libd2019

11
Analyzing BrainSeq Phase II and generating the
recount-brain resource
Leonardo Collado-Torres
@fellgernon
Data Science I with Andrew Jaffe

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Questions I’ll answer today
• Who am I?
• What is the BrainSeq Phase II project?
• What is the recount-brain resource?

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What defines me
• Bioinformatics
• R and Bioconductor
• Reproducibility and best practices
• Outreach and community building
• Back in 2005:
I like math and coding; biology provides the challenging problems

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History
2005-2009
Undergrad in
Genomic Sciences
2009-2011 2011-2016 August 2016+
Data Science
Division Leader
!
!
PIs:
• Jeff Leek: 2012+
• Andrew Jaffe: 2013+
Ph.D. Biostatistics Staff Scientist
Data Science Team I

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2008+
• BioC 2008-2011, 2014, 2017
• useR!2013
• rOpenSci unconf 2018
• RStudio::conf 2019
@fellgernon
2010+
Interests
!
@LIBDrstats
2018+
@CDSBMexico
2018+
Defunct: BmoreBiostats, Biostats Cultural Mixers
Guest
@RLadiesBmore
Blog: http://lcolladotor.github.io
2011+
FB: 75k, Tw: 66k
weekly

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Software tools I use every day
Mercurial/git user 2009+
https://github.com/lcolladotor
From: Feb 21, 2019
Communication tool for work and communities

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My path to LIBD
2005-2009
Undergrad in
Genomic Sciences
2009-2011 2011-2016 August 2016+
Data Science
Division Leader
!
!
PIs:
• Jeff Leek: 2012+
• Andrew Jaffe: 2013+
Ph.D. Biostatistics Staff Scientist
Data Science Team I
Microarrays,
little RNA-seq
N = 12
(bacteria)
N = 59
N = 72
potential for 1,000
BSP2: N = 900
+ many more

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Ph.D. overview
The goal was to develop statistical methods and software [...] in
RNA-seq […]. We applied these methods to further our
understanding of neuropsychiatric disorders using the Lieber Institute
for Brain Development human brains collection (> 1000 samples).

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Lieber Institute for Brain Development
• Role: Staff Scientist
• PI: Andrew Jaffe
Team:
• Staff Scientist: Emily Burke
• Research Associate: Madhavi Tippani
• Postdocs: Amanda Price
• Grad students: Matt Nguyen, Brianna Barry,
Kira Perzel Mandell
• Research Assistant: Nick Eagles, Stephen Semick*
• Close collaborators: Carrie Wright, Nina Rajpurohit
• Role details: like a postdoc (major role in some projects) with some support projects
* Now at University of Maryland in med school
Lab at Amy Peterson’s MPH capstone presentation

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Papers since 2016

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Papers since 2016
RNA-seq
WGBS
Biostatistics
RNA-seq
> 70,000 samples

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Lieber Institute for Brain Development
• miRNA kit prep comparison biorxiv.org/content/early/2018/10/30/445437
• DNAm on WGBS across development & cell types biorxiv.org/content/early/2018/09/29/428391
• RNA-seq DE in Schizophrenia disorder & 2 brain regions biorxiv.org/content/early/2018/09/26/426213
• RNA-seq from stem cells biorxiv.org/content/early/2018/07/31/380758
Peer reviewed:
• DNAm and gene DE in Alzheimer’s disease doi.org/10.1007/s00401-019-01966-5
• RNA-seq smoking during pregnancy doi.org/10.1038/s41380-018-0223-1
• RNA-seq DE in Schizophrenia disorder on DLPFC doi.org/10.1038/s41593-018-0197-y
• Histamine signaling in autism spectrum disorder doi.org/10.1038/tp.2017.87
Pre-prints:

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Today
BrainSeq Phase II
brain

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BrainSeq: A Human Brain Genomics Consortium
DLPFC
495
samples
BrainSeq Phase I
polyA+
Jaffe et al., Nature Neuroscience, 2018
DLPFC
453
samples
HIPPO
447
samples
BrainSeq Phase II
RiboZero
THE SCIENTIFIC FRONTIER
Neuron, 2015
Collado-Torres et al, bioRxiv, 2018
Manuscript status: under revision at Neuron

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DATA
16
BrainSeq Phase II RNA-seq samples
DLPFC HIPPO total
adult (age >= 18) 374 370 744
prenatal 29 28 57
0 <= age < 18 50 49 99
total 453 447 900
• Non-psychiatric control and schizophrenia affected individuals
• Two brain regions: dorsolateral prefrontal cortex and hippocampus
All
samples

