GWAS summary statistics

This study constructs a bipartite Genotype and Phenotype Network to analyze genetic associations and improve phenome-wide association studies by identifying pleiotropic loci and controlling false discovery rates.

This study evaluates over 3,000 congenital heart disease-risk variants using SNP Bind-n-Seq and MPRA to identify variants with allelic TF binding and regulatory activity.

This study uses large biobanks to identify gene-trait pairs with causal evidence, rediscover approved drug targets, and develop a predictive model for drug development.

The paper performs genome-wide association analysis to identify genetic loci and candidate genes associated with uncapping behavior in Varroa-resistant honeybees, highlighting a polygenic basis for this trait.

This study combines MAVEs and population-free VEPs to improve variant classification across ancestries in hereditary cancer genes, reclassifying over 90% of variants of uncertain significance.

The paper develops a joint statistical model to show that higher Mapuche genetic ancestry and accelerating BMI trajectories independently increase the likelihood of early menarche in admixed girls.

The paper identifies aging profiles using cognitive, affective, and brain reserve measures from UK Biobank data, revealing distinct patterns of emotional wellbeing and cognitive decline.

This study uses Mendelian Randomization to test if TNF pathway genes are genetically linked to Parkinson's disease or RBD, finding no evidence of such a link.

This study uses Mendelian randomization to assess how BMI affects COVID-19 severity through inflammatory cytokines and finds no significant mediation effect.

This study shows that individuals of Indian ancestry with high genetic risk for obesity can reduce their risk through favorable lifestyle choices, as demonstrated in two cohorts.

This study examines the psychological impact of viewing depression polygenic risk scores (PRS) in a real-world setting, finding no significant difference in depression or anxiety symptoms between those who viewed their results and those who did not.

LocusPackRat streamlines candidate gene prioritization from GWAS intervals by integrating diverse evidence packets, enhancing experimental validation in model organisms.

BrainBridge integrates single-cell transcriptomic data with phenotypes to model brain biology, prioritizing genes and cell types linked to Alzheimer's, aging, and sex differences.

This study uses Trio-GCTA to analyze direct and indirect genetic effects on educational achievement using parent-offspring trios, finding genetic contributions and gene-environment correlations.

GENPHIRE uses a large language model to convert genotype profiles into embedded vectors for disease risk prediction, outperforming existing polygenic risk scores in UK Biobank data.

This study identifies maternal and fetal genetic contributions to preeclampsia through genome-wide association analyses, revealing distinct genetic architectures and biological pathways.

This paper quantifies how polygenic scores are affected by ancestry stratification using a theoretical framework and applies it to UK Biobank data.

This study creates an age-aware genotype-phenotype atlas for 87 neurodevelopmental disorders using data from the Simons Searchlight cohort, identifying gene-specific risk profiles and age-dependent penetrance patterns.

This study examines genetic and environmental factors influencing mental health disorders in Mexican youth, finding that genetic liabilities and adversity interact to predict internalizing and externalizing traits.

This paper conducts a GWAS of distressing premenstrual symptoms (DPS) in two Nordic populations, identifying a SNP associated with DPS and genetic correlations to psychiatric disorders.

This study integrates large-scale omics and electronic health records to identify risk proteins and drugs for cancer prevention, finding 101 novel proteins and three drugs associated with reduced colorectal cancer risk.

This study identifies genetic associations between lysosomal genes and Parkinson's disease risk across diverse populations using GWAS and whole genome sequencing data.

This paper uses Polygenic Risk Scores and Mendelian Randomization to evaluate the causal effect of mitochondrial DNA copy number on Alzheimer's and Parkinson's disease risk.

ML-MAGES uses machine learning to improve genetic association analysis by shrinking inflated effect sizes and clustering variant associations across multiple traits.

The paper integrates GWAS meta-analysis with human brain cell mapping to identify genetic loci and cell types involved in tinnitus, implicating the amygdala and midbrain.

DeepCAST-GWAS improves GWAS by using deep learning to prioritize regulatory SNPs, increasing the reliability and replicability of genetic associations.

S4-Multi improves polygenic score prediction across diverse ancestries by reducing genetic variants while maintaining accuracy in non-European populations.

The paper introduces two fine-mapping methods to account for ancestral and environmental heterogeneity in GWAS, improving resolution of causal variants across diverse populations.