polygenic risk

XPRS decomposes polygenic risk scores into gene and SNP contributions using SHAP values and provides visualization tools to enhance interpretability and clinical communication.

The paper introduces blockLASSO, a method for training polygenic scores that is computationally efficient and effective across multiple phenotypes and ancestry groups.

This paper develops a multi-trait polygenic risk score framework to improve POAG prediction across ancestries by integrating glaucoma endophenotypes.

This study compares polygenic scores from genotyping arrays and whole-genome sequencing across traits and populations, finding that WGS generally improves prediction accuracy but with context-dependent advantages.

The paper uses a Bayesian model to identify shared genetic components across complex diseases, finding potential drug targets with lower side effects and repurposing opportunities.

This paper demonstrates that deep learning can generate polygenic risk scores with high accuracy, even with missing data, and shows that a single model can predict multiple scores.

The paper develops an improved polygenic score for idiopathic Parkinson's disease, showing higher discriminative performance and explaining variable penetrance in genetic forms of the disease.

The paper assesses bias in polygenic prediction of externalising behaviours using twin-pairs, finding minimal rGE bias but less variance prediction for callous-unemotional traits.

This study evaluates polygenic scores for hypertrophic cardiomyopathy (HCM) in the general population and clinical settings, showing increased risk with higher scores and potential for clinical application.

The paper identifies novel structural variants in Han Chinese genomes using long-read sequencing and links them to phenotypic diversity and disease susceptibility.

The paper introduces IQI, an individualized QTL index, to detect and characterize differences in QTL effects across individuals and populations, enabling more precise genetic analysis.

This paper introduces PGSUS, a method to assess the influence of stratification and assortative mating effects on polygenic scores, revealing that direct genetic effects often contribute little to PGS variation.

This paper models diagnostic code dropout in EHR data to improve phenotyping for schizophrenia, enhancing polygenic score transferability across ancestries.

This paper introduces mr.mash-rss, a method for polygenic prediction using GWAS summary statistics, showing improved accuracy over existing methods in various scenarios.

This paper introduces a haplotype-based framework for heritability estimation and reference-free polygenic prediction, showing improved performance over genotype-based methods in depression and schizophrenia.

This study uses CombGWAS to analyze genetic correlations between psychiatric disorder combinations and cardiovascular disease, identifying shared and unique genetic factors.

Genal provides a Python toolkit for genetic risk scoring and Mendelian Randomization, streamlining population genetic analyses with user-friendly functions.

This study shows that within-family GWAS does not improve prediction accuracy in admixed populations, suggesting genetic architecture differences, not confounding factors, drive the decline in polygenic index performance.

This paper evaluates polygenic risk scores (PRS) for CAD and blood lipids to predict CAD incidence in UK Biobank participants.

This paper introduces a statistical framework for regularized ensemble polygenic risk prediction using GWAS summary statistics, showing improved performance across traits and populations.

The paper analyzes genomic data from the Picene culture to explore genetic continuity and change during the Iron Age and Roman expansion in Central Italy.

This paper presents a non-invasive preimplantation genetic testing method using spent embryo culture medium, achieving high accuracy for monogenic and polygenic disease detection without modifying IVF workflows.

The paper develops a polygenic risk model for prostate cancer using VA data and validates it in external cohorts, showing its clinical utility in risk stratification.

The paper investigates how antidepressants affect suicidal ideation in patients with major depressive disorder, finding that genetic predisposition to psychiatric disorders influences this risk differently across antidepressant types.

MegaLMM improves genomic predictions in new environments by incorporating environmental covariates into a statistical model, showing higher accuracy than traditional methods in maize breeding.

This paper improves GWAS performance in underrepresented groups by reassigning ambiguous ethnicity codes, using ancestry-matched imputation panels, and incorporating environmental covariates.

This study evaluates deep learning methods for polygenic prediction, showing that MLP benefits most from larger sample sizes and one-hot encoding improves performance across methods.

This paper introduces a Transformer model called G2PT to predict metabolic traits from genetic variants and shows it outperforms existing methods across populations.

The GWAS Catalog now holds 625,113 curated SNP-trait associations from 6,921 publications plus >85K summary statistics datasets, with a new GWAS-SSF submission standard and GRCh38 harmonisation.