Amanda L. Dobbyn

The paper constructs a tissue-level phenome-wide network map linking genes and phenotypes using colocalized signals from the UK Biobank and Genotype-Tissue Expression project.

This study uses transcriptomic imputation to translate AN GWAS findings into gene expression associations, revealing 47 genes linked to AN and their clinical consequences in a biobank cohort.

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

Meta-analysis of 41,917 European-ancestry bipolar disorder cases versus 371,549 controls yields 64 risk loci (33 novel), neuronal and synaptic gene enrichment, and 15 eQTL-prioritized genes.

This study identifies robust gene-phenotype associations by analyzing rare loss-of-function variants in exome-wide data linked to electronic health records.

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 examines common genetic variants' contribution to bipolar disorder risk in multiplex families and finds higher polygenic risk scores in affected individuals compared to controls and unaffected family members.

This study identifies 73 genome-wide significant loci for mood disorders, revealing shared and distinct genetic factors between major depressive disorder and bipolar disorder.

This study identifies 30 genome-wide significant loci associated with bipolar disorder, including novel genes involved in ion channels, neurotransmitters, and synaptic components.

The paper identifies SNRNP35 as a trauma type-specific PTSD gene through analysis of genetically regulated gene expression in PTSD cases and controls.

The paper examines RNA editing changes in schizophrenia by analyzing postmortem brain samples and identifies genetic regulators and associations with schizophrenia GWAS loci.

The paper introduces a Bayesian framework to prioritize risk genes for complex diseases by integrating gene-set information with variant counts, identifying novel epilepsy risk genes and linking them to immune pathways and drug targets.

mTADA jointly analyzes de novo mutation data from multiple neuropsychiatric disorders to identify shared and specific risk genes.

This paper uses transcriptomic imputation to identify gene-tissue associations linked to Anorexia Nervosa risk, finding significant links in both brain and metabolic tissues.

The paper uses Bayesian modeling to analyze rare exonic variants in schizophrenia and neurodevelopmental disorders, identifying 1,551 risk genes for schizophrenia and 98 new risk genes for developmental 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.