Karthik A. Jagadeesh

This paper develops computational tools for allelic imbalance analysis using single-nucleus RNA-seq and demonstrates their application in a Parkinson's disease cohort.

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.

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

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 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.

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.

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.

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.

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.

The paper introduces a cryptographic algorithm that allows private DNA profile matching against a CODIS database without revealing the profile to the database provider.

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.

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

AMELIE 3 automatically reanalyzes undiagnosed Mendelian disease patients using new literature, achieving 80% diagnostic yield with 0.5-1 alerts per patient per year.

This study uses single-nucleus and spatial transcriptomics to analyze pancreatic cancer before and after neoadjuvant treatment, revealing changes in cell states and immune infiltration that may inform therapy.

AMELIE 2 improves Mendelian disorder diagnosis by matching patient phenotypes and genotypes to primary literature, achieving high accuracy and efficiency.

Across 1,176,683 airway and lung cells from 22 datasets, ACE2 rises with age, in males and in smokers' multiciliated cells, and is lowest in young children.

AVADA automatically curates a database of pathogenic variants from full-text literature, outperforming existing methods and providing more variants than ClinVar.

This paper develops secure protocols to protect patient privacy during disease diagnosis by enabling secure computations on both genotypic and phenotypic data in two scenarios: cohort discovery and gene prioritization.

S-CAP improves pathogenicity prediction for splicing-relevant variants by reducing uncertain variants by 41% with 95% sensitivity.

Phrank improves Mendelian disease prioritization by ranking candidate genes or diseases based on phenotype similarity, outperforming existing methods in real patient exome data.

This paper develops secure multiparty computation methods to identify causal variants in Mendelian diseases without exposing patient genomes.

AMELIE automates Mendelian disorder diagnosis by analyzing literature and prioritizing candidate genes, achieving high diagnostic accuracy for singleton patients.