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DATA
17
BrainSeq Phase II RNA-seq samples: by case status
DLPFC HIPPO total
adult 222 238 460
prenatal 29 28 57
0 <= age < 18 49 48 97
total 300 314 614
DLPFC HIPPO total
adult 152 132 284
prenatal 0 0 0
0 <= age < 18 1 1 2
total 153 133 286
Control
Schizophrenia
cases

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DATA ANALYSIS
18
Focus on being conservative
1.Use well established processing methods
2.Apply strict expression cutoffs
3.Use replication when possible
4.Adjust for RNA quality degradation confounding
• Using the qSVA method
5.Avoid potential batch effects
• Drop problematic samples
6.Take into account correlation at the individual level

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RNA-SEQ APPROACH
19
BrainSeq Phase II
Pre-natal
Adult
ACTG
Birth
Unaffected
Controls
Patients with
Schizophrenia
RNA Sequencing
Genotyping
+ +
+
Gene Exons Expressed Regions
Transcripts Junctions
Age
CC CA AA
SZ CONT
DLPFC
HIPPO
+ region differences
For public data, check recount2

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DATA ANALYSIS
20
Main processing steps
1. Quality check (QC) on raw reads (FastQC)
2. Failed QC? Then trim reads (Trimmomatic)
3. Align reads to the genome (HISAT2)
4. Count features (featureCounts + others)
5. Calculate coverage (bam2wig)
6. Quantify transcripts (Salmon)
7. Create count tables (R)
8. Genotype samples (samtools + vcftools)
Emily E. Burke
Nextflow version in
preparation with
Winter Genomics
and Nick Eagles

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DATA ANALYSIS
21
Filter features with low expression
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0.0 0.2 0.4 0.6 0.8 1.0
20000 30000 40000 50000
mean expression cutoff
Number of features > cutoff
Feature Cutoff Unit
Gene 0.25 RPKM
Exon 0.30 RPKM
Jxn 0.46 RP10M
Tx 0.38 TPM
Gene
• Used mean across all 900 samples
• All analyses with filtered data
Mean
RPKM
# genes >
cutoff
Based on the segmented R package:
break-point estimation
jaffelab::expression_cutoff()

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DATA ANALYSIS
22
Differential expression models
• Region-specific for adult or fetal ages
• Using only adult samples or only prenatal samples
• Test for differences between DLFPC and HIPPO
• Development
• Linear age splines with breakpoints at developmental stages
• Test for interaction between age and brain region at these splines
• Case-control
• By brain region
• Test for differences between non-psychiatric controls and individuals with
schizophrenia
• For the first two models, we account for the fact that an individual can have two
correlated samples: one for each brain region
limma::duplicateCorrelation()

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DATA ANALYSIS
23
Differential expression models
• Region-specific for adult or prenatal ages
• Alternative: !"#$ = &' + )*+ + ,+" +
∑./0
1 23#45. + 6789:)8+ + 898);<227*3+=>+3+ + :?@ + :+*793
• Development
• Alternative: !"#$ = &' + )*+ ∗ :+*793 + B+8); ∗ :+*793 +
C7$8ℎ ∗ :+*793 + 73B)38 ∗ :+*793 + Eℎ7;= ∗ :+*793 +
8++3 ∗ :+*793 + )=F;8 ∗ :+*793 + ,+" +
∑./0
1 23#45. + 6789:)8+ + 898);<227*3+=>+3+ + :?@ + :+*793
• Case-control
• Alternative: !"#$ = &' + )*+ + ,+" + 6789:)8+ +
$:@∑./0
1 23#45. + 898);<227*3+=>+3+ + :?@ + $+*793,#+E7B7EK,L2 + M7)*39272

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DATA ANALYSIS
24
Using BrainSpan for replication: region-specific model
• P-value < 0.05 in BrainSpan, consistent direction
Similar results for the development model
prenatal adult
gene exon jxn
p<0.05
p<0.01
p<0.001
p<1e−04
p<1e−05
p<1e−06
p<0.05
p<0.01
p<0.001
p<1e−04
p<1e−05
p<1e−06
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
p−value threshold
Replication rate

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DATA ANALYSIS
25
Region-specific by age results
• For adults: 1,612 DE genes with Bonferroni < 0.01 that replicate in BrainSpan
• For prenatal samples: 32 DE genes
23
6
2
0
3
0
3
gene exon
jxn
DE features grouped by gene id (prenatal)
328
422
778
23
839
647
388
gene exon
jxn
DE features grouped by gene id (adult)

