GWAS summary statistics

GWASHub is a cloud-based platform for GWAS meta-analysis that automates data handling, QC, and analysis workflows to improve reproducibility and reduce researcher burden.

The paper identifies an epistatic interaction between RBMS3 and CDKN2B-AS1 in CAD risk using VariantSpark and BitEpi, with validation in UK Biobank and All of Us.

This study identifies shared genetic factors between insomnia, depression, and anxiety, highlighting synaptic processes as potential drug targets.

This paper examines the feasibility of using Mendelian randomization to study the causal effects of childhood maltreatment on mental health, highlighting challenges in interpreting MR assumptions and results.

MAP-PRS improves polygenic risk score fairness and reliability across populations by combining ancestry-specific models with portfolio optimization and computational tools.

This paper introduces SSLPRS, a Polygenic Risk Score method using Spike-and-Slab LASSO, showing improved variable selection and prediction accuracy over existing methods.

The paper develops population-specific polygenic risk scores for Han Chinese using genomic and electronic health record data, showing strong predictive performance for multiple diseases.

The Taiwan Precision Medicine Initiative (TPMI) recruits a large cohort for genetic and electronic medical record studies to advance precision medicine.

This paper introduces a statistical framework to identify genetically driven heterogeneous treatment effects using causal transfer learning, applied to RCTs to find key determinants of treatment efficacy.

The paper introduces an effect size index and automated functions to assess specific genetic associations between external traits and common factors, applied to cognitive ability and Alzheimer's disease indicators.

The paper introduces COAST-SS, a method to identify allelic series using summary statistics, showing it is effective and robust to LD matrix misspecification.

This paper introduces a framework for quantifying uncertainty in polygenic risk scores, showing that prediction intervals vary with genetic and environmental factors.

The paper uses Mendelian randomization to test causal links between serum urate, gout, and prostate cancer in European males, finding a causal effect of gout on prostate cancer but not of urate.

This paper conducts GWAS on cannabis use frequency and lifetime use, identifies genetic loci, and shows genetic correlations with other traits and diseases.

This study uses in-silico analysis to explore the PAM gene's potential role in T2DM, identifying genetic variants and structural features that may disrupt its function.

This paper introduces EPIK, a deep learning framework for detecting higher-order epistasis in GWAS data, showing improved performance on simulated and real datasets and identifying new gene interactions in Parkinson's Disease.

The paper develops a mutation-selection-drift balance model for complex diseases, finding that directional selection has little role in common variation but may affect rare variants, and suggests heritability estimates may be biased.

This study identifies 118 new genetic loci associated with Alzheimer's disease across diverse ancestries and discovers 48 novel risk loci, including those enriched in neuronal cell types.

This study investigates causal links between T2D genetic clusters and non-cardiovascular comorbidities, identifying potential drug repurposing opportunities.

This study identifies pathway-level polygenicity in anorexia nervosa by constructing pathway-based polygenic risk scores and finding 497 associated pathways.

The paper quantifies genetic influences across educational milestones in two Norwegian cohorts, finding stage-specific heritability and declining genetic overlap with later degrees.

The paper identifies genetic variants near SLC2A2 that influence glycemic response to glimepiride in type 2 diabetes patients.

This study identifies twelve novel blood pressure loci and shows smoking modifies genetic effects on BP through immune-related pathways in over 1 million individuals.

The paper introduces MIXER, a framework that combines multiple metrics for feature selection and predictive modeling, outperforming individual criteria in Alzheimer's disease analysis.

This paper examines how genetic interactions with ancestry affect polygenic scores and shows that models of local and global ancestry interactions can be distinguished using partial polygenic scores when causal variants are known.

The paper integrates GWAS data with a CRISPRi screen in osteoblasts to identify genes affecting bone mineral density and reveals pleiotropy across non-bone tissues.

This study conducts a cross-ancestral GWAS and fine mapping of head and neck squamous cell carcinoma subsites, identifying 29 novel variants and highlighting gene-environment interactions.

This paper introduces a formal statistical replication analysis to reduce false positives in lung cancer GWAS, finding it significantly lowers false discovery rate and improves polygenic risk score efficiency.

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.