A two-part investigation into atopic asthma: severity prediction using machine learning and genomic architecture analysis of significant SNPs

Ghinwa Ababidi; Dima Joujeh; Mohamad Taher Anan

A two-part investigation into atopic asthma: severity prediction using machine learning and genomic architecture analysis of significant SNPs

Abstract

Background: Asthma is a widespread chronic condition characterized by fluctuating respiratory symptoms and limited airflow. It affects approximately 334 million people globally.

Aims: This study investigated the genetic and clinical factors influencing atopic asthma severity in a cohort of 138 patients.

Methods: The research employed a dual-pathway parallel framework. It utilized a cross-sectional design to investigate 138 patients diagnosed with atopic asthma, implementing a clinical machine learning approach to predict disease severity based strictly on patient-level data. In a separate, parallel in silico component, linkage disequilibrium (LD) and haplotype block analysis was performed using the LDpair tool on published GWAS single nucleotide polymorphisms to explore the independent genomic architecture associated with the disease.

Results: First, a predictive model was developed to forecast asthma severity using clinical data, including patient age, gender, duration of illness, and family relatedness. Both a Neural Network (NN) and a Support Vector Machine (SVM) were trained, with the NN achieving an overall accuracy of 92% and the SVM achieving a a high level of discriminative accuracy on the test set. These results highlight the significant predictive power of familial and clinical variables. The genetic analysis revealed a complex genetic architecture, with heterogeneous LD patterns across chromosomes and the presence of strong haplotype blocks, particularly on Chromosome 9. These findings suggest that GWAS signals may be driven by co-inherited genetic blocks rather than individual variants.

Conclusion: The study concludes that a parallel, complementary framework combining independent bioinformatics insights with clinical predictive modeling is highly effective for understanding and assessing atopic asthma severity, offering a robust foundation for future clinical risk assessment tools.

Keywords

Supporting Institution

The authors declared that this study has received no financial support.

Ethical Statement

This study was conducted using clinical data obtained from patients diagnosed with atopic asthma at the Department of Pulmonology, Aleppo University Hospital. The clinical data were collected from patients attending the department and were recorded on specialized forms between April 2022 and October 2024. All cases were clinically examined and diagnosed by Prof. Dr. Abdullah Khoury. According to the official institutional attestation provided by Aleppo University Hospital, all necessary verbal and/or written informed consents were obtained from the patients at the time of clinical examination for scientific research purposes, in accordance with Syrian law. The use of the clinical data for this study was approved and attested by the Director General of Aleppo University Hospital. Patient confidentiality and privacy were maintained throughout the study.

References

Lemmetyinen, R. E., Toppila-Salmi, S. K., But, A., Renkonen, R., Pekkanen, J., Haukka, J., & Karjalainen, J. (2024). Comorbidities associated with adult asthma: a population-based matched cohort study in Finland. BMJ open respiratory research, 11(1), e001959 https://doi.org/10.1136/bmjresp-2023-001959
Chen, R., Piao, L. Z., Liu, L., & Zhang, X. F. (2021). DNA methylation and gene expression profiles to identify childhood atopic asthma associated genes. BMC Pulmonary Medicine, 21(1), 292. https://doi.org/10.1186/s12890-021-01655-8
Ho, C. H., Gau, C. C., Lee, W. F., Fang, H., Lin, C. H., Chu, C. H., Huang, Y. S., Huang, Y. W., Huang, H. Y., Tsai, H. J., & Yao, T. C. (2022). Early-life weight gain is associated with non-atopic asthma in childhood. World Allergy Organization Journal, 15(8), 100672. https://doi.org/10.1016/j.waojou.2022.100672
Gerday, S., Schleich, F., Henket, M., Guissard, F., Paulus, V., & Louis, R. (2022). Revisiting differences between atopic and non-atopic asthmatics: when age is shaping airway inflammatory profile. World Allergy Organization Journal, 15(6), 100655. https://doi.org/10.1016/j.waojou.2022.100655
Global Initiative for Asthma. Global Strategy for Asthma Management and Prevention, 2025. Available from: https://ginasthma.org/
Sio, Y. Y., & Chew, F. T. (2021). Risk factors of asthma in the Asian population: a systematic review and meta-analysis. Journal of Physiological Anthropology, 40(1), 22. https://doi.org/10.1186/s40101-021-00273-x
Chowdhury, N. U., Guntur, V. P., Newcomb, D. C., & Wechsler, M. E. (2021). Sex and gender in asthma. European Respiratory Review, 30(162), 210067. https://doi.org/10.1183/16000617.0067-2021
Ntontsi, P., Photiades, A., Zervas, E., Xanthou, G., & Samitas, K. (2021). Genetics and epigenetics in asthma. International journal of molecular sciences, 22(5), 2412. https://doi.org/10.3390/ijms22052412

