Gabriel Hoffman

Associate Professor Genetics and Genomics Sciences

Icahn School of Medicine at Mount Sinai

As Director of Statistical Neurogenomics in the Center for Disease Neurogenomics at the Icahn School of Medicine at Mount Sinai, my group aims to map the chain of causality from DNA variants through epigenetic variation to clinical phenotype. We develop and apply sophisticated statistical models and high performance computing to study the link between genetic risk variants, functional genomics and neuropsychiatric disease.

Interests

Statistical methods in functional genomics
Cross-individual variation in single cell biology
Neuropsychiatric disease
Computational statistics
Machine learning in genomics

Education

Postdoctoral Fellow, 2015

Icahn School of Medicine at Mount Sinai, New York, NY
PhD in Genetics, 2013

Cornell University, Ithaca, NY
BS in Molecular Biology, 2007

University of Maryland, College Park, MD

Research

Our work focuses on three major research topics

Genomics of neuropsychiatric disease from post mortem brain collections
Statistical methods for single cell and integrative genomics
Statistically rigorous, open source software

Ongoing work includes

Statistical methods to identify differentially expressed genes from single cell transcriptomics data across 100s-1000s of individuals
Characterizing genetic regulation of chromatin accessibility in the human brain and its role in mediating risk of neuropsychiatric disease
Understanding effects of genetic regulation and Alzheimer’s disease on gene expression and chromatin accessibility in human microglia

Data: Single cell/nucleus RNA-seq, bulk RNA-seq and ATAC-seq, genotyping, WGS

Selected Publications

Zeng B, et al., Hoffman GE, P Roussos (2024). Single-Nucleus Atlas of Cell-Type Specific Genetic Regulation in the Human Brain. submitted.

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Zeng B^*, J Bendl^*, et al., JF Fullard^#, Hoffman GE^#, P Roussos^# (2024). Genetic regulation of cell type–specific chromatin accessibility shapes brain disease etiology. Science 384(6698).

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Hoffman GE, et al. (2023). Efficient differential expression analysis of large-scale single cell transcriptomics data using dreamlet. biorxiv.

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Hoffman GE^#, AE Jaffe^#, MJ Gandal^#, et al. (2023). Comment on: What genes are differentially expressed in individuals with schizophrenia? A systematic review. Molecular Psychiatry 28, 523–525.

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Kosoy R^*, Fullard JF^*, Zeng B^*, Bendl J^*, et al., Hoffman GE^#, Roussos P^# (2022). Genetics of the human microglia regulome refines Alzheimer’s disease risk loci. Nature Genetics 54, 1145–1154.

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Zeng B, Bendl J, Fullard JF, Hoffman GE^#, Roussos P^# (2022). Multi-ancestry eQTL meta-analysis of human brain identifies candidate causal variants for brain-related traits. Nature Genetics 54, 161–169.

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Hoffman GE*, et al. (2022). Sex Differences in the Human Brain Transcriptome of Cases With Schizophrenia. Biological Psychiatry 91(1):92-101.

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Schrode N, Seah C, Deans PJM, Hoffman GE^#, Brennand KJ^# (2021). Analysis framework and experimental design for evaluating synergy-driving gene expression. Nature Protocols. 16: 812–840.

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Hoffman GE, P Roussos (2020). dream: Powerful differential expression analysis for repeated measures designs. Bioinformatics. 37(2) 192-201.

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Hoffman GE, J Bendl, K Girdhar, P Roussos (2020). decorate: Differential Epigenetic Coregulation Test. Bioinformatics. 36(9): 2856–2861.

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Hoffman GE, et al. (2019). Functional Interpretation of Genetic Variants Using Deep Learning Predicts Impact on Epigenome. Nucleic Acids Research, 47(20): 10597–10611.

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Hoffman GE, et al. (2019). CommonMind Consortium provides transcriptomic and epigenomic data for Schizophrenia and Bipolar Disorder. Scientific Data. 6:180.

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Hoffman GE, et al. (2019). New considerations for hiPSC-based models of neuropsychiatric disorders. Molecular Psychiatry. 24, 49–66.

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Girdhar K*, Hoffman GE*, et al. (2018). Cell-specific histone modification maps in the human frontal lobe link schizophrenia risk to the neuronal epigenome. Nature Neuroscience 21, 1126–1136.

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Hoffman GE, KJ Brennand (2017). Mapping regulatory variants in hiPSC models. Nature Genetics 50, 1–2.

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Hoffman GE, et al. (2017). Transcriptional signatures of schizophrenia in hiPSC-derived NPCs and neurons are concordant with signatures from post mortem adult brains. Nature Communications 8:2225.

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Carcamo-Orive I*, GE Hoffman*, P Cundiff*, ND Beckmann*, et al. (2017). Analysis of Transcriptional Variability in a Large Human iPSC Library Reveals Genetic and Non-genetic Determinants of Heterogeneity. Cell Stem Cell, 20(4): 518–532.

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Hoffman GE, EE Schadt (2016). variancePartition: interpreting drivers of variation in complex gene expression studies. BMC Bioinformatics 17:483.

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Hoffman GE, JG Mezey, EE Schadt (2014). lrgpr: interactive linear mixed model analysis of genome-wide association studies with composite hypothesis testing and regression diagnostics in R. Bioinformatics, 30(21): p3134–3135.

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Hoffman GE (2013). Correcting for Population Structure and Kinship Using the Linear Mixed Model: Theory and Extensions. PLoS ONE 8(10): e75707.

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Hoffman GE, BA Logsdon, JG Mezey (2013). PUMA: A Unified Framework for Penalized Multiple Regression Analysis of GWAS Data. PLoS Comput Biol 9(6): e1003101.

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Software

dreamlet

Efficient differential expression analysis of large-scale single cell transcriptomics data

decorate

decorate: Differential Epigenetic Coregeulation Test

dream

dream: Powerful differential expression analysis for repeated measures designs

Brain eQTL meta-analysis

Joint fine-mapping of large-scale post-mortem brain resource with many public GWAS datasets

CommonMind Consortium provides transcriptomic and epigenomic data for Schizophrenia and Bipolar Disorder

Public resource of functional genomic data from the dorsolateral prefrontal cortex (DLPFC; Brodmann areas 9 and 46): RNA-seq and SNP genotypes on 980 individuals, and ATAC-seq on 269 individuals from 4 separate brain banks.