fine-mapping

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 mQTLs for serum and urine metabolites across ancestries, revealing ancestry-specific genetic effects and linking them to disease traits.

The paper identifies shared and distinct genetic architectures for cardiac structure and function using UK Biobank data, revealing common variants influencing multiple chambers and phenotypic variability.

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 analyzes genetic variation, ancestry, and DNA methylation in Hispanic/Latino participants using long-read sequencing, identifying novel variants and phenotype associations.

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 evaluates AlphaGenome's ability to decode regulatory mechanisms in ALS, finding it fails to accurately recover variant effects and assign regulatory signals to correct genes.

The paper constructs a genomic atlas of cardiac and adiposity imaging phenotypes using whole-genome sequencing data from UK Biobank, identifying genetic associations and polygenic scores linked to cardiometabolic diseases.

This study identifies cell type-specific eQTLs in the human pancreas using single-cell RNA-seq data and deconvoluted bulk RNA-seq data, revealing new genetic signals and associations with metabolic disease.

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

The paper quantifies the contribution of structural variants to complex trait heritability using MiXeRSV, finding SVs explain up to 32% of heritability in some traits.

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.

The paper identifies ancestry-specific genetic loci associated with autism spectrum disorder using admixture mapping across diverse populations.

This study identifies genetic variants associated with lipoma susceptibility and links them to telomere biology, showing a positive association between genetically predicted longer telomeres and lipoma risk.

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.

The paper identifies shared genetic and epigenetic mechanisms at the MOBP locus linking ALS and PSP, with distinct patterns from MSA.

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 paper introduces scDRS-FM, a method to distinguish causal from tagging disease associations in scRNA-seq data, showing it improves power and accuracy in identifying disease-relevant cell populations.

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