from discovery to translation in cardiovascular...

From Discovery to Translation in Cardiovascular

Genetics

Nathan Stitziel

DBBS Precision Medicine Pathway

Washington University School of Medicine

September 2017

Why study human genetics?

1. Understand biology

Identify processes that define and alter disease

2. Understand disease

Identify and validate therapeutic targets

3. Understand risk

Identify those who benefit from early preventive therapy

Manolio et al. Nat Rev Gen 2009

Mendelian (monogenic) Complex (polygenic)

Single allele of large effect accounts

for phenotypic variance

Cardiomyopathies

Arrhythmia Syndromes

Lipids

Vascular Syndromes

Clustering within families but not

due to single gene mutations

Coronary disease

Blood pressure

Lipids

Etc, etc, etc

Mapping causal genes• Genetic Linkage

• Candidate gene sequencing

• Direct causal gene sequencing

Genomic sequencing

WGS Advantages

Whole genome coverage

WGS Disadvantages

Cost (~$1250)

Whole genome coverage

Limited interpretability

WES Advantages

Covers protein coding regions

Interpretable variation

Cost (~$250)

WES Disadvantages

Missing 99% genome coverage (best case)

Uneven capture/coverage (worst case)

Exome: ~33Mb per individualGenome: ~3Gb per individual

Exons

Sequencing reads

Ashley EA, Nat Rev Genet 2016

Sequencing based mapping example:35 year old with Aortic dissection

M298R

Homo sapiens WHSCHQHYHSMDEFSHYDLLDA Homo sapiens

Mus musculus WHSCHQHYHSMDEFSHYDLLDA Mus musculus

Gallus gallus WHSCHQHYHSMDEFSHYDLLDA Gallus gallus

Danio rerio WHSCHQHYHSMDEFSHYDLLDA Danio rerio

Gasterosteus aculeatus WHSCHQHFHSMDEFSHYELLDA Gasterosteus aculeatus

Xenopus tropicalis WHSCHQHYHSMDEFSHYDLLDA Xenopus tropicalis

Oikopleura dioica WHACHGHYHSMERFIDYDLMHV Oikopleura dioica

Signal peptide

Propeptide

Catalytic domain

D

M298R








Signal peptide

Propeptide

Catalytic domain

D

Autosomal dominant aortic dissection

Lee et al, PNAS 2016

Resolving 3 billion possibilities

Identify variation co-segregating

with phenotype

Identify variation that alters

encoded protein

Allele frequency

Leverage population

genetics

Leverage locally-sequenced

Mendelian cases

Genotype in remaining

pedigree


WGS identified missense candidate mutation in

lysyl oxidase (LOX) as the most likely cause of

disease

No human phenotype described for this gene

M298R








Signal peptide

Propeptide

Catalytic domain

D

Autosomal dominant aortic dissection

NormalHuman

MutationLee et al, PNAS 2016

Heterozygous Homozygous

Lox+/+ Lox+/M292R2.5

3.0

3.5

4.0

Length

(m

m)

Asc Aorta Length

****

Systolic Diastolic0

50

100

150Blood Pressure

Pre

ssure

(m

mH

g)

Lox+/+

Lox+/M292R

0 25 50 75 100 125 150 1750

200

400

600

800

L.Carotid Compliance

Dia

mete

r (u

m)

Lox+/+

Lox+/M292R

*

Pressure (mmHg)

0 25 50 75 100 125 150 1750

500

1000

1500

2000

Asc Aorta Compliance

Dia

mete

r (u

m)

Lox+/+

Lox+/M292R** ***

Pressure (mmHg)

A B

C D

Universal perinatal lethality


Chong et al, AJHG 2015

Lessons from Mendelian genetics, or “Why studying rare diseases is broadly applicable”

• Provides fundamental insights into human biology

• Identifies pathways relevant to human disease that can be therapeutically manipulated even in individuals without Mendelian mutations (i.e. HMG-CoA reductase)

Okay, but why sequence for mapping complex disease?

