180 papers
Genetic and genomic medicine explores how our DNA shapes health, disease risk, and responses to treatment. This rapidly evolving field moves beyond simple family trees to examine the complex molecular instructions that guide every cell in the human body. By decoding these biological blueprints, researchers aim to unlock personalized therapies that target the root causes of illness rather than just treating symptoms.
On Gist.Science, we bring the latest discoveries directly from medRxiv, the leading preprint server for health sciences. We process every new submission in this category as it arrives, transforming dense academic findings into both detailed technical breakdowns and clear, plain-language summaries. This ensures that groundbreaking research is accessible to clinicians, scientists, and curious readers alike without the usual barriers of jargon.
Below are the most recent papers in genetic and genomic medicine, organized for your review.
This study establishes that loss-of-function variants in the MAP3K7 gene cause dilated cardiomyopathy, expanding the gene's known disease spectrum from syndromic conditions to include apparently isolated heart failure across the lifespan.
This paper describes the design, implementation, and AI-assisted harmonization workflow of the SysBio Common Data Model, which extends the OMOP framework to enable the findability and interoperability of multimodal -omics data from the Accelerating Medicines Partnership within a federated data ecosystem.
The paper introduces ICONIC, an R package that unifies genetic instrumental variables and negative-control calibration within a proximal causal inference framework to address unmeasured confounding in multi-omic studies, offering diverse estimators, robust diagnostics, and real-world applications in placental and tumor biology.
The paper introduces TRACE, a scalable, AI-driven pipeline that leverages a fine-tuned biomedical language model to automate the curation of literature and rank FDA-approved drug candidates for repurposing by aligning drug-gene effect directions with transcriptome-wide association study (TWAS) signals, thereby bridging genetic discovery and therapeutic hypothesis generation.
This study presents the first large-scale cross-trait and multi-polytranscriptomic score analysis of Parkinson's disease, demonstrating that integrating transcriptomic data with machine learning models identifies novel trait associations and significantly improves disease prediction accuracy beyond traditional polygenic scores.
This study analyzes pharmacogenomic data from 1,159 Russian children and newborns to demonstrate that current adult-based interpretation algorithms often fail to account for developmental changes in drug metabolism, leading to the proposal of a new age-oriented pediatric PGx reporting model that integrates ontogenetic adjustments and evidence stratification.
This case-control study in a Saudi Arabian population reveals that specific genetic variants within the Wnt signaling pathway genes (APC, AXIN2, DKK4, SFRP3, and LRP6) are associated with cardiovascular disease risk in an age- and gender-dependent manner, while variants in CTNNB1, TCF7L2, and DKK3 showed no significant association.
The paper introduces scDRS-FM, a novel method that integrates single-cell RNA-seq and GWAS data to distinguish causal disease-critical cell populations from correlated tagging populations by modeling conditional polygenic enrichment, thereby enabling more accurate fine-mapping of disease-relevant cellular contexts across diverse traits and cell types.
This study introduces an extended polygenic risk score (ePRS) framework that incorporates non-additive multilocus interactions and gene-environment effects to significantly improve genetic risk prediction for complex diseases like type 2 diabetes and celiac disease by capturing high-risk individuals missed by traditional additive models.
A genome-wide association study of 94,730 Chinese adults from the China Kadoorie Biobank identified 19 independent copy number variant associations across 15 loci influencing anthropometric and cardiometabolic traits, including novel findings and replicated dosage-sensitive regions, thereby expanding the genetic understanding of these traits in East Asian populations.