Finding risk genes in

psychiatric disorders

Mark Daly, PhD

Chief, Analytic and Translational Genetics UnitMassachusetts General Hospital

&Institute Member, Broad Institute and

Stanley Center for Psychiatric Research

Why Genetics?

• Correlation = causation

• Unique insights into biological causes

– Particular potential for mental health where direct biological measures, blood tests, etc. not available

• Durable foundation for rational therapeutic development

Despite diagnostic challenges, common

psychiatric diseases are extremely heritable

Sullivan, Daly, O’Donovan 2012

Gottesman 1991Cowan, Kopnisky, Hyman 2002

Traditional approach to

gene discovery

First Century Genetics

1860s: Mendel’s laws of inheritance – discrete, transmissible units of inherited variation resulting in phenotypic differences

A B C

1910s: Sturtevant and Morgan create the first genetic map

Aa AA Aa Aa AA AA Aa AA

Aa AA

1940s-1950s: Principles of linkage

analysis developed

‘Mendelian’ diseases travel predictably

and consistently in families

Aa AA Aa Aa AA AA Aa AA

Aa AA

Dominant transmission

Thousands of diseases or traits caused by mutations in a single gene

(e.g., Huntington’s, CF, muscular dystrophy)

Family-based linkage analysis

AC

AA

AC

AC

AC

AC

AA

AA

AA

AA

A/C Disease GeneSaw dramatic successes in the 1980s-90s

for the localization of genes underlying

countless Mendelian disorders:

Huntington’s, CF, DMD, early onset forms

of breast cancer, Alzheimers, diabetes…

Tracking ‘co-segregation’ of DNA

polymorphisms with disease

status permits identification of

region containing responsible

gene and mutations

Glazier, Nadeau and Aitman, Science 2002

Wildly successful for rare diseases, this simply

does not work for common ones…

Dark Ages of complex trait genetics

If not Mendelian, what is the genetic

architecture of traits that are

1. highly heritable &

2. very common

1900-1925: the Dawn of Polygenicity

Biometricians recognize many traits are highly heritable but do not apparently adhere to Mendel’s laws…violently opposed by Mendelians

Key experiments in plants, flies demonstrate that large phenotypic differences can arise from the sum of many contributors

Fisher synthesizes model wherein large number of small ‘Mendelian’ factors can explain high heritability of continuous traits

Failure of linkage not difficult to

understand

disease state

phenotype1

phenotype2

phenotype3

phenotype4 phenotype5

Exposures / environment

genotype

other

genes

“We suggest that evolutionary changes in anatomy and way of life are more often based on changes in the mechanisms controlling the expression of genes than on sequence changes in proteins. We therefore propose that regulatory mutations account for the major biological differences between humans and chimpanzees.” –King & Wilson. Science. April, 1975.

Many genes vs. 1

Incomplete/low penetrance

Progress has required many

fundamental paradigm shifts

Understanding the genomeand the fundamental nature of human variation

Dramatic technological advances in our ability to access genomes

If instead of one gene, there are

hundreds of contributors, how do

we proceed?

Study all common variation to find weak,

often regulatory risk factors? (CVAS/GWAS)

- OR -

Sequence a limited number of cases and controls

to find rare, high-impact variants? (RVAS)

Genomewide Association to common DNA variants

2005-present

10M or so common variants:

typically shared across populations

Gabriel et al, Science 2002 Rosenberg et al, Science 2002

10M or so common variants:

typically shared across populations

Gabriel et al, Science 2002 Rosenberg et al, Science 2002

The vast majority of genetic differences between individuals reside in common variants

(Lewontin 1972)

Therefore, most genetic variation in common traits should be explained by common variation

Really not a new idea

Observation: Extremely high heritability across cultures, backgrounds and relative pairs

Model: given frequency, heritability and lack of Mendelian segregation, polygenic inheritance of “constitutional predisposition” or “liability” in the terminology of the then recent work of Falconer’s extension of quantitative genetics models to inherited risk of ‘all or none’ traits.

50 years ago…

GWAS 2005-present:the primary driver of discovery in common disease

First efforts in psychiatry were not successfulGenomewide association in schizophrenia with 3500 cases

International Schizophrenia Consortium 2009

Why did GWAS seem to work more readily in immune-mediated and

cardio/metabolic disease?