Manolio et al. Nat Rev Gen 2009

~80 genetic loci for CAD

Deloukas et al, Nat Genet 2013

1/3 map to known risk factors

(genes for lipids and BP)

2/3 potentially highlight novel

pathways underlying CAD

(genes unclear)

~80 genetic loci for CAD

Significant limitations to gene mapping through GWAS

Balding, Nat Rev Gen 2006

10:781

1. Uncertain gene

2. Uncertain variant

3. Uncertain

mechanism

Sequencing-based gene mapping aims to overcome these limitations

Balding, Nat Rev Gen 2006

10:781

1. Test direct association

with causal genes

2. Test direct association

with causal variants

3. Test direction of effect

1:2< 1:1000

“Common”“Rare”

Ca

se

sC

on

tro

ls

Ca

se

sC

on

tro

ls

Allele Frequency

Sequencing needed to

discover and replicate

Majority of human genetic

variation

Majority of human

genetic variance

Genotyping sufficient for

study

Lek et al. Nature 2016

Exome sequencing in N>60,000

Kiezun et al. Nat Genet 2012

Early MI

cases

N=5,000

MI-free

controls

N=5,000

Detect changes

across 20,000 genes

How do we identify

causal genes?

Cases ControlsGene

Cases with

mutations=8

Controls with

mutations=2

Repeat for each gene

Sequence exome

Fundamental

challenge:

What variants

do you include?

Among 20,000 genes sequenced in >

5,000 MI cases and 5,000 controls,

two definitively associated with MI.

Do, Stitziel, and Won, et al. Nature 2015

Low-density

lipoprotein

receptor

(LDLR)

Low-density lipoprotein receptor

(LDLR): Exon 4

Known FH

mutations with

documented

loss of function

=

Receptor responsible for

cellular uptake of LDL

cholesterol

~50% of rare deleterious

variants in LDLR seen in

only 1 individual

Low-density

lipoprotein receptor

(LDLR)

0

50

100

150

200

250

300

350

400

450

Non-carriers Any mutation Deleterious Disruptive

LD

L C

ho

les

tero

l (m

g/d

L)

134

mg/dL

142

mg/dL

189

mg/dL

282

mg/dL

p<0.0001

LDLR mutations increase MI risk

LDLR

mutation

class

Percent MIPercent

controls

Odds of

MIP

Any NS

mutation6.1% 4.1% 1.5 4x10-6

Deleterious 1.9% 0.5% 4.2 3x10-11

Disruptive 0.5% 0.04% 13.0 9x10-5

Biological insights into coronary disease

• Beyond LDL pathway, triglyceride-rich lipoproteins via the LPL pathway emerge as key risk pathway

• Multiple potential therapeutic targets identified

Substantial limitations:

Only assessed coding regions

Limited sample size and power for realistic models of selection

Limited ability to assess SVs

Centers for Common Disease Genomics

E. Lander, S. Gabriel,

M. Daly, S. KathiresanRichard Gibbs Ira Hall, Nathan Stitziel,

Susan Dutcher

Goal: Comprehensive studies in common disease

Study the complete spectrum of genome variation in diverse sets of

diseases and populations

Under the NHGRI Genome Sequencing Program (includes CMG, AC, CC)

Five themes: CVD, Immune-related disease, Neuropsych, Common

controls, Data processing

Robert Darnell

Centers for Common Disease Genomics

Sekar KathiresanEric Boerwinkle Ira Hall & Nathan Stitziel

CVD Working Group

WGS and WES in 55,000 participants

(3:2 case/control):

Early-onset CAD (70%), Hemorrhagic Stroke (30%), risk factors

Multi-ethnic study (NFE, FE, HA, AA, SA)

Lessons from complex disease genetics, or “Why weak effect alleles are broadly applicable”

• Complex disease studies identify genes and pathways underlying human disease

• First era of well powered sequencing-based studies of complex disease underway

• Challenge to field remains systematic high-throughput means of assessing functional impact of genetic variation

Okay, but what can you do with all of this?






3. Understand risk


Causal

pathway

Drug

target

Compare outcomes over years

• Efficacious?

