institutet (grant no. 2020-0007 and 2022-02303).

Author contributions

T.G., B.B., and C.A. conceived of the study. T.G., B.B., C.A., R.K.H., C.L., Y.L., N.T., and A.A. designed the study, T.G. and J.P. performed the analyses, A.H. and Y.L. contributed to data preparation, all authors (T.G., R.K.H., A.H., C.L., A.S., K.L., A.A., Y.L., N.T., J.P., C.A., B.B.) contributed to the interpretation of results, T.G. and B.B. prepared the first draft of the manuscript, all authors (T.G., R.K.H., A.H., C.L., A.S., K.L., A.A., Y.L., N.T., J.P., C.A., B.B.) approved the final version for submission.

Peer review

Peer review information

Communications Biology thanks Christopher Arehart and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary handling editors: Helene Choquet and Rosie Bunton-Stasyshyn. A peer review file is available.

Funding

Open access funding provided by Karolinska Institute.

Data availability

The quantitative genetic and polygenic risk score analyses are based on original data held by the Swedish National Board of Health and Welfare, Statistics Sweden, and the Swedish Twin Registry, https://ki.se/en/research/the-swedish-twin-registry. Due to Swedish data storage laws, we cannot make the data publicly available, however, any researcher can access the data by obtaining ethical approval and then asking the registers for the original data. Pseudonymized data may also be provided by the PI upon request if providing a reasonable proposal and if an appropriate data-sharing agreement with Karolinska Institutet can be established. All other analyses use publicly available data.

Code availability

Codes and scripts used for all statistical analyses can be shared upon request.

Competing interests

The authors have no conflict of interest to declare except for Professor Nicholas Talley (NJT). NJTs involvement is all outside the submitted work: Norgine (2021)(IBS interest group), personal fees from Allakos (gastroduodenal eosinophilic disease) (2021), twoXAR Viscera Labs, (USA 2021) (IBS-diarrhea), IsoThrive (2021) (esophageal microbiome), BluMaiden (microbiome advisory board) (2021), Rose Pharma (IBS) (2021), Intrinsic Medicine (2022) (human milk oligosaccharide), Comvita Mānuka Honey (2021) (digestive health), Astra Zeneca (2022). In addition, Dr. Talley has a patent Nepean Dyspepsia Index (NDI) 1998, a patent Licensing Questionnaires Talley Bowel Disease Questionnaire licensed to Mayo/Talley, “Diagnostic marker for functional gastrointestinal disorders” Australian Provisional Patent Application 2021901692, ”Methods and compositions for treating age-related neurodegenerative disease associated with dysbiosis” US Application No. 63/537,725.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
==== Refs
References

