Ke Qiu | PubMed Health

PubMed2026

Conventional and Shared Genetic Association Analysis Between Diabetes Mellitus and Sensorineural Hearing Loss.

PURPOSE: This study aims to investigate the epidemiological and genetic associations between diabetes mellitus (DM) and sensorineural hearing loss (SNHL) across different subtypes. METHODS: We analyzed 502,490 participants from the UK Biobank using multivariate logistic regression to examine the association between DM and SNHL, considering gender, age, and HbA1c levels. Genetic correlations and causality were examined by linkage disequilibrium score regression and bidirectional Mendelian randomization. Cross-trait meta-analyses identified shared loci between DM and SNHL, followed by gene annotation, functional analysis, and drug candidate exploration for the shared traits. RESULTS: Observational analysis revealed significant associations between DM and SNHL, consistent in subgroups based on age, sex, and certain HbA1c levels. A positive genetic correlation was found between type 2 diabetes mellitus (T2D) and SNHL (Rg = 0.0982, p = 0.0095) between T2D and SNHL, and four loci were identified, with ARHGEF28 and TCF7L2 prioritized as credible pleiotropic genes. Enrichment was indicated in glucose metabolism and organogenesis, with shared heritability in metabolic tissues and outer hair cells. Metformin was identified as potential drug candidates for the T2D-SNHL comorbidity. CONCLUSION: These findings progress our understanding of the epidemiological association, shared genetic basis, and potential therapeutic targets between T2D and SNHL, which might contribute to the management of their comorbidity.

Humans↗

PubMed2026

A token-pruning framework enables efficient representation of the human genome for RNA modification analysis.

MOTIVATION: Modelling long genomic sequences remains challenging due to extreme sequence length, high redundancy, and the need for biological interpretability. Although Transformer-based architectures have achieved strong performance across genomic tasks, their high computational cost and reliance on fixed tokenization strategies limit their scalability and ability to focus on biologically informative regions. RESULTS: We propose ATSFormer, a token-pruning Transformer framework for efficient and biologically informed genomic sequence modelling. ATSFormer incorporates an attention-guided and parameter-free Adaptive Token Sampling (ATS) module into Transformer layers. Guided by attention-derived importance scores, ATS dynamically retains informative tokens while probabilistically discarding redundant ones, thereby reducing sequence length, FLOPs, and memory usage without introducing additional learnable parameters or extra training procedures. Importantly, the retained tokens correspond to key contributors to model predictions, enabling ATSFormer to highlight biologically meaningful sites and sequence motifs. We evaluated ATSFormer on four benchmark RNA modification datasets derived from RMVar 2.0, covering A-to-I, m1A, m5C, and m7G. Experimental results show that ATSFormer consistently outperforms existing state-of-the-art methods while achieving substantial computational savings. Furthermore, structural analysis using AlphaFold3 supports the biological relevance of the motifs identified by ATSFormer. AVAILABILITY AND IMPLEMENTATION: The source data and code are freely available at GitHub (https://github.com/1gao2/ATSFormer) and Zenodo (https://doi.org/10.5281/zenodo.21813541).

Humans↗