• Safe?

Drug

target

Drug

target

Compare outcomes over lifetime

• Efficacious?

• Safe?

Wild-type gene

(Placebo)

Mutant gene

(Drug)

NPC1L1

Does it also reduce risk of heart attack?

Inhibiting NPC1L1 reduces LDL-C

Identified rare NPC1L1 loss of

function mutations in

>110,000individuals

Stitziel et al., NEJM 2014

Rare loss of function mutations in NPC1L1

In >110,000 individuals:

82 NPC1L1 mutation carriers

Lifelong inactivation of one NPC1L1 copy

Carriers estimate lifelong effect of inhibitory drug

Associated with 12 mg/dL lowerLDL (p=0.04)

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

MI-freeControls

MICases

Ca

rrie

rs (

%) Odds Ratio for

MI = 0.47

P = 0.008

Stitziel et al., NEJM 2014

1)

2)

Lowers LDL cholesterol

Reduces risk of heart attack





Identify therapeutic targets

3. Understand risk


Deloukas et al, Nat Genet 2013

Do these genetic factors in aggregate predict prospective risk in

populations?

Calculated genetic risk for >48,000 participants of four statin therapy trials

Genetic risk score

27 genetic markers associated with

MI

Incre

asin

g r

isk

540 Number of risk alleles

Genetic risk score


MI

Incre

asin

g r

isk


Genetic risk score

Genetic risk score


MI

Incre

asin

g r

isk


Genetic risk score

“Low risk”

Bottom 20%

“Intermediate

risk”

“High risk”

Top 20%

Genetic Risk

Score Category Ratio 95% CI

Low

Intermediate

High

Ratio (95% CI)

Lower Risk Higher Risk

1.25 2.00.80 1.0

Reference

1.34

1.72

1.22-1.47

1.55-1.92

P-Value

<0.0001

<0.0001

*Analysis adjusted for traditional CV risk factors

Genetic score stratifies risk in placebo arms

Mega and Stitziel et al, Lancet 2015

For those at high risk, can it be modified?

Can we modify polygenic risk if

60% of loci are non-lipid?

Gold standard: Randomized trial

• Test the hypothesis that genetic information will improve risk stratification and that those individuals benefit from early therapy

Young individuals

without ASCVD

Men 30-40,

Women 40-50

High genetic

risk

Low genetic

risk

Randomize:

Statin vs Placebo

Randomize:

Statin vs Placebo

Follow for

clinical events

Many years

Calculated genetic risk for >48,000 participants of four statin therapy trials

High genetic risk greater benefit from statin therapy

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Ab

so

lute

Ris

k R

ed

ucti

on

s (

%)

Low Genetic Risk High Genetic Risk

0

0.5

1

1.5

2

2.5

3

3.5

0

1

2

3

4

5

6

7

-2

-1

0

1

2

3

4

5

6

7

8

Intermediate Genetic Risk

JUPITER ASCOT CARE PROVE IT

TIMI 22

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Ab

so

lute

Ris

k R

ed

ucti

on

s (

%)

Low Genetic Risk High Genetic Risk

0

0.5

1

1.5

2

2.5

3

3.5

0

1

2

3

4

5

6

7

-2

-1

0

1

2

3

4

5

6

7

8

Intermediate Genetic Risk

JUPITER ASCOT CARE PROVE IT

TIMI 22

Number needed to treat

1 / Absolute risk reduction

Low genetic risk ASCOT trial

NNT ~ 100

High genetic risk ASCOT trial

NNT ~ 33







3. Understand risk


Acknowledgements

Funding / Support

Research Participants

CollaboratorsVivian Lee Shamil Sunyaev

Robert Mecham Sung Chun

Sekar Kathiresan Danish Saleheen

Amit Khera Kiran Musunuru

Susan Dutcher Ira Hall

Stitziel Lab

In-Hyuk Jung

Arturo Alisio

Katherine Santana

Teresa Roediger

Salwa Mikhail

Sofia Luna

Jae-Hee Lee

Erica Young

stitziellab.org

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