1. To T Global asthma prevalence in adults: findings from the cross-sectional world health survey BMC Public Health 2012 12 204 10.1186/1471-2458-12-204 22429515
To, T. et al. Global asthma prevalence in adults: findings from the cross-sectional world health survey. BMC Public Health 12, 204 (2012).22429515 10.1186/1471-2458-12-204
2. Broers C Tack J Pauwels A Review article: gastro-oesophageal reflux disease in asthma and chronic obstructive pulmonary disease Aliment Pharm. Ther. 2018 47 176 191 10.1111/apt.14416
Broers, C., Tack, J. & Pauwels, A. Review article: gastro-oesophageal reflux disease in asthma and chronic obstructive pulmonary disease. Aliment Pharm. Ther. 47, 176–191 (2018).10.1111/apt.14416
3. Vakil N Disease definition, clinical manifestations, epidemiology and natural history of GERD Best. Pract. Res. Clin. Gastroenterol. 2010 24 759 764 10.1016/j.bpg.2010.09.009 21126691
Vakil, N. Disease definition, clinical manifestations, epidemiology and natural history of GERD. Best. Pract. Res. Clin. Gastroenterol. 24, 759–764 (2010).21126691 10.1016/j.bpg.2010.09.009
4. Kim SY Min C Oh DJ Choi HG Bidirectional association between GERD and asthma: two longitudinal follow-up studies using a national sample cohort J. Allergy Clin. Immunol. Pr. 2020 8 1005 1013 e1009 10.1016/j.jaip.2019.10.043
Kim, S. Y., Min, C., Oh, D. J. & Choi, H. G. Bidirectional association between GERD and asthma: two longitudinal follow-up studies using a national sample cohort. J. Allergy Clin. Immunol. Pr. 8, 1005–1013 e1009 (2020).10.1016/j.jaip.2019.10.043
5. Althoff M Ghincea A Wood L Holguin F Sharma S Asthma and three colinear comorbidities: obesity, OSA and GERD J. Allergy Clin. Immunol. Pr. 2021 9 3877 3884 10.1016/j.jaip.2021.09.003
Althoff, M., Ghincea, A., Wood, L., Holguin, F. & Sharma, S. Asthma and three colinear comorbidities: obesity, OSA and GERD. J. Allergy Clin. Immunol. Pr. 9, 3877–3884 (2021).10.1016/j.jaip.2021.09.003
6. Koshiyama S Gastroesophageal reflux-like symptoms are associated with hyposalivation and oropharyngeal problems in patients with asthma Respir. Investig. 2021 59 114 119 10.1016/j.resinv.2020.06.004 32665193
Koshiyama, S. et al. Gastroesophageal reflux-like symptoms are associated with hyposalivation and oropharyngeal problems in patients with asthma. Respir. Investig. 59, 114–119 (2021).32665193 10.1016/j.resinv.2020.06.004
7. Mallah N Turner JM Gonzalez-Barcala FJ Takkouche B Gastroesophageal reflux disease and asthma exacerbation: a systematic review and meta-analysis Pediatr. Allergy Immunol. 2022 33 e13655 10.1111/pai.13655 34448255
Mallah, N., Turner, J. M., Gonzalez-Barcala, F. J. & Takkouche, B. Gastroesophageal reflux disease and asthma exacerbation: a systematic review and meta-analysis. Pediatr. Allergy Immunol. 33, e13655 (2022).34448255 10.1111/pai.13655
8. Tariq K Sputum proteomic signature of gastro-oesophageal reflux in patients with severe asthma Respir. Med. 2019 150 66 73 10.1016/j.rmed.2019.02.008 30961953
Tariq, K. et al. Sputum proteomic signature of gastro-oesophageal reflux in patients with severe asthma. Respir. Med. 150, 66–73 (2019).30961953 10.1016/j.rmed.2019.02.008
9. Perotin, J. M. et al. Vulnerability to acid reflux of the airway epithelium in severe asthma. Eur. Respir. J. 6010.1183/13993003.01634-2021 (2022).
10. Porsbjerg C Menzies-Gow A Co-morbidities in severe asthma: clinical impact and management Respirology 2017 22 651 661 10.1111/resp.13026 28328160
