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Mark Daly - Finding risk genes in psychiatric disorders
1. Finding risk genes in
psychiatric disorders
Mark Daly, PhD
Chief, Analytic and Translational Genetics Unit
Massachusetts General Hospital
&
Institute Member, Broad Institute and
Stanley Center for Psychiatric Research
2. 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
3. Despite diagnostic challenges, common
psychiatric diseases are extremely heritable
Sullivan, Daly,
O’Donovan 2012
Gottesman 1991
Cowan, Kopnisky, Hyman 2002
5. 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
6. ‘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)
7. Family-based linkage analysis
A
C
A
A
A
C
A
C
A
C
A
C
A
A
A
A
A
A
A
A
A/C Disease Gene
Saw 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
8. 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
9. If not Mendelian, what is the genetic
architecture of traits that are
1. highly heritable &
2. very common
10. 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
11. 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
12. Progress has required many
fundamental paradigm shifts
Understanding the genome
and the fundamental nature
of human variation
Dramatic technological
advances in our ability to
access genomes
13. 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)
15. 10M or so common variants:
typically shared across populations
Gabriel et al, Science 2002 Rosenberg et al, Science 2002
16. 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
17. 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…
19. First efforts in psychiatry were not successful
Genomewide association in schizophrenia with 3500 cases
International Schizophrenia Consortium 2009
20. Why did GWAS seem to work more
readily in immune-mediated and
cardio/metabolic disease?
Another contributor:
Natural Selection
21. 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
22. 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
23. 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
24. PGC statistical analysis group
Stephan Ripke
Ben Neale
Naomi Wray
Frank Dudbridge
Peter Holmans
Danyu Lin
Edwin van den Oord
Shaun Purcell
Sarah Medland
Nick Craddock
Danielle Posthuma
Ken Kendler
PGC Schizophrenia group
Michael 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:
Collaboration
rather than competition
is the key
Psychiatric
Genomics
Consortium (PGC)
300+ investigators
80 institutions
20 countries
25. q
SCZ - Ancient times – 2009 (ISC)
2601 cases, 3345 controls
0 genome wide significant sites
26. q
9394 cases, 12462 controls
5 genome wide significant sites
PGC - The Past - 2011
27. More than 100 distinct regions of
associated to schizophrenia!!!
PGC SCZ v2: Genomewide association in
schizophrenia with 37,000 cases
July 22, 2014
DRD2
C4
SLC39A8
28. 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)
29. 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
31. 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
(-log10(p))
Steve McCarroll, Aswin Sekar
Sekar et al, Nature, Feb 11 2016
32. 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
33. 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
34. 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)
35. Measuring heritability
ISC, Nature 2009
Yang et al. 2010 Nat Genet
Lee 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
36. Polygenic risk scores
Use largest GWAS meta-analysis
For each individual, calculate
weighted sum of their risk
alleles across the entire genome
Single predictive measurement
describing the risk carried by
that genetic background
37. 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))
38. 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
39. 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
40. Common polygenic variation –
even of weak effect – can
provides critical insights into
the root biological causes of
mental illness
Gottesman & Shields 1967
41. 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.
42. 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
44. 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
45. 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
46. De novo variation and autism
AA AA
AB
4000 ASD trios with
deep exome sequence
compiled to date…
Autism Sequencing Consortium (ASC) founded to
leverage further emerging sequence data collaboratively
Key Investigators: Buxbaum, Daly, Devlin, Roeder, State
Barrett, Cutler, Palotie, Scherer, Sanders, Talkowski, Walsh, Zwick
47. Rateofdenovotruncatingmutationsper
exome
***
***
***
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
48. 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
0100002000030000400005000060000 Sample Size (N) and Ancestral Diversity
1000 Genomes, ESP, ExAC
IndividualswithExomeSequenceData
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)
49. Rateperexome
***
***
***
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
50. pLI separates signal from noise in
schizophrenia exome study
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.
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=0.0025
OR=1.022
p=2.7e-13
OR=1.42
p=0.60
OR=0.99
Giulio Genovese, Kaitlin Samocha, Steve McCarroll, Pat Sullivan, Pam Sklar, Christina Hultman
51. 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=0.0025
OR=1.022
p=2.7e-13
OR=1.42
p=0.60
OR=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.
52. Next Steps in Gene Discovery
Additional Gene Discovery
Fine-Mapping and Functional Studies
Exome/Genome Meta Analysis
54. 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
55. A lot more to be learned from
GWAS...
Specific loci and significantly
improved polygenic instruments
56. Beyond GWAS: Fine-mapping
Example 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
57. Finding the ultra-rare high-impact variants
Not as easy in case-control data
265
1271
3722
8100
11853
16372
31682
34165
266
977
3099
5273
7293
9564
21115
23562
0
10000
20000
30000
2010 2011 2012 2013 2014 2015 2016 2017
Year
Cumulativenumberofindividualssequenced
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.
58. 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
59. 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 in
intolerant genes, we ensure that variants
are either de novo or VERY young
60. AcknowledgmentsElise Robinson
- Dan Weiner
- Emilie Wigdor
Hailiang Huang
Kaitlin Samocha
Christine Stevens
Jack Kosmicki
Stephan Ripke
Kyle Satterstrom
Ben Neale & Lab
Daniel MacArthur & Lab
Aarno Palotie & Lab
ExAC team
Hail team
ALSPAC
Beate St. Pourcain
George Davey Smith
Support from NIMH (Thomas Lehner), NHGRI, Simons Foundation,
Stanley Center
iPSYCH team
Preben Mortensen, Anders Børglum
Thomas Werge, Merete Noredntoft,
Ole Mors, David Hougaard,
Mads Hollegaard, Jonas Grauholm
Jakob Grove, Ditte Demontis
Autism Sequencing Consortium
Joe Buxbaum
Matt State
Jeff Barrett
Ed Cook
Dave Cutler
Bernie Devlin
Aarno Palotie
Kathryn Roeder
Silvia de Rubeis
Stephan Sanders
Mke Talkowski
Mike Zwick
Inspiration:
Steve Hyman
Ed Scolnick
Families and clinicians
contributing to:
PGC schizophrenia studies
worldwide
Simons Simplex Collection
SVIP, AGRE, AGP,
Autism Consortium
PGC-ASD
Ric Anney
Dan Arking
Bernie Devlin
Stephan Ripke
and many more…
61. 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
