Cross-phenotype genome-wide association study supports shared genetic etiology between skin and gastrointestinal tract diseases

Bo Peng , Minghui Jiang , Si Li , Xingyu Chen , Shanshan Cheng , Xingjie Hao

Journal of Biomedical Research ›› 2026, Vol. 40 ›› Issue (2) : 172 -184.

PDF (5675KB)
Journal of Biomedical Research ›› 2026, Vol. 40 ›› Issue (2) :172 -184. DOI: 10.7555/JBR.39.20250166
Original Article
research-article
Cross-phenotype genome-wide association study supports shared genetic etiology between skin and gastrointestinal tract diseases
Author information +
History +
PDF (5675KB)

Abstract

The comorbidity of skin and gastrointestinal tract (GIT) diseases, primarily driven by the gut-skin axis (GSA), is well established. However, the genetic contribution to the GSA remains unclear. Here, using genome-wide association study (GWAS) summary statistics from European populations, we performed a genome-wide pleiotropic analysis to investigate the shared genetic basis and causal associations between skin and GIT diseases. We observed extensive genetic correlations and overlaps between skin and GIT diseases. A total of 298 pleiotropic loci were identified, 75 of which were colocalized, and 61 exhibited pleiotropic effects across multiple trait pairs, including 2p16.1 (PUS10), 6p21.32 (HLA-DRB1), 10q21.2 (ZNF365), and 19q13.11 (SLC7A10). Additionally, five novel loci were identified based on the pleiotropic analysis; among them, the 15q22.2 locus harboring RORA was validated by the latest inflammatory bowel disease GWAS. Gene-based analysis identified 394 unique pleiotropic genes, which were enriched in GSA-associated tissues and the immune system, and protein-protein interaction analysis further revealed that the GPCR-cAMP, chromatin remodeling, JAK-STAT, and HLA-mediated immunity pathways were involved in GSA comorbidity. Notably, the JAK-STAT pathway showed strong potential for drug repurposing, with adalimumab targeting tumor necrosis factor and ustekinumab targeting interleukin-12 subunit beta already being used to treat both skin and GIT diseases. Finally, Mendelian randomization analysis identified five significant causal associations, and subsequent mediation analysis identified three potential microbiota-GIT-skin pathways. Taken together, our study demonstrated that the shared genetic factors between skin and GIT diseases were widely distributed across the genome. These findings will enhance our understanding of the genetic mechanisms underlying GSA comorbidity.

Keywords

gut-skin axis / gastrointestinal tract diseases / skin diseases / pleiotropic analysis / Mendelian randomization

Cite this article

Download citation ▾
Bo Peng, Minghui Jiang, Si Li, Xingyu Chen, Shanshan Cheng, Xingjie Hao. Cross-phenotype genome-wide association study supports shared genetic etiology between skin and gastrointestinal tract diseases. Journal of Biomedical Research, 2026, 40(2): 172-184 DOI:10.7555/JBR.39.20250166

登录浏览全文

4963

注册一个新账户 忘记密码

Funding

This study was supported by grants from the National Natural Science Foundation of China (Grant No. 32470658) and the National Key Research and Development Program of China (Grant Nos. 2022YFC2502400 and 2022YFC2502402).

Acknowledgments

The authors thank all study participants and all investigators of the UKB, FinnGen, and GTEx.

References

[1]

O'Neill CA, Monteleone G, McLaughlin JT, et al. The gut-skin axis in health and disease: A paradigm with therapeutic implications[J]. Bioessays, 2016, 38(11): 1167-1176. doi: 10.1002/bies.201600008

[2]

Kim M, Choi KH, Hwang SW, et al. Inflammatory bowel disease is associated with an increased risk of inflammatory skin diseases: A population-based cross-sectional study[J]. J Am Acad Dermatol, 2017, 76(1): 40-48. doi: 10.1016/j.jaad.2016.08.022

[3]

Piontkowski AJ, Sharma D, Ungar B. Rosacea and gastrointestinal diseases: A case-control study in the All of Us database[J]. Dermatology, 2024, 240(5-6): 875-878. doi: 10.1159/000541469

[4]

Mahmud MR, Akter S, Tamanna SK, et al. Impact of gut microbiome on skin health: Gut-skin axis observed through the lenses of therapeutics and skin diseases[J]. Gut Microbes, 2022, 14(1): 2096995. doi: 10.1080/19490976.2022.2096995

[5]

De Pessemier B, Grine L, Debaere M, et al. Gut-skin axis: Current knowledge of the interrelationship between microbial dysbiosis and skin conditions[J]. Microorganisms, 2021, 9(2): 353. doi: 10.3390/microorganisms9020353

[6]

Bycroft C, Freeman C, Petkova D, et al. The UK Biobank resource with deep phenotyping and genomic data[J]. Nature, 2018, 562(7726): 203-209. doi: 10.1038/s41586-018-0579-z

[7]

Kurki MI, Karjalainen J, Palta P, et al. FinnGen provides genetic insights from a well-phenotyped isolated population[J]. Nature, 2023, 613(7944): 508-518. doi: 10.1038/s41586-022-05473-8

[8]

The GTEx Consortium. The GTEx Consortium atlas of genetic regulatory effects across human tissues[J]. Science, 2020, 369(6509): 1318-1330.

