Yakir Reshef

This paper introduces VIMA, a method for analyzing spatial molecular data to identify disease-associated spatial features, showing improved performance over existing methods in simulations and real datasets.

This study identifies granzyme K as a driver of a novel complement activation pathway independent of classical, lectin, or alternative pathways.

The paper introduces GeNA, a tool to identify genetic variants influencing cell state abundance in single-cell data, finding five loci associated with immune cell state shifts.

The paper develops SCENT, a method to create cell-type-specific enhancer-gene maps from single-cell multimodal data, identifying causal variants and genes for diseases.

The paper maps cell-type-specific eQTLs for HLA genes across multiple tissues and identifies dynamic regulation of HLA-DQ genes in immune cells.

The paper deconstructs RA synovium using single-cell data to identify six cell type abundance phenotypes associated with distinct inflammatory states.

This paper introduces CNA, a method to identify disease-associated cell states by analyzing transcriptional neighborhoods across samples, showing improved performance over cluster-based approaches in simulations and real datasets.

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 develops ACLR to estimate genomic autocorrelation of causal SNP effects and finds significant negative autocorrelation at short distances in complex traits.

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 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.

The paper develops a machine learning system using convolutional neural networks to predict cell type-specific epigenetic and transcriptional profiles from DNA sequence, showing alignment with eQTL causal variants.

This study connects schizophrenia GWAS findings to specific genes, tissues, and regulatory mechanisms through transcriptome-wide and chromatin activity analyses.

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.

This paper partitions heritability across functional genomic elements using GWAS summary statistics to identify trait-specific enrichments.