368 papers
Genomics is the study of an organism's complete set of DNA, offering a deep dive into the biological instructions that shape life. This field explores how genetic information influences traits, health, and evolution, moving beyond single genes to understand the complex interplay within entire genomes. From uncovering the roots of disease to mapping biodiversity, genomics provides the foundational data for many modern medical breakthroughs.
At Gist.Science, we process every new preprint in this category as it appears on bioRxiv, ensuring you stay ahead of the curve. Each paper is accompanied by both a clear, plain-language overview and a detailed technical summary, making cutting-edge research accessible to everyone regardless of their background. Below are the latest papers in genomics, freshly summarized and ready for you to explore.
This study investigates the relative impacts of genetics and heat-related environmental factors on pig whole-blood gene expression, identifying thousands of differentially expressed genes and eQTLs while highlighting specific genetic mechanisms and candidate genes involved in thermoregulation and production traits.
This study demonstrates that the regulatory impact of genetic variants in Massively Parallel Reporter Assays is highly dependent on their position within the DNA sequence, primarily due to context-specific transcription factor binding and structural constraints like Alu element promoter requirements, highlighting the complexity of cis-regulatory grammar.
The authors developed in vivo multiomic Perturb-seq, a novel platform that overcomes inefficient nuclear gRNA recovery to enable high-fidelity, single-nucleus multiomic profiling, which they used to uncover cell-type-specific transcriptomic and epigenomic phenotypes of neurodevelopmental disorder risk genes in the developing cortex.
This paper presents a comprehensive, structure-aware framework for interpreting genomic variants in genetic skeletal disorders by integrating experimental and AlphaFold2-derived protein structures with clinical data to reveal structural knowledge gaps, emphasize the importance of multimeric interfaces, and enable mechanistic interpretation of pathogenic and uncertain variants.
This study presents a scalable, cost-effective metagenomic surveillance framework leveraging Brazil's existing rabies monitoring program to detect diverse zoonotic viruses in urban bats, including a novel filovirus, thereby validating a practical One Health model for epidemic preparedness in low- and middle-income countries.
This study demonstrates that inferences about a trait's genetic architecture, particularly regarding SNP heritability and the inferred direction of allelic effects, are significantly influenced by study design and cohort representation, with the latter often driven by the skewness of the trait distribution within the specific biobank.
By integrating GWAS data with a high-resolution map of dexamethasone-induced chromatin dynamics in human trabecular meshwork cells, this study identifies 78 candidate causal genes and elucidates the regulatory mechanisms underlying elevated intraocular pressure and primary open-angle glaucoma pathogenesis.
This study analyzes mutation data from over 20,000 cancer patients to reveal that stop-loss mutations, which generate C-terminal protein extensions, are significantly enriched in oncogenes and tumor suppressor genes and can functionally alter protein processing, as demonstrated by the impaired cleavage of the immunostimulatory peptide thymosin alpha 1 in PTMA.
This study utilizes CAGE technology to map active transcriptional start sites and enhancers during honeybee worker metamorphosis, revealing a lineage-specific regulatory network where the transcription factor tramtrack (ttk) likely controls key developmental genes like Broad complex (Br-c) through enhancers conserved specifically within the Apis genus.
The paper introduces "Combine," a scalable group lasso framework that integrates local ancestry dosages with genotype data to significantly enhance polygenic risk prediction accuracy and interpretability in admixed populations, outperforming existing multi-ancestry methods while matching optimized individual-level models.