Silviu‐Alin Bacanu

The paper identifies rare and common genetic variants associated with acute myeloid leukemia using whole-genome sequencing data from the All of Us Research Program.

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 paper identifies 58 genome-wide significant loci for major anxiety disorders using a large meta-analysis and highlights GABAergic signaling as a potential mechanism.

The paper shows genotype- and phenotype-based genetic instruments explain largely independent components of liability to psychiatric disorders, supported by theoretical analysis and simulations.

The paper identifies genetically distinct patient subgroups across diseases by analyzing polygenic risk and pathway convergence.

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.

AutoComplete improves phenotype imputation in biobank data, enhancing GWAS power and genetic discovery.

GAUSS provides an R package for ancestry-informed imputation and TWAS analysis of GWAS summary statistics without requiring individual-level data.

This study uses machine learning and functional weights to identify rare variants associated with AUDIT-P, finding significant associations in ADH1C and THRA genes.

This study identifies somatic copy number variants (sCNVs) in schizophrenia cases, revealing their contribution to risk and specific NRXN1 disruptions.

The paper introduces PA-FGRS, a method to estimate genetic liability from family records, and shows it improves MDD classification and GWAS power when combined with genotypes.

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.

The paper analyzes rare functional variation in genes related to ethanol metabolism in severe alcohol dependence cases using a case-only design and WES data from the island of Ireland.

The paper uses brain cell-type-specific protein interactomes to interpret genetic data in schizophrenia and identifies enriched networks and genes associated with the disease.

This paper improves genetic studies of MDD by integrating shallow and deep phenotypes, enhancing GWAS power and PRS accuracy while preserving specificity.

A genome-wide association study of 69,369 schizophrenia cases and 236,642 controls identifies 270 risk loci, prioritizes 130 genes, and links common and rare risk variants to neuronal synaptic biology.

This study identifies sex-dependent genetic risk loci shared and specific to mood and psychotic disorders using genome-wide interaction analysis.

The paper compares ten polygenic score methods for psychiatric disorders across multiple cohorts, finding that some methods significantly improve prediction statistics over the baseline PC+T method.

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.

This paper conducts a genome-wide association study of suicide death and shows polygenic scores predict suicide case-control status and related psychiatric traits.

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 study identifies traits associated with polygenic risk for depression using UK Biobank data and finds causal links between depression risk and brain structure, as well as environmental interactions.

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

This study finds that subthreshold psychosis symptoms in a population-based cohort are associated with genetic risks for schizophrenia, ADHD, autism, and neuroticism, with notable sex differences.

The paper compares genome-wide data from opioid use and dependence cases to identify genetic associations and differences between exposure and dependence.

This study shows that using unscreened or super-normal controls in GWA studies biases genetic correlation estimates between disorders upward, with stronger effects at higher prevalence and lower true genetic correlation.