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DATA ANALYSIS
26
Region-specific by age results: adult enriched biological processes by region
G: gene, E: exon, J: exon-exon junction
D: DLPFC, H: HIPPO
cell−matrix adhesion
extracellular matrix organization
ameboidal−type cell migration
microtubule bundle formation
axoneme assembly
cilium movement
establishment of synaptic vesicle localization
synaptic vesicle transport
synaptic vesicle localization
signal release from synapse
neurotransmitter secretion
presynaptic process involved in chemical synaptic transmission
positive regulation of GTPase activity
regulation of GTPase activity
synaptic vesicle exocytosis
neurotransmitter transport
regulation of calcium ion−dependent exocytosis
synaptic vesicle cycle
regulation of synapse organization
positive regulation of synaptic transmission
regulation of neuron projection development
regulation of small GTPase mediated signal transduction
extracellular structure organization
positive regulation of nervous system development
positive regulation of neurogenesis
positive regulation of cell development
axon development
axonogenesis
regulation of hormone levels
cardiac chamber development
cardiac septum development
kidney epithelium development
nephron development
urogenital system development
cardiac chamber morphogenesis
cardiac septum morphogenesis
renal system development
kidney development
regulation of transmembrane transport
actomyosin structure organization
regulation of trans−synaptic signaling
modulation of chemical synaptic transmission
synapse organization
regulation of cell morphogenesis
synaptic transmission, glutamatergic
regulation of synaptic transmission, glutamatergic
potassium ion transmembrane transport
cellular potassium ion transport
potassium ion transport
G.D
(609)
G.H
(573)
E.D
(1101)
E.H
(1087)
J.D
(850)
J.H
(823)
GeneRatio
0.02
0.03
0.04
0.05
0.06
0.01
0.02
0.03
0.04
p.adjust
ontology: BP

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Pluripotency Glial
Fetal
Infant
C
hild
Teens
Adult
50+
Fetal
Infant
C
hild
Teens
Adult
50+
0.4
0.6
0.8
0.0
0.1
0.2
0.3
Age Group
Cell Type Proportion
Region
DLPFC
Hippo
DATA ANALYSIS
27
Development model: similar composition in prenatal across regions by DNAm
Stephen A. Semick

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DATA ANALYSIS
28
Development model results
• 5,982 (~55%) genes contain differentially expressed exons and splice junctions that
replicated in BrainSpan (Bonferroni < 1%)
2354
2260
1762
243
5982
8501
1558
gene exon
jxn
DE features grouped by gene id

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DATA ANALYSIS
29
• Normalized expression over age for GABRD
−2 0 2 4 6
log2(CPM + 0.5)
14 16 18 20 22
PCW
0.0 0.2 0.4 2 4 6 8 12 14 16 18 2020 30 40 50 50 55 60 65 70 75 80 85
ENSG00000187730.7 GABRD p−bonf 0
Age
DLPFC HIPPO

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DATA ANALYSIS
30
• Normalized expression over age after removing effect of terms from the null model
jaffelab::cleaningY() and jaffelab::agePlotter()
−2 −1 0 1 2 3
log2(CPM + 0.5) − covariate effects removed
14 16 18 20 22
PCW
0.0 0.2 0.4 2 4 6 8 12 14 16 18 2020 30 40 50 50 55 60 65 70 75 80 85
ENSG00000187730.7 GABRD p−bonf 0
Age
DLPFC HIPPO

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qSVA WORKFLOW
31 Slide adapted from Amy Peterson
Jaffe et al., PNAS, 2017
Model 1 (6429 genes)
Log2 FC Dx
Log2 FC Degradation
http://research.libd.org/rstatsclub/2018/12/11/quality-surrogate-variable-analysis/

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DEqual HIPPO
32
Model 1 (6429 genes)
Model 1. Naïve model
E / = 10
+ 11
45
DEqual plots demonstrate effectiveness of statistical correction
r = 0.412
Slide adapted from Amy Peterson
Log2 FC Dx
Log2 FC Degradation
HIPPO
333
samples

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DEqual HIPPO
33
Model 1 (6429 genes) Model 2 (63 genes)
E / = 10 + 1145
Model 2. Added RNA-quality and demographic covariates
E / = 10 + 1145 + 12FGH + 13JH5+ 14LMNOPFNH+ 15RPSTRFte + 16GHVHPFNH
+ 17PXS + ∑Z[\
] γMJV_`aM
r = 0.412 r = 0.0712
Log2 FC Dx Log2 FC Dx, adj. cov
Log2 FC Degradation
Log2 FC Degradation
HIPPO
333
samples

