aha council for high blood pressure research rdannual...

AHA Council for High Blood Pressure Research 63rd Annual Scientific Conference

Genes, Molecules, Systems Biology and Epidemiology in Hypertension: Bringing it all Together in Framingham

Daniel Levy, MDFramingham Heart StudyCenter for Population StudiesNational Heart, Lung, & Blood Institute

Genetics of Hypertension

• Blood pressure is a major cardiovascular disease risk factor

• Previous studies confirmed a large heritable component (h2 0.4-0.6)

• Genes for hypertension exist, but to date, few genetic variants associated with BP have been identified and replicated in the general population

Identifying Blood Pressure Genes

• Several methods for identifying blood pressure genes:– Study of rare Mendelian disorders– Pursuit of candidate genes – Genome-wide association studies

Genome-Wide Association Studies (GWAS)

• Genome-wide screening technology can test 1,000,000 genetic variants in a single DNA specimen using chip technology

• We’ve entered the GWAS era

Blood Pressure GWAS Comes of Age in 2009

Nature Genetics May 10, 2009

CHARGE Genome-wide Association Study of Blood Pressure

Hypothesis• Blood pressure variation

in the general population is due, in part, to multiple genetics variants with small effects, and that very large study samples are needed to identify them


CHARGE Genome-wide Association Study of Blood Pressure

• CHARGE Consortium– Age Gene/Environment Susceptibility Study (n=3,219)– Atherosclerosis Risk in Communities Study (n=8,047)– Cardiovascular Health Study (n=3,277)– Framingham Heart Study (n=8,096)– Rotterdam Study (n=4,737)– Rotterdam Extension Study (n=1,760)

• Total of 29,136 individuals


Phase I

• Separate GWAS conducted by each of 6 CHARGE study sites (n=29,136)

• Meta-analysis using inverse variance weighting


Phase II

• Top 10 CHARGE SNPs for SBP, DBP and HTN exchanged for look up in the Global BPgen Consortium (n=34,433)

• Results from CHARGE and Global BPgen were meta-analyzed

• Phase II sample size 63,569• P value threshold 5x10-8


Genetic Risk Score

• A genetic risk score was developed using the top 10 SBP and DBP risk alleles

• The number of risk alleles was calculated for each individual with weighting of each risk allele by its effect size on BP (beta coefficient)

• The weighted risk score was then studied in relation to SBP and DBP


Genome-wide Significant Loci for Systolic Blood Pressure

SNP Chr Position GeneCHARGEP value

BPgenP value

CHARGE+BPgen P value

rs1004467 10 104,584,497 CYP17A1 1.99E-06 1.08E-05 1.28E-10

rs381815 11 16,858,844 PLEKHA7 5.76E-07 2.72E-04 1.89E-09

rs2681492 12 88,537,220 ATP2B1 3.01E-11 4.07E-03 3.76E-11

rs3184504 12 110,368,991 SH2B3 5.73E-07 6.36E-04 4.52E-09


Genome-wide Significant Loci for Diastolic Blood Pressure

SNP Chr Position GeneCHARGEP value

BPgenP value

CHARGE+BPgen P value

rs9815354 3 41,887,655 ULK4 7.88E-07 3.79E-04 2.54E-09

rs11014166 10 18,748,804 CACNB2 8.82E-07 1.46E-03 1.24E-08

rs2681472 12 88,533,090 ATP2B1 3.74E-08 2.43E-03 1.47E-09

rs3184504 12 110,368,991 SH2B3 1.68E-08 2.83E-07 2.58E-14

rs2384550 12 113,837,114 TBX3,5 1.32E-07 1.06E-02 3.75E-08

rs6495122 15 72,912,698 CSK/ULK3 8.10E-07 3.98E-05 1.84E-10


Systolic Blood Pressure Risk Score


Diastolic Blood Pressure Risk Score


GWAS Conclusions• Multiple common genetic variants contribute to inter-

individual blood pressure variation• The effect sizes of risk alleles are small, 1 mmHg for

SBP, 0.5 mmHg for DBP, but the aggregate effect of multiple risk alleles on BP is substantial and sufficient to increase CVD risk

• Observational data indicate that a prolonged increase in diastolic BP of 5 mm Hg is associated with a 34% increase in risk for stroke and a 21% increase in risk of coronary events1


1. MacMahon et al. Lancet 1990

Genes, Molecules, Systems Biology and Epidemiology in Hypertension

• Recent advances in genetics of hypertension

• Systems medicine strategy• Bringing it all together

SABRe CVD Initiative

Systems Approach to Biomarker Research in NHLBI’s Framingham Heart Study

• SABRe CVD aims:– Characterize the biomarker signatures of

• Atherosclerosis • Metabolic syndrome risk factors

SABRe CVD Initiative: Design

• Use high-throughput technology to measure a multitude of biomarkers:– Discovery proteomics, metabolomics, and

lipomics (Project 1)– Targeted immunoassays (Project 2)– Gene expression profiling (Project 3)– microRNA profiling (Project 4)

SABRe CVD Initiative Project 1: “Omic” Discovery

• Discovery proteomics, metabolomics, and lipomics via case-control studies– Atherosclerosis cases vs. matched controls– Metabolic risk factor cases vs. matched controls– New onset CVD cases vs. matched controls

• Validation of promising biomarkers in ~7500 Framingham samples

• (Replication in other observational studies will be proposed)

SABRe CVD Initiative Project 2: Targeted Immunoassays

• Targeted immunoassays for 180 protein biomarkers to be selected by a Nominating Committee based on:– Literature review– Discovery proteomics– Analysis of genome-wide association– Results of expression profiling

SABRe CVD Initiative Project 3: Gene Expression Profiling

SABRe CVD Initiative Project 4: microRNA Profiling

imgm.de/images/bild_rna_microrna5.jpg

Genes, Molecules, Systems Biology and Epidemiology in Hypertension

• Recent advances in genetics of hypertension

• Systems medicine strategy• Bringing it all together

Genomic Convergence Identifies Childhood Asthma Gene

GWAS identifies region on Chr 17q21

for asthma

17q21 region Replicated in 2

independent studies

Asthma SNPs Associated with

ORMDL3 expression

Systems Biology: A Complex Network of Data

Transcriptome(miRNA Profiling)TranscriptomeTranscriptome

(miRNA Profiling)(miRNA Profiling)

Genome(Genetic Variation)

GenomeGenome(Genetic Variation)(Genetic Variation)

Metabolome(Small Molecules)

MetabolomeMetabolome(Small Molecules)(Small Molecules) Proteome

(Gene Products)ProteomeProteome

(Gene Products)(Gene Products)

Transcriptome(RNA Profiling)

TranscriptomeTranscriptome(RNA Profiling)(RNA Profiling)

Risk Factors andDiseases

Risk Factors andRisk Factors andDiseasesDiseases

Bioinformatics Bioinformatics Data IntegrationData IntegrationApplied AnalysesApplied Analyses

Data MiningData Mining

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