Brien P. Riley

The paper introduces co-expression-based models to improve eQTL predictions and identifies novel schizophrenia-associated genes using brain RNA-seq data.

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

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.

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 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.

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

This study integrates mouse brain gene expression data with human genome-wide association data to identify novel gene networks associated with alcohol dependence.

The paper identifies TCF4 binding sites and their association with schizophrenia risk genes, showing TCF4 regulates neural development genes and interacts with SZ-related loci.

The paper constructs a polygenic risk atlas for 33 traits in emerging adults and finds genetic risks for schizophrenia, neuroticism, and depression predict various phenotypes.

The paper confirms a polygenic effect on negative symptoms in schizophrenia using GWAS, identifies novel loci, and highlights the need for larger samples.

This paper investigates how global polygenic risk scores, sex, and their interaction predict schizophrenia risk using a classification model, finding sex and polygenic risk are the strongest predictors.

FIQT estimates Z-score means from genome scans more accurately and efficiently than existing methods, improving risk prediction in schizophrenia studies.