transcriptome-wide association study of schizophrenia and ... · metabolic syndrome in men...

Transcriptome-wide association study of schizophrenia and

chromatin phenotypes

Alexander (Sasha) Gusev

Harvard TH Chan School of Public Health

Challenges of studying complex disease

• Goal: specific mechanistic hypotheses.

• Schizophrenia is highly polygenic, with up to 70% of 1MB regions harboring an association

• For an associated SNP, causal mechanism often not through nearest gene [… or mediated by CNV / TFBS / etc]

• Relevant molecular features (RNA-seq/ChIP-seq in brain) are difficult to collect

Loh et al. 2015b Nat. Genet.; Sekar et al. 2016 Nature

Methods for molecular + GWAS data:

Gusev et al. 2016 Nat. Genet.

Outline

1. TWAS: [cis] molecular features can be predicted into un-assayed samples

2. TWAS to find expression associated with schizophrenia

3. TWAS to find expression associated with ChIP-Seq chromatin activity

TWAS using individual-level data

C T G T A

G T C A A

C A G T C

~

C A G T C

C A G T C

C A C A A

G T G A C

~

individual GWAS data

β1 β2 β3 β4 β5

C A C T C

G T C T A

Linear genetic

predictor

60-80% prediction

accuracy

expression reference

Gamazon et al. 2015 Nat Genet; Gusev et al. 2016 Nat Genet

TWAS using summary-level data

2 1 2 5 1

7

R2 > 0.9 w/

individual-level

prediction

summary GWAS data

C T G T A

G T C A A

C A G T C

~

β1 β2 β3 β4 β5

Linear genetic

predictor

60-80% prediction

accuracy


Gusev et al. 2016 Nat Genet; Barbeira et al. biorxiv

Outline




TWAS for schizophrenia

CommonMind Consortium (CMC) 1: 500 brain RNA-seq alternative splice variants 2 Netherlands Twin Registry (NTR)3: 1,200 blood array METabolic Syndrome In Men (METSIM): 600 adipose RNA-seq Young Finns Study (YFS): 1,300 blood array

4 Expression Reference Panels

GWAS: Psychiatric Genetics Consortium ~80,000 SCZ samples [PGC 2014 Nature]

[1] Fromer et al. 2016 Nat Neuro; [2] Li et al 2016 Science; [3] Wright et al. 2014 Nat Genet

C T G T A

G T C A A

C A G T C

~

2 1 2 5 1

7 TWAS

157 gene associations (45 in novel loci) 80 splice-variant associations

conditioning on expression explains all genome-wide significant effect at known loci

TWAS for schizophrenia Z-

sco

re

chromosomes

polygenic effects across thousands of genes

5e−0

8

1e−0

6

1e−0

4

0.00

1

0.01

0.05 0.

1

0.2

0.5 1

CMC/brain genes

P−value threshold

liabi

lity−

scal

e R

2

0.00

0.01

0.02

0.03

0.04

0.05

5e−0

8

1e−0

6

1e−0

4

0.00

1

0.01

0.05 0.

1

0.2

0.5 1

CMC/brain splicing

P−value threshold

liabi

lity−

scal

e R

2

0.00

0.01

0.02

0.03

0.04

0.05

5e−0

8

1e−0

6

1e−0

4

0.00

1

0.01

0.05 0.

1

0.2

0.5 1

METSIM/adipose

P−value threshold

liabi

lity−

scal

e R

2

0.00

0.01

0.02

0.03

0.04

0.05

5e−0

8

1e−0

6

1e−0

4

0.00

1

0.01

0.05 0.

1

0.2

0.5 1

YFS/blood

P−value threshold

liabi

lity−

scal

e R

2

0.00

0.01

0.02

0.03

0.04

0.05

5e−0

8

1e−0

6

1e−0

4

0.00

1

0.01

0.05 0.

1

0.2

0.5 1

NTR/blood

P−value threshold

liabi

lity−

scal

e R

2

0.00

0.01

0.02

0.03

0.04

0.05

5e−0

8

1e−0

6

1e−0

4

0.00

1

0.01

0.05 0.

1

0.2

0.5 1

ALL jointly

P−value threshold

liabi

lity−

scal

e R

2

0.00

0.01

0.02

0.03

0.04

0.05brain 0.05

0.04

0.03

0.02

0.01

polygenic score using predicted expression, evaluated in independent SCZ cohort

P-value threshold

R2

w/ SCZ status

more associations with brain and splicing

5e−0

8

1e−0

6

1e−0

4

0.00

1

0.01

0.05 0.

1

0.2

0.5 1

CMC/brain genes

P−value threshold

liabi

lity−

scal

e R

2

0.00

0.01

0.02

0.03

0.04

0.05

5e−0

8

1e−0

6

1e−0

4

0.00

1

0.01

0.05 0.

1

0.2

0.5 1

CMC/brain splicing

P−value threshold

liabi

lity−

scal

e R

2

0.00

0.01

0.02

0.03

0.04

0.05

5e−0

8

1e−0

6

1e−0

4

0.00

1

0.01

0.05 0.

1

0.2

0.5 1

METSIM/adipose

P−value threshold

liabi

lity−

scal

e R

2

0.00

0.01

0.02

0.03

0.04

0.05

5e−0

8

1e−0

6

1e−0

4

0.00

1

0.01

0.05 0.

1

0.2

0.5 1

YFS/blood

P−value threshold

liabi

lity−

scal

e R

2

0.00

0.01

0.02

0.03

0.04

0.05

5e−0

8

1e−0

6

1e−0

4

0.00

1

0.01

0.05 0.

