GWAS summary statistics

This study uses genome and transcriptome data from apple species to show how selection and gene flow shaped the cultivated apple genome, revealing distinct gene pools and adaptive introgression patterns.

This paper identifies a novel genetic locus associated with olfactory dysfunction through a genome-wide association study meta-analysis and shows causal links to various health outcomes.

This study identifies distinct frailty domains with varying causal relationships to Alzheimer's disease using genetic methods.

This study investigates pleiotropy across species and trait types, finding common features and selection pressures in highly pleiotropic genes.

This study identifies PTPRD as a shared risk gene for MDD, BPD, and ADHD through multivariate GWAS and proposes a mechanistic role in dopaminergic neuroplasticity.

This paper shows the HOXB13 p.G84E variant increases prostate cancer risk by threefold in men, but does not lead to more aggressive cancer.

This study combines rare variant burden scores and common variant polygenic risk scores to improve genetic risk prediction for schizophrenia, hypertrophic cardiomyopathy, and major depressive disorder.

The paper introduces MoEPRS, a Mixture-of-Experts framework for personalized polygenic risk prediction, showing modest accuracy improvements over single-source models across diverse biobanks.

This paper improves polygenic prediction of nasal shape by using multivariate GWAS, optimized phenotypes, and benchmarked PGS methods, achieving higher variance explained than traditional approaches.

This study identifies genetic links between PTSD and brain circuitry traits using GWAS meta-analysis and multiple genetic methods, finding shared loci and causal relationships.

This paper introduces SMuGLasso, a method to address population stratification in GWAS by identifying population-specific SNPs, showing its superiority over MuGLasso in simulations and real data.

This study identifies genetic subtypes of obesity that uncouple adiposity from cardiometabolic risks, revealing distinct biological pathways and protein associations.

This study identifies and characterizes immune-related genetic factors in gout, showing their potential as therapeutic targets beyond traditional urate-lowering treatments.

This study identifies novel genetic signals associated with severe obesity through GWAS and shows strong genetic overlap with BMI, highlighting shared comorbidities and lifestyle interactions.

The paper identifies genetic variants associated with erythrocyte traits and iron metabolism in a Japanese population, revealing a linkage disequilibrium block linked to hypochromic erythropoiesis.

This study evaluates the portability of polygenic indexes for behavioral traits across genetic ancestries, finding reduced accuracy in non-European populations and factors contributing to this loss.

The paper evaluates a genetic risk-based breast cancer screening model in younger women, showing its feasibility, clinical utility, and acceptability.

The paper uses pathway-specific polygenic scores to identify gene-adiposity interactions affecting liver biomarkers, finding more specific and interpretable results compared to standard polygenic scores and GWIS.

The paper introduces int2MR, a Mendelian randomization method for detecting exposure-by-group interactions using GWAS summary statistics, showing its effectiveness in identifying sex- and age-group-specific risk factors for ADHD and Alzheimer's disease.

This study compares genotype-forward and phenotype-forward approaches for detecting monogenic cardiovascular diseases in the UK Biobank, finding the genotype-forward method has a higher diagnostic yield.

The paper identifies high-confidence breast cancer predisposition genes using multi-ethnic genomic data and integrates GWAS, TWAS, and PWAS to refine the genetic architecture of heritable breast cancer.

This study uses machine learning to assess how 3D genome structure affects height by analyzing GWAS data and predicting variant impacts on chromatin contacts.

The paper introduces acmgscaler, an R package and Colab notebook for calibrating gene-level variant effect scores within the ACMG/AMP framework using MAVEs and VEPs.

This study shows that combining GWAS summary statistics with phenotype data can leak genomic information, with higher risk for non-European populations.

The paper shows that common genetic variants influence disease risk and severity in monogenic diabetes, modifying clinical outcomes in individuals with pathogenic variants.

The paper shows that variable information across SNPs in GWAS data can lead to false colocalisation inferences and proposes a proportional colocalisation test to address this issue.

This study identifies 10 novel genomic loci associated with intrinsic capacity using GWAS on UK Biobank and CLSA data, revealing genetic correlations with cellular processes and traits related to healthy aging.

This study identifies 80 genome-wide significant associations for endometriosis and adenomyosis in 1.4 million women, uncovering novel loci and pathways involved in disease mechanisms.

This study examines how polygenic scores and socioeconomic status jointly influence risk for 19 diseases, finding distinct and partly interacting effects across populations.

This study identifies 40 genome-wide significant loci for SSRI use and finds genetic correlations with psychiatric and medical traits, suggesting SSRI use is a proxy for MDD with distinct genetic architecture.