Leonard C. Schalkwyk

The paper introduces PIPPA, a pipeline for processing large-scale ATAC-seq data, showing that peak calling performance varies by dataset and recommending parameters for high-confidence chromatin accessibility profiles.

The paper evaluates polygenic indices for neuropsychiatric conditions and brain endophenotypes across UK samples, finding environmental mediation in NPGIs and limited power in EPGIs.

The paper identifies neuron-specific DNA methylation differences in schizophrenia using cell type-resolved epigenome-wide association study in prefrontal cortex nuclei.

The paper identifies stable and variable DNA methylation loci in human whole blood using the IlluminaEPIC array, showing their genomic distribution and genetic control.

The paper develops methods to improve the solubility and stability of intrabodies by optimizing linker design, domain orientation, and charge properties, enabling effective intracellular targeting of cytoplasmic proteins.

The paper provides guidance for designing and analyzing cell-type specific epigenetic epidemiology studies using DNA methylation data from purified cell populations.

The paper develops and validates deconvolution models using DNA methylation data to estimate cell-type proportions in the human cortex, finding glial cells are twice as abundant as neurons and linking cell-type proportions to Alzheimer's disease neuropathology.

The paper introduces a two-step normalization strategy for DNA methylation microarray data to reduce sex bias by separately normalizing autosomal and sex chromosome CpGs.

This paper introduces and validates a metric to assess the accuracy of cellular deconvolution in DNA methylation studies, showing it identifies biases in different demographic groups.

The paper develops a cerebellum-specific DNA methylation clock and finds it outperforms existing clocks, revealing the cerebellum ages more slowly than other brain tissues.

This study compares Nanopore sequencing with DNA methylation microarrays to evaluate its viability for epigenetic epidemiology, finding high correlation and significant smoking-associated methylation sites.

The paper identifies sex-associated differentially methylated CpG sites and regions in whole blood using the Illumina EPIC array, revealing their genomic locations and enrichment for sex-related transcription factors.

The paper presents a DNA methylation-based sex classifier that predicts sex and identifies sex chromosome aneuploidy using CpG sites from whole blood samples.

This study performs a meta-analysis of DNA methylation data from Alzheimer's disease patients to identify differentially methylated loci across brain regions and cortex.

This study uses long-read RNA sequencing to analyze isoform diversity in human and mouse cerebral cortex, revealing widespread alternative splicing and novel transcripts.

The paper examines DNA methylation differences between human blood and sperm, finding limited correlation and no significant obesity-related changes in sperm methylation.

The paper develops a novel DNA methylation clock optimized for human cortex tissue, outperforming existing clocks and highlighting issues with previous associations between DNA methylation age and neurodegenerative phenotypes.

This study identifies DNA methylation changes in blood samples from psychosis patients, including treatment-resistant schizophrenia, and links them to biological age, smoking, and cellular composition.

The paper identifies mQTLs and pleiotropic associations between DNAm, gene expression, and complex traits using SMR and a large UK cohort.

This study identifies widespread H3K27ac acetylomic variation in AD cases compared to controls, linking it to disease-related pathways and AD-associated genetic variants.

The paper examines the properties of the Horvath epigenetic clock in older individuals and finds age acceleration measures are age-dependent and may lead to misleading associations with diseases like Alzheimer's.

The paper quantifies 5-hydroxymethylcytosine (5hmC) changes across 71 human fetal brain samples and identifies sex differences and genetic associations.