Network-based approaches for the analysis of gene-disease associations
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Network-based gene- phenotype association analysis Casey Greene Assistant Professor Systems Pharmacology and Translational Therapeutics
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Transcript of Network-based approaches for the analysis of gene-disease associations
Network-based gene-phenotype association
analysis
Casey Greene
Assistant Professor Systems Pharmacology and Translational Therapeutics
Tissue-specific Expression
vs.
Discover Tissue-specific Genes.
in silico
in vivo
IHC to confirm �podocyte predictions
A B C
D E F
G H I
Example novel podocyte proteins A B C
D E F
G H I
Known podocyte markers
W Ju*, CS Greene*, et al. Genome Research, 2013.
In silico predictions are highly podocyte specific
0 20 40 60 80
Podocyte
%
In silico
In vivo mouse experiment
Background frequency
W Ju*, CS Greene*, et al. Genome Research, 2013.
Now that I know where proteins are expressed, what other proteins are they working with?
Tissue-specific IL-1β response
CS Greene*, A Krishnan*, AK Wong* et al. Nature Genetics. 2015.
Tissue-specific TF Activity
CS Greene*, A Krishnan*, AK Wong* et al. Nature Genetics. 2015.
Tissue-specific TF Activity
CS Greene*, A Krishnan*, AK Wong* et al. Nature Genetics. 2015.
Genome-wide Association Study
Cases
Controls
Genome-wide Association Study
genomic position
-log(
p)
Cases
Controls Low statistical strength
- low frequency mutations - small effect sizes - epistasis
-log(p)
Positives: nominally significant genes
SVM
neg
pos
Negatives: non- significant genes
Network-wide Association Study Top GWAS hits are potentially enriched with disease-associated genes
+
Tissue-network Tissue-specific NetWAS Genes
Kidney NetWAS of Hypertension
NetWAS Phenotype Integration
NetWAS Identifies Tissues Kidney Liver Heart
Network-based analysis of genetic variants associated with hippocampal volume in Alzheimer’s disease: a study of ADNI cohorts�2016 @ BioData Mining �http://github.com/greenelab/adni-netwas
HetNets
HetNets: Integrated performance is strong.
Heterogeneous Network Edge Prediction: A Data Integration Approach to Prioritize Disease-Associated Genes 2015 @ PLOS Computational Biology �https://github.com/dhimmel/hetio Genetic Association Guided Analysis of Gene Networks for the Study of Complex Traits Minor Revisions @ Circulation Cardiovascular Genetics https://github.com/dhimmel/snplentiful
Tissue-networks
+
EHR
Tissue to
Phenotype Associations
Gene to
Phenotype Associations
+
Biorepository
Drug to
Phenotype Associations
Input 0 10
100 1000 10000
PCA Only
2 Hidden Nodes
3 Hidden Nodes
4 Hidden Nodes
Semi-Supervised Learning of the Electronic Health Record with Denoising Autoencoders for Phenotype Stratification �bioRxiv: On the way!�github: http://github.com/greenelab/DAPS
Greene Lab: Jie Tan (Grad Student) Gregory Way (Grad Student) Brett Beaulieu-Jones (Grad Student) Sammy Klasfeld (Rotation Student) René Zelaya (Programmer) Matt Huyck (Programmer) Dongbo Hu (Programmer) Kathy Chen (Undergrad) Mulin Xiong (Undergrad) Tim Chang (Undergrad)
Data: All investigators who publicly release their gene expression data.
Images: Artists who release their work under a Creative Commons license.
Funding: G&B Moore Investigator in Data-Driven Discovery National Science Foundation Cystic Fibrosis Foundation American Cancer Society
Find us online: http://www.greenelab.com Twitter: @GreeneScientist
