Integrating multiple lines of evidence to identify candidate AD drivers

Ben Logsdon,Sage Bionetworks

AMP-AD

➢ 5 year U01 grant with joint funding from NIA and the FNIHa. 6 academic centers (Broad-Rush, UFL-Mayo-ISB, Emory, Mt.

Sinai, Duke, Harvard)b. 4 industry partners (Lilly, Biogen Idec, AbbVie, GSK)

➢ Goal: identify new targets for intervention in Alzheimer’s diseasea. each academic center must provide list of targets for further

preclinical validation at end of grant➢ All data released on an aggressive quarterly timeline

a. RNAseq, exome sequencing, microRNA, DNA methylation, histone (H3K9) acetylation, genotype, clinical, etc...

➢ All data deposited into the AMP-AD Knowledge Portala. https://www.synapse.org/project/AMP_AD_Knowledge_Portal

ROS/MAP

➢ Combination of two longitudinal studies (ROS and MAP) to study Alzheimer’s disease in normal aging populations1,2

➢ RNA sequencing data available on post mortem brains from 592 patients (202 patients with clinically diagnosed AD)

➢ After processing and filtering (Broad Pipeline) there are FPKM gene expression levels on 22,894 genes (using ENSEMBL gene models)

1. Bennett et al. (2012) (http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3409291/)2. Bennett et al. (2012) (http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3439198/)

ROS/MAP

➢ Combination of two longitudinal studies (ROS and MAP) to study Alzheimer’s disease in normal aging populations1,2

➢ RNA sequencing data available on post mortem brains from 592 patients (202 patients with clinically diagnosed AD)

➢ After processing and filtering (Broad Pipeline) there are FPKM gene expression levels on 22,894 genes (using ENSEMBL gene models)

1. Bennett et al. (2012) (http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3409291/)2. Bennett et al. (2012) (http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3439198/)

Correlation between covariates

1. RIN and batch effects shows some correlation

1. Cognition scores and RIN are highly correlated

1. Cognition scores are also concordant with age at death and visit

PMI

BATC

H

RIN

cogd

x

span

ishraceedu

sex

Age D

x

Apoe

Age v

isit

Age d

eath

Correlation between mRNA expression and covariates

Based on 22894 genes and 592

samples

Correlation between mRNA expression and covariates

Based on 22894 genes and 592

samples

Batch effects CogDx

RINAge

PMI

Genie32

WGCNA7LASSO6

ARACNe4

Tigress3

Ridge5

SPARROW1

Generate networks with multiple methods

2nd Generation Network Inference Methods

1st Generation Network Inference Methods

Metanetwork Approach

Ben Logsdon, SageThanneer Perumal, SageLara Mangravite, Sage

Generate Consensus Network and Modules

Network

ModulesEdge RankConsensus network

14 distinct penalized regression methods to infer undirected graphs

Modules identified in network

Network Inference Pipeline

Starcluster + MPI Integration

Modularising Networks

Modularising Networks

Network Modules: Algorithm

* Finding community structure in very large networks (http://arxiv.org/pdf/cond-mat/0408187v2.pdf)

Hierarchical Agglomeration* a. Implementation: Fast greedy algorithm of igraphb. Complexity: O(m . d . log n) ~ O(n log2 n)c. Modularity:

a. Interpretation: How different our modules are from random network

b. Objective: To maximise Q by merging all the leaf nodes (i.e., communities) in a dendrogram of communities

** Zhang, Y. et al. (2014). J Neurosci 34, 11929-11947, doi:10.1523/JNEUROSCI.1860-14.2014

Module Evaluation in AD Rank Consensus (ROSMAP)

Network Properties# Genes 22899# Edges 11117

Module Properties# of Modules (size > 20) 21

# of genes in the above 36 modules 4804Module Name Size Cell Markers** Odds Ratio

red 870 Endothelial 23

purple 348 Microglia 65

blue 447 MyelinOligos 74

brown 776 Astrocyte 23

black 259 Neuron 8

greenyellow 156 Neuron 11

tan 111 OPC 19

Rank Consensus AD Network (ROSMAP)

Endothelial

Microglia

Astrocyte

Myelin Oligodendrocytes

Ribosome

mRNA splicingNeuron

Response to Dicycoverine (Muscarine receptor inhibitor)

