Steven Gazal

The paper assesses and improves Mendelian disease-derived pathogenicity scores for common disease using stratified LD score regression and a gradient boosting framework.

This paper introduces PolyFun, a method that improves fine-mapping accuracy by integrating genome-wide functional data, leading to more causal variant identifications and polygenic localization of trait heritability.

The paper improves deep learning SNP annotations for complex diseases by integrating them with SNP-to-gene linking strategies and gradient boosting, showing increased informativeness for autoimmune diseases.

A large-scale GWAS in 212,453 Japanese individuals identified 45 novel susceptibility loci for 22 diseases, highlighting the importance of non-European population studies.

LDpred-funct adds baseline-LD functional priors to polygenic scores: average prediction R2 0.144 across 21 UK Biobank traits, a 4.6% relative gain over SBayesR.

This paper introduces a new association method, LT-FH, that combines case-control status and family history to increase power in genetic association studies, outperforming existing methods across multiple diseases.

The paper constructs cell-type-specific TF binding annotations by combining sequence-based predictions with chromatin data, showing increased heritability enrichment for 49 diseases.

The paper analyzes 41 diseases and traits to show transposable elements contribute uniquely to disease heritability, with older elements and SINEs showing specific enrichment patterns.

This paper compares S-LDSC and LDAK models, showing the baseline-LD model better fits UK Biobank data and produces more consistent functional enrichment estimates.

The paper assesses gene network connectivity's role in disease heritability using LD score regression and finds that network annotations are enriched but largely explained by regulatory annotations.

IMPACT improves regulatory element annotation by integrating TF binding profiles with epigenomic data, showing better performance in predicting eQTLs and capturing RA heritability.

The paper finds that disease heritability enrichment is higher in ancient, conserved regulatory elements compared to younger or less conserved ones.

The paper quantifies frequency-dependent genetic architectures and negative selection effects across 25 UK Biobank traits using a statistical model and imputed genotypes.

This paper introduces a method to enhance GWAS power by incorporating functional annotations, resulting in increased detection of genome-wide significant loci.

The paper partitions low-frequency and common variant heritability across functional annotations, finding non-synonymous coding variants and conserved regions explain significant portions of low-frequency heritability.

The paper shows that polygenicity of complex traits is explained by negative selection, which flattens genetic signal across the genome.

The paper introduces a method to detect directional effects of transcription factor binding on disease risk using GWAS data and applies it to identify new causal genes and mechanisms.

The paper quantifies genetic components of population differentiation in UK Biobank traits to detect polygenic selection signals across geographic gradients.

The paper uses molecular QTL annotations to identify genetic variants associated with disease heritability, finding stronger enrichments using causal posterior probabilities than standard annotations.

The paper introduces a faster version of BOLT-LMM for GWAS on large biobank datasets, showing increased power and detecting more loci for height and other traits.

The paper compares methods for estimating heritability and genetic architecture using whole genome data and finds that larger imputation panels improve accuracy of causal variant frequency estimates.

The paper uses gene expression data and GWAS summary statistics to identify disease-relevant tissues and cell types by detecting heritability enrichments in gene-specific expression regions.

The paper quantifies linkage disequilibrium (LD) dependent architecture of complex traits, finding that low LD SNPs have higher heritability, likely due to negative selection.

This study partitions the heritability of local and distal gene expression regulation across 15 human tissues, identifying enriched functional categories and tissue-specific patterns.