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