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.

This study finds that cell segmentation errors affect numerous downstream applications of spatial transcriptomics data and provides a method to correct these errors by factorizing molecular neighborhoods.

Pangenome Mutation-Annotated Network (PanMAN) is a pangenome data structure that encodes shared mutational and evolutionary histories across microbial genomes, providing both high compression and enhanced representative power.

De novo and inherited dominant variants in genes encoding U4 and U6 small nuclear RNAs are identified in individuals with retinitis pigmentosa. The variants cluster at nucleotide positions distinct from those implicated in neurodevelopmental disorders.

This study uses a high-dimensional proteomics panel to explore protein-level genetic associations with type 2 diabetes in a Ugandan cohort.

Protein quantitative trait loci show enrichment of trans effects among proteins in the same interaction networks and among missense variants at interaction interfaces, highlighting pathways impacted by trait-associated variants.

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.

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.