Genetic Associations and Epidemiology

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 introduces a DNA foundation model to assess functional genetic variation across diverse populations, demonstrating its utility in understanding Alzheimer's disease risk and ancestry-specific gene expression.

The paper estimates direct effects of genetic ancestry on complex traits in a Mexican population using family-based analyses and finds significant associations for height and type 2 diabetes but not for educational attainment.

The paper investigates how polygenic scores and copy-number variants interact, finding that assortative mating explains their positive correlation and contributes to genetic risk compounding.

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.

The paper identifies Serpina3n as a major regulator of cardiac gene expression in heart failure by integrating genetic and epigenetic data from mouse strains.

The paper identifies co-eQTLs that alter gene co-expression relationships in specific cell types, linking these variants to immune disease associations.

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.

The paper identifies and quantifies phantom epistasis using genealogical data to distinguish spurious SNP interactions from true biological effects.

The paper identifies genes associated with obesity and fat distribution using genetic and functional data, then validates their roles through CRISPR knockdown in cell lines.

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

This paper introduces TWRCI, an algorithm that identifies root causal genes and their causal graph using genetic and bulk RNA-seq data, outperforming existing methods in uncovering disease mechanisms.

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.

This study compares pooled analysis and meta-analysis for multi-ancestry GWAS, finding pooled analysis offers better statistical power while controlling population structure.

This paper provides guidance for reducing bias in case-control GWAS by addressing genetic relatedness, allelic interactions, and control genotype biases, introducing the AlleliC-GWAS tool.

This paper develops a semi-empirical Bayesian framework to calibrate weak instrumental bias in sex-specific Mendelian randomization studies, revealing significant sex differences in causal effects of sleep phenotypes on cardiovascular diseases.

This paper identifies genome-wide pleiotropic loci linking blood pressure and cognitive performance measures using longitudinal data from multiple cohorts.

The paper shows that environmental variance increases with age, reducing heritability and PRS accuracy for multiple traits and diseases, suggesting younger cohorts are better for risk prediction models.

The paper identifies 505 protein quantitative trait loci (pQTL) in a Scottish cohort, linking proteins to complex traits and diseases, and highlights potential drug targets.

The paper identifies causal genetic mechanisms at CAD GWAS loci using CRISPR screens and chromatin analysis, revealing enhancer-gene links and ancestry/sex-specific risk factors.

The paper uses single-cell eQTL data to identify cell-type-specific effector genes associated with 69 diseases and 31 biomarker traits, revealing distinct immune cell contributions to disease.

The paper analyzes four immunity genes associated with severe COVID-19 and other immune-related diseases, identifying SNPs under positive selection and their association with disease severity.

IDEAL-GENOM streamlines GWAS analysis by integrating tools like PLINK and GCTA into a Python-based framework for reproducible and customizable genomic analysis.

This study links the developmental expression of risk genes to the age of onset of neuropsychiatric disorders, showing that disease peaks align with gene expression peaks in the prefrontal cortex.

The paper maps eQTLs in stimulated iPSC-derived macrophages and identifies condition-specific reQTLs that colocalize with disease-associated variants.

This paper improves polygenic risk scores for chronic back pain by combining multiple traits, showing better performance in clinical samples.

SAGA is a user-friendly, automated pipeline that simplifies GWAS by integrating PLINK, GMMAT, and SAIGE, enabling non-experts to perform reproducible analyses.

This study examines the relationship between renal impairment and cardiac structure/function using CMR and genetic methods, finding associations between eGFR reduction and cardiac parameters.

This paper introduces sfFDR to enhance variant discovery by integrating related trait summary statistics, increasing power and discovering additional SNPs in GWAS.