Patrick F. Sullivan

The paper presents a connection-level genetic atlas of human cortical functional connectivity, identifying genetic loci associated with resting and task states.

Ultra-rare damaging coding variants in 87,959 schizophrenia cases and 150,587 controls implicate 40 genes (16 exome-wide, 24 at 5% FDR), half overlapping autism, developmental delay or bipolar genes.

The paper uses CRISPR-based functional genomics to identify noncoding regulatory elements and variants affecting schizophrenia-associated gene expression.

This paper maps genetic, proteomic, and metabolomic effects along human white-matter tracts using UK Biobank data, revealing localized associations with neurodegenerative and psychiatric traits.

This paper shows that astrocytes reprogram neuronal gene expression and chromatin accessibility, affecting thousands of genes and regulatory elements, including those linked to schizophrenia and Alzheimer's disease.

This paper introduces RVGA, a framework for efficient genome-wide association analysis in imaging genetics, identifying novel loci and genetic correlations in brain structures using UK Biobank data.

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

This study identifies hundreds of new cis-regulatory element-gene connections in the MHC locus using CRISPR screens and single-cell transcriptomics, revealing cell type-specific regulatory mechanisms.

The paper identifies rare protein-coding variants associated with multi-organ MRI traits using exome-wide association analysis in the UK Biobank.

The paper integrates GWAS meta-analysis with human brain cell mapping to identify genetic loci and cell types involved in tinnitus, implicating the amygdala and midbrain.

This paper benchmarks 20 methods for mapping trait-cell type associations by integrating GWAS and scRNA-seq data, finding CATCH to be the most robust approach.

This study integrates genetic analysis with single nucleus RNA-seq to identify cell-type specific transcriptomic perturbations in schizophrenia, highlighting mitochondrial dysfunction and protein modification as key factors.

This paper introduces launch-dCas9, a machine learning framework to predict guide RNA effects in CRISPR epigenome editing, showing high accuracy and improved gRNA selection.

The paper investigates epigenetic age acceleration in schizophrenia, finding age- and sex-specific effects and associations with polygenic risk scores.

The paper integrates genomic data with single-cell transcriptomics to identify brain cell types and regions associated with psychiatric disorders and traits.

The paper investigates the genetic impact of the X-chromosome and sex differences in the human brain, identifying 72 genome-wide significant trait-locus pairs linked to brain-related disorders.

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

TOGA integrates gene annotation and orthology inference across hundreds of genomes, improving ortholog detection and creating large comparative gene resources.

A 240-species placental mammal alignment resolves constraint to single bases, estimating at least 10.7% of the human genome is constrained, with 48.5% of constrained bases unannotated.

The paper uses evolutionary constraint scores to identify functional genomic regions and finds they are enriched for variants associated with common diseases.

The paper uses genetic data to predict imaging-derived phenotypes from the UK Biobank, creating polygenic scores that identify disease risk in external cohorts.

OTTERS is a TWAS framework that uses summary-level eQTL reference data to estimate eQTL weights and perform omnibus TWAS, showing improved power and applicability.

mBAT-combo improves gene-trait association detection in GWAS by accounting for SNP effect directions, revealing masking effects in complex traits.

DeepGWAS enhances GWAS signals for neuropsychiatric disorders using deep neural networks, improving locus detection in schizophrenia and validating results in other disorders.

This study uses whole exome sequencing in families with multiple affected individuals to identify rare variants in genes associated with both Mendelian and complex neuropsychiatric syndromes.

The paper identifies synaptic gene sets that can discriminate cortical neuronal identities with the same power as all genes, revealing functional categories and compartments with cell type-specific expression.

The paper finds that rare copy number variants (CNVs) are more common in treatment-resistant psychosis patients compared to a general schizophrenia cohort, with duplications at 15q11.2-q13.1 and 16p11.2 linked to treatment resistance.

TwinEQTL provides a fast and powerful method for eQTL and GWAS analysis in twin studies by splitting twin samples and combining results with minimal power loss.

THUNDER is a deconvolution method that infers cell type proportions from bulk Hi-C data, outperforming existing methods in accuracy.