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 paper argues that the claim of a 55% heritability for human lifespan is fundamentally flawed because it incorrectly assumes susceptibility to "extrinsic" causes of death, such as infections and accidents, is non-genetic, thereby introducing selection bias that artificially inflates heritability estimates.
This study demonstrates that residual plasma from routine clinical chemistry heparin separator tubes, when processed promptly and refrigerated, serves as a viable and high-quality source for circulating cell-free DNA biobanking and molecular testing, showing strong concordance with standard specialized collection methods.
This paper introduces PheMED, a scalable statistical method that leverages GWAS summary statistics to quantify and adjust for genome-wide effect size dilution caused by phenotype misclassification, thereby improving the accuracy of downstream replication, heritability analyses, and meta-analyses.
This French study reveals that while parental support for expanding newborn screening to include genomic technologies is generally high, it is nuanced and primarily driven by perceived health benefits and emotional attitudes, with specific concerns regarding uncertainty and ethical implications requiring culturally adapted communication and clear governance for successful implementation.
This study proposes an integrated deep learning framework that combines data augmentation, feature selection, and explainable graph neural networks to achieve high-accuracy, biologically interpretable classification of small-sample RNA-Seq datasets, demonstrating superior performance on chromophobe renal cell carcinoma and other diseases.
This study describes the recruitment and initial characterization of 1,180 Generation Scotland participants with a history of antidepressant treatment, who completed a detailed questionnaire to establish clinically meaningful phenotypes of treatment response that will be validated against electronic health records and linked with biological samples to investigate the mechanisms underlying variability in antidepressant efficacy.
This study utilizes saturation genome editing to comprehensively map the functional impact of nearly 11,000 BARD1 variants, successfully resolving over 95% of variants of uncertain significance and confirming the gene's critical role in tumor suppression through homology-directed DNA repair.
This study identifies 14 individuals with severe developmental and epileptic encephalopathy caused by rare biallelic *RNU2-2* variants, characterizing a distinct clinical phenotype and demonstrating that aberrant splicing events are detectable via RNA sequencing in fibroblast but not blood tissues.
This paper introduces COXMM, a novel Cox proportional hazard mixed model that accurately estimates time-to-event heritability from longitudinal biobank data, revealing that disease progression often has lower heritability than initial incidence and providing a robust framework for improving polygenic risk score predictions in survival analyses.
By applying dominant and recessive genetic models to a genome-wide association study of over 30,000 individuals, researchers identified five novel risk signals for Idiopathic Pulmonary Fibrosis, including variants in the PMF1 and EPN3 genes, thereby uncovering new mechanistic insights into the disease's pathogenesis.