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DEqual HIPPO
34
Model 1 (6429 genes) Model 2 (63 genes) Model 3 (48 genes)
E / = 10 + 1145
Model 2. Added RNA-quality and demographic covariates
+ 17PXS + ∑Z[\
] γMJV_`aM
Model 3. Added qSVs
+ 17PXS + ∑Z[\
] γMJV_`aM
+ ∑Z[\
d eMfghM
HIPPO
333
samples
r = 0.412 r = 0.0712 r = -0.00173
Log2 FC Dx Log2 FC Dx, adj. cov Log2 FC Dx, adj
qSVs
Log2 FC Degradation
Log2 FC Degradation
Log2 FC Degradation

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−6 −4 −2 2 4 6
−6 −4 −2 0 2 4 6
0
t−statistic DLPFC
t−statistic BSP1
r = 0.809
DATA ANALYSIS
35
Case-control: by region
• 48 DE genes at FDR <5% in hippocampus, 243 in DLPFC (FDR <5%)
• DLPFC results agree with BrainSeq Phase I
Amy Peterson
−6 −4 −2 2 4
−4 −2 0 2 4
0
t−statistic HIPPO
t−statistic DLPFC
r = 0.644
Control > SCZD
SCZD > Control
DLPFC FDR<10%, HIPPO FDR<20%

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axon development
axonogenesis
regulation of axonogenesis
regulation of neuron projection development
neutrophil chemotaxis
regulation of B cell proliferation
regulation of B cell activation
positive regulation of B cell activation
B cell receptor signaling pathway
B cell activation
lymphocyte activation
regulation of leukocyte cell−cell adhesion
regulation of cell−cell adhesion
positive regulation of alpha−beta T cell activation
regulation of leukocyte activation
positive regulation of lymphocyte activation
positive regulation of cell activation
positive regulation of leukocyte activation
leukocyte migration
positive regulation of leukocyte cell−cell adhesion
positive regulation of T cell activation
leukocyte chemotaxis
response to organophosphorus
ATF6−mediated unfolded protein response
positive regulation of cell adhesion
cell chemotaxis
positive regulation of cell−cell adhesion
protein folding
protein folding in endoplasmic reticulum
HgC
(137)
HeC
(306)
DgC
(297)
DeS
(250)
GeneRatio
0.025
0.050
0.075
0.100
0.01
0.02
0.03
0.04
p.adjust
ontology: BP
DATA ANALYSIS
36
• BP enrichment in Control > SCZD at gene level
• Immune processes
g: gene, e: exon, j: exon-exon junction
D: DLPFC, H: HIPPO, C: control, S: SCZD
Case-control: by region

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Gene Individual 1 Individual 2
1 10 15
2 5 22
…
DLPFC
Gene Individual 1 Individual 2
1 10 17
2 6 16
…
HIPPO
correlation( (10, 5, …), (10, 6, …) ) =~ 0.9
correlation( (15, 22, …), (17, 16, …) ) =~ 0.7
Individual 1
Individual 2
Control vs SCZD

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A B
C D
Control SCZD
0.76 0.77 0.78 0.79
cleaned expr (keeping Dx) − geneRpkm
p−value: 0.0164
SCZD diagnosis
Correlation
Control SCZD
0.71 0.72 0.73 0.74 0.75 0.76
cleaned expr (keeping Dx) − exonRpkm
p−value: 0.0499
SCZD diagnosis
Correlation
Control SCZD
0.54 0.55 0.56 0.57
cleaned expr (keeping Dx) − jxnRp10m
p−value: 1.72e−05
Correlation
Control SCZD
0.47 0.48 0.49 0.50 0.51 0.52
cleaned expr (keeping Dx) − txTpm
p−value: 0.00992
Correlation
Correlation
Correlation
Correlation
Correlation
Gene: 0.0164
Exon: 0.0499
Jxn: 1.72 * 10-5
Tx: 0.00992

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DATA ANALYSIS
39
HIPPO eQTLs
• 11,237,357 eQTL associations (FDR <1%) across genes, exons and junctions
corresponding to 17,719 genes
Emily E. Burke
2061
3183
2163
274
5945
2915
1178
gene exon
jxn
eQTLs grouped by gene id
Includes 60 risk SNPs from PGC2
8
+,332
E
0 1 2
−1 0 1 2 3 4
chr2:5822204358229ï-[Q
rs74563533:58250433:G:A
r2:58 250 433
Residualized Expression
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0 1 2
−1 0 1 2 3 4
chr2:5822204358229ï-[Q
rs75575209:58138192:A:T
r2:58 138 192
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p=5.75e−34
p=3.82e−81
F