Ranjbar, M., Whetstone, C. E., Omer, H., Power, L., Cusack, R. P., & Gauvreau, G. M. (2022). The genetic factors of the airway epithelium associated with the pathology of asthma. Genes, 13(10), 1870. https://doi.org/10.3390/genes13101870
Khajotia. R. (2008). Classifying asthma severity and treatment determinants: national guidelines revisited. Malaysian family physician: the official journal of the Academy of Family Physicians of Malaysia, 3(3), 131.
Cerezo, M., Sollis, E., Ji, Y., Lewis, E., Abid, A., Bircan, K. O., Hall, P., Hayhurst, J., John, S., Mosaku, A., Ramachandran, S., Foreman, A., Ibrahim, A., McLaughlin, J., Pendlington, Z., Stefancsik, R., Lambert, S. A., McMahon, A., Morales, J., Keane, T., Inouye, M., Parkinson, H., & Harris, L. W. (2025). The NHGRI-EBI GWAS Catalog: standards for reusability, sustainability and diversity. Nucleic Acids Research, 53(D1), D998–D1005. https://doi.org/10.1093/nar/gkae1070
Machiela, M. J., & Chanock, S. J. (2015). LDlink: a web-based application for exploring population-specific haplotype structure and linking correlated alleles of possible functional variants. Bioinformatics, 31(21), 3555–3557. https://doi.org/10.1093/bioinformatics/btv402
Molfino, N. A., Turcatel, G., & Riskin, D. (2024). Machine Learning Approaches to Predict Asthma Exacerbations: A Narrative Review. Advancement in Therapy, 41(2), 534–552. https://doi.org/10.1007/s12325-023-02743-3
Pooja, M. R., Ravi, V., Lokesh, G. H., Al Mazroa, A., & Ravi, P. (2024). A Prognostic Model to Improve Asthma Prediction Outcomes Using Machine Learning. The Open Bioinformatics Journal, 17, 1–11. https://doi.org/10.2174/0118750362306414240624113350
Williams A. T., Shrine, N., Naghra-van Gijzel, H., Betts, J. C., Chen, J., Hessel, E. M., John, C., Packer, R., Reeve, N. F., Yeo, A. J., Abner, E., Åsvold, B. O., Auvinen, J., Bartz, T. M., Bradford, Y., Brumpton, B., Campbell, A., Cho, M. H., Chu, S., Crosslin, D. R., Feng, Q. P., Esko, T., Gharib, S. A., Hayward, C., Hebbring, S., Hveem, K., Järvelin, M. R., Jarvik, G. P., Landis, S. H., Larson, E. B., Liu, J., Loos, R. J. F., Luo, Y., Moscati, A., Mullerova, H., Namjou, B., Porteous, D. J., & Quint, J. K. (2023). Genome-wide association study of susceptibility to hospitalised respiratory infections. Wellcome Open Research, 6, 290. https://doi.org/10.12688/wellcomeopenres.17230.2
Wu, C. P., Sleiman, J., Fakhry, B., Chedraoui, C., Attaway, A., Bhattacharyya, A., Bleecker, E. R., Erdemir, A., Hu, B., Kethireddy, S., Meyers, D. A., Rashidi, H. H., & Zein, J. G. (2024). Novel Machine Learning Identifies Five Asthma Phenotypes Using Cluster Analysis of Real-World Data. The Journal of Allergy and Clinical Immunology: In Practice, 12(8), 2084–2091. https://doi.org/10.1016/j.jaip.2024.04.035