[9]

Lees CW, Barrett JC, Parkes M, et al. New IBD genetics: Common pathways with other diseases[J]. Gut, 2011, 60(12): 1739-1753. doi: 10.1136/gut.2009.199679

[10]

Schaefer AS, Jochens A, Dommisch H, et al. A large candidate-gene association study suggests genetic variants at IRF5 and PRDM1 to be associated with aggressive periodontitis[J]. J Clin Periodontol, 2014, 41(12): 1122-1131. doi: 10.1111/jcpe.12314

[11]

Zouboulis CC, Desai N, Emtestam L, et al. European S1 guideline for the treatment of hidradenitis suppurativa/acne inversa[J]. J Eur Acad Dermatol Venereol, 2015, 29(4): 619-644. doi: 10.1111/jdv.12966

[12]

Ray D, Chatterjee N. A powerful method for pleiotropic analysis under composite null hypothesis identifies novel shared loci between type 2 diabetes and prostate cancer[J]. PLoS Genet, 2020, 16(12): e1009218. doi: 10.1371/journal.pgen.1009218

[13]

Gong W, Guo P, Li Y, et al. Role of the gut-brain axis in the shared genetic etiology between gastrointestinal tract diseases and psychiatric disorders: A genome-wide pleiotropic analysis[J]. JAMA Psychiatry, 2023, 80(4): 360-370. doi: 10.1001/jamapsychiatry.2022.4974

[14]

Jiang M, Hao X, Jiang Y, et al. Genetic and observational associations of lung function with gastrointestinal tract diseases: Pleiotropic and mendelian randomization analysis[J]. Respir Res, 2023, 24(1): 315. doi: 10.1186/s12931-023-02621-0

[15]

Koskeridis F, Fancy N, Tan P, et al. Multi-trait association analysis reveals shared genetic loci between Alzheimer's disease and cardiovascular traits[J]. Nat Commun, 2024, 15(1): 9827. doi: 10.1038/s41467-024-53452-6

[16]

Bulik-Sullivan B, Finucane HK, Anttila V, et al. An atlas of genetic correlations across human diseases and traits[J]. Nat Genet, 2015, 47(11): 1236-1241. doi: 10.1038/ng.3406

[17]

Shi H, Mancuso N, Spendlove S, et al. Local genetic correlation gives insights into the shared genetic architecture of complex traits[J]. Am J Hum Genet, 2017, 101(5): 737-751. doi: 10.1016/j.ajhg.2017.09.022

[18]

Purcell S, Neale B, Todd-Brown K, et al. PLINK: A tool set for whole-genome association and population-based linkage analyses[J]. Am J Hum Genet, 2007, 81(3): 559-575. doi: 10.1086/519795

[19]

The 1000 Genomes Project Consortium. A global reference for human genetic variation[J]. Nature, 2015, 526(7571): 68-74. doi: 10.1038/nature15393

[20]

Quinlan AR, Hall IM. BEDTools: A flexible suite of utilities for comparing genomic features[J]. Bioinformatics, 2010, 26(6): 841-842. doi: 10.1093/bioinformatics/btq033

[21]

Wang K, Li M, Hakonarson H. ANNOVAR: Functional annotation of genetic variants from high-throughput sequencing data[J]. Nucleic Acids Res, 2010, 38(16): e164. doi: 10.1093/nar/gkq603

[22]

MacArthur J, Bowler E, Cerezo M, et al. The new NHGRI-EBI Catalog of published genome-wide association studies (GWAS Catalog)[J]. Nucleic Acids Res, 2017, 45(D1): D896-D901. doi: 10.1093/nar/gkw1133

[23]

Giambartolomei C, Vukcevic D, Schadt EE, et al. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics[J]. PLoS Genet, 2014, 10(5): e1004383. doi: 10.1371/journal.pgen.1004383

[24]

de Leeuw CA, Mooij JM, Heskes T, et al. MAGMA: Generalized gene-set analysis of GWAS data[J]. PLoS Comput Biol, 2015, 11(4): e1004219. doi: 10.1371/journal.pcbi.1004219

[25]

Watanabe K, Taskesen E, van Bochoven A, et al. Functional mapping and annotation of genetic associations with FUMA[J]. Nat Commun, 2017, 8(1): 1826. doi: 10.1038/s41467-017-01261-5

[26]

Wishart DS, Feunang YD, Guo AC, et al. DrugBank 5.0: A major update to the DrugBank database for 2018[J]. Nucleic Acids Res, 2018, 46(D1): D1074-D1082. doi: 10.1093/nar/gkx1037

[27]

Finucane HK, Bulik-Sullivan B, Gusev A, et al. Partitioning heritability by functional annotation using genome-wide association summary statistics[J]. Nat Genet, 2015, 47(11): 1228-1235. doi: 10.1038/ng.3404

