polygenic risk

This paper introduces DendroPRS, a machine learning method that improves genotype-to-phenotype prediction across diverse ancestries by incorporating phylogenetic relationships.

This study examines differences in polygenic risk score distributions across European populations and shows how country-specific calibration can improve breast cancer risk prediction.

This study uses SNP analysis to show that white clover populations in Brazil have high within-population genetic diversity, with some populations showing admixture with cultivars and others having distinct genetic backgrounds, confirming its naturalization.

This paper introduces edLMM, a method that detects low-effect genetic variants using moderate sample sizes, improving missing heritability estimation.

This study evaluates how imputation panels affect polygenic risk score accuracy across different ethnic populations, finding only marginal improvements when panels match the target population.

This study performs GWAS on BMD traits and fracture risk using 30,000 UK Biobank samples, identifies novel loci, and prioritizes drug targets for osteoporosis prevention.

This study demonstrates high concordance between whole-genome amplified embryonic DNA and the genome of the subsequently born child, validating PGT-WGS for detecting genetic variants and polygenic risk scores.

PRS-Net improves disease prediction by modeling genetic nonlinearity using deep learning and identifies key genes and interactions influencing Alzheimer's and multiple sclerosis risk.

This study identifies genetic links between early vocabulary development and ADHD and cognition traits using genome-wide analyses.

This paper introduces improved fine-mapping methods that model infinitesimal effects, showing better calibration and increased PRS accuracy for causal variant identification in complex traits.

The paper describes the Gene-SOEP dataset, a longitudinal genetic study of 2,598 individuals in Germany, providing polygenic indices for research on genetic-environment interactions.

The paper evaluates dual-systems models of impulsive personality traits using genomic structural equation modeling and genetic correlations with neuroimaging phenotypes, finding shared and unique genetic influences.

This paper introduces a transfer learning framework with false negative control to improve polygenic risk prediction across heterogeneous datasets.

This study uses a polygenic risk score derived from genome-wide data to identify individuals at higher risk of severe COVID-19 in a Russian population.

This paper validates LDpred2-auto for inferring genetic parameters and shows improved heritability capture and predictive performance with an expanded variant set.

The paper identifies a genetic region associated with systemic necrosis in Arabidopsis thaliana infected with turnip mosaic virus, showing a trade-off between resistance and fitness costs.

Intronic GBA1 variant rs3115534-G, common in African and African admixed ancestry but rare elsewhere, raises Parkinson's disease risk (OR 1.58) and lowers onset age.

This paper introduces EFA, a model that factorizes polygenic epistasis to improve prediction and uncover biological pathways in complex traits.

This paper uses a polygenic risk score to identify undiagnosed diabetes cases, finding that combining BMI and PRS captures most undiagnosed cases, including those with normal weight.

This study simulates genomic data from diverse populations to assess GWAS tool performance and proposes an optimized framework for analyzing GWAS data in non-European populations.

The paper estimates polygenic risk scores (PRS) for 12 diseases in a diverse cohort and finds higher disease incidence and earlier clinical manifestation in top PRS individuals.

The paper introduces CT-SLEB, a method for multi-ancestry polygenic prediction that improves performance in non-European populations using GWAS summary statistics and simulation studies.

The paper identifies a striatal gene set linked to schizophrenia risk that predicts in vivo dopamine synthesis and striatal activation in neurotypical and patient cohorts.

This paper evaluates how down-sampling affects GWAS summary statistics and recommends reporting specific analyses to support their use.

This study shows that polygenic risk scores from multi-ancestry GWAS perform better in understudied populations than single-ancestry scores, except when the understudied group is small.

This paper introduces gVAMP, an algorithm for analyzing whole genome sequence data to identify genetic variants associated with complex traits like height, showing improved accuracy over existing methods.

The paper tests for polygenic epistasis in biobank-scale data and finds 16 trait-loci pairs with strong evidence of interaction effects.

This study shows pathway-specific polygenic scores improve cross-ancestry prediction of psychosis risk and clinical outcomes compared to genome-wide polygenic scores.

HAPNEST generates and evaluates large-scale synthetic genotype and phenotype datasets for polygenic risk score analysis.

This paper uses whole-genome sequencing of kakapo to analyze genetic diversity and breeding values, showing how conservation management has maintained genetic diversity and providing tools for future recovery efforts.