View Slide

DATA ANALYSIS
40
Region dependent eQTLs
• 205,618 region-dependent eQTLs (FDR <1%) corresponding to 1,484 genes
• Includes 3 PCG2 schizophrenia risk loci
Emily E. Burke
19394
10734
6493
2622
3937
4398
34259
gene exon
jxn
eQTLs grouped by SNP id
488
246
319
18
99
63
251
gene exon
jxn
eQTLs grouped by gene id

View Slide

A B
0.D 1.D 2.D 0.H 1.H 2.H
4.0 4.4 4.8 5.2
LRP1(16(
(Exon)
rs324015
p=3.91e−05
0.D 1.D 2.D 0.H 1.H 2.H
−1 0 1 2 3
2YHUODSVJHQH15*1
FKUï-[Q
rs12293670:124612932:A:G
p=5.27e−26
C D
0.D 1.D 2.D 0.H 1.H 2.H
−1 0 1 2 3 4
2YHUODSVJHQH1*()
FKUïï-[Q
rs4144797:233562197:T:C
p=3.5e−14
0.D 1.D 2.D 0.H 1.H 2.H
6.8 7.2 7.6 8.0
LRP1
FKUï-[Q
rs324015
p=2.02e−06
D: DLPFC, H: HIPPO
DATA ANALYSIS
Region dependent eQTLs
These 3 SNPs are also
meQTLs (Jaffe et al, 2016)

View Slide

Overall eQTLs at FDR < 1% - rAggr eQTL analysis with PCG2 risk SNPs
Hippocampus DLPFC
# unique SNPs (unique index SNPs) 5510 (103) 6780 (116)
# unique features 1731 2525
# Unique genes 123 171
# Unique transcripts 244 332
# Unique exons 857 1363
# Unique junctions 507 659
Emily E. Burke
21 8
95
DLPFC HIPPO
In BSP2: 163/179 (91.1%)
FDR <1%: 124/163 (76.6%)

View Slide

• We will soon update our eQTL browser at http://eqtl.brainseq.org/ Bill Ulrich

View Slide

TWAS
Gusev et al, Nature Genetics, 2016

View Slide

TWAS
SNP ID chromosome position
rs101 12 1
rs102 13 10000
…
rs101:A:T 12 1
rs102 13 10005
…
rs101 12 1
rs102 13 10000
…
LD reference LIBD BSP2 SNPs
GWAS summary SNPs
https://github.com/LieberInstitute/brainseq_phase2/blob/master/twas/README.md
1,190,321 82,393,211
6,500,475
CLOZUK+PGC2

View Slide

TWAS
rs101:A:T 12 1
rs102 13 10005
…
rs101:A:T 12 1
rs102 13 10005
…
rs101:A:T 12 1
rs102 13 10005
…
LD reference LIBD BSP2 SNPs
GWAS summary SNPs
https://github.com/LieberInstitute/brainseq_phase2/blob/master/twas/README.md
1,022,527
1,022,546
4,316,013
CLOZUK+PGC2

View Slide

TWAS
DLPFC HIPPO
gene 7,683 (32.8%) 5,866 (25.1%)
exon 61,678 (16.2%) 47,583 (12.5%)
jxn 33,754 (12.5%) 27,088 (10.1%)
tx 14,118 (15.9%) 11,567 (13%)
Features with TWAS weights
Removed sex, snp PCs 1-5, expr PCs
Kept diagnosis
jaffelab::cleaningY()

View Slide

TWAS
Among loci
considered?
DLPFC HIPPO
Yes 99 (85.3%) 87 (84.5 %)
No 17 16
116 103
PGC2 loci with eQTLs (FDR<1%): considered in TWAS?
? DLPFC HIPPO
Yes 48 (48.5%) 45 (51.7%)
No 51 42
99 87
Locus has a TWAS p-value among all features?
? DLPFC HIPPO
Yes 26 (54.2%) 25 (55.5%)
No 22 20
48 45
TWAS p-value <1x10-8?