Porsbjerg, C. & Menzies-Gow, A. Co-morbidities in severe asthma: clinical impact and management. Respirology 22, 651–661 (2017).28328160 10.1111/resp.13026
11. Kim SY Bidirectional association between GERD and asthma in children: two longitudinal follow-up studies using a national sample cohort Pediatr. Res. 2020 88 320 324 10.1038/s41390-020-0749-1 31923915
Kim, S. Y. et al. Bidirectional association between GERD and asthma in children: two longitudinal follow-up studies using a national sample cohort. Pediatr. Res. 88, 320–324 (2020).31923915 10.1038/s41390-020-0749-1
12. Freuer D Linseisen J Meisinger C Asthma and the risk of gastrointestinal disorders: a Mendelian randomization study BMC Med. 2022 20 82 10.1186/s12916-022-02283-7 35292014
Freuer, D., Linseisen, J. & Meisinger, C. Asthma and the risk of gastrointestinal disorders: a Mendelian randomization study. BMC Med. 20, 82 (2022).35292014 10.1186/s12916-022-02283-7
13. Zheng Z Luo Y Li J Gao J Randomised trials of proton pump inhibitors for gastro-oesophageal reflux disease in patients with asthma: an updated systematic review and meta-analysis BMJ Open 2021 11 e043860 10.1136/bmjopen-2020-043860 34376437
Zheng, Z., Luo, Y., Li, J. & Gao, J. Randomised trials of proton pump inhibitors for gastro-oesophageal reflux disease in patients with asthma: an updated systematic review and meta-analysis. BMJ Open 11, e043860 (2021).34376437 10.1136/bmjopen-2020-043860
14. Gibson, P. G., Henry, R. & Coughlan, J. J. L. Gastro-oesophageal reflux treatment for asthma in adults and children. Cochrane Database Syst. Rev. 10.1002/14651858.Cd001496 (2003).
15. Brew BK Comorbidity of atopic diseases and gastro-oesophageal reflux: evidence of a shared cause Clin. Exp. Allergy 2022 52 868 877 10.1111/cea.14106 35132702
Brew, B. K. et al. Comorbidity of atopic diseases and gastro-oesophageal reflux: evidence of a shared cause. Clin. Exp. Allergy 52, 868–877 (2022).35132702 10.1111/cea.14106
16. Hellgren J Olin AC Toren K Increased risk of rhinitis symptoms in subjects with gastroesophageal reflux Acta Otolaryngol. 2014 134 615 619 10.3109/00016489.2014.890739 24665868
Hellgren, J., Olin, A. C. & Toren, K. Increased risk of rhinitis symptoms in subjects with gastroesophageal reflux. Acta Otolaryngol. 134, 615–619 (2014).24665868 10.3109/00016489.2014.890739
17. Kung YM Allergic rhinitis is a risk factor of gastro-esophageal reflux disease regardless of the presence of asthma Sci. Rep. 2019 9 15535 10.1038/s41598-019-51661-4 31664063
Kung, Y. M. et al. Allergic rhinitis is a risk factor of gastro-esophageal reflux disease regardless of the presence of asthma. Sci. Rep. 9, 15535 (2019).31664063 10.1038/s41598-019-51661-4
18. Thomsen SF Epidemiology and natural history of atopic diseases Eur. Clin. Respir. J. 2015 2 24642 10.3402/ecrj.v2.24642
Thomsen, S. F. Epidemiology and natural history of atopic diseases. Eur. Clin. Respir. J. 2, 24642 (2015).10.3402/ecrj.v2.24642
19. Polderman TJC Meta-analysis of the heritability of human traits based on fifty years of twin studies Nat. Genet. 2015 47 702 709 10.1038/ng.3285 25985137
Polderman, T. J. C. et al. Meta-analysis of the heritability of human traits based on fifty years of twin studies. Nat. Genet. 47, 702–709 (2015).25985137 10.1038/ng.3285
20. Tsuo K Multi-ancestry meta-analysis of asthma identifies novel associations and highlights the value of increased power and diversity Cell Genom. 2022 2 100212 10.1016/j.xgen.2022.100212 36778051
