Gabriel E. Hoffman

The paper introduces a probabilistic whitening transformation for high-dimensional genetic data, showing it is faster and more accurate than existing methods for imputing z-statistics and identifying genetic regulatory signals.

This study compares transcriptome and proteome profiles of three microglia sources, identifying shared surface markers and HERV expression patterns, suggesting MDMi as a practical model for functional studies.

GROMTools, a sparse-weight C++/R engine streaming PLINK2 genotypes, reproduces PrediXcan and PLINK2 GReX imputations with about 100-fold less CPU time and 33-fold less peak memory.

Crumblr is a statistical method for analyzing changes in cell type composition across studies, showing increased power over existing methods in simulations and applied to multiple single-cell RNA-seq datasets.

The paper introduces dreamlet, an R package for efficient differential expression analysis of large-scale single-cell transcriptomics data, showing improved speed and memory usage compared to existing methods.

This study performs a single-nucleus transcriptome-wide association study to identify gene-trait associations in brain disorders, revealing cell-type-specific genetic regulation and ancestry-specific patterns.

The paper constructs a single-nucleus atlas of genetic regulation in the human brain, identifying cell-type-specific regulatory effects and disease-associated genes.

The paper profiles human myeloid cells across aging and Alzheimer's disease, identifies AD-associated changes, and links GPNMB to neuroprotection via TREM2.

The paper identifies three gene regulatory states associated with cognitive resilience and pathogenesis in Alzheimer's disease using single-cell data from the dorsolateral prefrontal cortex.

The paper constructs a single-cell transcriptomic atlas of the human dorsolateral prefrontal cortex across neurodegenerative and neuropsychiatric diseases, revealing shared and distinct molecular mechanisms.

This study creates a single-nucleus transcriptomic atlas of Parkinson's disease progression, identifying cell-type-specific gene expression changes and linking them to genetic risk factors and disease stages.

The paper presents a long-read RNA-seq atlas of microglia isoforms, identifying novel isoforms and their associations with genetic risk loci in Alzheimer's and Parkinson's disease.

This paper presents a computational drug repurposing framework that identifies FDA-approved drugs for COVID-19 by integrating genetic expression data and EMR data, finding four compounds with protective effects in validation.

The paper maps transcriptomic and chromatin accessibility profiles across 25 human brain regions and identifies genes linked to neuropsychiatric disorder risk.

This study creates a single-nucleus transcriptomic atlas of the human DLPFC across the lifespan, revealing three transcriptomic phases and ten age-related trajectories linked to neurodevelopmental and neurodegenerative disease risk.

The paper analyzes transcriptomic differences in human brain cells with nine high-risk neuropsychiatric CNVs compared to non-carriers, finding widespread gene expression disruptions and shared functional pathways.

The paper identifies 46,802 enhancers and 164 psychiatric disorder variants with regulatory effects in the developing cortex and organoids using massively parallel reporter assays and deep learning.

The paper processes single-nuclei multi-omics data from 388 human brains to identify regulatory elements and build cell-type networks, revealing changes in aging and neuropsychiatric disorders.

The paper identifies cell-type specific chromatin accessibility QTLs and their functional impact on brain disease risk.

The paper evaluates deconvolution methods on human brain data, finding dtangle and bMIND perform best, and identifies decon-eQTLs that explain more schizophrenia heritability than bulk or single-cell eQTLs.

The paper generates a molecular neuroanatomical map of the human prefrontal cortex using spatial transcriptomics and single nucleus RNA-sequencing, revealing novel spatial domains and cell-cell interactions linked to psychiatric disorders.

This study identifies 240 novel genetic loci associated with mosaic loss of chromosome Y through a multi-ancestry GWAS of the Million Veteran Program and UK Biobank.

DeepGAMI improves phenotype prediction from multimodal data by integrating biological knowledge and imputing missing modalities, outperforming existing methods and providing interpretable insights.

This paper introduces PheMED, a tool to quantify effect size dilution in GWAS due to phenotypic misclassification, and demonstrates its applications in improving meta-analysis.

This study maps chromatin accessibility in Alzheimer's disease using ATAC-seq, identifying regulatory changes and 3D genomic alterations linked to disease.

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

The paper identifies IL10RB as a key regulator of COVID-19 host susceptibility and severity through integrative analysis of genetic and transcriptomic data, and validates potential drug repositioning candidates.

The paper identifies regulatory mechanisms for 21 Alzheimer's disease risk loci in human microglia using transcriptome and chromatin accessibility profiling.

The paper identifies acetylated chromatin domains associated with dysfunction in schizophrenia and bipolar disorder by analyzing histone modifications in prefrontal cortex samples.

The paper performs a trans-ethnic eQTL meta-analysis to identify regulatory variants and candidate causal genes for brain-related traits.