Ben Logsdon, Sage Bionetworks - Emory...
Transcript of Ben Logsdon, Sage Bionetworks - Emory...
-
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)
-
: : : : :
...
...
...
...
...
...
...
...
DriverEvent
Patie
nts
Genes with expression under similar selection across patients 1-5
11
1
11
.6.3 .3.6 .6 .6
.3
.3
.3
.3
Correlation among expression levels
E-driver
: : :
...
...
...
...
...
...
...
...
Conditioning on e-driver
11
1
11
-.40 0-.4 -.4 -.4
0
0
0
0
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)
-
: : :
...
...
...
...
...
...
...
...
Patie
nts
: : :
...
...
...
...
...
...
...
...
: : :
...
...
...
...
...
...
...
...
Driver Events (Unobserved)
Candidate E-driversSynaptic Pruning Protein Misfolding
: : :
...
...
...
...
...
...
...
...
-
: : :
...
...
...
...
...
...
...
...
AD pathway genes
G1 G2 Gm
Patie
nts
β1
β2
β3
...
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
: : : : :
β1 β2 β3 βp
Learned G1 Sparse Basis: β1, β2, β3 = 1/3; β4,..., βp =0
G1 ~3000 Possible E-drivers
...
-
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
g
gg
gg
g g
gg
g
g
gg
gg
g g
gg
g
g
gg
gg
g g
gg
g
~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