Tsuo, K. et al. Multi-ancestry meta-analysis of asthma identifies novel associations and highlights the value of increased power and diversity. Cell Genom. 2, 100212 (2022).36778051 10.1016/j.xgen.2022.100212
21. An J Gastroesophageal reflux GWAS identifies risk loci that also associate with subsequent severe esophageal diseases Nat. Commun. 2019 10 4219 10.1038/s41467-019-11968-2 31527586
An, J. et al. Gastroesophageal reflux GWAS identifies risk loci that also associate with subsequent severe esophageal diseases. Nat. Commun. 10, 4219 (2019).31527586 10.1038/s41467-019-11968-2
22. Ferreira MAR Genetic architectures of childhood- and adult-onset asthma are partly distinct Am. J. Hum. Genet 2019 104 665 684 10.1016/j.ajhg.2019.02.022 30929738
Ferreira, M. A. R. et al. Genetic architectures of childhood- and adult-onset asthma are partly distinct. Am. J. Hum. Genet 104, 665–684 (2019).30929738 10.1016/j.ajhg.2019.02.022
23. Amarasiri DL Pathmeswaran A de Silva HJ Ranasinha CD Response of the airways and autonomic nervous system to acid perfusion of the esophagus in patients with asthma: a laboratory study BMC Pulm. Med. 2013 13 33 10.1186/1471-2466-13-33 23724936
Amarasiri, D. L., Pathmeswaran, A., de Silva, H. J. & Ranasinha, C. D. Response of the airways and autonomic nervous system to acid perfusion of the esophagus in patients with asthma: a laboratory study. BMC Pulm. Med. 13, 33 (2013).23724936 10.1186/1471-2466-13-33
24. Ferreira MA Shared genetic origin of asthma, hay fever and eczema elucidates allergic disease biology Nat. Genet. 2017 49 1752 1757 10.1038/ng.3985 29083406
Ferreira, M. A. et al. Shared genetic origin of asthma, hay fever and eczema elucidates allergic disease biology. Nat. Genet. 49, 1752–1757 (2017).29083406 10.1038/ng.3985
25. Waage J Genome-wide association and HLA fine-mapping studies identify risk loci and genetic pathways underlying allergic rhinitis Nat. Genet. 2018 50 1072 1080 10.1038/s41588-018-0157-1 30013184
Waage, J. et al. Genome-wide association and HLA fine-mapping studies identify risk loci and genetic pathways underlying allergic rhinitis. Nat. Genet. 50, 1072–1080 (2018).30013184 10.1038/s41588-018-0157-1
26. Sliz E Uniting biobank resources reveals novel genetic pathways modulating susceptibility for atopic dermatitis J. Allergy Clin. Immunol. 2022 149 1105 1112.e1109 10.1016/j.jaci.2021.07.043 34454985
Sliz, E. et al. Uniting biobank resources reveals novel genetic pathways modulating susceptibility for atopic dermatitis. J. Allergy Clin. Immunol. 149, 1105–1112.e1109 (2022).34454985 10.1016/j.jaci.2021.07.043
27. Kichaev G Leveraging polygenic functional enrichment to improve GWAS power Am. J. Hum. Genet. 2019 104 65 75 10.1016/j.ajhg.2018.11.008 30595370
Kichaev, G. et al. Leveraging polygenic functional enrichment to improve GWAS power. Am. J. Hum. Genet. 104, 65–75 (2019).30595370 10.1016/j.ajhg.2018.11.008
28. Ong JS Multitrait genetic association analysis identifies 50 new risk loci for gastro-oesophageal reflux, seven new loci for Barrett’s oesophagus and provides insights into clinical heterogeneity in reflux diagnosis Gut 2022 71 1053 1061 10.1136/gutjnl-2020-323906 34187846
Ong, J. S. et al. Multitrait genetic association analysis identifies 50 new risk loci for gastro-oesophageal reflux, seven new loci for Barrett’s oesophagus and provides insights into clinical heterogeneity in reflux diagnosis. Gut 71, 1053–1061 (2022).34187846 10.1136/gutjnl-2020-323906
