Genetic Associations and Epidemiology

SIEVE is a deep-learning framework for variant and gene prioritization in exome studies, achieving strong discrimination and recovering known associations across diseases.

Contrasting a pretrained genomic language model's attention between case and control haplotypes ranked HBB first on chromosome 11 and recovered more known β-thalassemia variants than GWAS, without per-variant tests.

TLS-Tractor integrates external GWAS summary statistics with individual-level data for ancestry-aware association analysis in admixed populations, improving power and calibration.

This study estimates the heritability of age-related macular degeneration in the Amish population using SNP and pedigree-based methods, finding substantial genetic contributions.

This paper develops a multi-ancestry polygenic risk score for BMI that predicts longitudinal weight change and shows strong associations across diverse genetic ancestries.

R package DetectGxT fits allelic-additivity nonlinear models with optional donor or polygenic random effects, raising simulated power to detect gene-by-treatment interaction QTLs over linear regression.

FORGEPHAST simulates large synthetic cohorts with controlled genetic and phenotypic properties, showing polygenic risk score performance varies by phenotype architecture, ancestry, and variant coverage.

The paper introduces EdgeMap to assess conditional heritability of ligand-receptor gene annotations in spatial transcriptomics data, finding higher edge z-scores in biologically matched tissues.

The paper identifies a causal allele in the CD40 locus and demonstrates context-specific trans-effects in B cells using CITE-seq and CRAFT-seq.

This study integrates CAD GWAS with transcriptomic data to identify genes and networks associated with CAD and subclinical atherosclerosis.

The paper introduces a cell village-based fitness screening approach and a Dirichlet regression framework to analyze genetic factors affecting cell proliferation and environmental sensitivity in human neural progenitor cells.

This paper identifies a proteomic and metabolomic pathway linking insulin resistance to coronary heart disease through genetic analysis and mediation modeling.

This study identifies latent regulatory programs from disease-associated variants to reveal biological mechanisms and clinical subtypes in complex diseases.

The paper identifies a young, brain-regulatory subset of schizophrenia risk variants that concentrate heritability after accounting for age, frequency, and selection signals.

The paper identifies eQTLs and eGenes in tonsil-derived immune cells from children, linking them to childhood atopic diseases and expanding known genetic contributors.

The paper introduces the omnicausal model to assess how phenome-wide risk factors collectively explain causality in complex diseases, showing they explain more variance than known risk factors.

The paper introduces an interpretable neural network architecture for genomic prediction, outperforming existing methods in disease risk prediction and capturing non-linear genetic interactions.

The paper uses genome-wide association testing on embeddings from a medical-history foundation model to identify genetic signals linked to biological pathways and drug targets.

This paper shows that meta-analysis of GWAS can be biased by cryptic relatedness between studies, especially in family-based datasets, and recommends avoiding such analyses.

This study investigates genetic overlaps between psychiatric disorders, cardiometabolic diseases, and brain structure to understand shared and distinct genetic pathways contributing to comorbidity.

This study expands the human plasma proteome analysis using an enhanced SomaScan platform, identifying new genetic associations and disease mechanisms beyond the classical secretome.

The paper introduces SPADE, a QC metric that reduces bias from gene-sex interaction signals in large-scale GWAS by adjusting for sex-specific disease prevalence and preserving informative variants.

This paper shows that differences in power and linkage disequilibrium affect eQTL portability across ancestries and introduces a method to correct for these factors.

The PMBB Geno-Pheno Toolkit provides modular pipelines for cross-biobank association analyses, supporting GWAS, ExWAS, and PheWAS with containerized workflows.

This study investigates the association between genetic predisposition to longer lifespan, lifestyle factors, and all-cause mortality using a 17-year cohort, finding that genetic factors and lifestyle factors like smoking and sex independently influence mortality risk.

The paper evaluates gene-environment interactions in blood pressure traits across diverse populations and finds they explain additional variance, though they do not improve prediction accuracy.

This paper compares two Bayesian methods for polygenic risk scores (PRS) for hypertension across two ancestry groups, finding improved performance with updated LD panels and functional annotations, but not equitable results between groups.

GenoSim simulates diploid SNP genotypes with population stratification and pedigree constraints, providing outputs compatible with clinical and population genetics formats.

VN1K provides a comprehensive multi-omics and phenomics resource for the Vietnamese population, identifying novel variants and functional associations with disease traits and drug responses.

The paper conducts a GWAS on antipsychotic-induced changes in total cholesterol and BMI in a multi-ancestry cohort, identifying genetic loci and pathways associated with these metabolic effects.