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 paper improves depression prediction by integrating psychiatric traits, showing stronger associations across development, families, and populations.

LRCQ estimates phenome-wide heritability enrichment of genomic loci using GWAS summary statistics, identifying enriched regions and their functional annotations.

LRCP identifies functionally related SNPs and genes by estimating phenome-wide genetic-effect correlations from GWAS summary statistics, improving accuracy over naive methods.

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

This study identifies polygenic trends in morning chronotype evolution in modern humans above 20 degrees north, showing increased morningness compared to Neanderthals.

The paper benchmarks variant effect predictors using a leakage-controlled dataset and develops a gradient-boosting consensus model that outperforms existing tools.

This study conducts ancestry-specific and multi-ancestry GWAS of restless legs syndrome, identifying shared and ancestry-specific risk loci across African, Latin American, and European populations.

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.

BLocPRS improves polygenic risk score prediction by combining locus-specific weights from different PRSs, achieving higher accuracy in UK Biobank and extending to non-European populations.

The paper examines how social determinants of health modify polygenic risk scores for cardiometabolic diseases and shows that integrating social and genetic factors improves disease prediction.

The paper identifies 16 genetic loci interacting with sleep duration to influence glycemic traits in over 480,000 individuals, revealing distinct biological mechanisms for short and long sleep.

The paper introduces MutQA, a cross-validated Q&A dataset linking genetic mutations to their functional consequences, and demonstrates its utility in improving LLM performance.

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.

The paper identifies rare SNPs associated with hypo-inflammation and adverse outcomes in trauma patients, highlighting type 17 immune responses as central to critical illness.

This study identifies two genetically distinct axes of hand and shoulder soft-tissue disorders and shows divergent responses to adiposity and incretin therapy.

The paper introduces a probabilistic whitening transformation for high-dimensional genetic data, showing it is faster and more accurate than existing methods for imputing z-statistics and identifying genetic regulatory signals.

This paper introduces PGI, a system that automates post-GWAS interpretation, achieving high recovery of findings and enabling cross-study biological evidence accumulation.

The paper systematically adjudicates genetic evidence for Alzheimer's disease and related dementias, identifying 111 high-confidence loci through a tiered framework applied to large-scale genome-wide studies.

The paper shows that variant effect predictors influence ClinVar classifications, creating circularity in variant classification and prediction.

The paper introduces a family-aware benchmark for polygenic prediction using All of Us data, enabling comparison of PRS and machine learning models across 14 traits with strong predictive performance.

This study identifies five genetic loci associated with age at onset of Parkinson's disease in European populations, including novel associations at HIP1R and FSCB.

This paper develops a polygenic score for smoking persistence and finds it is associated with earlier or later cessation ages across diverse populations.

The paper introduces a pipeline to create curated, balanced variant-trait association datasets for complex disorders using GWAS and functional annotations.

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.

This study conducts a GWAS of Lyme disease, identifying five genetic loci and linking it to post-infectious diseases like Bell palsy and joint pain.

This study uses genomic structural equation modelling to assess the genetic overlap between insomnia, depression, and anxiety, finding insomnia is the best-supported location of the genetic overlap.

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

Varformer prioritizes drug targets and variants by integrating variant and gene features using a transformer model, achieving high precision across diverse benchmarks.

LKOM identifies genetic variants associated with longitudinal phenotypes by jointly modeling mean and variability signals while controlling the false discovery rate.