Genetic Associations and Epidemiology

The paper identifies 693 SNPs associated with transcription factor binding in liver ATAC-seq data by analyzing footprint quantitative trait loci (fpQTLs).

This paper develops a method to protect polygenic score association tests from confounding by ancestry variation using a new approach to estimate population structure.

The paper introduces Clorinn, a convex optimization method to decompose genetic variant associations into shared and distinct components across complex traits, showing improved performance in simulations and application to GWAS data.

This study identifies 21 infertility loci and over 400 reproductive hormone loci through GWAS across multiple cohorts, finding limited genetic correlation between reproductive hormones and infertility.

The paper introduces a method to improve genetic discovery and fine-mapping by analyzing latent factors from high-dimensional traits, showing enhanced power compared to traditional approaches.

This study identifies latent genetic factors underlying stroke and its comorbid conditions in European and East Asian populations using genomic structural equation modeling.

The paper computes genetic correlations between BMI and brain morphology traits using UK Biobank data and identifies molecular mechanisms through eQTL and Mendelian randomization.

This paper introduces a method to classify gene-by-treatment interactions using Bayesian model selection and applies it to molecular count phenotypes with a software package.

The paper links genetic risk for neurodegenerative diseases to microglial pathways using genomic and computational methods.

This study uses polygenic scores to predict impulsivity and substance use in adolescents, finding age-related changes in genetic associations and highlighting caffeine as an early risk factor.

LDSC++ improves heritability and genetic correlation estimation in multivariate GWAS by enhancing LD score regression with segmented regression and better handling of shared genetic variants and imputation quality.

The paper applies genomICA to GWAS data from brain MRI phenotypes to identify genetic components associated with neurobiological processes and behaviors.

This study shows that angiographic CAD burden independently contributes to adverse cardiovascular outcomes, even when traditional risk factors are accounted for.

This study identifies 22 novel gene-sleep duration interaction loci for blood pressure traits across 811,405 individuals, highlighting distinct genetic influences of short and long sleep on cardiovascular health.

This paper reassesses the measurement error assumption in twin studies and introduces a hierarchical model to improve heritability estimation accuracy.

This study identifies shared genetic variants and causal relationships between coronary artery disease and stroke using GWAS and Mendelian randomization.

The paper presents a framework to analyze multi-omic risk factors for coronary artery disease using TWAS and PWAS, identifying 10 genes with shared associations and colocalized eQTLs and pQTLs.

This study uses biobank-linked drug purchase data to investigate genetic factors influencing medication dosing, finding significant polygenic and pharmacogenomic associations with statins, metoprolol, and warfarin.

The paper investigates genetic and neural overlaps between alcohol use disorder and BMI, finding polygenic overlap despite minimal genetic correlation and identifying shared brain regions involved in executive function and reward.

RatXcan extends transcriptome-wide association studies to rats, enabling cross-species integration of GWAS and gene expression data to identify shared genetic mechanisms of complex traits.

The paper identifies six HLA alleles associated with reduced likelihood of becoming a cognitively healthy centenarian, revealing pleiotropic effects on disease susceptibility and longevity.

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

Neur-Ally predicts regulatory non-coding SNPs in brain tissues and validates their effect in neurological disorders using deep learning and epigenomic data.

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

This study evaluates transdiagnostic polygenic risk scores for psychiatric disorders using the All of Us Research Program data, finding that a common psychiatric genetic factor PRS better predicts comorbidity burden than single-disorder scores.

This study develops a polygenic risk score (metaGRS) for predicting recurrent stroke in the Japanese population using data from the BioBank Japan cohort, finding it associates with increased recurrence risk, particularly in individuals without hypertension.

The paper fine maps CAD loci in UK Biobank whole genome sequencing data using PolyFun and ML tools, showing improved sensitivity over traditional methods and demonstrating cost and speed benefits of cloud-based analysis.

The paper uses EHRs and biobanks to identify pharmacogenetic associations with drug response in cardiometabolic diseases, finding novel genetic predictors for personalized treatment.

This paper uses a vision transformer autoencoder to extract brain imaging phenotypes and identifies 10 novel genetic loci associated with brain structure, 3 of which were previously unreported.

This study identifies shared genetic variants and causal genes between type 2 diabetes and stroke using GWAS data, revealing novel molecular targets.