polygenic risk

This study develops and evaluates HLA-ARC, a polygenic risk score for type 1 diabetes, showing improved performance over existing methods in diverse ancestry groups.

The paper introduces gact, an R package that integrates GWAS summary statistics with genomic resources to identify causal genes and pathways, demonstrating its application in coronary artery disease and type 2 diabetes.

The paper evaluates polygenic risk scores for Parkinson's disease in a South African cohort, finding modest predictive performance and no ancestry-specific risk variants.

This paper develops endophenotype-informed polygenic risk scores for POAG in African ancestry individuals, showing improved clinical interpretability and risk stratification compared to aggregate scores.

This study evaluates genome prediction accuracy in Brassica napus using nested association mapping, finding that rrBLUP and RKHS models and SNaP markers improve prediction accuracy for agronomic traits.

The paper introduces the BPC approach to improve polygenic score calibration for binary disorders, achieving better calibration than existing methods in simulated and empirical data.

PTL-PRS improves polygenic risk score prediction in non-European populations using pseudovalidation and software optimizations without requiring individual-level data.

This study benchmarks GWAS causal gene prioritization methods for drug discovery using 445 diseases, finding the nearest gene method performs as well as more complex approaches.

This paper introduces ELAG, an ensemble learning framework for polygenic risk prediction that improves accuracy in high-dimensional genomics data by using policy gradient methods to guide variant selection.

The paper develops and validates multi-ancestry polygenic risk scores for LUAD in East Asian never-smokers, showing improved risk prediction compared to East Asian-only models.

This study combines rare variant burden scores and common variant polygenic risk scores to improve genetic risk prediction for schizophrenia, hypertrophic cardiomyopathy, and major depressive disorder.

The paper introduces MoEPRS, a Mixture-of-Experts framework for personalized polygenic risk prediction, showing modest accuracy improvements over single-source models across diverse biobanks.

This paper improves polygenic prediction of nasal shape by using multivariate GWAS, optimized phenotypes, and benchmarked PGS methods, achieving higher variance explained than traditional approaches.

The paper identifies genetic trade-offs and antagonistic pleiotropy in Florida Scrub-Jays by analyzing fitness components and their genomic associations.

This paper provides guidance for reducing bias in case-control GWAS by addressing genetic relatedness, allelic interactions, and control genotype biases, introducing the AlleliC-GWAS tool.

The paper shows that environmental variance increases with age, reducing heritability and PRS accuracy for multiple traits and diseases, suggesting younger cohorts are better for risk prediction models.

This paper improves polygenic risk scores for chronic back pain by combining multiple traits, showing better performance in clinical samples.

This study uses a multivariate GWAS approach to identify genetic loci associated with reading ability and dyslexia, finding 35 new genome-wide significant loci and estimating SNP heritability at 24%.

This study uses genome-wide meta-regression to analyze social behavior across different contexts, ages, and reporters, identifying six genetic loci and showing modest heritability.

This study introduces VAE-PRS, a deep learning model that improves polygenic prediction for blood cell traits by capturing interaction effects and outperforming traditional methods in 14 out of 16 traits.

The paper develops SVrefiner to refine structural variations, enhancing heritability estimates and risk prediction by integrating refined SVs with SNPs and other variants.

This paper develops and validates polygenic scores for within-family disease risk prediction, showing strong performance across ancestries and demonstrating their clinical utility in embryo screening.

The paper develops sex-specific transcriptome-based polygenic risk scores for depression and evaluates their associations with brain structure and symptoms in adolescents.

This study improves allele frequency and ancestry proportion estimation by adjusting genotype likelihoods to reduce mapping bias in ancient DNA sequencing.

The paper identifies two functional domains in a genomic region that influence adult migration timing in Chinook salmon, showing how a simple genetic system interacts with genomic backgrounds to generate ecotypic diversity.

This paper introduces PRSFNN, a neural empirical Bayesian method for polygenic prediction that outperforms existing methods on 17 of 18 traits in the UK Biobank.

This study identifies 93 genetic loci associated with alcohol-related diseases using FinnGen data and shows polygenic risk scores can stratify risk for these diseases and related outcomes.

This study compares imputation accuracy and polygenic score performance between genotyping arrays and low coverage whole-genome sequencing across diverse populations, finding that low coverage sequencing performs competitively and better in underrepresented groups.

This paper develops a pipeline for translating type 1 diabetes polygenic risk scores into clinical use, showing they can distinguish type 1 from type 2 diabetes and identify high-risk individuals.

This study develops and validates a polygenic score for cardiorespiratory fitness (CRF) and finds it is associated with reduced cardiovascular disease and mortality risks.