Genetics and Genomics for the Prevention and Treatment of Cardiovascular Disease: Update: A Scientific Statement From the American Heart Association Santhi K. Ganesh, Donna K. Arnett, Thermistocles L. Assimes, Craig T. Basson, Aravinda Chakravarti, Patrick T. Ellinor, Mary B. Engler, Elizabeth Goldmuntz, David M. Herrington, Ray E. Hershberger, Yuling Hong, Julie A. Johnson, Steven J. Kittner, Deborah A. McDermott, James F. Meschia, Luisa Mestroni, Christopher J. O'Donnell, Bruce M. Psaty, Ramachandran S. Vasan, Marc Ruel, Win-Kuang Shen, Andre Terzic and Scott A. Waldman Circulation. 2013;128:2813-2851; originally published online December 2, 2013; doi: 10.1161/01.cir.0000437913.98912.1d Circulation is published by the American Heart Association, 7272 Greenville Avenue, Dallas, TX 75231 Copyright © 2013 American Heart Association, Inc. All rights reserved. Print ISSN: 0009-7322. Online ISSN: 1524-4539

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AHA Scientific Statement Genetics and Genomics for the Prevention and Treatment of Cardiovascular Disease: Update A Scientific Statement From the American Heart Association Santhi K. Ganesh, MD, FAHA, Co-Chair*; Donna K. Arnett, PhD, MSPH, FAHA, Co-Chair; Thermistocles L. Assimes, MD, PhD; Craig T. Basson, MD, PhD, FAHA; Aravinda Chakravarti, PhD; Patrick T. Ellinor, MD, PhD, FAHA; Mary B. Engler, PhD, FAHA*; Elizabeth Goldmuntz, MD; David M. Herrington, MD, MHS, FAHA; Ray E. Hershberger, MD, FAHA; Yuling Hong, MD, PhD, FAHA; Julie A. Johnson, PharmD, FAHA; Steven J. Kittner, MD, FAHA; Deborah A. McDermott, MS, CGC; James F. Meschia, MD; Luisa Mestroni, MD; Christopher J. O’Donnell, MD, MPH*; Bruce M. Psaty, MD, PhD, FAHA; Ramachandran S. Vasan, MD, FAHA; Marc Ruel, MD, FAHA; Win-Kuang Shen, MD, FAHA; Andre Terzic, MD, FAHA; Scott A. Waldman, MD, PhD, FAHA; on behalf of the American Heart Association Council on Functional Genomics and Translational Biology, Council on Epidemiology and Prevention, Council on Basic Cardiovascular Sciences, Council on Cardiovascular Disease in the Young, Council on Cardiovascular Surgery and Anesthesia, Council on Clinical Cardiology, Council on Cardiovascular and Stroke Nursing, and Stroke Council

C

ardiovascular diseases (CVDs) are a major source of morbidity and mortality worldwide. Despite a decline of ≈30% over the past decade, heart disease remains the leading killer of Americans.1 For rare and familial forms of CVD, we are increasingly recognizing single-gene mutations that impart relatively large effects on individual phenotype. Examples include inherited forms of cardiomyopathy, arrhythmias, and aortic diseases. However, the prevalence of monogenic disorders typically accounts for a small proportion of the total CVD observed in the population. CVDs in the general population are complex diseases, with several contributing genetic and environmental factors. Although recent progress in monogenic disorders has occurred, we have seen a period of intense investigation to identify the genetic architecture of more common forms of CVD and related traits.

Genomics serves several roles in cardiovascular health and disease, including disease prediction, discovery of genetic loci influencing CVD, functional evaluation of these genetic loci to understand mechanisms, and identification of therapeutic targets. For single-gene CVDs, progress has led to several clinically useful diagnostic tests, extending our ability to inform the management of afflicted patients and their family members. However, there has been little progress in developing genetic testing for complex CVD because individual common variants have only a modest impact on risk. The study of the genomics of complex CVDs is further challenged by the influence of environmental variables, phenotypic heterogeneity, and pathogenic complexity. Characterization of the clinical phenotype requires consideration of the clinical details of the diseases and traits under study.

