Vladimir I. Vladimirov

The paper conducts a GWAS on antipsychotic-induced changes in total cholesterol and BMI in a multi-ancestry cohort, identifying genetic loci and pathways associated with these metabolic effects.

This study analyzes 173 multiplex Portuguese families to identify rare genetic variants associated with schizophrenia, mood disorders, and autism, finding a CHD2 mutation linked to diverse mental illness phenotypes.

The paper performs rare variant association analysis using whole-genome sequencing data from the Portuguese Island Collection to identify shared genetic pathways in schizophrenia and bipolar disorder.

This study identifies shared and distinct genetic factors underlying suicidality phenotypes using GWAS and structural equation modelling.

This study identifies ADARB1 overexpression in T21 fetal brain due to trisomy 21, leading to increased A-to-I RNA editing and altered GRIA gene recoding.

This paper introduces MR-GSE, a multi-omics Mendelian randomization method for gene set enrichment in psychiatric disorders, showing increased detection of significant gene sets compared to non-MR methods.

This paper identifies risk genes and pathways for neuropsychiatric and substance use disorders using XWAS analyses in blood and brain tissues, finding shared signals in the MHC region and other genomic areas.

This study integrates DNA methylation and gene expression data from two cohorts to better understand epigenomic regulatory mechanisms and challenges assumptions about cis DNAm-GE relationships.

The paper identifies shared and unique biological processes in PFC and NAc associated with chronic alcohol abuse using network analysis and genetic regulatory mechanisms.

The paper profiles lncRNA and protein coding gene expression in nucleus accumbens from alcohol-dependent and control subjects, identifying differentially expressed genes and their functional roles in alcohol dependence.

The paper introduces JEPEGMIX2-P, a TWAS pathway method that improves computational efficiency and performance for psychiatric disorder GWAS, showing increased power over existing methods like MAGMA.

This paper improves psychiatric GWAS by re-imputing summary statistics using a larger, more diverse reference panel, revealing new signals from rare variants.

HiCcompare normalizes multiple Hi-C datasets and detects differential chromatin interactions, outperforming single-dataset normalization methods in identifying true interaction changes.

JEPEGMIX2 improves gene-level eQTL analysis by using updated cis-eQTL SNPs and adjusting for background enrichment, reducing false positives in cosmopolitan cohorts.