1

0.2

0.5 1

NTR/blood

P−value threshold

liabi

lity−

scal

e R

2

0.00

0.01

0.02

0.03

0.04

0.05

5e−0

8

1e−0

6

1e−0

4

0.00

1

0.01

0.05 0.

1

0.2

0.5 1

ALL jointly

P−value threshold

liabi

lity−

scal

e R

2

0.00

0.01

0.02

0.03

0.04

0.05brain brain splice-var

adipose blood blood 0.05

0.04

0.03

0.02

0.01

P-value threshold

all predicted cis-expression explain 26% of total SCZ SNP-heritability (upper bound on mediated effect)

R2

w/ SCZ status

polygenic score using predicted expression, evaluated in independent SCZ cohort

Outline




Chromatin variation is under genetic control

[CEU] Waszak et al. 2015 Cell:

Local chromatin-expression interactions form modules.

[YRI] Grubert et al. 2015 Cell:

Chromatin variation and expression under shared genetic control,

genome

connecting genes to chromatin peaks with top hits

for every gene:

expression - QTLs

for every peak

chromatin - QTLs

TWAS for expression-chromatin associations

C T G T A

G T C A A

C A G T C

~

C A G T C

C A G T C

C A C A A

G T G A C

~

individual epigenomic data

β1 β2 β3 β4 β5

C A C T C

G T C T A

linear genetic

predictor


TWAS identifies 10x more gene/chromatin associations than eQTL overlap approach

0 100 200 300 400 500

0.0

0.2

0.4

0.6

0.8

1.0

Power to detect gene−mark association

# chromatin samples

Pow

er

A

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TWAS

eQTL/cQTL overlap

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TWAS

eQTL/cQTL overlap

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0

20

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80

European TWAS

Max distance to gene boundar y# s

ign

ific

an

t a

sso

cia

tio

ns

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100bp 1kb 2kb 10kb 50kb 100kb 250kb 500kb

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H3K4ME1

H3K4ME3

H3K27AC

PU1

RPB2

B

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0

20

40

60

80

European QTL

Max distance to gene boundar y

# s

ign

ific

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t a

sso

cia

tio

ns

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H3K4ME1

H3K4ME3

H3K27AC

PU1

RPB2

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0

50

100

150

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250

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Yoruban TWAS

Max distance to gene boundar y

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t a

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cia

tio

ns

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H3K4ME1

H3K4ME3

H3K27AC

DHS

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0

50

100

150

200

250

300

Yoruban QTL

Max distance to gene boundar y#

sig

nific

an

t a

sso

cia

tio

ns

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H3K4ME1

H3K4ME3

H3K27AC

DHS

chromatin-TWAS overlapping top hits

distance to the gene TSS

Validating TWAS gene-chromatin associations

brain/blood reference

expression

Discovery: TWAS gene–chromatin

associations

Validating TWAS gene-chromatin associations

brain/blood reference

expression

blood target expression

Discovery: TWAS gene–chromatin

associations Replication: measured LCL RNA-seq

expression in chromatin samples

Chromatin variation is highly associated with expression at predicted gene-peak associations

replication R2 ≈ total expression cis-hg2

ChIP-seq and expression from Waszak et al. 2015 Cell

NTR CMC

PU1

R2(m

ark

,GE

)

0.0

0.1

0.2

0.3

0.4

0.5

TWAS−linked peaks

all nearby peaks

random

* *

NTR CMC

RPB2

R2(m

ark

,GE

)

0.0

0.1

0.2

0.3

0.4

0.5

TWAS−linked peaks

all nearby peaks

random

****

**** ****

NTR CMC

H3K4ME1

R2(m

ark

,GE

)

0.0

0.1

0.2

0.3

0.4

0.5

TWAS−linked peaks

all nearby peaks

random

** *** ** ***

NTR CMC

H3K4ME3

R2(m

ark

,GE

)

0.0

0.1

0.2

0.3

0.4

0.5

TWAS−linked peaks

all nearby peaks

random

**

*

***

**

NTR CMC

H3K27AC

R2

(ma

rk,G

E)

0.0

0.1

0.2

0.3

0.4

0.5

TWAS−linked peaks

all nearby peaks

random

** **** ** ****

R2 chromatin-expression

(in LCLs)

blood brain

0.5

0.3

0.0

predicted gene-mark pairs background genes

Enrichment for chromatin associations with SCZ TWAS genes

157 SCZ TWAS genes 42 with chromatin TWAS associations

4x background (P=1x10-11)

significant enrichment for SCZ splice variants with chromatin associations

2 1 2 5 1

7

TWAS provides mechanistic hypotheses

108 SCZ loci 26/108 have TWAS splicing association

48/108 have TWAS splicing or gene association (76% not nearest gene)

34/48 also have chromatin association

(85% not in the promoter)

N=80,000

N=3,500

N=150

Conclusion / Future Work

• TWAS implicates specific genes and their regulators.

• Epigenetic mediators can be a common disease mechanism

• Yields zozens of mechanistic hypotheses for schizophrenia loci (following up with experimental validation)

Nick Mancuso Hilary Finucane Yakir Reshef Lingyun Song Alexias Safi Edwin Oh Steve McCarroll Ben Neale Mick O’Donovan Roel Ophoff

recruiting in new lab at Dana Farber Cancer Institute &

Harvard Medical School

gusevlab.org

Gregory Crawford Nicholas Katsanis Patrick F Sullivan Bogdan Pasaniuc Alkes L Price

Psychiatric Genomics Consortium CommonMind Consortium PsychENCODE 1000 Genomes

pre-print on biorxiv

transcriptome-wide association study of schizophrenia and ... · metabolic syndrome in men...

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