*This update was prepared, in part, by Drs Mary Engler, Santhi Ganesh, and Christopher O’Donnell in their personal capacity. The opinions expressed in this article are the authors’ own and do not reflect the view of the National Institutes of Health, the US Department of Health and Human Services, or the US government. The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention. The American Heart Association makes every effort to avoid any actual or potential conflicts of interest that may arise as a result of an outside relationship or a personal, professional, or business interest of a member of the writing panel. Specifically, all members of the writing group are required to complete and submit a Disclosure Questionnaire showing all such relationships that might be perceived as real or potential conflicts of interest. This statement was approved by the American Heart Association Science Advisory and Coordinating Committee on October 9, 2013. A copy of the document is available at http://my.americanheart.org/statements by selecting either the “By Topic” link or the “By Publication Date” link. To purchase additional reprints, call 843-216-2533 or e-mail [email protected]. The American Heart Association requests that this document be cited as follows: Ganesh SK, Arnett DK, Assimes TL, Basson CT, Chakravarti A, Ellinor PT, Engler MB, Goldmuntz E, Herrington DM, Hershberger RE, Hong Y, Johnson JA, Kittner SJ, McDermott DA, Meschia JF, Mestroni L, O’Donnell CJ, Psaty BM, Vasan RS, Ruel M, Shen W-K, Terzic A, Waldman SA; on behalf of the American Heart Association Council on Functional Genomics and Translational Biology, Council on Epidemiology and Prevention, Council on Basic Cardiovascular Sciences, Council on Cardiovascular Disease in the Young, Council on Cardiovascular Surgery and Anesthesia, Council on Clinical Cardiology, Council on Cardiovascular and Stroke Nursing, and Stroke Council. Genetics and genomics for the prevention and treatment of cardiovascular disease: update: a scientific statement from the American Heart Association. Circulation. 2013;128:2813–2851. Expert peer review of AHA Scientific Statements is conducted by the AHA Office of Science Operations. For more on AHA statements and guidelines development, visit http://my.americanheart.org/statements and select the “Policies and Development” link. Permissions: Multiple copies, modification, alteration, enhancement, and/or distribution of this document are not permitted without the express permission of the American Heart Association. Instructions for obtaining permission are located at http://www.heart.org/HEARTORG/General/CopyrightPermission-Guidelines_UCM_300404_Article.jsp. A link to the “Copyright Permissions Request Form” appears on the right side of the page. (Circulation. 2013;128:2813-2851.) © 2013 American Heart Association, Inc. Circulation is available at http://circ.ahajournals.org

DOI: 10.1161/01.cir.0000437913.98912.1d

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2814  Circulation  December 24/31, 2013 This update expands the prior scientific statement on the relevance of genetics and genomics for the prevention and treatment of CVDs.2 In the earlier report, we focused on the current status of the field, which consisted of predominantly family-based linkage studies and single-gene or mendelian mutations of relatively large phenotypic effect sizes. The past several years have seen many advances in genetics technologies that have allowed a new generation of genetic association discoveries and an explosion of reports in the literature of these new associations. Approaches such as genome-wide association studies (GWASs) and whole-genome sequencing have led to the discovery of novel genomic determinants of CVDs, and we focus on these findings. Unlike candidate gene association studies, hypothesis-generating GWASs and whole-genome sequencing studies have the potential to identify genes with previously unknown (and even unsuspected) roles in CVD. These findings are in various stages of translation to both the clinical setting and basic functional research aimed at uncovering new mechanisms operative in CVDs. The basic methodology for GWASs is to analyze singlenucleotide polymorphisms (SNPs) across the genome in association with dichotomous (case-control) or continuous traits. SNPs are analyzed by genotyping arrays capable of interrogating millions of SNPs directly and predicting millions more genotypes via imputation. Multiple test corrections and replication of findings in independent samples are done to demonstrate that statistical findings are robust. The remarkable discoveries from GWASs have brought enormous progress to identifying and understanding a portion of the cause of complex CVD. The basis for the remaining portion of genomic cause is uncertain but has been hypothesized to reside in less common, rare, or very rare sequence variants.3–5 Further information on GWAS methodology can be found in several excellent reviews.6–8 In addition to genomic technologies reviewed in the initial statement2 and GWASs, methods such as massively parallel next-generation sequencing9,10 are greatly extending our knowledge of human genetic variation and genetic markers of CVD and related traits. The exome, defined as the ≈1% to 2% of the coding portion of the human genome, encompasses ≈19 000 genes. Exome or genome sequencing provides a novel and powerful research strategy for those conditions driven by uncommon or rare variants.11 In this context, uncommon usually means allele frequencies of 0.5% to 5%; rare, 500 000 single-nucleotide variants identified, 86% had an allele frequency 22 000 cases and 64 000 controls, and identified 13 novel susceptibility loci for CAD.23 In parallel, the Coronary Artery Disease (C4D) Genetics Consortium identified an additional 4 novel loci by pooling together 2 cohorts of European ancestry with others of South Asian ancestry.20

