Integrating Genetic Susceptibility and Air Pollution Exposure to Predict Asthma Risk in an Urban Kazakh Population

Abstract

Background

Bronchial asthma (BA) is a complex respiratory disease resulting from interactions between genetic susceptibility and environmental exposures [1]. Air pollutants contribute to BA via oxidative stress and inflammation, especially in genetically susceptible individuals [2]. This study aimed to investigate genetic susceptibility to BA in an urban Kazakh population by integrating genome-wide SNP genotyping with environmental exposure data to enhance personalized risk prediction.

Methods

A case-control study was conducted in Almaty involving 288 participants: 144 asthma patients and 144 matched controls. Genome-wide genotyping was performed using the Infinium Global Screening Array-24 v3.0 on an iScan platform. SNP associations were analyzed using PLINK and annotated via ClinVar, dbSNP, and 1000 Genomes. Environmental exposure metrics (PM₂.₅, PM₁₀, SO₂) were monitored digitally over 7 months. Gene-environment interactions were evaluated to determine pollutant-modulated SNP effects.

Results

Several SNPs demonstrated significant associations with BA risk and specific pollutant exposures. Protective variants included rs12413578 in GSDMB (OR = 0.24, 95% CI: 0.09- 0.67), rs907092 in IKZF3 (OR = 0.59, 95% CI: 0.40-0.86), and rs17293632 in SMAD3 (OR = 0.26, 95% CI: 0.09-0.75) under elevated PM10 exposure. Increased risk was observed for rs1837253 in TSLP (OR = 1.67, 95% CI: 1.03-2.69) in relation to PM2.5, and rs1295686 in IL13 (OR = 0.22, 95% CI: 0.06-0.85) showed a protective effect under SO2 exposure.

Conclusions

This study identified several SNPs significantly associated with asthma risk in Almaty population, including GSDMB rs12413578 and IKZF3 rs907092 as protective variants, and TSLP rs1837253 as a PM2.5 dependent risk marker for patients with BA. Additionally, SMAD3 and IL13 variants showed pollutant-specific associations with PM10 and SO2, respectively. The proposed integration of gene-environment interactions into risk models may enhance asthma prediction and inform more targeted strategies in precision medicine.

Acknowledgement

This study was funded by the Committee of Science of the Ministry of Science and Higher Education of the Republic of Kazakhstan under Grant No. AP23488865

Key words: bronchial asthma, gene–environment interaction, SNP genotyping, air pollutants.

References:

1. Meyers D.A., Bleecker E.R., Holloway J.W., Holgate S.T. Asthma genetics and personalised medicine. Lancet Respir Med. 2(5):405-15 (2014).
2. Guarnieri M., Balmes J.R. Outdoor air pollution and asthma. Lancet. 383(9928):1581- 92 (2014).