Penn Medicine BioBank

The paper identifies shared and distinct genetic architectures for cardiac structure and function using UK Biobank data, revealing common variants influencing multiple chambers and phenotypic variability.

The paper uses polygenic scores to associate psychiatric disorder risk with laboratory tests, identifying physiological biomarkers across different ancestries.

This paper develops a two-step genetic risk model for brain arteriovenous malformations (bAVM) using rare Mendelian variants and a polygenic risk score, identifying high-risk individuals with improved screening efficiency.

The paper uses admixture mapping in the All of Us Research Program to identify genetic ancestry associations with 22 traits, finding 71 novel loci where African ancestry explains phenotypic variation.

This study identifies 207 height-associated genes using rare protein-altering variants from over 1.4 million individuals, showing large effect sizes and overlap with Mendelian height syndromes.

This study links a polygenic risk score for coronary artery disease to coronary atherosclerotic burden measured by clinical CT angiograms.

The paper establishes a global consortium to discover rare genetic associations by integrating data from multiple biobanks, identifying 514 gene-trait associations, many detectable only through cross-ancestry meta-analysis.

This study evaluates the GIRA approach in a health system, finding 30.4% of patients labeled high-risk based on genomic data, with ancestry differences and social deprivation influences.

The paper shows that statistical uncertainty explains disagreement in type 2 diabetes polygenic scores and proposes using uncertainty measures to improve risk prediction.

This study uses genomic structural equation modeling to identify genetic overlaps between psychiatric and physical illnesses, finding shared risk factors across multiple domains.

DRIVE-KG integrates multi-omics data into a knowledge graph to discover SNP-phenotype associations and predict endometriosis status, uncovering new variant-phenotype links and improving classification performance.

This paper examines how genetic interactions with ancestry affect polygenic scores and shows that models of local and global ancestry interactions can be distinguished using partial polygenic scores when causal variants are known.

The paper evaluates proteomic and polygenic risk scores for disease prediction, finding proteomic scores outperform polygenic scores in most traits and show temporal responsiveness.

This study shows that angiographic CAD burden independently contributes to adverse cardiovascular outcomes, even when traditional risk factors are accounted for.

The paper identifies a higher prevalence of NF1 pathogenic variants using a genotype-first approach, finding many cases with somatic mosaicism and increased cancer risk.

This study identifies 91 risk loci for gallstone disease through a trans-ancestry GWAS, including 37 novel loci, and links them to metabolic and liver-related traits.

The paper identifies BSN variants as a cause of a new neurodevelopmental disorder with a broad phenotypic range, using clinical data and computational phenotyping.

The paper develops a deep learning method to quantify 3D aortic geometry in large biobanks, expanding available phenotypes for studying aortic degeneration.

The paper examines how covariates affect polygenic score performance across European and African cohorts, finding significant differences in R2 and interaction effects, and proposes methods to improve model performance.

This study evaluates the reliability of coronary artery disease polygenic risk scores across diverse populations, finding poor individual agreement despite equivalent population-level performance.

This study finds that universal exome sequencing in critically ill adults yields a 25% diagnostic rate and highlights race-based disparities in genetic diagnosis documentation.

This study investigates mtDNA copy number (mtCN) in a diverse cohort, finding ancestry-specific genetic and phenotypic associations and highlighting the impact of blood cell composition on results.

This paper evaluates adding polygenic risk scores to the RECODe model for predicting cardiovascular outcomes in type 2 diabetes patients, finding no significant improvement in predictive performance.

The paper examines immune responses in vaccinated individuals during SARS-CoV-2 breakthrough infection, showing early activation of memory T cells and subsequent neutralizing antibody production.

This study identifies 13.9% of neuroblastoma patients with pathogenic germline variants in cancer predisposition genes, associated with worse survival and enriched in DNA repair genes like BARD1.