Another contributor:Natural Selection

From: Fecundity of Patients With Schizophrenia, Autism, Bipolar Disorder,

Depression, Anorexia Nervosa, or Substance Abuse vs Their Unaffected Siblings

JAMA Psychiatry. 2013;70(1):22-30. doi:10.1001/jamapsychiatry.2013.268

Ramifications:

Common and low frequency

variants can be plentiful at very

low effect sizes (OR < 1.1)

Large effect alleles must be

extremely rare

DRAMATICALLY

REDUCED

FITNESS IN

SCHIZOPHRENIA

AND AUTISM

Implication of

PNAS Dec. 2013

Modest ORs (2-10)

- Sweet spot for lipids,

CVD, Alzheimers, AMD,

immune disease

- De novo studies will not

flag these (most are

inherited and found in

unaffecteds)

- Selection keeps them

almost impossibly rare

to detect

High OR =

de novo detection

only

High frequency =

GWAS detection

Conceptually, a polygenic

model could fit per Gottesman

& Shields, but if so it would

require a much larger scale to

gain access to the individual

components

PGC statistical analysis groupStephan Ripke

Ben Neale Naomi Wray

Frank DudbridgePeter Holmans

Danyu Lin Edwin van den Oord

Shaun PurcellSarah MedlandNick Craddock

Danielle PosthumaKen Kendler

PGC Schizophrenia groupMichael O'Donovan

Pamela Sklar

Patrick Sullivan Doug Levinson

Ed Scolnick

Pablo Gejman

Aiden Corvin

Anil Malhotra

Ayman Fanous

D Blackwood

Hugh Gurling

Kenneth Kendler

Michael Gill

Michael Owen

Leena Peltonen

Ole Andreassen

Roel Ophoff

David St. Clair

Sven Cichon

Thomas Schulze

Peter Holmans

Thomas Lehner

Aarno Palotie

Tonu Esko

Alan Sanders

Thomas Werge

Dan Rujescu

BryanMowry

MathewKeller

Fundamental Shift:

Collaborationrather than competition

is the key

Psychiatric Genomics Consortium (PGC)

300+ investigators80 institutions20 countries

qSCZ - Ancient times – 2009 (ISC)

2601 cases, 3345 controls

0 genome wide significant sites

q

9394 cases, 12462 controls

5 genome wide significant sites

PGC - The Past - 2011

More than 100 distinct regions of

associated to schizophrenia!!!

PGC SCZ v2: Genomewide association in schizophrenia with 37,000 cases

July 22, 2014DRD2

C4

SLC39A8

Common variants can abound, but only at

extremely modest ORs permitted by this

selective pressure

Odds-ratio vs risk allele frequency, PGC-schizophrenia 2014 results

(N ~ 35,000 cases, n=128 genome-wide significant variants)

Common variants can abound, but only at

extremely modest ORs permitted by this

selective pressure

Odds-ratio vs risk allele frequency, PGC-schizophrenia 2014 results

(N ~ 35,000 cases, n=128 genome-wide significant variants)

GWAS: 100s of biological clues

available, >20,000 cases required to

start harvesting them

Biology begins to emerge from

the shadows…

Discovery: Alleles of C4 shape schizophrenia risk in

proportion to their effects on expression of C4A

Genetic result: (n=62,000, p<10-20)

(from brain tissue,

n=100, p<10-4)

Chromosome

(-lo

g1

0(p

))

Steve McCarroll, Aswin Sekar

Sekar et al, Nature, Feb 11 2016

C4 shapes the extent of

synaptic pruning

Allison Bialas, Matt Baum, Mike Carroll, Beth Stevens

WT C4 +/- C4 -/-

In C4 -/- mice,TRNs retain

multiple, overlapping inputs

C4 is expressed by RGCs during

“critical period” for pruning

A potential piece of the puzzle...

Huttenlocher, 1979

Excessive synaptic

pruning may play a role

Schizophrenia patient

Control

Schizophrenia patient

Birth Child Adult

Glantz & Lewis

Arch Gen Psychiatry

2000

Another example: SLC39A8

• Zn and Mn transporter

• A functional allele (A391T) corresponding to lower serum

Mn levels is convincingly associated to schizophrenia

risk

• Mendelian recessive deficiency of this gene recently

described as disorder of glycosylation

• Important biological clue and potentially a public health

intervention – 15% of Europeans carry this low

functioning, high-risk variant

Other emerging lessons• Nearly all associated variants have the same effect across global

populations

• Very few have unusual modes of association– Effect differences by sex, parent-of-origin– Epistasis (gene-gene interactions)

• Many/most are shared across diagnostic boundaries– Considerable shared genetics across behavioral and cognitive

traits/diagnoses

• Polygenic risk provides many insights– Degree of shared genetic predisposition between traits– Enrichment highlights specific cells/tissues of relevance– Causal relationships between biomarkers and disease can be evaluated via

Mendelian Randomization

All these observations are consistent with what is seen in other disease areas (e.g., immune-mediated diseases)

Measuring heritability

ISC, Nature 2009Yang et al. 2010 Nat GenetLee et al. 2011 AJHG

Heritability explained by genome-wide significant SNPs

< SNP heritability <Total narrow sense heritability

SNP heritability = heritability explained by all SNPs genotyped on a standard array.

