Hepatitis B virus (HBV) vertical transmission remains a major health challenge, and the characteristics of the placental immune microenvironment in chronic infection remain unclear. This study aimed to use mass cytometry to comprehensively analyze phenotypic changes in placental immune cell subsets.

We collected placental tissues from 20 pregnant women (6 healthy controls and 14 with chronic HBV infection). CD45+ leukocytes were detected using a 35-marker antibody panel. Unsupervised clustering identified 14 clusters, of which 13 immune clusters were analyzed.

HBV mainly caused functional remodeling of placental immune cells rather than changes in cell composition (P > 0.05 for all subsets). In natural killer (NK) cells, CD38 (P = 0.015) and CD16 (P = 0.0020) were notably upregulated and strongly correlated (ρ = 0.872, P < 0.001), suggesting potentially enhanced ADCC function. T cells showed upregulation of CD27, CD38, and CXCR5. Basophils exhibited the most pronounced changes: 9 differential markers (including chemokine receptors CCR4 and CCR7) were consistently downregulated. Classical monocytes showed an M1 polarization tendency (CD38↑, CD163↓). All P-values were raw; no markers remained significant after false discovery rate correction at an FDR threshold of < 0.1, indicating nominal significance. In addition, the coordinated expression patterns of markers within multiple cell subsets were weakened after infection.

These exploratory findings suggest that HBV may reshape placental immunity through enhanced NK cell activation, broad basophil suppression, and disrupted marker coordination. The sample size was limited (n = 20), so the results need to be validated in larger cohorts. These findings provide new perspectives for understanding the mechanisms of mother-to-child transmission.

Inter-individual variability in drug efficacy and toxicity remains a major obstacle to precision therapeutics. Candidate-gene pharmacogenomics and star-allele-based guidelines have established clinically useful examples, but they cannot capture the full spectrum of mechanisms that shape drug response. Pharmacogenomic genome-wide association studies (pharmacoGWAS) extend this framework by enabling discovery beyond known pharmacogenes and can identify human genetic variants associated with efficacy, adverse drug reactions, dose requirements, pharmacokinetics, and pharmacodynamics. This mini-review aims to summarize practical principles for human pharmacoGWAS, with emphasis on study design, phenotype and exposure definition, reproducible bioinformatics pipelines, gene-expression-based functional interpretation, and clinical translation. This review discusses randomized trials, prospective cohorts, biobanks, electronic health records, claims databases, and rare adverse-event designs, highlighting the specific biases that arise because drug response is defined among exposed individuals. It then outlines core analytical steps, including genotype quality control, imputation, ancestry-aware association testing, mixed models, survival and longitudinal analyses, rare-variant aggregation, replication, and meta-analysis. Particular attention is given to expression quantitative trait loci, splicing quantitative trait loci, and protein quantitative trait loci, tissue prioritization informed by the Genotype-Tissue Expression project, transcriptome-wide association studies, and colocalization as tools for prioritizing candidate genes and plausible mechanisms. Finally, we propose a translation framework connecting discovery to clinical validity, guideline development, electronic health record decision support, and equitable implementation across diverse populations. When combined with rigorous epidemiology and functional genomics, pharmacoGWAS may help translate genome-wide signals into safer and more effective prescribing.