GWAS summary statistics

This study shows that excluding low-frequency variants and indels in fine-mapping leads to false discoveries and introduces SuSiE-OUT to address this issue.

This study identifies genetic risk loci for pilonidal sinus disease and finds strong genetic overlap with hidradenitis suppurativa and hair patterning, but not obesity.

The paper fine-maps GWAS risk variants for PDAC and identifies candidate target genes through chromatin conformation capture, finding functional interactions and eQTLs at specific loci.

Single-cell eQTL mapping in 5.4 million PBMCs from 1,925 individuals with matched WGS finds common-variant eQTLs for 83% and rare-variant signals for 47% of 21,404 genes.

This paper introduces a haplotype-based fine-mapping method called happler to identify causal variants for complex traits, showing improved performance over traditional methods in detecting haplotype effects.

SUMMIT improves heritability and genetic correlation estimation across diverse ancestries using LD scores and GWAS summary statistics, showing better accuracy in non-European populations.

This paper identifies 116 risk loci for melanoma through GWAS, links susceptibility to immunity and Wnt signaling, and develops a PRS improving risk prediction.

This paper uses GWAS in dogs and humans to identify a gene network linked to compulsive disorders, revealing biological pathways not detectable in human studies alone.

The paper decomposes polygenic risk of coronary artery disease into distinct biological processes using latent factor decomposition of genetic variants.

The paper uses polygenic risk scores to analyze genetic contributions to neurodegenerative diseases across brain cell types, finding shared and disease-specific genetic risks.

The paper evaluates how expression predictions from sequence models correlate with genetic associations in Alzheimer's disease risk loci, finding modest but significant relationships that suggest their utility as complementary evidence.

This study uses multi-omics fine-mapping to identify a pleiotropic intestinal enhancer variant linked to pig growth and fatness through PMAIP1 regulation.

This study analyzes breast cancer GWAS across multiple ancestries, finding similar heritability and genetic correlations, and identifies shared regulatory and cellular contexts.

Targeted-learning GWAS of 1,268 UK Biobank ME/CFS cases found 176 FDR-significant variants; seven replicated in disjoint UK Biobank or All of Us cohorts.

This study examines the genetic contributions to emotional and behavioural symptoms in childhood and adolescence using common and rare variants from two large cohorts.

FM-GPT is a Bayesian fine mapping method that identifies causal genes in phenome-wide transcriptome-wide association studies, improving accuracy and reducing false positives.

This study identifies shared genetic architecture between schizophrenia and substance use disorders using GWAS and genomic methods, finding significant genetic correlations and bidirectional causal relationships.

This study conducts a multi-ancestry GWAS meta-analysis of 2.5 million participants to identify genetic variants associated with migraine and link them to biological pathways and therapeutic targets.

This study benchmarks four Bayesian colocalization methods to validate MR-identified protein-trait associations for cardiometabolic traits, finding SharePro most frequently supports significant associations.

This paper introduces CT-FM and CT-FM-SNP methods to identify causal cell types and variants for diseases, finding most SNPs affect traits via a single cell type set.

TRIDENT is a framework for robust multi-trait GWAS that identifies 66 novel osteoarthritis signals by combining earlier and larger GWAS meta-analyses and applying translational annotation.

SuSiNE improves genetic fine-mapping by incorporating signed functional priors and multi-basin ensembling to better resolve LD ambiguity and uncertainty.

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

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 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 study uses a pleiotropy-informed GWAS method to identify new orofacial cleft risk loci by leveraging genetic associations with facial shape.

The paper introduces Tractor-Mix, a mixed model method for GWAS in admixed cohorts with high relatedness, improving ancestry-specific association detection and controlling false positives.

PanvaR integrates GWAS and fine-mapping in one tool, providing LD analysis, SNP effect predictions, and candidate gene lists with visualizations.

CuGen is a GPU-accelerated framework for large-scale genomics that enables efficient GWAS and fine-mapping with robust confounding control.