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Systematic comparisons of different masking approaches for rare variant association tests across 54 traits in UK Biobank highlight strategies for gene-level burden analyses that increase study power and replicability.
By mapping chromatin accessibility over transposable elements (TEs) across normal hematopoietic cells and the various cell types of primary acute myeloid leukemia (AML), we identified TE subfamilies with altered chromatin state in leukemia stem cells. Signatures of TE chromatin accessibility were able to predict clinical outcomes in cohorts of patients with AML, linking repetitive DNA elements to stemness.
Mutations may be enriched in tumor samples because they promote carcinogenesis or because they promote clonal expansions in healthy tissue. This study mathematically disentangles these two possibilities by analyzing tumor and normal tissue sequencing datasets.
The Single Cell Notebooks provide multilingual, open-access training materials for single-cell and spatial transcriptomics analysis through reusable notebooks. They have evolved into a community-driven platform for education and capacity building, lowering language and computational barriers to support equitable participation in omics research.
This study identifies distinct transposable element subfamilies as genetic determinants of stemness properties in normal and leukemic stem populations with clinical implications for patients with acute myeloid leukemia.
This study explores the clonal architecture of aplastic anemia across age using single-cell approaches. Somatic inactivation of specific human leukocyte antigen risk alleles is a frequent event and often occurs in multiple independent events.
Genetic prediction of type 1 diabetes is one of the most successful for complex traits. A machine learning approach now improves this further and discovers multiple non-linear locus–locus interactions and molecular subclusters with differing clinical features.
Genome-wide association and fine-mapping analyses of type 1 diabetes (T1D) identify multiple genetic risk signals. Furthermore, a machine learning model, T1GRS, improves the prediction of T1D in individuals with complex risk profiles and identifies genetic subgroups.
Multi-ancestry genome-wide association analyses coupled with multi-omics integration identify new risk loci for endometriosis and adenomyosis, shed light on underlying molecular mechanisms and suggest potential therapeutic interventions.
This review examines how ancient DNA has transformed our understanding of human adaptation by enabling genetic changes to be tracked directly through time, revealing how past shifts in environment, diet and disease shaped present-day human variation.
INSPIRE addresses challenges to integrating diverse spatial transcriptomics datasets by combining deep learning with non-negative matrix factorization, revealing shared and context-specific spatial gene programs and tissue organization across scales.
Profiling of pleural mesothelioma samples from 91 patients identifies four DNA methylation subtypes that correlate with response to immune checkpoint inhibition and survival