Hilary Finucane, Brendan Bulik-Sullivan, Ben Neale

Polygenic risk scoresUse largest GWAS meta-analysis

For each individual, calculate weighted sum of their riskalleles across the entire genome

Single predictive measurement describing the risk carried by that genetic background

Polygenic risk scores 0.22

0.20

0.18

0.16

0.14

0.12

0.10

0.08

0.06

0.04

0.02

0.00

Roughly 20% of the variance in SCZ risk in a new sample is captured by 2016 PRS(Previous meetings: 3% (2009), 8% (2011), 18% (2014))

Polygenic risk scores

Established genomewide significant hit regions explain only 20-25% of this!Many more pointers to biology will become definitively established as we expand GWAS

0.22

0.20

0.18

0.16

0.14

0.12

0.10

0.08

0.06

0.04

0.02

0.00

Many uses of PRS / MR / LD score regression

• Overlap between diseases helps to clarify shared pathways

• Enrichment of gene expression and epigenetic marks clarifies relevant cell types

• Understanding the relevance and relationships of continuous traits across population and prodromal phenotypes to disease

• Understanding the relevance of variation in functional assays and models by linkage to the heritable biological variation of disease

Common polygenic variation –

even of weak effect – can

provides critical insights into

the root biological causes of

mental illness

Gottesman & Shields 1967

Strengths & limitations of common polygenic

risk

• Enables recognition of:

– Etiologic overlap

between diseases

– Evaluating causal

relationships between

biomarkers and disease

– Points to specific

cells/tissues relevant

– Can now resolve many

to single/few variants

45 best-resolved associations to IBD

Coding

TFBS

Epigenetics

eQTL

• Interpretation is

challenging

Despite gut and immune cells

being accessible and well-

studied, more than half of the

non-coding map to no known

enhancer, promoters, etc.

Effect sizes in complex disease

42

Associations discovered by GWAS

(IBD pictured here)

- Most discoveries (~80%) are non-

coding

- Those that are coding have

dramatically larger range of effects

OR = 1.25

Effect sizes in complex disease

43

OR = 1.25

LDLR, APOB, LPL, APOA5, LPA,

PCSK9…

Myocardial infarction

RNF186, CARD9, NOD2, IL23R, …

Inflammatory Bowel Disease

SLC30A8, HNF1A, PAX4, …

Type 2 diabetes

APOE, ABCA7, TREM2, …

Alzheimer’s

CFH, CFI, C3, C9

AMD

Effect sizes in complex disease

44

OR = 1.25

LDLR, APOB, LPL, APOA5, LPA,

PCSK9…

Myocardial infarction

RNF186, CARD9, NOD2, IL23R, …

Inflammatory Bowel Disease

SLC30A8, HNF1A, PAX4, …

Type 2 diabetes

APOE, ABCA7, TREM2, …

Alzheimer’s

CFH, CFI, C3, C9

AMD

These types of variants, at frequencies we have power to

detect

cannot in theory and

do not in empirical data

exist in autism, schizophrenia and traits with similar

selection

Natural selection prevents strong alleles from achieving

any measurable population frequency, and therefore

meaningful contribution to heritability –

de novo mutations, however, are exempt

AA AA

AC

Rare variant studies seem

hopelessly underpowered – even

OR=2 has no chance to become

even a 0.1% polymorphism

Beneficial exception: de novo

mutations!

• Can have any effect size

• Easy to find – low background

rate

De novo variation and autism

AA AA

AB

4000 ASD trios with deep exome sequence

compiled to date…Autism Sequencing Consortium (ASC) founded toleverage further emerging sequence data collaboratively

Key Investigators: Buxbaum, Daly, Devlin, Roeder, StateBarrett, Cutler, Palotie, Scherer, Sanders, Talkowski, Walsh, Zwick

Rate

of de n

ovo t

runcating m

uta

tions p

er

exom

e

***

***

***RR = 1.55

***

* P < 0.01

** P < 0.001

*** P < 0.0001

(compared to

unaffected ASD siblings)

