Meta-analysis reveals rs1799793 polymorphism linked to head and neck cancer, underscoring genetic factors in carcinogenesis. Future studies needed for accuracy. #CancerResearch #Genetics

Studies in large cohorts exposed to the same triggers associated with Epstein-Barr virus (EBV) reactivation and the follow-up of post-acute outcomes m…

Researchers have extended complexity theory to encompass a broader range of game structures, definitively classifying the building blocks needed to verify computational processes and, as a result, proving that certain computational problems, specifically those linked to odd cycles, cannot be solved by computers.

Purpose To evaluate the associations of single-nucleotide polymorphisms (SNPs) in 17 loci with primary open-angle glaucoma (POAG) and subtypes in Chinese and Japanese.Methods 17 SNPs in 17 genes, selected from a recent genome-wide association study, were genotyped in a Hong Kong Chinese cohort of 1093 POAG patients, including 557 high-tension glaucoma (HTG) and 536 normal-tension glaucoma (NTG) patients, and 584 controls. Seven SNPs showing association in the Hong Kong cohort were genotyped in a Shantou Chinese cohort of 155 POAG patients and 380 controls and an Osaka Japanese cohort of 254 POAG patients and 207 controls. The SNP-disease association of individual and pooled cohorts was analysed.Results In combined Chinese and Japanese subjects, three SNPs were significantly associated with POAG: AFAP1 rs938604 (Pmeta =6.40x10–5, OR=0.70), FNDC3B rs62283813 (Pmeta =0.0050, OR=1.24) and GAS7 rs9913911 (Pmeta =0.0060, OR=1.19). In subgroup analysis, these three SNPs showed stronger association with HTG: AFAP1 rs938604 (Pmeta =8.00x10–6, OR=0.59), FNDC3B rs62283813 (Pmeta =6.00x10–7, OR=1.55) and GAS7 rs9913911 (Pmeta =0.0040, OR=1.24). In addition, SPRED2 rs4414666 (Pmeta =8.00x10–4, OR=1.29) was also significantly associated with HTG. No SNP showed a significant association with NTG.Conclusions This study confirms AFAP1 rs938604, FNDC3B rs62283813, GAS7 rs9913911 and SPRED2 rs4414666 as HTG-specific loci in Chinese and Japanese populations. These findings provide new insights into the genetic architecture of POAG, highlighting distinct molecular mechanisms between HTG and NTG subtypes.

**Abstract:** Current personalized pharmacogenomics largely focuses on single-gene polymorphisms. This research introduces a novel framework, Predictive Polymorphism Analysis via Bayesian Network Fusion (PPABNF), that integrates multi-omic data (genomics, proteomics, metabolomics) with electronic health records (EHR) to predict individual drug response with significantly higher accuracy. PPABNF leverages Bayesian network fusion to identify complex, non-linear relationships between genetic predispositions, physiological states, and drug efficacy.

Variation in antibody (Ab) responses contributes to variable disease outcomes and therapeutic responsiveness, the determinants of which are incompletely understood. This study demonstrates that polymorphisms in immunoglobulin (IG) light chain loci dictate the composition of the Ab repertoire, establishing fundamental baseline differences that influence functional Ab-mediated responses. Using long-read genomic sequencing of the IG kappa (IGK) and IG lambda (IGL) loci, we resolve genetic variation, including structural variants, single nucleotide variants, and gene alleles. By integrating these genetic data with Ab repertoire profiling, we find that all forms of IG germline variation contribute to inter-individual gene usage differences for >70% of light chain genes in the repertoire, directly impacting the amino acids of expressed light chain transcripts. The genomic locations of usage-associated variants in both intergenic and coding regions indicate that IG polymorphisms modulate g

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CONCLUSION: These results indicate that the PDCD1 rs11568821 G>A polymorphism may contribute as a potential susceptibility and prognosis marker for breast cancer, especially for ER+/PR+ subtypes, considering the importance of PD-1 inhibitor effect over anti-tumor response.

**Abstract:** This research proposes a novel deep learning framework, *PolyMetaNet*, for accurately predicting the impact of human drug metabolism enzyme polymorphisms (DMEPs) on in vivo drug distribution. Polymorphisms in enzymes like CYP450s significantly influence drug clearance and bioavailability, often leading to interindividual variability in therapeutic outcomes. PolyMetaNet integrates pharmacokinetic (PK) modeling, genomic data representing DMEPs, and a transformer-based neural network architecture to provide personalized predictions of drug exposure profiles.