Research

Our work focuses on three major research topics

  • Statistical and machine learning methods for integrative genomics
  • Genomics of psychiatric disease from post mortem brain collections
  • Induced pluripotent stem cell models of human disease

Ongoing work includes

  • Interpretation of non-coding variation and its role in regulatory genomics
  • An epigenomics reference map of the human brain
  • Genetic regulation of histone modification in the human brain
  • Integrative network analysis of the human brain transcriptome

Data: RNA-seq, histone ChIP-seq, ATAC-seq, array genotyping, WES, WGS

Selected Publications

(2019). decorate: Differential Epigenetic Coregulation Test. coming soon.

Project

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

PDF

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

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(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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(2013). PUMA: A Unified Framework for Penalized Multiple Regression Analysis of GWAS Data. PLoS Comput Biol 9(6): e1003101.

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Software

decorate

decorate: Differential Epigenetic Coregeulation Test

dream

dream: Powerful differential expression analysis for repeated measures designs

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.

DeepFIGV

Functional Interpretation of Genetic Variants Using Deep Learning Predicts Impact on Epigenome.

variancePartition

Interpreting drivers of variation in complex gene expression studies with linear mixed models

Data and code resources for an hiPSC model

RNA-seq data from hiPSC-derived neural progenitor cells and neurons from controls and patients with childhood onset schizophrenia

Design hiPSC experiments

Design powerful transcriptome experiments given cost constraints

Referance map of human brain epigenome

Data from ChIP-seq for H3K4me3 (promoters) and H3K27ac (enhancers and promoters) from 2 brain regions from 17 individuals

Epigenomics

PsychENCODE

lrgpr

Interactive linear mixed model analysis of genome-wide association studies with composite hypothesis testing and regression diagnostics in R

Resources

decorate

decorate: Differential Epigenetic Coregeulation Test

dream

dream: Powerful differential expression analysis for repeated measures designs

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.

DeepFIGV

Functional Interpretation of Genetic Variants Using Deep Learning Predicts Impact on Epigenome.

variancePartition

Interpreting drivers of variation in complex gene expression studies with linear mixed models

Data and code resources for an hiPSC model

RNA-seq data from hiPSC-derived neural progenitor cells and neurons from controls and patients with childhood onset schizophrenia

Design hiPSC experiments

Design powerful transcriptome experiments given cost constraints

Referance map of human brain epigenome

Data from ChIP-seq for H3K4me3 (promoters) and H3K27ac (enhancers and promoters) from 2 brain regions from 17 individuals

Epigenomics

PsychENCODE

lrgpr

Interactive linear mixed model analysis of genome-wide association studies with composite hypothesis testing and regression diagnostics in R

Contact