polygenic risk

GAUDI improves polygenic risk prediction in admixed populations by modeling ancestry-specific and shared effects, outperforming existing methods in simulations and real data.

Meta-analysis of harmonized GWAS from up to 18 biobanks (six ancestry groups) across 14 endpoints replicated 320 known loci and identified 188 apparently novel loci.

This study shows polygenic risk scores for substance use disorders predict substance use milestones and progression, with differences by sex and population group.

This study models ancient Eurasian selection landscapes and shows ancestry-specific genetic risk associations with modern phenotypes.

This paper introduces a framework to enhance polygenic prediction by combining multiple polygenic scores, showing significant accuracy improvements for psychiatric disorders and other traits.

The paper introduces an improved polygenic score method, S4, which enhances accuracy across multiple ancestries and phenotypes using summary statistics.

This paper introduces FairPRS, a method to create polygenic risk scores that are fair across ethnic groups by using invariant risk minimization, showing improved phenotype prediction and reduced racial bias.

The paper estimates how parents' cognitive and non-cognitive skills affect offspring education through environmental effects using polygenic scores and multiple study designs.

This paper uses gradient boosted trees to improve polygenic risk prediction by capturing non-linear genetic effects and SNP interactions, showing significant increases in variance explained for multiple complex traits.

This study controls for geographic confounding in GWASs of 56 traits, showing that regional differences in SES reduce genetic signals for traits like education and income.

This paper introduces TL-Multi, a transfer learning method to improve polygenic risk prediction in non-European populations using European GWAS data, achieving better accuracy than existing methods in SLE and asthma risk prediction.

The paper identifies misspecifications in GWAS summary statistics and shows how to correct them to improve polygenic score predictive performance.

The paper shows rapid polygenic adaptation in ash trees due to a fungal epidemic, with a 31% juvenile population decline estimated through simulations.

This paper develops a polygenic risk score (metaPRS) to predict sudden cardiac arrest in patients with coronary artery disease using genetic and clinical data from two cohorts.

This study identifies genetic resilience factors for Alzheimer's disease by constructing polygenic resilience scores that reduce genetic risk penetrance and correlate with polygenic risk scores in unaffected individuals.

This paper translates the PRS-CS polygenic risk score method to Julia, achieving 5.5x faster computation with maintained accuracy across nine traits.

This paper improves genetic scores for adiposity traits in East Asian populations by using trans-ancestry meta-analysis, increasing variance explained compared to European-derived scores.

This study combines polygenic risk scores with family history to improve disease risk prediction across diverse populations, showing significant gains in accuracy.

This paper develops a trans-ancestry polygenic risk score for type 2 diabetes and validates it across diverse populations to improve equitable risk prediction.

The paper constructs and validates a polygenic risk score for Parkinson's disease in a Latino cohort, finding ancestry-related biases and haplotype associations at the SNCA locus.

This paper introduces LOG-TRAM, a method to improve trans-ancestry association mapping by leveraging local genetic structure, enhancing power and reducing bias in under-represented populations.

The paper develops a latent factor model to capture additional genetic risk from family history, improving polygenic risk prediction for height and 11 complex diseases.

The paper defines 21 ancestry groups from UK Biobank data and develops a method to measure ancestry proportions from GWAS summary statistics using allele frequencies and principal components.

This study integrates rare large-effect expression variants into polygenic risk scores, showing improved prediction of complex traits by including these variants in risk assessment.

This paper introduces a framework to reconstruct SNP allele and genotype frequencies from GWAS summary statistics and presents three applications, showing high performance in power and error control.

This paper develops polygenic risk scores for breast cancer prediction in women of African ancestry by combining African and European ancestry datasets, improving prediction accuracy.

This paper introduces PRS-CSx, a method that improves polygenic prediction across ancestrally diverse populations by integrating GWAS summary statistics and sharing genetic effects.

This paper introduces a framework for estimating variant effect sizes from GWAS summary statistics, improving polygenic score accuracy by jointly inferring individual variant effects and distributional properties.

A genome-wide association study of 69,369 schizophrenia cases and 236,642 controls identifies 270 risk loci, prioritizes 130 genes, and links common and rare risk variants to neuronal synaptic biology.

This paper conducts a genome-wide association study of happiness, identifying genetic loci and correlations with personality traits, and shows consistent genetic effects across age and ancestry.