cardiovascular continuum sampling from extremes padmanabhan s et al. plos genet 2011

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Cardiovascular Continuum Sampling from Extremes Padmanabhan S et al. PLoS Genet 201

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Cardiovascular ContinuumSampling from Extremes

Padmanabhan S et al. PLoS Genet 2011

Cardiovascular ContinuumSampling from Extremes

Padmanabhan S et al. PLoS Genet 2011

Cardiovascular ContinuumUMOD Gene

Padmanabhan S et al. PLoS Genet 2011

Cardiovascular ContinuumUMOD: a Novel Hypertension Candidate Gene

Padmanabhan S et al. PLoS Genet 2011

Monogenic TraitsUMOD Gene

Köttgen A et al. Nat Genet 2009

Monogenic Traits

Padmanabhan S et al. Hypertension 2014

Uromodulin and Blood Pressure

Monogenic TraitsBlood Pressure in Umod+/+ (WT) and Umod−/− (KO)

Graham LA et al. Hypertension 2014

Monogenic Traits

Graham LA et al. Hypertension 2014

Umod+/+ Umod-/-

Salt Sensitivity in Umod+/+ (WT) and Umod−/− (KO)

Monogenic Traits

Graham LA et al. Hypertension 2014

Pressure Natriuresis Curves

Monogenic Traits

What have we learned from GWAS?

Monogenic Traits

New Targets?

What Have we Learned from GWAS?

NHGRI GWA Catalogwww.genome.gov/GWAStudieswww.ebi.ac.uk/fgpt/gwas/

Published Genome-Wide Associations through 12/2012Published GWA at p≤5X10-8 for 17 trait categories

Monogenic TraitsUromodulin?

Padmanabhan S et al. Hypertension 2014

Monogenic Traits

Risk Prediction/Stratification?

What Have we Learned from GWAS?

Monogenic TraitsRisk Prediction?

Padmanabhan S et al. Trends Genet 2012

Monogenic Traits

No direct genetic linksbetween CKD and Hypertension

(Exception: UMOD)

What Have we Learned from GWAS?

Monogenic Traits

Current and future strategies

Thomas SR 2009

Monogenic Forms of Hypertension

Cardiovascular ContinuumDetection of Rare/Private Mutations

Lifton RP et al. Nat Genet 2008

Cardiovascular ContinuumDetection of rare/private mutations

Lifton RP et al. Nat Genet 2008

Rare (private) mutations could

explain the "missing heritability",

i.e. heritability that is not explained

by common genetic variants.

"Missing Heritability"

Systems Biology and "Omics"

DNA

mRNA

Protein

Metabolitessmall molecules

Proteomics

Metabolomics

Genomics

TranscriptomicsmiRNAs

Monogenic Traits

DNA Methylation Histone Modification

Non-coding RNAs, microRNAs

Epigenetics

Friso S et al. Translat Res 2014

Cardiovascular ContinuumCardiovascular Continuum

Dzau V et al. Circulation 2006

Risk factors

Oxidative andmechanical stress

Inflammation

Early tissue dysfunction

Atherothrombosis andprogressive CV disease

Tissue injury(MI, stroke, renal

insufficiency,peripheral arterial

insufficiency)

Pathologicalremodeling

Target organ damage

End-organ failure(CHF, ESRD)

Death

Altered gene expression

Altered protein expression

Genome

BHF Glasgow Cardiovascular Research Centre

Log Rank (Mantel-Cox) P=0.021

CAD Score: Survival Analysis in ASCOT

< Mean> Mean

Brown C et al. SCF 2013

Collagen alpha-1(II) chain

10 100 1000 10000 100000

0

50

100

150

200

250

ID:35339

eGF

R (

MD

RD

) m

l/min

/1,7

3m²

Collagen alpha-1(III) chain

1 10 100 1000 10000 100000 1000000

0

20

40

60

80

100

120

140

160

180

ID:156878

eGF

R (

MD

RD

) m

l/min

/1,7

3m²

Roscioni SS et al. Diabetologia 2013

Normo Micro Micro Macro

Prediction of Diabetic Nephropathy

Roscioni SS et al. Diabetologia 2013

Normo Normo Normo Micro Micro Micro Micro Macro

Prediction of Diabetic Nephropathy

WTCCC

Why did WTCCC find "hits" for many diseases, but not for hypertension?

WTCCC. Nature 2007

Cases and Controls in WTCCC

Collection 58C UKBS BD CAD CD HT RA T1D T2D No Samples 1480 1458 1868 1926 1748 1952 1860 1963 1924 % Male / %Female

50/50 48/52 37/63 79/21 39/61 40/60 25/75 51/49 58/42

Eastern 11 12 3 10 25 16 20 17 26 E&WRidings 9 6 1 26 0 1 10 10 0 London 8 5 7 2 22 18 2 4 10 Midlands 9 12 24 6 1 4 16 6 1 Northern 8 10 9 10 20 3 3 8 15 North Midlands

7 3 6 15 1 13 8 7 5

Northwestern 11 11 3 8 1 3 19 11 3 Southeastern 7 11 5 4 1 8 2 3 2 Southern 8 8 6 4 14 5 12 8 18 Southwestern 8 9 6 6 1 3 1 9 19 Scotland 10 9 10 5 14 24 3 10 0 Wales 5 5 21 5 0 1 1 7 1

If 5% of controls would meet the definition of cases, then loss of power of the GWAS is approximately the same as that due to

the reduction of sample size by 10%.

"Caseness" of Controls

WTCCC. Nature 2007

Challenges

• Possible caseness of controls• Accurate definition of the phenotype• Precise assessment of the phenotype• Multiple pathways, multiple genes

What can be done?

Cardiovascular ContinuumIncreasing the Sample Size

OR=1.5

CHARGE Consortium

Levy D et al. Nat Genet 2009

CHARGE Consortium: 29,136 Subjects

Levy D et al. Nat Genet 2009