Neuron

Neuron

OPC

** Zhang, Y. et al. (2014). J Neurosci 34, 11929-11947, doi:10.1523/JNEUROSCI.1860-14.2014

Module Evaluation in NCI Rank Consensus (ROSMAP)

Network Properties# Genes 22899# Edges 11117

Module Properties# of Modules (size > 20) 21

# of genes in the above 36 modules 4804Module Name Size Cell Markers** Odds Ratio

brown 312 Endothelial 32

pink 675 Microglia 30

yellow 432 MyelinOligos 97

turquoise 803 Astrocyte 24

magenta 210 Neuron 7

greenyellow 189 Neuron 13

blue 106 OPC 17

Rank Consensus NCI Network (ROSMAP)

Endothelial

MicrogliaAstrocyte

Myelin Oligodendrocyte

Neuron

NeuronPrefrontal Cortex

Chromatin/Alzheimer’s Disease Signatures

Ion Channel

Ribosome OPC

Rank Consensus AD Network (ROSMAP)

Endothelial

Microglia

Astrocyte

Myelin Oligodendrocytes

Ribosome

mRNA splicingNeuron

Response to Dicycoverine (Muscarine receptor inhibitor)

Neuron

Neuron

OPC

NCI

Cogdx Networks

MCI

AD

https://www.synapse.org/#!Synapse:syn5553756

BRAAK12

BRAAK Networks

BRAAK34

BRAAK56

https://www.synapse.org/#!Synapse:syn5553756

Overlap

Odds Ratios (using Fisher’s exact test) comparing edge overlaps between ROSMAP networks. (All are significant fdr < 1e-16)

NCI Microglia Module

Enrichment analysis of microglia module

Gene Set Name Odds Ratio FDR

TF-LOF_Expression_from_GEO

foxa2_20483781_p15_lung_lof_mouse_gpl1261_gse19204_up 9.22 4.07E-34

myc_20940306_e13dot5_erythroblast_purified_from_liver_gof_mouse_gpl6885_gse18558_up 5.55 1.50E-22

glis2_17618285_kidney_lof_mouse_gpl2897_gds2817_up 4.31 6.10E-16

irf8_00000000_splenic_cd11bplusgrdash1_hdash2b_gen_background_lof_mouse_gpl6887_gse39228_down 3.69 2.99E-12

gfi1b_22201127_amulv_gof_mouse_gpl6246_gds4302_up 3.46 1.12E-10

Chip Experimental Analysis

IRF8-21731497-J774-MOUSE 5.27 6.10E-08

RUNX1-20887958-HPC-7-MOUSE 3.31 2.77E-11

NR1H3-23393188-ATHEROSCLEROTIC-FOAM-HUMAN 3.31 6.90E-08

Mouse Microglial Overlaps (2 Months)

Mouse Microglial Overlaps (4 Months)

Mouse Microglial Overlaps (6 Months)

Mouse Microglial Overlaps (8 Months)

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DriverEvent

Patie

nts

Genes with expression under similar selection across patients 1-5

11

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11

.6.3 .3.6 .6 .6

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.3

.3

Correlation among expression levels

E-driver

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Conditioning on e-driver

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-.40 0-.4 -.4 -.4

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Partial correlations among expression levels

Residual expression

* Logsdon et al. , Sparse expression bases in cancer reveal tumor drivers, Nucleic Acids Research (2015): gku1290

Driver Mutation

Active e-driver

Pathway genesunderselection

Expression Drivers (e-drivers)

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Driver Events (Unobserved)

Candidate E-driversSynaptic Pruning Protein Misfolding

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AD pathway genes

G1 G2 Gm

Patie

nts

β1

β2

β3

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β4

βp

Selection frequency in sparse basis for apoptosis inhibition genes (across G1, ...,Gm)

0

1

Sparse basis parameters (ordered by how often they are selected, i.e. βj ≠ 0)

Candidate E-drivers

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β1 β2 β3 βp

Learned G1 Sparse Basis: β1, β2, β3 = 1/3; β4,..., βp =0

G1 ~3000 Possible E-drivers

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Identifying candidate e-drivers

•Learn a graph structure with SPARROW*

•Use the hub genes as top candidate e-drivers

•Rank selected genes based on e-driver score

genes

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~700patients

AMP- AD expression data

* Logsdon et al. , Sparse expression bases in cancer reveal tumor drivers, Nucleic Acids Research (2015): gku1290

Top Hubs in NCI Network