Charles E. Breeze

This study constructs a high-resolution placental mQTL resource and investigates how placental DNA methylation relates to early- and later-life traits and their shared vulnerability.

EpiATLAS provides a comprehensive collection of human reference epigenomes to advance epigenomic research and understanding of cellular function and disease.

LDscore provides a cloud-based, Python 3 web platform for LDSC analysis, improving accessibility and scalability for heritability and genetic correlation studies.

The paper develops and validates multi-ancestry polygenic risk scores for LUAD in East Asian never-smokers, showing improved risk prediction compared to East Asian-only models.

The paper identifies placental DNA methylation (DNAm) as a potential mediator of genetic risk for schizophrenia, bipolar disorder, and major depressive disorder using mQTL and GWAS data.

This study uses twin-based Mendelian randomisation to find bidirectional causation between smoking and blood DNA methylation at multiple genomic sites.

FORGEdb integrates genomic annotations to prioritize functional variants and target genes for complex diseases.

The paper develops universal epigenetic clocks that accurately estimate age across mammalian tissues using DNA methylation data from 185 species.

The paper develops a mammalian methylation array to profile methylation levels at conserved sequences across species, enabling cross-species comparisons and epigenetic biomarker development.

This paper develops epigenetic clocks for rhesus macaques using methylation data from multiple tissues, showing high cross-species correlation with vervet monkeys.

This paper extends eFORGE to analyze cell type-specific effects in EWAS, identifying target cell types and tissues even below significance thresholds.

This study uses DNA methylation profiles from bats to show that age and longevity are linked to specific methylation patterns, with longevity associated with immune and cancer-related genes.

The paper critiques a recent commentary on DNA methylation analysis, highlighting misconceptions in algorithm evaluation and reporting contradictory results on real EWAS datasets.

The paper creates a mouse regulatory DNA atlas by mapping DNase I hypersensitive sites across development and shows dynamic changes in regulatory elements and transcription factor activity.

The paper evaluates existing statistical algorithms for identifying cell-type-specific DNA methylation associations in EWAS and highlights biases in prior comparative studies.

This paper introduces CellDMC, a statistical algorithm that identifies differentially methylated cell-types in EWAS, achieving high sensitivity and specificity in detecting methylation changes in smoking-related lung cancer.

This paper compares reference-based deconvolution algorithms for correcting cell-type heterogeneity in EWAS, finding that non-constrained methods like robust partial correlations outperform constrained approaches.