polygenic risk

This paper improves depression prediction by integrating psychiatric traits, showing stronger associations across development, families, and populations.

This paper develops a stable polygenic risk score for colorectal cancer by integrating multiple existing scores, improving individual-level risk classification consistency without sacrificing population-level performance.

BLocPRS improves polygenic risk score prediction by combining locus-specific weights from different PRSs, achieving higher accuracy in UK Biobank and extending to non-European populations.

The paper examines how social determinants of health modify polygenic risk scores for cardiometabolic diseases and shows that integrating social and genetic factors improves disease prediction.

The paper introduces a family-aware benchmark for polygenic prediction using All of Us data, enabling comparison of PRS and machine learning models across 14 traits with strong predictive performance.

This paper develops a polygenic score for smoking persistence and finds it is associated with earlier or later cessation ages across diverse populations.

SUMMIT improves heritability and genetic correlation estimation across diverse ancestries using LD scores and GWAS summary statistics, showing better accuracy in non-European populations.

The paper introduces KASM to integrate functional annotations and estimate heritability of rare protein coding variants across 786,871 exomes, finding higher heritability than previous estimates.

This paper introduces S4-Oligo, an optimized polygenic score method for clinical use, which matches or exceeds existing models' accuracy while using fewer SNPs.

This study introduces PGSa,d, a polygenic scoring framework that incorporates both additive and dominance effects, showing improved accuracy for traits with significant dominance contributions.

The paper uses sequence-based variant-effect predictions to show that different regulatory layers contribute variably to the heritability of complex traits within tissues.

The paper detects stabilizing selection on human plasma proteins by integrating genetic and fitness-related evidence across multiple approaches.

This paper identifies 116 risk loci for melanoma through GWAS, links susceptibility to immunity and Wnt signaling, and develops a PRS improving risk prediction.

This study evaluates polygenic score prediction accuracy and cross-ancestry portability across 3,994 PGSs in diverse populations, identifying factors affecting their performance and equity.

The paper identifies a bias in local genetic correlation estimation due to significance filtering and proposes a correction method with a diagnostic tool.

The paper develops and validates polygenic risk scores (PRS) for 150 diseases and mortality using UK Biobank and FinnGen data, showing genetic information provides the largest predictive gains for certain diseases but not others.

The paper generates and validates genome-wide genetic data for the 45 and Up Study cohort, enhancing its utility for disease risk and precision health research.

GeneSIS improves polygenic score transferability by incorporating gene-by-sex interactions, enhancing prediction accuracy in non-European populations for 32 traits.

The paper identifies estrogen-modulated SNVs associated with breast cancer risk and shows their interaction with reproductive factors using polygenic risk scores.

This study shows that adverse polygenic profiles for dementia are linked to higher risks of dementia, disability, or death in middle-aged and older adults.

The paper identifies individuals with extreme polygenic risk scores (PRS) for Alzheimer's disease (AD) and shows they have significantly elevated AD risk across multiple ancestries and APOE diplotypes.

The paper evaluates 1,752 polygenic risk score models for Alzheimer's disease and finds significant variability in results across methods and ancestries.

The paper identifies shared genetic variants between major psychiatric disorders and retinal traits, revealing molecular mechanisms linking retinal morphology to neurodevelopmental and neurotransmission processes.

This study evaluates when nonlinear machine learning methods outperform standard polygenic risk scores, finding their effectiveness depends on genetic architecture and sample size.

This study meta-analyzes 156 comparisons across 76 studies to assess mtDNA copy number changes in neurodegenerative diseases, finding no overall difference but significant heterogeneity.

This paper develops a framework to improve polygenic risk scores by using correlated traits, showing that helper traits can significantly enhance prediction accuracy for type 2 diabetes.

This paper introduces PGSC, a polygenic score framework that incorporates locus-specific gene-context interactions, showing improved prediction across multiple traits and ancestries.

This paper presents a pipeline for polygenic risk score calculation that includes data curation, statistical methods, and reporting, resulting in a curated database and interpretable reports for PRS analysis.

The paper analyzes herbicide resistance in blackgrass, showing TSR mutations arose recently while NTSR evolved from pre-existing genetic variation.

This study evaluates how principal component and mixed model approaches affect polygenic risk score portability across different ancestries in the UK Biobank.