Agaz Hussain Wani

DNA Methylation as a Mediator of Cardiovascular Disease Risk in Relation to PTSD Severity: Identification of Potential Epigenetic Biomarkers.

The paper investigates DNA methylation mediation between PTSD severity and cardiovascular disease risk, identifying six CpGs as potential epigenetic biomarkers.

Time-varying mediation analysis for incomplete data with application to DNA methylation study for PTSD

The paper develops time-varying mediation analysis methods for DNA methylation data to study dynamic mediation effects of trauma on PTSD using the Detroit Neighborhood Health Study.

Differentially Expressed Heterogeneous Overdispersion Genes Testing for Count Data

The paper introduces DEHOGT, a method for detecting differentially expressed genes in RNA-seq data that accounts for overdispersion, and shows it outperforms DESeq and EdgeR on synthetic data and detects more DE genes in microglial cells under stress hormone treatments.

Multi-ancestry GWAS of major depression aids locus discovery, fine-mapping, gene prioritisation, and causal inference

This paper conducts a multi-ancestry GWAS of major depression, identifying 190 loci and 354 genes, with findings showing increased fine-mapping power and bidirectional BMI relationship in European ancestry samples.

DNA methylation of Nuclear Factor of Activated T Cells 1 mediates the prospective relation between exposure to different traumatic event types and post-traumatic stress disorder

The paper examines how DNA methylation at NFATC1 CpG sites mediates the relationship between different trauma types and PTSD risk in a prospective study.

The Impact of Psychopathology, Social Adversity and Stress-relevant DNAm on Prospective Risk for Post-traumatic Stress: A Machine Learning Approach

The paper uses machine learning to identify factors predicting prospective risk of post-traumatic stress symptoms, finding that prior PTSS, loneliness, and a specific DNA methylation site are key predictors.

RANDOMIZE: A Web Server for Data Randomization

RANDOMIZE randomizes metadata to reduce batch effects in DNA methylation microarray data by evenly distributing samples across factors like row, chips, and plates.