29. Zhu Z A genome-wide cross-trait analysis from UK Biobank highlights the shared genetic architecture of asthma and allergic diseases Nat. Genet 2018 50 857 864 10.1038/s41588-018-0121-0 29785011
Zhu, Z. et al. A genome-wide cross-trait analysis from UK Biobank highlights the shared genetic architecture of asthma and allergic diseases. Nat. Genet. 50, 857–864 (2018).29785011 10.1038/s41588-018-0121-0
30. Ahn K Mendelian randomization analysis reveals a complex genetic interplay among atopic dermatitis, asthma, and gastroesophageal reflux disease Am. J. Respir. Crit. Care Med 2023 207 130 137 10.1164/rccm.202205-0951OC 36214830
Ahn, K. et al. Mendelian randomization analysis reveals a complex genetic interplay among atopic dermatitis, asthma, and gastroesophageal reflux disease. Am. J. Respir. Crit. Care Med. 207, 130–137 (2023).36214830 10.1164/rccm.202205-0951OC
31. Zhang M Hou Z-K Huang Z-B Chen X-L Liu F-B Dietary and lifestyle factors related to gastroesophageal reflux disease: a systematic review Therapeut. Clin. Risk Manag. 2021 17 305 323 10.2147/TCRM.S296680
Zhang, M., Hou, Z.-K., Huang, Z.-B., Chen, X.-L. & Liu, F.-B. Dietary and lifestyle factors related to gastroesophageal reflux disease: a systematic review. Therapeut. Clin. Risk Manag. 17, 305–323 (2021).10.2147/TCRM.S296680
32. Lehto, K., Pedersen, N. L., Almqvist, C., Lu, Y. & Brew, B. K. Asthma and affective traits in adults: a genetically informative study. Eur. Respir. J. 53. 10.1183/13993003.02142-2018 (2019).
33. Zhu, Z. et al. Shared genetics of asthma and mental health disorders: a large-scale genome-wide cross-trait analysis. Eur. Respir. J. 54. 10.1183/13993003.01507-2019 (2019).
34. Zamani, M., Alizadeh-Tabari, S., Chan, W. W. & Talley, N. J. Association between anxiety/depression and gastroesophageal reflux: a systematic review and meta-analysis. Am. J. Gastroenterol. 10.14309/ajg.0000000000002411 (2023).
35. Lichtenstein P The Swedish Twin Registry: a unique resource for clinical, epidemiological and genetic studies J. Intern. Med. 2002 252 184 205 10.1046/j.1365-2796.2002.01032.x 12270000
Lichtenstein, P. et al. The Swedish Twin Registry: a unique resource for clinical, epidemiological and genetic studies. J. Intern. Med. 252, 184–205 (2002).12270000 10.1046/j.1365-2796.2002.01032.x
36. Zagai U Lichtenstein P Pedersen NL Magnusson PKE The Swedish Twin Registry: content and management as a research infrastructure Twin Res. Hum. Genet. 2019 22 672 680 10.1017/thg.2019.99 31747977
Zagai, U., Lichtenstein, P., Pedersen, N. L. & Magnusson, P. K. E. The Swedish Twin Registry: content and management as a research infrastructure. Twin Res. Hum. Genet. 22, 672–680 (2019).31747977 10.1017/thg.2019.99
37. Magnusson PK The Swedish Twin Registry: establishment of a biobank and other recent developments Twin Res. Hum. Genet 2013 16 317 329 10.1017/thg.2012.104 23137839
Magnusson, P. K. et al. The Swedish Twin Registry: establishment of a biobank and other recent developments. Twin Res. Hum. Genet. 16, 317–329 (2013).23137839 10.1017/thg.2012.104
38. Lagergren J Bergström R Lindgren A Nyrén O Symptomatic gastroesophageal reflux as a risk factor for esophageal adenocarcinoma N. Engl. J. Med. 1999 340 825 831 10.1056/NEJM199903183401101 10080844
Lagergren, J., Bergström, R., Lindgren, A. & Nyrén, O. Symptomatic gastroesophageal reflux as a risk factor for esophageal adenocarcinoma. N. Engl. J. Med. 340, 825–831 (1999).10080844 10.1056/NEJM199903183401101
