Bryce van de Geijn

The paper links SNPs to genes using functionally informed strategies, revealing enhancer-related and master-regulator genes contribute to autoimmune disease heritability.

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.

This study evaluates how well deep learning annotations predict disease heritability and finds they provide limited unique information beyond existing functional annotations.

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.

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