Details

Primary Language

English

Subjects

Bioinformatics

Journal Section

Research Article

Authors

Ghinwa Ababidi This is me
0009-0005-3129-5598
Syria

Dima Joujeh ^*
Syria

Mohamad Taher Anan This is me
Syria

Early Pub Date

June 22, 2026

Publication Date

-

Submission Date

November 13, 2025

Acceptance Date

June 17, 2026

Published in Issue

Year 2026 Number: Advanced Online Publication

IZ

https://izlik.org/JA42LA68RM

Cite

RIS / Bibtex

APA

Ababidi, G., Joujeh, D., & Anan, M. T. (2026). A two-part investigation into atopic asthma: severity prediction using machine learning and genomic architecture analysis of significant SNPs. Trakya University Journal of Natural Sciences, Advanced Online Publication. https://izlik.org/JA42LA68RM

AMA

1.Ababidi G, Joujeh D, Anan MT. A two-part investigation into atopic asthma: severity prediction using machine learning and genomic architecture analysis of significant SNPs. Trakya Univ J Nat Sci. 2026;(Advanced Online Publication). https://izlik.org/JA42LA68RM

Chicago

Ababidi, Ghinwa, Dima Joujeh, and Mohamad Taher Anan. 2026. “A Two-Part Investigation into Atopic Asthma: Severity Prediction Using Machine Learning and Genomic Architecture Analysis of Significant SNPs”. Trakya University Journal of Natural Sciences, no. Advanced Online Publication. https://izlik.org/JA42LA68RM.

EndNote

Ababidi G, Joujeh D, Anan MT (June 1, 2026) A two-part investigation into atopic asthma: severity prediction using machine learning and genomic architecture analysis of significant SNPs. Trakya University Journal of Natural Sciences Advanced Online Publication

IEEE

[1]G. Ababidi, D. Joujeh, and M. T. Anan, “A two-part investigation into atopic asthma: severity prediction using machine learning and genomic architecture analysis of significant SNPs”, Trakya Univ J Nat Sci, no. Advanced Online Publication, June 2026, [Online]. Available: https://izlik.org/JA42LA68RM

ISNAD

Ababidi, Ghinwa - Joujeh, Dima - Anan, Mohamad Taher. “A Two-Part Investigation into Atopic Asthma: Severity Prediction Using Machine Learning and Genomic Architecture Analysis of Significant SNPs”. Trakya University Journal of Natural Sciences. Advanced Online Publication (June 1, 2026). https://izlik.org/JA42LA68RM.

JAMA

1.Ababidi G, Joujeh D, Anan MT. A two-part investigation into atopic asthma: severity prediction using machine learning and genomic architecture analysis of significant SNPs. Trakya Univ J Nat Sci. 2026. Available at https://izlik.org/JA42LA68RM.

MLA

Ababidi, Ghinwa, et al. “A Two-Part Investigation into Atopic Asthma: Severity Prediction Using Machine Learning and Genomic Architecture Analysis of Significant SNPs”. Trakya University Journal of Natural Sciences, no. Advanced Online Publication, June 2026, https://izlik.org/JA42LA68RM.

Vancouver

1.Ghinwa Ababidi, Dima Joujeh, Mohamad Taher Anan. A two-part investigation into atopic asthma: severity prediction using machine learning and genomic architecture analysis of significant SNPs. Trakya Univ J Nat Sci [Internet]. 2026 Jun. 1;(Advanced Online Publication). Available from: https://izlik.org/JA42LA68RM