View Slide

TWAS
ENSG00000188511.12
ENSG00000280224.1
ENSG00000279597.1
ENSG00000264139.1
ENSG00000212939.2
ENSG00000213279.2
ENSG00000235111.1
ENSG00000226954.1
ENSG00000223142.1
ENSG00000214727.2
ENSG00000100425.18
ENSG00000279345.1
ENSG00000236867.1
ENSG00000260613.1
ENSG00000229409.1
ENSG00000277771.1
ENSG00000279494.1
ENSG00000100426.6
ENSG00000182858.13
ENSG00000273192.1
ENSG00000184164.14
ENSG00000278869.1
ENSG00000198355.4
ENSG00000276753.1
ENSG00000188263.10
ENSG00000138892.11
ENSG00000100427.15
ENSG00000073146.15
ENSG00000280395.1
ENSG00000073150.13
ENSG00000170638.9
ENSG00000273188.1
ENSG00000273253.2
ENSG00000073169.13
ENSG00000272836.1
ENSG00000273137.1
ENSG00000128159.11
ENSG00000100429.17
ENSG00000188130.13
ENSG00000185386.14
ENSG00000196576.14
ENSG00000205593.11
ENSG00000227484.1
ENSG00000279182.1
ENSG00000100239.15
ENSG00000100241.20
ENSG00000128165.8
ENSG00000100253.12
ENSG00000100258.17
ENSG00000025770.18
ENSG00000130489.13
ENSG00000272821.1
ENSG00000025708.13
ENSG00000177989.13
ENSG00000272666.1
ENSG00000273272.1
ENSG00000226738.1
ENSG00000130487.5
ENSG00000217442.3
ENSG00000205560.12
ENSG00000254413.8
ENSG00000100288.19
ENSG00000205559.3
ENSG00000272940.1
ENSG00000008735.13
ENSG00000100299.17
ENSG00000212569.1
ENSG00000251322.7
ENSG00000206841.1
ENSG00000225929.1
ENSG00000100312.10
ENSG00000254499.1
ENSG00000213683.4
ENSG00000184319.15
ENSG00000079974.17
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49.4 49.6 49.8 50.0 50.2 50.4 50.6
0
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chr 22 physical position (MB)
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Can also identify conditionally independent features
GWAS: grey
TWAS: blue
CRELD2

View Slide

TWAS
ENSG00000188511.12
ENSG00000280224.1
ENSG00000279597.1
ENSG00000264139.1
ENSG00000212939.2
ENSG00000213279.2
ENSG00000235111.1
ENSG00000226954.1
ENSG00000223142.1
ENSG00000214727.2
ENSG00000100425.18
ENSG00000279345.1
ENSG00000236867.1
ENSG00000260613.1
ENSG00000229409.1
ENSG00000277771.1
ENSG00000279494.1
ENSG00000100426.6
ENSG00000182858.13
ENSG00000273192.1
ENSG00000184164.14
ENSG00000278869.1
ENSG00000198355.4
ENSG00000276753.1
ENSG00000188263.10
ENSG00000138892.11
ENSG00000100427.15
ENSG00000073146.15
ENSG00000280395.1
ENSG00000073150.13
ENSG00000170638.9
ENSG00000273188.1
ENSG00000273253.2
ENSG00000073169.13
ENSG00000272836.1
ENSG00000273137.1
ENSG00000128159.11
ENSG00000100429.17
ENSG00000188130.13
ENSG00000185386.14
ENSG00000196576.14
ENSG00000205593.11
ENSG00000227484.1
ENSG00000279182.1
ENSG00000100239.15
ENSG00000100241.20
ENSG00000128165.8
ENSG00000100253.12
ENSG00000100258.17
ENSG00000025770.18
ENSG00000130489.13
ENSG00000272821.1
ENSG00000025708.13
ENSG00000177989.13
ENSG00000272666.1
ENSG00000273272.1
ENSG00000226738.1
ENSG00000130487.5
ENSG00000217442.3
ENSG00000205560.12
ENSG00000254413.8
ENSG00000100288.19
ENSG00000205559.3
ENSG00000272940.1
ENSG00000008735.13
ENSG00000100299.17
ENSG00000212569.1
ENSG00000251322.7
ENSG00000206841.1
ENSG00000225929.1
ENSG00000100312.10
ENSG00000254499.1
ENSG00000213683.4
ENSG00000184319.15
ENSG00000079974.17
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49.4 49.6 49.8 50.0 50.2 50.4 50.6
0
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49.4 49.6 49.8 50.0 50.2 50.4 50.6
0
2
4
6
8
chr 22 physical position (MB)
−log10(P−value)
GWAS signal
eQTL signal
CRELD2

View Slide

TWAS
Feature DLFPC HIPPO TWAS conditionally
independent?
Gene 29 (43.3%) 20 (40 %) Yes
38 30 No
Exon 42 (48.3%) 31 (40.3) Yes
45 46 No
Jxn 50 (51%) 40 (40.8%) Yes
48 48 No
Tx 35 (46.1%) 26 (40%) Yes
41 39 No