[28]

de Leeuw CA, Stringer S, Dekkers IA, et al. Conditional and interaction gene-set analysis reveals novel functional pathways for blood pressure[J]. Nat Commun, 2018, 9(1): 3768. doi: 10.1038/s41467-018-06022-6

[29]

Szklarczyk D, Gable AL, Lyon D, et al. STRING v11: Protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets[J]. Nucleic Acids Res, 2019, 47(D1): D607-D613. doi: 10.1093/nar/gky1131

[30]

Sharma A, Menche J, Huang CC, et al. A disease module in the interactome explains disease heterogeneity, drug response and captures novel pathways and genes in asthma[J]. Hum Mol Genet, 2015, 24(11): 3005-3020. doi: 10.1093/hmg/ddv001

[31]

Blondel VD, Guillaume JL, Lambiotte R, et al. Fast unfolding of communities in large networks[J]. J Stat Mech: Theory Exp, 2008, 2008(10): P10008. doi: 10.1088/1742-5468/2008/10/P10008

[32]

Kojaku S, Masuda N. A generalised significance test for individual communities in networks[J]. Sci Rep, 2018, 8(1): 7351. doi: 10.1038/s41598-018-25560-z

[33]

Yu G, Wang L, Han Y, et al. clusterProfiler: An R package for comparing biological themes among gene clusters[J]. OMICS, 2012, 16(5): 284-287. doi: 10.1089/omi.2011.0118

[34]

Shannon P, Markiel A, Ozier O, et al. Cytoscape: A software environment for integrated models of biomolecular interaction networks[J]. Genome Res, 2003, 13(11): 2498-2504. doi: 10.1101/gr.1239303

[35]

Hemani G, Tilling K, Davey Smith G. Orienting the causal relationship between imprecisely measured traits using GWAS summary data[J]. PLoS Genet, 2017, 13(11): e1007081. doi: 10.1371/journal.pgen.1007081

[36]

Verbanck M, Chen CY, Neale B, et al. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases[J]. Nat Genet, 2018, 50(5): 693-698. doi: 10.1038/s41588-018-0099-7

[37]

Burgess S, Butterworth A, Thompson SG. Mendelian randomization analysis with multiple genetic variants using summarized data[J]. Genet Epidemiol, 2013, 37(7): 658-665. doi: 10.1002/gepi.21758

[38]

Bowden J, Davey Smith G, Burgess S. Mendelian randomization with invalid instruments: Effect estimation and bias detection through Egger regression[J]. Int J Epidemiol, 2015, 44(2): 512-525. doi: 10.1093/ije/dyv080

[39]

Bowden J, Davey Smith G, Haycock PC, et al. Consistent estimation in Mendelian randomization with some invalid instruments using a weighted median estimator[J]. Genet Epidemiol, 2016, 40(4): 304-314. doi: 10.1002/gepi.21965

[40]

Hartwig FP, Davey Smith G, Bowden J. Robust inference in summary data Mendelian randomization via the zero modal pleiotropy assumption[J]. Int J Epidemiol, 2017, 46(6): 1985-1998. doi: 10.1093/ije/dyx102

[41]

Zhao Q, Wang J, Hemani G, et al. Statistical inference in two-sample summary-data Mendelian randomization using robust adjusted profile score[J]. Ann Stat, 2020, 48(3): 1742-1769. doi: 10.1214/19-AOS1866

[42]

Zhao J, Ming J, Hu X, et al. Bayesian weighted Mendelian randomization for causal inference based on summary statistics[J]. Bioinformatics, 2020, 36(5): 1501-1508. doi: 10.1093/bioinformatics/btz749

[43]

Rühlemann MC, Hermes BM, Bang C, et al. Genome-wide association study in 8956 German individuals identifies influence of ABO histo-blood groups on gut microbiome[J]. Nat Genet, 2021, 53(2): 147-155. doi: 10.1038/s41588-020-00747-1

[44]

Sanna S, van Zuydam NR, Mahajan A, et al. Causal relationships among the gut microbiome, short-chain fatty acids and metabolic diseases[J]. Nat Genet, 2019, 51(4): 600-605. doi: 10.1038/s41588-019-0350-x

[45]

Cheung MWL. Comparison of methods for constructing confidence intervals of standardized indirect effects[J]. Behav Res Methods, 2009, 41(2): 425-438. doi: 10.3758/BRM.41.2.425

[46]

Liu Z, Liu R, Gao H, et al. Genetic architecture of the inflammatory bowel diseases across East Asian and European ancestries[J]. Nat Genet, 2023, 55(5): 796-806. doi: 10.1038/s41588-023-01384-0

[47]

Li Q, Patrick MT, Sreeskandarajan S, et al. Large-scale epidemiological analysis of common skin diseases to identify shared and unique comorbidities and demographic factors[J]. Front Immunol, 2023, 14: 1309549. doi: 10.3389/fimmu.2023.1309549

PDF (5675KB)

0

Accesses

0

Citation

Detail

Sections
Recommended

/