208 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 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.
This study leverages UK Biobank and TriNetX cohort data to characterize the genetic overlap, shared biological pathways, and comorbidity patterns across autoimmune diseases through polygenic risk score analysis and pathway mapping, revealing both common and distinct genetic architectures that support the concept of poly-autoimmunity.
This study presents a comprehensive analysis of 392,030 whole genomes from the All of Us Research Program, utilizing a unified "All by All" framework to identify nearly 50,000 genetic associations across thousands of traits and, through meta-analysis with the UK Biobank, uncover novel gene-phenotype links while providing public tools for global research.
This study demonstrates that the Huntingtin CAG repeat length acts as a continuous quantitative modifier of brain structure and neuropsychiatric vulnerability across the general population, challenging the traditional view of it as a purely categorical determinant of Huntington's disease.
This study presents a comprehensive single-cell map of chromatin accessibility across 3.5 million human immune cells from over 1,000 donors, revealing that integrating cell type-specific caQTLs with multi-omics data significantly enhances the identification of disease-associated regulatory mechanisms and gene networks compared to traditional approaches.
This study presents a computational framework integrating machine learning, protein-protein interaction network analysis, and transcriptomic validation to identify six promising protein biomarkers (OSM, IL6R, LMNB1, HIF1A, NPM1, and CSF1) for the early diagnosis, prognosis, and therapeutic discovery of CLN3 disease.