View Slide

Authors
• Leonardo Collado-Torres
o [email protected]
o @fellgernon
• Emily E. Burke
• Amy Peterson
• Joo Heon Shin
• Richard E. Straub
• Anandita Rajpurohit
• Stephen A. Semick
• William S. Ulrich
• Cristian Valencia
• Ran Tao
• Amy Deep-Soboslay
• Thomas M. Hyde
• Joel E. Kleinman
• Daniel R. Weinberger+
• Andrew E. Jaffe+
o [email protected]
o @andrewejaffe
53
• BrainSeq Consortium
• LIBD @lieberinstitute
Funding
github.com/LieberInstitute/brainseq_phase2

View Slide

View Slide

https://jhubiostatistics.shinyapps.io/recount/

View Slide

http://rail.bio/
Slide adapted from Ben Langmead
by Abhinav Nellore

View Slide

http://blogs.citrix.com/2012/10/17/announcing-general-availability-of-sharefile-with-storagezones/

View Slide

GTEx TCGA
slide adapted from Shannon Ellis

View Slide

SRA

View Slide

jx 1 jx 2 jx 3 jx 4
jx 5
jx 6
Coverage
Reads
Gene
Isoform 1
Isoform 2
Potential
isoform 3
exon 1 exon 2 exon 3 exon 4
Expressed region 1:
potential exon 5
doi.org/10.12688/f1000research.12223.1

View Slide

doi.org/10.12688/f1000research.12223.1

View Slide

> library('recount')
> download_study( 'ERP001942', type='rse-gene')
> load(file.path('ERP001942 ', 'rse_gene.Rdata'))
> rse <- scale_counts(rse_gene)
https://github.com/leekgroup/recount-analyses/

View Slide

slide adapted from Jeff Leek

View Slide

related projects
• Bioconductor recountWorkflow: doi.org/10.12688/f1000research.12223.1
• Shannon Ellis & Leek: phenotype prediction doi.org/10.1093/nar/gky102
• Jack Fu & Taub: transcript estimations doi.org/10.1101/247346
• Madugundu & Pandey (JHU):
proteomics
• Luidi-Imada & Marchionni (JHU):
cancer and FANTOM
• Kuri-Magaña & Martínez-Barnetche (INSP Mexico):
immune expression doi.org/10.3389/fimmu.2018.02679
• Ryten (UCL):
Guelfi: validating expressed regions (ERs) eQTLs
Zhang: improving the detection of ERs doi.org/10.1101/499103

View Slide

expression data for ~70,000 human samples
samples
phenotypes
?
GTEx
N=9,962
TCGA
N=11,284
SRA
N=49,848
samples
expression
estimates
gene
exon
junctions
ERs
Answer meaningful
questions about
human biology and
expression

View Slide

Category Frequency
F 95
female 2036
Female 51
M 77
male 1240
Male 141
Total 3640
Even when information is provided, it’s not always
clear…
sra_meta$Sex
“1 Male, 2 Female”, “2 Male, 1 Female”,
“3 Female”, “DK”, “male and female”
“Male (note: ….)”, “missing”, “mixed”,
“mixture”, “N/A”, “Not available”, “not
applicable”, “not collected”, “not
determined”, “pooled male and female”,
“U”, “unknown”, “Unknown”

View Slide

Goal :
to accurately
predict critical
phenotype
information for
all samples in
recount
gene, exon, exon-exon junction and expressed region RNA-Seq data
SRA
Sequence Read Archive
N=49,848
TCGA
The Cancer Genome
Atlas
N=11,284
GTEx
Genotype Tissue Expression
Project
N=9,662

View Slide

Goal :
to accurately
predict critical
phenotype
information for
all samples in
recount
SRA
N=49,848
GTEx
Project
N=9,662
divide
samples
build and
optimize
phenotype
predictor
training
set
test
accuracy
of
predictor
test
set
TCGA
The Cancer Genome
Atlas
N=11,284

View Slide

Goal :
to accurately
predict critical
phenotype
information for
all samples in
recount
SRA
N=49,848
GTEx
Project
N=9,662
divide
samples
build and
optimize
phenotype
predictor
training
set
test
accuracy
of
predictor
predict
phenotypes
across
samples in
TCGA
test
set
TCGA
The Cancer Genome
Atlas
N=11,284

View Slide

Goal :
to accurately
predict critical
phenotype
information for
all samples in
recount
SRA
N=49,848
GTEx
Project
N=9,662
divide
samples
build and
optimize
phenotype
predictor
training
set
predict
phenotypes
across SRA
samples
test
accuracy
of
predictor
predict
phenotypes
across
samples in
TCGA
test
set
TCGA
The Cancer Genome
Atlas
N=11,284

View Slide

Sex
prediction is
accurate
across data
sets
Number of Regions 20 20 20 20
Number of
Samples (N)
4,769 4,769 11,245 3,640
99.8% 99.6% 99.4%
88.5%

