Douglas M. Ruderfer

The paper investigates how POGZ loss-of-function affects transcription factor binding and gene expression in neurons, revealing shared molecular changes with other NDD-related genes.

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

The paper identifies convergent coexpressed genes shared between common and rare variant risk genes in six neuropsychiatric disorders, revealing shared biological mechanisms and therapeutic targets.

This study sequenced 35,024 individuals with predominantly African ancestry to address gaps in genomics and healthcare, finding under-documented disease variants and novel associations with drug adverse events and glycemic control.

The paper develops a method to identify deleterious noncoding variants by analyzing changes in CTCF binding activity across genomic loci and finds that loss of CTCF binding is associated with rare variants.

The paper develops a framework to model cell-type-specific gene expression dynamics in neurons and applies it to identify genes associated with schizophrenia and other complex diseases.

This study evaluates how different modeling choices affect the performance of clinical risk prediction models integrating genetic and clinical data, finding that PRS inclusion benefits vary by integration method and data type.

This study quantifies the expansion of clinical genetic testing at Vanderbilt University Medical Center from 2002 to 2022, showing increased testing rates, broader disease coverage, and a growing number of variants of uncertain significance.

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

This study uses EHR data to estimate TRD probability and identifies two genome-wide significant loci linked to metabolic traits, suggesting shared biology with related conditions.

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

This study identifies modifiable comorbidities of schizophrenia by analyzing EHR data and polygenic risk scores, finding conditions with no shared genetic risk that may be influenced by treatment or environment.

The paper identifies novel autism risk genes by analyzing convergent coexpression patterns in brain samples, showing these genes may not be detected by sequencing studies.

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

This study identifies 114 genome-wide significant rare CNV-disease associations across 30 neurological disorders, revealing 38 novel triplosensitive genes and developing a model to predict dosage sensitivity.

This study relaxes GWAS significance thresholds and uses GRIN to identify additional genes linked to suicidal behavior, which replicate across cohorts and show stronger biological connections.

The paper develops a model to identify patients likely to have a genetic disease using EHR data, achieving high accuracy in predicting genetic testing needs.

The paper introduces methods to clean and analyze clinical lab data linked to EHRs, finding polygenic scores associated with lipid levels and coronary artery disease biomarkers.

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.

This study identifies genetic variants and pathways associated with cognitive resilience to Alzheimer's disease, finding a novel locus linked to bile acid metabolism and methylation changes.

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.

This study identifies genetic risk factors for major depressive disorder and loneliness as predictors of coronary artery disease risk, particularly in women.

The paper investigates the role of single-gene copy number variants in schizophrenia risk using exome-sequencing data from a Swedish cohort, identifying significant deletions in voltage-gated calcium channels.

This study evaluates polygenic risk scores for schizophrenia in electronic health records, finding strong associations with schizophrenia and pleiotropic effects on other psychiatric and medical conditions.

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

This paper identifies significant heritability of suicide attempt using two independent datasets and shows genetic overlap with insomnia and psychiatric disorders.

The study finds that rare copy number variants (CNVs) contribute to bipolar disorder risk primarily in schizoaffective disorder bipolar type cases, with differences in CNV burden and schizophrenia polygenic risk scores between subtypes.

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.

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.