The cross-population Sum of Single Effects (SuSiEx) model is a robust and computationally efficient method for conducting multi-ancestry fine-mapping of genome-wide association signals, producing smaller credible sets and capturing population-specific causal variants.

Analysis of whole-genome sequencing data from over 10,000 tumor samples spanning 35 cancer types identifies putative driver genes and highlights new therapeutic opportunities.

The Human Endometrial Cell Atlas integrates single-cell transcriptomic datasets from women with and without endometriosis. Novel and known cell types are registered using spatial transcriptomics to provide a comprehensive map of the human endometrium in controls and endometriosis cases.

Yin Yang 1 (YY1) aids in the formation of enhancer–promoter (E–P) loops independently of cohesin. YY1 maintains a subset of E–P interactions in interphase and establishes an overlapping yet distinct set after mitotic exit.

This study uses a combination of in silico and experimental techniques to ascribe target genes to 170 risk loci for colorectal cancer.

CREME is an extensible computational tool for investigating cis-regulation via in silico perturbations of neural network-based DNA sequence models such as Enformer, identifying complex interactions between a gene’s regulatory elements.

Single-cell profiling of lamina-associated domains (LADs) during early mouse development reveals an overlap between preimplantation-specific LAD dissociation and noncanonical broad H3K27me3 domains. Loss of H3K27me3 restores canonical LAD profiles.

Analysis of human tumor datasets shows that all features that appear significantly associated with immunotherapy response and survival may be collapsed into five latent factors: tumor mutation burden, T cell effective infiltration, TGF-β activity in the microenvironment, prior treatment and tumor proliferative potential.

Massively parallel reporter assays in four plant species show that transcriptional regulatory elements are position dependent with enrichment downstream of the transcription start site, particularly GATC motifs with strong effects in vascular plants.

MILTON uses phenotype information in the UK Biobank to identify clinical biomarkers and other quantitative traits that characterize diseases. It then constructs augmented cohorts by predicting undiagnosed individuals, improving power to discover gene–disease relationships.

Calls for more diverse data in genetics studies typically fall short of offering further guidance. Here we summarize a policy framework from the Global Alliance for Genomics and Health designed to fill this gap. The framework prompts researchers to consider both what types of diversity are needed and why, and how aims can be achieved through choices made throughout the data life cycle.