Enrico Domenici

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

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

The paper links miR-137 and its target genes to schizophrenia risk through emotion processing in the prefrontal cortex, showing allele associations with brain activation patterns.

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

The paper identifies population-level variation in enhancer expression in the human brain and links enhancer eQTL to schizophrenia heritability.

The paper investigates the conservation of rare schizophrenia risk variants across diverse populations and finds consistent burden in constrained genes.

This paper performs a large-scale eQTL meta-analysis across cerebral cortical and cerebellar brain regions, identifying distinct eQTL patterns and demonstrating improved power over existing studies.

The paper functionally annotates rare structural variants in the human brain by integrating SV calling with RNA-seq data to assess their regulatory effects and prioritize pathogenic deletions.

The paper analyzes transcriptomes of zebrafish atoh7 mutant embryos to identify Atoh7-dependent genetic networks linked to retinal development and human eye diseases.

This study identifies 99 genes associated with autism spectrum disorder through large-scale exome sequencing, showing differences in mutation frequencies between ASD and severe neurodevelopmental delay groups.

The Italian Autism Network (ITAN) collects biological samples and clinical data from ASD patients and families to support genetic and biomarker research.

The paper analyzes allelic expression bias in schizophrenia using RNA-seq data to assess imprinted gene expression and its relationship with age, ancestry, and disease.

This study identifies 114 genome-wide significant loci for schizophrenia and bipolar disorder, with specific subphenotype associations and four genes distinguishing the disorders.

The paper uses transcriptomic imputation to identify schizophrenia risk genes across multiple brain regions, finding 71 associated genes and 36 enriched pathways.

This study uses transcriptomic imputation to identify 29 novel genes associated with bipolar disorder and its subtypes, revealing biological pathways related to oxidative stress and circadian rhythms.

The paper integrates GWAS and conditional eQTL data to identify schizophrenia risk loci with co-localization, finding 40 loci and six with conditional eQTL co-localization.