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Ganesh et al   Genetics and Genomics for CVD Prevention and Treatment   2815 More recently, these 2 large consortia joined forces to form the CARDIoGRAMplusC4D consortium and executed a largescale replication of the top findings from CARDIoGRAM using the Illumina Metabochip, which assays ≈200 000 SNP markers in genes of interest for metabolic and CVD traits.29,30 Metabochip data in 34 independent sample collections of European or South Asian descent comprising 41 513 cases and 65 919 controls were combined with existing CARDIoGRAM GWASs to uncover an additional 15 loci, bringing the total number of GWAS susceptibility loci for CAD to 46.30 A further 104 independent variants strongly associated with CAD at a 5% false discovery rate and, together with the 46 loci reaching genome-wide variants, explain ≈11% of the additive heritability of CAD. Together, CARDIoGRAM and C4D20,23,30 evaluated >200 000 individuals and have identified and replicated >30 novel loci for CAD susceptibility (Table 1). These GWAS investigations were comprehensive in that they also assessed gene expression (for genes in the implicated loci) in tissues using human cell lines, evaluated genome-wide maps of allelic expression imbalance, compared “hits” for CAD with those for common CVD risk factors, and performed complex network analyses.20,23,30 The functional impact of the identified loci was interpreted on the basis of the association of CAD-associated alleles with traditional CAD risk factors, through which the genetic risk for CAD is likely imparted,35 Interestingly, in these analyses adjusted only for age and sex, the lead SNP in 12 of the 46 validated CAD susceptibility loci was found to be significantly associated with a lipid trait in the Global Lipids Genetics Consortium data set (predominantly low-density lipoprotein [LDL] and triglycerides) and 5 with blood pressure (BP) in the International Consortium of Blood Pressure data set, underscoring the strong causal influence of these risk factors on CAD risk.30 Network analysis also indicated a causal association between pathways of inflammation and CAD.30 The functional impact of these loci has also been pursued through the use of molecular methods in transgenic mouse models for the 9p21 association with CAD.35 Finally, a recent modest-sized GWAS of ≈1500 Han Chinese subjects with CAD and 5019 control subjects followed by a large-scale replication of top findings in 15 460 cases and 11 472 controls replicated 4 loci uncovered in Europeans and implicated 4 novel loci.31 Two of the novel loci have also been implicated in hypertension in Europeans.31 This GWAS was unable to replicate an association between rs6903956 at 6p24.1 (c6orf105) and CAD uncovered by a previous smaller GWAS in a Chinese Han population despite adequate power to detect it.31,32 These studies highlight several key points. First, many CAD loci have consistent effects across studies of various ethnicity and for both men and women,20,31 although data on African Americans and other non-European ancestry ethnic groups remain relatively limited. Second, associations were generally stronger for early-onset CAD compared with lateronset CAD, suggesting that selecting individuals with premature CAD phenotypes enriches for genetic factors and may yield additional valuable insights in future studies.23 Third, associations were stronger for hard end points such as MI and angiographically defined CAD, again emphasizing the importance of careful phenotyping for GWASs.23 Fourth, as noted in