ASD

Unaffected ASD siblings

ID/DD

All Class 2 & Class 1 pLI ≥0.9

class 1 pLI < 0.9

0.22

0.20

0.18

0.16

0.14

0.12

0.10

0.08

0.06

0.04

0.02

0.00

RR = 3.24

RR = 6.70

RR = 2.15

NS

de novo mutations contribute to ASD & ID

Most reliable signal to date in

ASD – and much more strongly

in ID/DD and epileptic

encephalopathy – has been

excess of de novo truncating

mutations and CNVs

ExAC reference database

critical to interpretation

1000

Genomes

ESP

N=6500

ExAC

N=60,706

Latino

African

European

South Asian

East Asian

Other

1000 Genomes ESP ExAC

01

000

020

000

300

00

40

000

500

00

60

00

0Sample Size (N) and Ancestral Diversity

1000 Genomes, ESP, ExAC

Indiv

idu

als

with

Exom

e S

eq

uence D

ata

East Asian

South Asian

European

Middle Eastern

African

Native American ancestry

Diverse Other

World proportions

World Population

Scaled to ExAC height

Daniel MacArthurMonkol Lek Kaitlin Samocha

Enables recognition of 20% of

genes that do not tolerate

heterozygous truncating

mutations – see also Cassa et al

(2017, in press, Nat Gen);

Petrovski/Goldstein RVIS papers

Lek et al. Nature 536:

285-291 (2016)

Ra

te p

er

exo

me

***

***

***

RR = 1.55

***

* P < 0.01

** P < 0.001

*** P < 0.0001

compared to

unaffected ASD siblings

ASD de novo variants

Unaffected ASD siblings

de novo variants

ID/DD

de novo variants

All mutations in tolerant mutations in intolerant

genes genes

0.22

0.20

0.18

0.16

0.14

0.12

0.10

0.08

0.06

0.04

0.02

0.00

RR = 3.24

RR = 6.70

RR = 2.15

NS

Zeroing in on the critical de novo mutations

Jack Kosmicki

pLI separates signal from noise in

schizophrenia exome studyExome sequencing was performed on ~6,000 controls and

~5,000 cases with schizophrenia from Sweden. 0 genes

discovered. Slight signal for enrichment of singleton LoF

mutations in the cases.

Set All genes (n=19,131)

Haploinsufficient(pHI ≥ 0.95, n=2651)

Not haploinsufficient (pHI < 0.95, n=16480)

Synonymous p=NA

OR=0.97 p=0.81

OR=0.96 p=NA

OR=0.98

Missense p=NA

OR=0.97 p=0.13

OR=0.98 p=NA

OR=0.97

Loss-of-function

p=0.0025OR=1.022

p=2.7e-13OR=1.42

p=0.60OR=0.99

Giulio Genovese, Kaitlin Samocha, Steve McCarroll, Pat Sullivan, Pam Sklar, Christina Hultman

pLI separates signal from noise in

schizophrenia exome study

Set All genes (n=19,131)

Haploinsufficient(pLI ≥ 0.95, n=2651)

Not haploinsufficient (pLI < 0.95, n=16480)

Synonymous p=NA

OR=0.97 p=0.81

OR=0.96 p=NA

OR=0.98

Missense p=NA

OR=0.97 p=0.13

OR=0.98 p=NA

OR=0.97

Loss-of-function

p=0.0025OR=1.022

p=2.7e-13OR=1.42

p=0.60OR=0.99

Giulio Genovese, Kaitlin Samocha, Steve McCarroll, Pat Sullivan, Pam Sklar, Christina Hultman

Exome sequencing was performed on ~6,000 controls and

~5,000 cases with schizophrenia from Sweden. 0 genes

discovered. Slight signal for enrichment of singleton LoF

mutations in the cases.

Next Steps in Gene Discovery

Additional Gene Discovery

Fine-Mapping and Functional Studies

Exome/Genome Meta Analysis

Manhattan plot, EAS

53

Combining EUR and EAS

54

# cases # controls

EAS 13,305 16,244

EUR 33,640 43,456

COMBINED 46,945 59,700

# Loci # SNPs

EAS 12 12

EUR 108 128

COMBINED 140 181

Sample size Loci and SNPs associated

A lot more to be learned from

GWAS...

