Genome wide association studies will unlock the genetic basis of human hypertension

1. Genome wide association studies will unlock the genetic basis of human hypertension Anna F Dominiczak

2. associations with common SNPs identified in GWAS have proven robust and reproducible as per design – most of these SNPs are associated with a relative risk of 1.5 per copy or less – thus even in aggregate account for a small fraction of the overall inherited risk the mechanisms whereby DNA variation in most of these regions influences disease is not obvious  good, more work is needed! GWAS – a watershed in the history of genetics in medicine

3. WTCCC published in 2007 = first GWAS for hypertension – no SNPs crossing experimental threshold of significance (px10-7) 8 further GWAS for BP or hypertension have been published since ( European, Japanese, Korean, African Americans, and Amish ancestry) GWAS & hypertension

4. cluster of SNPs on chr 2q24.3 with serine theronine kinase 39 (STK39) initial P=8.9x10-6 (Amish cohort), meta-analysis P=1.6x10-7 (n>7000 individuals) & relevant protein interacts with cation transporters in the kidney KORA (Europeans), SNP upstream T-cadherin (CDH13) gene on chr 16q23.3 associated with DBP, initial P=5.55x10-5, replication P=5.3 x10-8 Korean cohort, two SNPs associated with SBP & DBP, initial P=9.1x10-7, replication P= 1.3x10-7, intragenic SNP near the ATPase calcium ion transporting membrane 1 gene (ATP2B1) - protein involved in calcium homeostasis Examples of success Wang et al, PNAS 2009 Org et al, Hum Mol Genet 2009 Cho et al, Nat Genet 2009

5. 2009, two large-scale meta-analyses of GWAS for BP and hypertension published (Global BPGen & CHARGE) more stringent significance threshold (p < 5x10-8) genome imputation approaches to combine data across cohorts that used different SNP genotyping chips SBP & DBP in both consortia plus genome scan for hypertensive genes in CHARGE both consortia identified genome-wide significant associations at 8 loci with 3 discovered by both consortia Meta-analysis of GWAS for BP & Hypertension

6. GWAS hits for blood pressure and hypertension (at p< 5x10-8) MTHFR NPPA CLCN6 NPPB AGTRAP CASZ1 ULK4 ULK4 PLEKHA7 FGF5 PRDM8 c4orf22 MDS1 MDS1 3 3 4 1 11 CACNB2 CACNB2 CACNB2 c10orf107 TMEM26 RTKN2 RHOBTB1 ARID5B c10orf107 TMEM26 RTKN2 RHOBTB1 ARID5B c10orf107 TMEM26 RTKN2 RHOBTB1 ARID5B PLCD3 ACBD4 HEX1M1 HEX1M2 ZNF652 PHB CSK ULK3 CYP1A1 CYP1A2 CSK LMAN1L ARID3B ATP2B1 ATP2B1 CYP17A1 AS3MT CNNM2 NT5C2 CYP17A1 AS3MT CNNM2 NT5C2 SH2B3 ATXN2 TBX3 TBX5 SH2B3 ATXN2 TBX3 TBX5 CDH13 10 12 16 15 17 Newton Cheh et al, Nat Gen 2009 (chr 1,3,10,12,15,17) Levy et al, Nat Gen 2009 (chr 3,10,11,12,15)

7. Meta-analyses of GWAS results have revealed 13 new candidates loci for BP and hypertension Two of 13 genomic regions contain genes that have been previously implicated in hypertension susceptibility: Chr 1p32, atrial natriuretic peptide A and B-type natriuretic peptide genes NPPA & B Chr10q24, CYP17A1 gene- (mutations  17-a-hydoxylase CAH) New Candidate Loci for Hypertension

8. small effects of multiple genes the modest fraction of heritability explained Critics of GWAS might tell you that: • and lack of overlap with our biologic understanding • all suggest a weakness of genomics  I and many colleagues disagree

9. genetic mapping turns hypothesis – driven research on its head as it is based on the theory that systematic genome-wide study of DNA variation will lead us to disease genes very large sample sizes are required to pinpoint novel disease causing genes and very stringent level of statistical significance is required i.e. only collaborative big ventures win The origins of dislike/hate

10. each SNP explains a very small proportion of the total variation in SBP & DBP, 0.05-0.10% or 1mmHg/allele systolic and 0.5mmHg/allele diastolic BP but the aggregate effects of several variants do produce meaningful population changes in risk 2mmHg SBP  6% reduction of stroke & 5% reduction of CAD there are many more common variants associated with BP that remain to be discovered The Genetic Architecture of BP

11. ongoing even larger meta-analyses of GWAS = International Consortium for BP-Genome-Wide Association Study (ICBP-GWAS) studies targeting individuals with extreme phenotypes careful analysis of CNVs data from the 1000 Genomes Project open the possibility of reliable imputation of rare variant genotypes resequencing (next-generation) of cases and controls for fine mapping & causal variants Future

12. Feasibility of identifying genetic variants by risk allele frequency and strength of genetic effect Manolio et al, Nature 2009

13. rather than seeking a new twist on a long-studied pathway or asking whether discoveries in model organisms are relevant to humans, researchers can explore hundreds of genes proven by GWAS to be relevant to human disease the challenge is to develop research methods to take us from genetic localisation to medically useful application GWAS & opportunities for physician scientists

14. “As for the future, your task is not to foresee, but to enable it” Antoine de Saint – Exupéry The Wisdom of the Sands