other GWASs, the effect sizes in these studies typically were modest (often with odds ratios 5%), structural variation, epigenetic modifications, microRNA, altered splicing, and posttranslational modification of proteins also contribute to the residual unexplained heritability.3,23,36 These observations underscore the importance of a multifaceted investigation strategy harnessing the tools of systems biology to better elucidate the genetic basis of CAD. Fifth, a minority of the loci implicated in CAD were associated with traditional CVD risk factors, including lipids and BP, whereas others were associated with myriad other common diseases and traits, including hematologic and biochemical traits and intracranial aneurysm, thereby pointing to the pleiotropic effects of these loci.15,20,23 Sixth, whereas the list of loci implicated in CAD is long, the exact causal variants and mechanisms by which these candidate variants and candidate genes mediate their effects remain to be elucidated for most loci. Such additional work involves careful experiments like those accomplished for the SORT1 locus, for which there is some evidence for causal associations with LDL cholesterol (LDL-C) and CAD.37 Finally, the contribution of these newer loci to risk prediction and their clinical utility remain to be clearly demonstrated in future studies, although improvement in this respect has been noted in some cohorts.38–41

Ischemic Stroke Stroke is a heterogeneous and complex disease composed of 3 main types: ischemic stroke, intracerebral hemorrhage, and subarachnoid hemorrhage. Within the phenotype of ischemic stroke, various subtypes appear to differ in degree of heritability.42 Here, we focus on ischemic stroke and its subtypes. GWASs have provided the strongest evidence for loci associated with the common form of ischemic stroke (Table 2). To date, all such loci have had 2 characteristics. First, most of the loci have been discovered in other mechanistically related conditions such as atrial fibrillation, CAD, and coagulation, with subsequent confirmation of associations with ischemic stroke. This is not unexpected, given the pathogenic associations between stroke and these other traits and because the GWASs of these other conditions have far greater sample sizes and statistical power compared with the GWASs of ischemic stroke. Second, the associations are specific to subtypes of ischemic stroke. For example, a locus on chromosome 4q25 adjacent to the transcription factor PITX2 (codes for pituitary homeobox 2), found to be associated with atrial fibrillation, was subsequently found to be associated with cardioembolic stroke.52 Additionally, a locus on chromosome 16q22 involving ZFHX3 (the zinc finger homeobox protein 3) has been associated with both atrial fibrillation and cardioembolic stroke.53 Similarly, a pooled analysis demonstrated that 6 SNPs in the chromosome 9p21 locus, first identified through a heart disease GWAS,26 are associated with atherosclerotic stroke independently of demographic variables, CAD, MI, and other

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2816  Circulation  December 24/31, 2013 Table 1.   Susceptibility Loci for CAD Uncovered Through GWASs Chromosomal Location

Genes* (Name)

Reference

1p13.3

SORT1 (sortilin 1)

1p32.2

PPAP2B (phosphatidic acid phosphatase type 2B)

1p32.3

PCSK9 (proprotein convertase subtilisin/kexin type 9)

1q21.3

IL6R (interleukin 6 receptor)

1q41

MIA3 (melanoma inhibitory activity family, member 3)

2p11.2

VAMP5 (vesicle-associated membrane protein 5), VAMP8 (vesicle-associated membrane protein 8)

30

2p21

ABCG8 [ATP-binding cassette, subfamily G (WHITE), member 8]

30

2p24.1

WDR35 (WD protein repeats domain 35)

31

2p24.1

APOB (apolipoprotein B)

30

2q22.3

ZEB2 (zinc finger E-box binding homeobox 2)

2q33.1

WDR12 (WD protein repeats domain 12)

19, 20, 23

3q22.3

MRAS (muscle RAS oncogene homolog)

20, 21, 23

4q31.22

EDNRA (endothelin receptor type A)

4q32.1

GUCY1A3 (guanylate cyclase 1, soluble, alpha 3)

5q31.1

SLC22A4 (solute carrier family 22, member 4)

30

6p21.2

KCNK5 (potassium channel, subfamily K, member 5)

30

6p21.31

ANKSIA (ankyrin repeat and sterile α motif domain containing 1A)

23

6p21.32

C6orf10 (chromosome 6 open reading frame 10)

6p24.1

PHACTR1 (phosphatase and actin regulator 1)

6p24.1

C6orf105 (chromosome 6 open reading frame 105)†

6q23.2

TCF21 (transcription factor 21)

6q25.3

LPA [lipoprotein(a)]

23, 32, 33

6q26

PLG (plasminogen)

30

7p21.1

HDAC9 (histone deacetylase 9)