39. Locke GR 3rd. Prevalence and clinical spectrum of gastroesophageal reflux: a population-based study in Olmsted County, Minnesota Gastroenterology 1997 112 1448 1456 10.1016/S0016-5085(97)70025-8 9136821
Locke, G. R. et al. 3rd. Prevalence and clinical spectrum of gastroesophageal reflux: a population-based study in Olmsted County, Minnesota. Gastroenterology 112, 1448–1456 (1997).9136821 10.1016/S0016-5085(97)70025-8
40. Zhou W Global biobank meta-analysis Initiative: Powering genetic discovery across human disease Cell Genom. 2002 2 100192 10.1016/j.xgen.2022.100192
Zhou, W. et al. Global biobank meta-analysis Initiative: Powering genetic discovery across human disease. Cell Genom. 2, 100192 (2002).10.1016/j.xgen.2022.100192
41. Shrine N Moderate-to-severe asthma in individuals of European ancestry: a genome-wide association study Lancet Respir. Med. 2019 7 20 34 10.1016/S2213-2600(18)30389-8 30552067
Shrine, N. et al. Moderate-to-severe asthma in individuals of European ancestry: a genome-wide association study. Lancet Respir. Med. 7, 20–34 (2019).30552067 10.1016/S2213-2600(18)30389-8
42. Ni G A comparison of ten polygenic score methods for psychiatric disorders applied across multiple cohorts Biol. Psychiatry 2021 90 611 620 10.1016/j.biopsych.2021.04.018 34304866
Ni, G. et al. A comparison of ten polygenic score methods for psychiatric disorders applied across multiple cohorts. Biol. Psychiatry 90, 611–620 (2021).34304866 10.1016/j.biopsych.2021.04.018
43. Neale, M. C. & Cardon, L. R. Methodology for Genetic Studies of Twins and Families. (Kluwer Academic/Plenum Publishers, 1992).
44. Plomin, R., DeFries, J. C. & McClearn, G. E. Behavioral Genetics. (Macmillan, 2008).
45. Bulik-Sullivan B An atlas of genetic correlations across human diseases and traits Nat. Genet. 2015 47 1236 1241 10.1038/ng.3406 26414676
Bulik-Sullivan, B. et al. An atlas of genetic correlations across human diseases and traits. Nat. Genet. 47, 1236–1241 (2015).26414676 10.1038/ng.3406
46. Grotzinger AD Genomic structural equation modeling provides insights into the multivariate genetic architecture of complex traits Nat. Hum. Behav. 2019 3 513 525 10.1038/s41562-019-0566-x 30962613
Grotzinger, A. D. et al. Genomic structural equation modeling provides insights into the multivariate genetic architecture of complex traits. Nat. Hum. Behav. 3, 513–525 (2019).30962613 10.1038/s41562-019-0566-x
47. Hemani G The MR-Base platform supports systematic causal inference across the human phenome eLife 2018 7 e34408 10.7554/eLife.34408 29846171
Hemani, G. et al. The MR-Base platform supports systematic causal inference across the human phenome. eLife 7, e34408 (2018).29846171 10.7554/eLife.34408
48. de Leeuw CA Mooij JM Heskes T Posthuma D MAGMA: generalized gene-set analysis of GWAS data PLoS Comput. Biol. 2015 11 e1004219 10.1371/journal.pcbi.1004219 25885710
de Leeuw, C. A., Mooij, J. M., Heskes, T. & Posthuma, D. MAGMA: generalized gene-set analysis of GWAS data. PLoS Comput. Biol. 11, e1004219 (2015).25885710 10.1371/journal.pcbi.1004219
49. Watanabe K Taskesen E van Bochoven A Posthuma D Functional mapping and annotation of genetic associations with FUMA Nat. Commun. 2017 8 1826 10.1038/s41467-017-01261-5 29184056
Watanabe, K., Taskesen, E., van Bochoven, A. & Posthuma, D. Functional mapping and annotation of genetic associations with FUMA. Nat. Commun. 8, 1826 (2017).29184056 10.1038/s41467-017-01261-5
