GWAS summary statistics

This paper identifies eleven genome-wide suggestive loci and seven transcriptome-wide significant genes associated with AMH levels in Samoan women using GWAS and TWAS.

VarDrug predicts variant-drug interactions using machine learning on PharmGKB data, achieving 90% F1 score for psychiatric disorder applications.

This study identifies 59 novel genome-wide significant loci shared between coronary artery disease and aortic aneurysms through joint meta-analysis and functional characterization.

The paper develops pathway-specific polygenic scores to predict lithium treatment response in bipolar disorder and finds associations with specific biological pathways.

The paper maps MAVE data to human reference sequences to improve variant functional assay utility for genomics applications.

Haplomatic uses deep learning to adaptively adjust genomic resolution in genetic mapping studies, achieving higher resolution without sacrificing accuracy.

This paper reviews methodological challenges in GWAS of biobank data with time-to-event outcomes, proposing a two-step martingale residual approach for efficient analysis.

This study identifies genetic risk loci and factors underlying clinical subtypes of bipolar disorder using GWAS and meta-analysis.

This study identifies causes and consequences of vascular dementia using polygenic risk scores, phenome-wide association, and Mendelian randomization analyses from UK Biobank data, highlighting cardiometabolic and educational factors as key modifiable risks.

The paper identifies novel genetic loci for circulating fatty acids and reveals their molecular mechanisms through GWAS and multi-omics integration.

This study identifies common and rare variant risk loci for ADHD in a Hong Kong population, implicating cerebellar development and immune genes.

This study identifies a shared genetic architecture between Parkinson's disease and sleep-related traits, implicating the MAPT locus in both conditions through common biological pathways.

The paper uses k-mer-based GWAS on a wheat collection to identify novel powdery mildew resistance loci, finding 27 new loci and a region for adult plant resistance.

This study evaluates how data representation bias and distribution shifts affect multi-population machine learning performance using synthetic genotype-phenotype datasets across five continental populations.

This study identifies genetic trade-offs between fertility, longevity, and disease risk, showing that disease-risk alleles can persist due to reproductive advantages.

This study identifies 123 genome-wide significant loci associated with foveal pit depth, revealing new biological pathways and genes involved in human foveal development.

This study calibrates polygenic risk scores (PRS) for African American adults based on genetic ancestry, improving risk stratification and effect estimation for type 2 diabetes and height.

This paper introduces a new statistical method, qJLS, to detect genetic associations in complex traits by accounting for unknown gene-gene or gene-environment interactions, and applies it to identify a novel variant in cystic fibrosis lung disease.

This paper develops a multi-Polygenic Risk Score (multiPRS) to predict diabetes complications in different ethnic groups, finding that logistic regression with Mondrian Cross-Conformal Prediction improves prediction accuracy in non-European populations.

MIXPRS improves multi-population polygenic risk score prediction by integrating GWAS summary statistics without individual-level data, outperforming existing methods across multiple traits and populations.

This study uses Mendelian randomization to assess how cardiometabolic drugs affect kidney function, finding specific drug targets influence eGFR and related biomarkers.

This study analyzes SARS-CoV-2 ORF8 sequence conservation and mutations across variants to assess how dataset size affects identification of top mutations.

This paper evaluates polygenic and methylation risk scores for predicting overweight-related hypertension in Chinese populations, finding improved accuracy with integrated multi-omics approaches.

This study identifies genetic risk factors for panic attacks and disorder through genome-wide meta-analysis and links them to multiple body systems and neuronal functions.

This study conducts the first genome-wide association analysis of trichotillomania and finds no significant common variants but detects higher polygenic risk and copy number variants associated with neuropsychiatric disorders.

CADET improves transcriptome-wide association studies in admixed populations by integrating local ancestry and eQTL data to enhance gene expression prediction and association testing.

SurfacOmics is an R shiny application that identifies gene biomarkers using Elastic-Net Regularization and integrates biological knowledge for interpretation.

This study uses Mendelian randomization to find coffee consumption is causally linked to later age-at-onset of Parkinson's disease but not to disease risk or progression.

This study identifies a novel genetic variant associated with widespread pain using UK Biobank data and finds sex-specific genetic differences.

This paper identifies a genetic locus associated with sepsis susceptibility in children using a genome-wide association study of a national cohort.