View Slide

Number of Regions 589 589 589 589 589
Number of Samples
(N)
4,769 4,769 613 6,579 8,951
97.3% 96.5% 91.0%
70.2%
Prediction
is more
accurate in
healthy
tissue
50.6%

View Slide

> library('recount')
> download_study( 'ERP001942', type='rse-gene')
> load(file.path('ERP001942 ', 'rse_gene.Rdata'))
> rse <- scale_counts(rse_gene)
> rse_with_pred <- add_predictions(rse_gene)
https://github.com/leekgroup/recount-analyses/

View Slide

●
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●
adipose tissue
adrenal gland
bladder
blood
blood vessel
bone
bone marrow
brain
breast
cervix
cervix uteri
colon
epithelium
esophagus
fallopian tube
heart
intestine
kidney
liver
lung
melanoma
monocytes
muscle
nerve
ovary
pancreas
penis
pituitary
placenta
prostate
salivary gland
skin
small intestine
spinal cord
spleen
stem cell
stomach
testis
thyroid
tonsil
umbilical cord
urinary bladder
uterus
vagina
0
3000
6000
9000
12000
0 1000 2000 3000
reported
predicted

View Slide

• 62 SRA studies
• 4,431 rows by 48 columns
Ashkaun Razmara, in prep.

View Slide

Sex Female Male
Age/Development Fetus Child Adolescent Adult
Race/Ethnicity Asian Black Hispanic White
Tissue Site 1 Cerebral cortex Hippocampus Brainstem Cerebellum
Tissue Site 2 Frontal lobe Temporal lobe Midbrain Basal ganglia
Tissue Site 3 Dorsolateral
prefrontal cortex
Superior temporal
gyrus
Substantia nigra Caudate
Hemisphere Left Right
Brodmann Area 1-52
Disease Status Disease Neurological control
Disease Brain tumor Alzheimer’s disease Parkinson’s disease Bipolar disorder
Tumor Type Glioblastoma Astrocytoma Oligodendroglioma Ependymoma
Clinical Stage 1 Grade I Grade II Grade III Grade IV
Clinical Stage 2 Primary Secondary Recurrent
Viability Postmortem Biopsy
Preparation Frozen Thawed

View Slide

https://github.com/LieberInstitute/recount-brain/tree/master/metadata_reproducibility
• Overall curation steps: starts by downloading SRA Run Table info, then info from the publications
• Details for each SRA study
Reproducibility document

View Slide


View Slide

Replicates part of the GTEx PMI paper by
Ferreira et al. doi.org/10.1038/s41467-017-
02772-x

View Slide

Code Example:
research.libd.org/recount-brain/example_PMI/example_PMI.html
research.libd.org/recount-brain/example_PMI/example_PMI.Rmd
Replicates part of the GTEx PMI paper by Ferreira et al.
doi.org/10.1038/s41467-017-02772-x

View Slide

* Jeff Leek presented Shannon Ellis’ prediction work in Toronto (around April 2018)
https://docs.google.com/presentation/d/1FgUZZU6pW91J7zH0OqrEgxfnV1Py_ZGL3ZKHfbOZskY/edit#slide=id.g2f831fd4ae_0_306
* Dustin J. Sokolowski from Michael D. Wilson’s lab is using recount2
* Dustin joins the project and merges recount-brain with GTEx and TCGA
recount_brain_v2
The SRA samples in recount-brain are complemented by 1,409 GTEx (GTEx Consortium
2015) and 707 TCGA (Brennan et al. 2013; Cancer Genome Atlas Research Network et
al. 2015) samples covering 13 healthy regions of the brain and 2 tumor types, respectively.
In total, there are 6,547 samples with metadata in recount-brain with 5,330 (81.4%)
present in recount2

View Slide

* Discussed with Sean Davis (NIH) at Biological Data Science #biodata18
* Potential for contributing recount-brain to SRAdbV2 github.com/seandavi/SRAdbV2
* Have mapped variables to ontologies
What about maintaining/growing it?

View Slide

The recount-brain team
Hopkins
Ashkaun Razmara
Shannon E. Ellis
Jeff T. Leek
University of
Toronto
Dustin J. Sokolowski
Michael D. Wilson
NIH
Sean Davis
LIBD
Andrew E. Jaffe
Funding
NIH R01 GM105705
NIH 1R21MH109956
NIH R01 GM121459
CIHR, NSERC
Ontario Ministry of Research
IDIES SciServer
Hosting recount2
github.com/LieberInstitute/recount-brain

View Slide

libd2019

libd2019

Leonardo Collado-Torres

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