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 paper introduces PAVS, a standardized, publicly accessible database integrating thousands of Saudi Arabian and global clinical cases with phenotype-genotype data to address the lack of population-specific resources and demonstrate high utility in prioritizing disease-causing genes for under-represented populations.
This study demonstrates that genomic ascertainment of heterozygous *PALB2* pathogenic variants in large population cohorts reveals significantly elevated risks for breast and pancreatic cancers, as well as increased all-cause mortality, with risks further modified by family history but generally lower than estimates derived from familial ascertainment.
This study utilizes functionality-informed fine-mapping on over one million individuals to dissect the highly polygenic architecture of coronary artery disease, identifying 36 high-confidence causal variants and three key biological pathways—lipoprotein metabolism, vascular homeostasis, and inflammation—that drive disease risk.
By analyzing sex-stratified adiposity genetics in 2 million individuals, this study reveals that female-specific genetic factors drive endometrial cancer risk through distinct hormone-responsive and insulin-leptin signaling pathways, identifying 26 shared loci and demonstrating that only a fraction of the cancer's genetic variance is directly mediated by adiposity.
This study confirms the pathogenicity of a novel TGFBR2 E431K variant in a patient with Loeys-Dietz syndrome by integrating AI-driven structural modelling with cell-based assays to demonstrate that the mutation disrupts protein stability and aberrantly alters TGF-β signalling.
By partnering with 18 patient organizations to utilize the publicly available GeniE tool for analyzing gnomAD data, this study demonstrates how democratizing genetic prevalence estimates for 22 rare conditions empowers patient advocacy and highlights the dynamic nature of these metrics as genomic databases evolve.
This multi-institute prospective study demonstrates that a novel fragmentomic metric called the regional motif diversity score (rMDS), derived from cell-free DNA end motifs, robustly distinguishes immunotherapy responders from non-responders in head and neck cancer and predicts improved disease-free survival, outperforming established biomarkers like PD-L1 expression and tumor fraction.
This study identifies FRMPD4 as a causal gene for X-linked non-syndromic sensorineural hearing loss, expanding its known phenotypic spectrum beyond intellectual disability and epilepsy through genetic analysis of affected families and functional validation across Drosophila, zebrafish, and mouse models.
This study demonstrates through genomic structural equation modeling that genetic factors influencing branched-chain amino acid (BCAA) metabolism independently contribute to the risk of type 2 diabetes and coronary artery disease, distinct from traditional risk factors like obesity and dyslipidemia.
This systematic review and meta-analysis of 15 studies reveals that while large language models augmented with external knowledge achieve higher diagnostic accuracy for rare diseases than standalone models, their performance is highly variable and dependent on benchmark disease composition, with all current evidence limited by high risk of bias and a lack of prospective clinical validation.