101 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.
The Biobank Rare Variant Analysis (BRaVa) consortium leverages a global meta-analysis of over 1.2 million individuals across ten diverse biobanks to discover 514 rare gene-trait associations, demonstrating that federated integration significantly enhances the detection of rare genetic variants and their biological mechanisms beyond the capacity of individual cohorts.
A survey of North American genetic counselors reveals that while AI chatbots are widely used for general purposes and viewed as promising tools for improving workflow efficiency and patient education, their clinical adoption remains low due to concerns regarding accuracy, patient comprehension, and a lack of structured training.
This study suggests that while ancient or polygenic selection on standing variation likely contributed to the higher cardiometabolic disease risk in South Asian populations compared to Europeans, recent selective sweeps and gene-gene or gene-environment interactions are unlikely to be the primary drivers of this disparity.
By constructing a multi-ancestry long-read sequencing panel to impute structural variants in 500,000 UK Biobank participants, this study enables large-scale genome-wide association analyses that uncover thousands of significant disease links and demonstrate the superior ability of structural variants to prioritize causal genes compared to traditional short-variant GWAS.
This study identifies that rare bi-allelic loss-of-function variants in the LRRK2 gene cause a recessive Mendelian form of interstitial lung disease characterized by lung fibrosis and alveolar type 2 cell dysfunction, distinguishing it from the Parkinson's disease associated with heterozygous activating variants.
This study leverages single-cell eQTL mapping across 28 peripheral immune cell types to construct a comprehensive catalogue of cell-type-specific effector genes for 69 diseases and 31 biomarker traits, revealing distinct immune contributions to complex conditions and identifying targets with a higher likelihood of regulatory approval.
This study introduces a novel statistical likelihood-ratio methodology (PS4-LR-Calculator) that successfully integrates large-scale unselected case-control data with nationally collected, enriched laboratory datasets to significantly enhance the power and precision of classifying breast cancer susceptibility gene variants.
By analyzing homozygous deletions across 125,730 individuals in the UK's National Genomic Research Library, this study establishes a comprehensive genome-wide map that enhances rare disease diagnosis, uncovers the significance of non-coding promoter deletions, and identifies 43 candidate novel disease-associated genes.
A retrospective review of Primary Children's Hospital's universal whole-genome sequencing protocol for neonates with congenital heart defects revealed that 19.4% of patients had clinically relevant genetic findings, with 10.6% receiving a causative diagnosis that often occurred before the emergence of extracardiac features, thereby enabling timely medical management and informed family decision-making.
This study integrates genomic, transcriptomic, and cytokine secretion data from 366 donors to identify key genetic loci, including CCR5-Δ32 and OAS1, that regulate monocyte cytokine production and reveal links to lipid metabolism and disease risk.