HLA-G in asthma and its potential as an effective therapeutic agent

SPIN: sex-specific and pathway-based interpretable neural network for sexual dimorphism analysis

Sexual dimorphism in prevalence, severity and genetic susceptibility exists for most common diseases. However, most genetic and clinical outcome studies are designed in sex-combined framework considering sex as a covariate. Few sex-specific studies have analyzed males and females separately, which failed to identify gene-by-sex interaction. Here, we propose a novel unified biologically interpretable deep learning-based framework (named SPIN) for sexual dimorphism analysis. We demonstrate that SPIN significantly improved the C-index up to 23.6% in TCGA cancer datasets, and it was further validated using asthma datasets. In addition, SPIN identifies sex-specific and -shared risk loci that are often missed in previous sex-combined/-separate analysis. We also show that SPIN is interpretable for explaining how biological pathways contribute to sexual dimorphism and improve risk prediction in an individual level, which can result in the development of precision medicine tailored to a specific individual's characteristics.

Genetic Susceptibility of HLA Alleles to Non-Steroidal Anti-Inflammatory Drug Hypersensitivity in the Taiwanese Population

BACKGROUND

Human leukocyte antigen (HLA) genes are important in many immune processes and contribute to many adverse drug reactions. Whether genetic variations in the HLA region are associated with non-steroid anti-inflammatory drug (NSAID) hypersensitivity remains uncertain. Therefore, the aim of our study was to identify HLA genetic variations in patients with NSAID hypersensitivity in the Taiwanese population.

METHODS

This hospital-based, retrospective case-control study enrolled 37,156 participants with NSAID exposure from the Taiwan Precision Medicine Initiative (TPMI), who were all genotyped and imputed to fine map HLA typing. Our study assigned 1217 cases to the NSAID allergy group and 12,170 controls to a matched group. Logistic regression analyses were utilized to explore associations between HLA alleles and NSAID hypersensitivity.

RESULTS

Overall, 13,387 patients were genotyped for eight major HLA alleles. Allele frequencies were different between the two groups. In the NSAID allergy group, the genotype frequencies of HLA-A*02:01, HLA-A*34:01, and HLA-DQA1*06:01 were found to be markedly elevated compared to the control group, a significance that persisted even after applying the Bonferroni correction. Furthermore, the risk of NSAID allergy demonstrated a significant association with HLA-A*02:01 (OR = 1.29, p < 0.001) and HLA-A*34:01 (OR = 9.90, p = 0.001), in comparison to their respective counterparts. Notably, the genotype frequency of HLA-B*46:01 exhibited a significant increase in the severe allergy group when compared with the mild allergy group.

CONCLUSIONS

We identified HLA genotypes linked to the onset and severity of NSAID hypersensitivity. Our findings establish a basis for precision prescription in future clinical applications.