Kushal K. Dey

Host control and regulation of human anellovirus persistence

This study identifies host factors and cellular contexts governing anellovirus persistence through genomic, transcriptomic, and functional analyses.

Perturbation-induced gene programs map the hidden regulatory architecture of complex diseases

This paper introduces Perturbscape, a framework that uses perturbation-induced gene programs to identify disease-relevant genes and pathways, showing higher heritability informativeness than steady-state approaches.

Engineered regulatory states resolve context-dependent genetics in macular degeneration

The paper uses CRISPR activation to engineer regulatory states in RPE-1 cells, revealing hidden genetic effects in AMD by analyzing chromatin accessibility and transcriptional interactions.

SPP1hi macrophages in fibrin niches promote hyperplastic tissue remodeling in rheumatoid arthritis synovium

SPP1hi macrophages in RA synovial fibrin deposits promote tissue hyperplasia.

A dish-to-biobank framework links β-cell nutrient-stress programs to genetic and dietary risk for Type 2 Diabetes

A stem cell knockout village reveals lineage rewiring and a non-canonical islet cell fate in monogenic diabetes

This study uses a knockout village framework to analyze how mutations in diabetes genes alter pancreatic beta cell development, revealing non-canonical cell fate shifts and identifying ISL1 as a key regulator.

PRISM: ancestry-aware integration of tissue-specific genomic annotations enhances the transferability of polygenic scores

PRISM improves polygenic score transferability across populations by integrating ancestry-aware tissue-specific genomic annotations, achieving a 13.10% performance boost for African ancestry.

Mapping disease critical spatially variable gene programs by integrating spatial transcriptomics with human genetics

Spacelink integrates spatial transcriptomics with genetics to identify spatially variable genes linked to disease, showing improved detection power and disease informativeness.

Population-scale sequencing resolves correlates and determinants of latent Epstein-Barr Virus infection

The paper uses population-scale whole genome sequencing to identify EBV DNA as a biomarker for respiratory, autoimmune, and cardiovascular diseases and identifies genetic determinants of EBV persistence.

Linking regulatory variants to target genes by integrating single-cell multiome methods and genomic distance

The paper introduces pgBoost, a framework that improves linking regulatory variants to genes by integrating single-cell multiome data and genomic distance, showing higher enrichment than existing methods.

Prevalent cross-cell type QTL trans-regulatory genetic effects impacting innate lymphoid cells in the small intestine

The paper identifies genetic variants affecting innate lymphoid cells in the gut using single-cell RNA-seq and validates their regulatory roles across cell types.

Genetic and functional analysis of unproductive splicing using LeafCutter2

LeafCutter2 identifies and quantifies unproductive splicing events from RNA-seq data, revealing their role in gene regulation and disease, including Alzheimer's.

A consensus variant-to-function score to functionally prioritize variants for disease

This paper introduces a consensus variant-to-function score to prioritize disease-causing variants, showing improved performance in heritability enrichment and fine-mapping.

CRISPR Screening Uncovers a Long-Range Enhancer for ONECUT1 in Pancreatic Differentiation and Links a Diabetes Risk Variant

The paper identifies a long-range enhancer for ONECUT1 involved in pancreatic differentiation and links a diabetes risk variant to disrupted transcription factor binding.

Tissue-specific enhancer-gene maps from multimodal single-cell data identify causal disease alleles

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.

Leveraging single-cell ATAC-seq to identify disease-critical fetal and adult brain cell types

The paper integrates GWAS with scATAC-seq and scRNA-seq data to identify disease-critical fetal and adult brain cell types for multiple traits.

Mapping the dynamic genetic regulatory architecture of HLA genes at single-cell resolution

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

An encyclopedia of enhancer-gene regulatory interactions in the human genome

ENCODE-rE2G, a classifier trained on K562 CRISPR perturbations, maps 13,455,443 enhancer-gene interactions across 352 biosamples, matching or exceeding prior models on CRISPR, eQTL, and GWAS benchmarks.

Single-cell multiome regression models identify functional and disease-associated enhancers and enable chromatin potential analysis

SCARlink predicts gene expression from chromatin accessibility in single-cell multiome data, identifies enhancers, and enables chromatin potential analysis for developmental trajectories.

Polygenic architecture of rare coding variation across 400,000 exomes

The paper quantifies the heritability explained by rare coding variants and compares it to common variants across 22 traits using 400,000 UK Biobank exomes, finding rare variants explain less phenotypic variance but are more concentrated in constrained genes.

Compressed Perturb-seq: highly efficient screens for regulatory circuits using random composite perturbations

Compressed Perturb-seq reduces cost and complexity of CRISPR screens by using random composite perturbations and computational decomposition, achieving similar accuracy to conventional methods with greater power to detect genetic interactions.

Systematically characterizing the roles of E3-ligase family members in inflammatory responses with massively parallel Perturb-seq

The paper uses Perturb-seq to systematically study 1,130 E3 ligases and their roles in inflammatory responses in dendritic cells, revealing co-functional modules and genetic interactions.

Set-based rare variant association tests for biobank scale sequencing data sets

The paper improves rare variant association testing by introducing SAIGE-GENE+ which reduces type I error and computational costs, identifying 551 gene-phenotype associations in UK Biobank WES data.

Polygenic enrichment distinguishes disease associations of individual cells in single-cell RNA-seq data

This paper introduces scDRS, a method to identify disease-associated cells in scRNA-seq data using polygenic risk scores, finding novel subpopulations linked to diseases like IBD and schizophrenia.

SNP-to-gene linking strategies reveal contributions of enhancer-related and candidate master-regulator genes to autoimmune disease

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

Combining SNP-to-gene linking strategies to pinpoint disease genes and assess disease omnigenicity

The paper develops a combined SNP-to-gene linking strategy (cS2G) to improve the identification of disease-causing genes and assess disease omnigenicity using GWAS data.

Identifying disease-critical cell types and cellular processes across the human body by integration of single-cell profiles and human genetics

The paper integrates single-cell RNA-seq, epigenomic maps, and GWAS data to identify disease-critical cell types and processes across multiple diseases and tissues.

A single-cell and spatial atlas of autopsy tissues reveals pathology and cellular targets of SARS-CoV-2

This study creates a single-cell and spatial atlas of autopsy tissues from COVID-19 patients, revealing altered cellular responses and potential therapeutic targets.

Improving the informativeness of Mendelian disease-derived pathogenicity scores for common disease

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

Evaluating the informativeness of deep learning annotations for human complex diseases

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