Detecting genome-wide directional effects of transcription factor

binding on polygenic disease risk

Yakir ReshefHarvard/MIT MD/PhD Program

Harvard University Computer ScienceOctober 18, 2017

GWAS + genomics biology

[Liu et al 2015 Nat Genet]

“inflammation”

Crohn’s GWAS

genes expressed in immune cells

[Pasaniuc & Price 2016 Nat Rev Genet; Maurano et al. 2012 Science;Pickrell 2014 AJHG;Gusev et al. 2014 AJHG; Farh et al. 2015 Nature; Finucane et al. 2015 Nat Genet; …]

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Signed annotations: stronger inference

“inflammation”

Crohn’s GWAS

genes expressed in immune cells

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[Degner et al. 2012 Nature; Lee et al. 2015 Nat Genet; Zhou et al. 2015 Nat Meth;Tehranchi et al. 2016 Cell; Tewhey et al. 2016 Cell; Kelley et al. 2016 Genome Res]

Signed annotations for transcription factors

“binding of IRF1”

Crohn’s GWAS

IRF1 Crohn’scausality

IRF1 “Inflammation” Crohn’smechanism

“Genome-wide, alleles increasing IRF1 binding tend to increase Crohn’s risk”

Outline

• Description of method

• Validation in simulations

• Proof of concept: analysis of molecular traits

• Analysis of 46 diseases and complex traits

Outline

• Description of method

• Validation in simulations

• Proof of concept: analysis of molecular traits

• Analysis of 46 diseases and complex traits

A thought experiment

What if oracle gave us

true causal effect of SNPs on disease

true causal effect of SNPs on TF binding

A thought experiment

effects on disease effects on TF binding

What do we have in practice?(Signed) marginal GWAS summary statistics

(Signed) binding predictions from DNA sequence

LD matrix from reference panel

Method: signed LD profile regression(Signed) marginal GWAS summary statistics

(Signed) binding predictions from DNA sequence

LD matrix from reference panel

[Details: p-values, generalized least-squares, minor allele effects]

Under model:

Outline

• Description of method

• Validation in simulations

• Proof of concept: analysis of molecular traits

• Analysis of 46 diseases and complex traits

SLDP is well-calibrated

[Reshef et al. 2017 BioRxiv]

no enrichment

SLDP is robust to unsigned enrichment

[Reshef et al. 2017 BioRxiv]

confounding byunsigned enrichment

Outline

• Description of method

• Validation in simulations

• Proof of concept: analysis of molecular traits

• Analysis of 46 diseases and complex traits

382 TF binding annotations analyzed

ENCODE ChIP-seq + Basset CNN model

Transcription factor Cell line

CTCF A459...

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IRF1 GM12878

[ENCODE Project; Kelly et al. 2016 Genome Res]

Trait: gene expression, across genes

Seeking:

TFs that affect expression inconsistent direction across genes

Strategy:

Meta-analysis across genes

GENE1

GENE2

GENE3

GENE4

SLDP reproducibly identifies activating TFs

[Reshef et al. 2017 BioRxiv; Hansen et al. 1994 Mol Chem Bio; Kimura et al. 1994 Science]

Known activator (UniProt)

Other

SLDP links TFs to epigenetic marks

[Reshef et al. 2017 BioRxiv; Ogryzko et al. 1996 Cell; Laiosa et al. 2006 Ann Rev Immun]

Known activator (UniProt)

Other

Outline

• Description of method

• Validation in simulations

• Proof of concept: analysis of molecular traits

• Analysis of 46 diseases and complex traits

46 diseases and complex traitsUKB + public (sumstats) avg N=289k, ~1M SNPs

Phenotype Sample size

Height N≈450k

Rheu. arthritis N≈36k...

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Lupus N≈14k

Schizophrenia N≈70k

[Loh et al. BioRxiv; BOLT-LMM UK Biobank summary statistics are publicly available]

SLDP identifies 77 TF-trait annotations

[Reshef et al. 2017 BioRxiv]

SLDP identifies 77 TF-trait annotations

…that form 12 independent signalsTotal results: 77Indep. signals: 12

Significant results at per-trait FDR < 5%, grouped into approx. independent signals.

[Reshef et al. 2017 BioRxiv]

0

20

BCL11A

[DDD 2015 Nature; Okbay et al. 2016 Nature; Bazak et al. 2016 JCI; Dias et al. 2016 AJHG]

Rare LOFsin BCL11A

intellectual disability

BCL11A EDU+

Genome-wide GWAS signalvs signed LD profile EDU Manhattan plot

0

0

[DDD 2015 Nature; Okbay et al. 2016 Nature; Bazak et al. 2016 JCI; Dias et al. 2016 AJHG]

CTCF Lupus-

Genome-wide GWAS signalvs signed LD profile Lupus Manhattan plot

0

0

0

20

CTCF

CTCF slows myeloiddifferentiation

Fine-mapped SLE SNPsmodify CTCF binding

ExAC:pLI(CTCF) = 1.00(> 99.9% of genes)

IRF1 Crohn’s+

Genome-wide GWAS signalvs signed LD profile Crohn’s Manhattan plot

0

0

-20

20

IRF1

[Jostins et al. 2012 Nature; Wright et al. 2014 Nat Genet]

IRF1 Crohn’s+

Genome-wide GWAS signalvs signed LD profile

0

0

[Jostins et al. 2012 Nature; Wright et al. 2014 Nat Genet]

-20

20

IRF1

IRF1

eQTL

z-s

core

Crohn’s Manhattan plot

Conclusions

• Signed annotations enable strong inference about disease mechanism

• Signed LD profile regression links signed annotations to GWAS

• Evidence for genome-wide directional effects of TFs on molecular and complex traits

AcknowledgementsHilary Finucane

David KelleyAlexander Gusev

Dylan KotliarJacob Ulirsch

Farhad Hormozdiari

Pier-Francesco Palamara

Luca Pinello

Nick Patterson

Ryan Adams

Alkes Price

CGTA, HMS research computing, C de Boer, L Dicker, J Engreitz, N Friedman, X Liu, M Mitzenmacher, J Perry, D Reshef, S Reilly, S Raychaudhuri, A Schoech, P Sabeti, R Tewhey, P Turley

Luke O’ConnorBryce van de Geijn

Po-Ru LohShari GrossmanGaurav BhatiaSteven Gazal

Detecting genome-wide directional effects of transcription factor binding on polygenic disease risk Reshef et al. 2017 bioRxiv:204685

Thank you