MultiSuSiE is an extension of the Sum of Single Effects fine-mapping model that allows causal effect sizes to vary across ancestries. Applying MultiSuSiE to quantitative traits in All of Us identifies fine-mapped variants not implicated by other methods.

This study associates p53 loss and brain metastasis in breast cancer. Mechanistically, p53-null tumors recruit astrocytes that provide substrates for enhanced fatty acid synthesis via upregulated SCD1 expression, representing a targetable axis in the disease.

Saturation mutagenesis screening examines 11,520 point mutations in the kinase domains of FGFR1, FGFR2, FGFR3 and FGFR4, identifying their activating and resistance properties to the FGFR inhibitors pemigatinib and futibatinib.

The authors present DNA-Diffusion, a generative AI framework that designs synthetic regulatory elements with tunable cell-type specificity. Experimental validation demonstrates their ability to reactivate AXIN2 expression, a leukemia-protective gene, in its native genomic context.

A single-cell epigenomic atlas of human immune cells highlights cell-type-specific features associated with genetic or environmental exposures.

Single-cell RNA sequencing and assay for transposase-accessible chromatin using sequencing profiling of human retinal samples from diverse ancestries create an epitranscriptomic atlas characterizing over 130 cell types. Integration with genome-wide association study and expression quantitative trait loci data provides further insights into gene regulation and disease etiology.

This study uses single-nucleus RNA sequencing to analyze lung tissue from 141 individuals with chronic obstructive pulmonary disease. Distinct patterns of spatially resolved changes in cell composition and cell states that correlate with clinical features are highlighted.

Genome-wide analyses identify 13 loci associated with susceptibility to infection with SARS-CoV-2 Omicron variants, including variation in ST6GAL1, previously associated with influenza susceptibility, and other genes involved in glycosylation processes.

AI co-scientists can act as virtual research collaborators in statistical genetics, accelerating genetic discovery and translation. Realizing this potential depends on robust domain-specific data and infrastructures, together with interdisciplinary teamwork to build community standards that ensure rigor and responsible deployment.