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.
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
Our work focuses on three major research topics
Ongoing work includes
Data: Single cell/nucleus RNA-seq, bulk RNA-seq and ATAC-seq, genotyping, WGS
Efficient differential expression analysis of large-scale single cell transcriptomics data
decorate: Differential Epigenetic Coregeulation Test
dream: Powerful differential expression analysis for repeated measures designs
Joint fine-mapping of large-scale post-mortem brain resource with many public GWAS datasets
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.
Functional Interpretation of Genetic Variants Using Deep Learning Predicts Impact on Epigenome.
Interpreting drivers of variation in complex gene expression studies with linear mixed models
RNA-seq data from hiPSC-derived neural progenitor cells and neurons from controls and patients with childhood onset schizophrenia
Design powerful transcriptome experiments given cost constraints
Data from ChIP-seq for H3K4me3 (promoters) and H3K27ac (enhancers and promoters) from 2 brain regions from 17 individuals
PsychENCODE
Interactive linear mixed model analysis of genome-wide association studies with composite hypothesis testing and regression diagnostics in R
Efficient differential expression analysis of large-scale single cell transcriptomics data
decorate: Differential Epigenetic Coregeulation Test
dream: Powerful differential expression analysis for repeated measures designs
Joint fine-mapping of large-scale post-mortem brain resource with many public GWAS datasets
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.
Functional Interpretation of Genetic Variants Using Deep Learning Predicts Impact on Epigenome.
Interpreting drivers of variation in complex gene expression studies with linear mixed models
RNA-seq data from hiPSC-derived neural progenitor cells and neurons from controls and patients with childhood onset schizophrenia
Design powerful transcriptome experiments given cost constraints
Data from ChIP-seq for H3K4me3 (promoters) and H3K27ac (enhancers and promoters) from 2 brain regions from 17 individuals
PsychENCODE
Interactive linear mixed model analysis of genome-wide association studies with composite hypothesis testing and regression diagnostics in R