Specific loci and significantly

improved polygenic instruments

Beyond GWAS: Fine-mappingExample from Inflammatory Bowel Disease

Immunochip: Specialized genotyping reagent focused on immune-mediated diseases:– Type 1 Diabetes (T1DGC)

– Autoimmune thyroid disease

– Ankylosing spondylitis

– Crohn’s disease

– Celiac disease

– IgA deficiency

– Multiple sclerosis

– Primary biliary cirrhosis

– Psoriasis

– Rheumatoid arthritis

– Systemic lupus erythematosus

– Ulcerative colitis

• Nearly 50,000 Cases in GWAS & Immunochip

• Huang, Jostins, Fang et al. (BioRxiv October 2015; Nature June 2017)

Crohn’s & Ulcerative Colitis (IBD)18 associations refined to a SINGLE VARIANT with greater than 95% posterior-p

• 200 bp into intron of TNFSFR6B• 5 kb downstream of GPR35• 500 bp from TSS of JAK2,

massive ENCODE peak• intronic to IL2RA

(MEF2A/MEF2C binding site)• RELA/NFKB binding site 40 kb

upstram IKZF1• 10 kb from TSS of NKX2-3, also

LINC01475 inbetween SNP and NKX2-3

• Intron LRRK2• 5kb downstream from HNF4A• 4 kb from TSS of PRDM1

• Intron of NOD2• NOD2 insC• NOD2 R702W• NOD2 G908R• NOD2 N289S

• IL23R V362I• CARD9 splice variant• IFIH1 I923V • SMAD3 I65V

Finding the ultra-rare high-impact variantsNot as easy in case-control data

265

1271

3722

8100

11853

16372

31682

34165

266977

3099

5273

7293

9564

21115

23562

0

10000

20000

30000

2010 2011 2012 2013 2014 2015 2016 2017

Year

Cu

mula

tive

num

be

r o

f in

div

idu

als

se

qu

ence

d

a

a

Schizophrenia

Control

With UK10K done at Sanger, total now past 25K cases!

Like early years of GWAS, many individual exome studies completed, later this year for the first time these will be assembled into a meta-analysis and results made immediately avail.

How are we getting along without trios?

.30

.25

.20

.15

.10

.5

0

SSC+ASC:De novo PTVs- high pLI genes only- not in ExAC

ASD con Dan: ASD con

Danish cases and control rates:all PTVs- high pLI genes only- not in ExAC- Rate difference significant p<10-13

Inherited variants carry modest signal - but are also rare so do not

wash out de novo signal.30

.25

.20

.15

.10

.5

0

SSC+ASC:inherited PTVs- high pLI genes only- not in ExAC- 18.5%-16% (p<.005)

ASD con Dan: ASD con

Danish cases and control rates:all PTVs- high pLI genes only- not in ExAC- Rate difference significant p<10-13

By focusing on damaging variants inintolerant genes, we ensure that variants are either de novo or VERY young

AcknowledgmentsElise Robinson- Dan Weiner- Emilie Wigdor

Hailiang HuangKaitlin SamochaChristine StevensJack KosmickiStephan RipkeKyle Satterstrom

Ben Neale & Lab

Daniel MacArthur & LabAarno Palotie & LabExAC teamHail team

ALSPACBeate St. PourcainGeorge Davey Smith

Support from NIMH (Thomas Lehner), NHGRI, Simons Foundation, Stanley Center

iPSYCH teamPreben Mortensen, Anders BørglumThomas Werge, Merete Noredntoft, Ole Mors, David Hougaard, Mads Hollegaard, Jonas GrauholmJakob Grove, Ditte Demontis

Autism Sequencing ConsortiumJoe BuxbaumMatt StateJeff BarrettEd CookDave CutlerBernie DevlinAarno PalotieKathryn RoederSilvia de RubeisStephan SandersMke TalkowskiMike Zwick

Inspiration:Steve HymanEd Scolnick

Families and clinicianscontributing to:PGC schizophrenia studies worldwide

Simons Simplex CollectionSVIP, AGRE, AGP, Autism Consortium

PGC-ASDRic AnneyDan ArkingBernie DevlinStephan Ripkeand many more…

Jakob Grove

Anders Børglum

iPSYCH+PGC ASD GWAS 2017 (17K cases, 1KG Phase 3 imputation)

Genome-wide significant

GWS after replication

+5 additional significant loci in MTAG analyses w/ Educational Attainment, SCZ & MDD

Appendix 1:Key references and figures regarding

polygenicity 1900-1925

Biometric / Mendelian debate

Karl Pearson William Bateson

East 1915: Inheritance of Corolla

Length in Nicotiana longiflora

Demonstration of polygenic inheritance through selective breeding

The first complex trait mapping:

Altenburg and Muller (1920)

Hermann Muller

Many Mendelian traits can sum

to a continuous distribution

Ronald Fisher (1918)