181 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 demonstrates that lung adenocarcinoma histological subtypes defined by WHO classification exhibit distinct immune cell and transcriptomic profiles that correlate with survival and immunotherapy responsiveness, offering a more prognostically valuable framework than mutation analysis alone.
This study demonstrates that a genotype-based severity scoring system, which classifies WFS1 variants by type and location, effectively correlates with the onset age of cardinal Wolfram syndrome symptoms, particularly diabetes mellitus and optic atrophy, thereby offering valuable insights for predicting disease progression and guiding clinical care.
This study presents a comprehensive resource of whole-genome DNA methylation patterns in 404 high-grade serous ovarian cancer samples, revealing that the regulatory methylome is established early and remains stable during chemotherapy, with widespread promoter hypermethylation in treatment-resistant ascites cells driving therapy failure and offering detectable epigenetic signatures for liquid biopsy monitoring.
This study employs an integrative cross-omic framework to demonstrate that the genetic relationship between Alzheimer's disease and type 2 diabetes is characterized not by a simple shared-risk model, but by locus-specific heterogeneity and antagonistic pleiotropy, where shared genetic signals often exert opposing effects on the two diseases.
CanVar-UK is a freely accessible online platform that supports the collaborative interpretation of germline variants in cancer susceptibility genes by aggregating diverse variant-level data, linking to clinical databases, and facilitating diagnostic discussions among a growing international community of users.
The paper introduces gnomAD v4, a massive database of 807,162 individuals, alongside refined loss-of-function annotation and a novel Bayesian framework integrating clinical literature to significantly enhance gene discovery and rare disease diagnosis.
This integrative systematic review and meta-analysis synthesizes data from 31 studies to characterize the general population's preferences for expanded carrier screening program attributes and estimates a median willingness-to-pay of $107, despite noting a high risk of bias in most included studies.
This study demonstrates that FTA Elute technology enables reliable, rapid, and cost-effective prenatal diagnosis of sickle cell disease via amniocentesis in resource-limited settings like Kinshasa, while also characterizing the socioeconomic and genetic profiles of the couples seeking these services.
This study presents an in silico framework demonstrating that integrating polygenic risk scores into clinical trial designs to enrich for high-risk participants significantly improves statistical power, reduces required sample sizes, and accelerates event accrual across various disease contexts, though optimal enrichment thresholds must be balanced against population availability.
The paper introduces S-MiXcan, a scalable summary-statistics-based framework that infers cell-type-specific transcriptome-wide associations from bulk transcriptomics and GWAS data by jointly modeling genetically regulated expression across multiple cell types without requiring individual-level data.