Genome-wide association analyses of Alzheimer’s disease stratified by APOE carrier status identify variants whose effects are attenuated or augmented by the ε4 risk allele.

Cancer treatment alters the selection of preexisting somatic mutations in the normal esophagus, including druggable mutants that confer treatment resilience to normal cells.

This study uses high-resolution, allele-specific chromatin architecture to identify causal gene-regulatory mechanisms in inflammatory diseases and uncovers a mechanism by which a neo-CTCF site promotes autoimmunity.

This study uses budding yeast as a model system to link genetic variants with quantitative changes in protein–protein interaction strength under different environmental perturbations by leveraging a protein-fragment complementation assay targeting 61 pairs of interacting proteins.

SAIGE-QTL is a framework for eQTL mapping that directly models single-cell read counts using a Poisson mixed model, achieving improved statistical efficiency and power and enabling rare variant set-based testing in the single-cell eQTL setting.

RSparsePro performs robust fine-mapping in the presence of linkage disequilibrium mismatch by coupling a probabilistic graphical model with a variational inference algorithm, identifying credible sets with higher power and coverage than existing methods.

T cells provide a unique setting for quantification of the relationship between genotype and phenotype. The authors introduce TCRi, an information-theoretic framework for this, and find mutual information to be a robust determinant of immunotherapeutic response.

Cross-scale spatial Omics Registration and Analysis via graph Learning is a deep generative model that integrates multiscale spatial multi-omics data using graph-attention aggregation mechanisms, enabling a comprehensive spatial analysis.

This longitudinal single-cell study of estrogen receptor (ER)-positive breast cancer suggests that strong estrogen signaling, ER-linked enhancers and active T cell responses could predict sensitivity to aromatase inhibitors, whereas resistance involves androgen signaling though hormone-sensing enhancer activity.

This study introduces a framework for integrating DNA methylation data across more than 90 tissues and cell types, characterizing genome-wide methylation patterns and predicting context-specific effects of genetic variants on DNA methylation and regulatory function using a deep learning model, Wreath.

Patient and family groups increasingly drive the development of drugs for rare diseases, from trial design to regulatory strategy. This model of patient-focused drug development can accelerate therapies for rare diseases but also generates recurring ethical tensions with academic, industry and regulatory partners that remain underdiscussed.