Warren Cheung

The paper uses long-read sequencing to analyze telomere inheritance patterns in human trios, finding parent-of-origin effects and heritable influences on telomere length.

The paper introduces a data model and dataset for rare disease research, enabling interoperable multi-omic data integration across clinical sites.

The paper expands the human genomic imprinting map using long-read sequencing and GWAS integration, revealing new disease-associated loci and maternal/paternal imprinting patterns.

This paper expands methylome analysis and detects quantitative trait loci by long-read sequencing of personal DNA, revealing tens of thousands of methylation QTLs associated with structural variants.

This study quantifies polygenic liability in rare disease probands and finds associations between rare disease phenotypes and polygenic scores, supporting complex trait influences on Mendelian variant interpretation.

The paper introduces OSMP, a tool for one-sided genomic matchmaking in rare disease patients, which efficiently filters variant occurrences to prioritize clinically relevant matches.

The paper identifies a common flanking variant linked to increased meiotic stability at the FGF14-SCA27B repeat locus.

The paper uses long-read whole genome sequencing and polygenic scores to identify structural variants linked to rare diseases and prioritize candidate genes for functional validation.

The paper uses pangenome graphs to improve detection of rare structural variants in a rare disease cohort, identifying novel diagnostic variants.

The paper uses HiFi sequencing to identify hypermethylation outliers in a rare disease cohort, finding allele-specific loss of regulatory elements linked to disease.

The paper introduces TRGT, a tool for tandem repeat genotyping, and a database of 937,122 TRs across 100 genomes, achieving high concordance and detecting repeat expansions, methylation, and mosaicism.

The paper presents diagnostic analyses of 960 pediatric rare disease families using genomic sequencing and machine learning, finding higher diagnostic rates with long-read sequencing and highlighting challenges in variant interpretation.

The paper analyzes DNA methylation changes in nasal samples from infants with respiratory viral infections, identifying virus-specific differentially methylated regions and their association with immune responses.

The paper identifies GSDMA as a gene in naive CD4+ T cells that modulates expression and methylation levels, driving the 17q12-21 asthma association.