IFNG and IFNGR1 polymorphisms are associated with tuberculosis: a case-control study.

Area of Science:

• Genetics and Immunology
• Infectious Disease Epidemiology
• Biomarker Discovery

Background:

• Tuberculosis (TB) remains a significant global health challenge.
• Previous research indicated potential roles for interferon gamma (IFNG) and its receptor IFNGR1 in TB pathogenesis.
• Genetic variations may influence individual susceptibility to TB.

Purpose of the Study:

• To investigate the association between IFNG and IFNGR1 gene single-nucleotide polymorphisms (SNPs) and TB in the Tibetan population.
• To develop a machine learning-based clinical prediction model for TB using genetic and clinical data.

Main Methods:

• A case-control study involving 613 TB patients and 603 healthy controls from the Tibetan population.
• Logistic regression analysis was employed to assess the association between SNPs and TB, adjusting for age and sex.
• Random forest (RF) machine learning was utilized to construct a predictive model integrating clinical data and identified SNPs.

Main Results:

• Specific SNPs and alleles in IFNG (rs1861494) and IFNGR1 (rs3799488, rs9376267, rs1327475, rs2234711) were found to be significantly associated with TB, with several showing protective effects.
• The RF model identified five key markers, achieving an area under the curve (AUC) of 0.6 in the training set and 0.59 in the test set, indicating predictive capability.
• The study identified significant associations between specific genetic variations and tuberculosis risk within the Tibetan cohort.

Conclusions:

• IFNG and IFNGR1 gene polymorphisms are significantly associated with tuberculosis in the Tibetan population.
• The identified genetic markers hold promise as potential diagnostic biomarkers for TB.
• Machine learning models integrating genetic data can aid in TB prediction.