7q22

BCAP29 (B-cell receptor–associated protein 29)

7q32.2

ZC3HC1 (zinc finger, C3HC-type containing 1)

23

8p21.3

LPL (lipoprotein lipase)

30

8q24.13

TRIB1 (tribbles homolog 1)

9p21

CDKN2A/2B (cyclin-dependent kinase inhibitor 2A/2B)

9q34.2

ABO (ABO blood group)

10p11.23

KIAA1462

10q11.1

CXCL12 (chemokine (C-X-C motif, ligand 12)

10q23

LIPA (lipase A, lysosomal acid, cholesterol esterase)

20

10q24.3

CYP17A1 (cytochrome P450, family 17, subfamily A, polypeptide 1)

23

11q22.3

PDGFD (platelet-derived growth factor D)

20

11q23.3

ZNF259 (zinc finger protein 259)

23

12q21.33

ATP2B1 (ATPase, Ca2+ transporting, plasma membrane 1)

12q24.12

SH2B3 (SH2B adaptor protein 3)

13q12.3

FLT1 (FMS-related tyrosine kinase 1)

30

13q34

COL4A1 (collagen, type IV, α1)

23

14q32.2

HHIPL1 (hedgehog interacting protein-like 1)

15q25.1

ADAMTS7 (a disintegrin-like and metallopeptidase with thrombospondin type 1, motif 7)

15q26.1

FURIN (paired basic amino acid cleaving enzyme)

30

17p11.2

SMCR3 (Smith-Magenis syndrome chromosome region, candidate 3)

23

17p13.3

SMG6 (SMG6 nonsense mediated mRNA decay factor)

23

17q21.32

UBE2Z (ubiquitin-conjugating enzyme E2Z)

19p13.2

LDLR (low-density-lipoprotein receptor)

21q22.11

MRPS6 (mitochondrial ribosomal protein S6)

CAD indicates coronary artery disease; and GWAS, genome-wide association study. *Genes are provided for those genes near or within the locus of the primary association signal. †This locus was not replicated in a subsequent larger GWAS.

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20, 23, 25 23 19, 20, 23 30 20, 23, 25

30

30 30, 31

31 19, 20, 23, 31 32 23, 31

30 20, 32

30 20, 23–26, 31 23 20 20, 23, 25

31 20, 23, 31, 34

23 20, 23

23 19, 20, 23 19, 23

Ganesh et al   Genetics and Genomics for CVD Prevention and Treatment   2817 Table 2.  Common Genes Associated With Hemorrhagic and Ischemic Stroke Locus

Gene Symbol*

Gene Name*

References

Familial cerebral amyloid angiopathies  21q21.3

APP

Amyloid β (A4) precursor protein

 20p11.21

CST3

Cystatin C

45

 18q12.1

TTR

Transthyretin

46

APOE

Apolipoprotein E

47

tRNA leucine

48

43, 44

Sporadic intracerebral hemorrhage  19q13.2

Familial ischemic stroke or stroke-like syndromes  Mitochondrial  19p13.2-p13.1  10q26.3

NOTCH3

Notch3

49

HTRA1

HtrA serine peptidase 1

50

Sporadic cardioembolic ischemic stroke  4q25

PITX2

Paired-like homeodomain 2

51

 16q22.3

ZFHX3

Zinc finger homeobox 3

51

TBD

TBD

51

HDAC9

Histone deacetylase 9

51

Sporadic large-vessel atherosclerotic stroke  9p21  7p21.1

TBD indicates to be determined. *Genes are provided for those genes near or within the locus of the primary association signal.

vascular risk factors.54 The 9p21 locus seems to affect stroke risk independently of the risk of MI. Genetic variants identified through GWASs of coagulation/fibrin phenotypes were subsequently examined for association with ischemic stroke. The rs505922 in the ABO gene was found to have a replicated association with large-vessel and cardioembolic stroke.28,55 GWASs have yet to identify a locus that imparts risk by modifying tissue sensitivity to ischemic injury. A genetic variant that imparts sensitivity of the brain parenchyma to focal ischemia might be expected to have an association across ischemic stroke subtypes while lacking an association with conventional atherosclerotic risk factors for stroke such as atrial fibrillation and hypertension (parenchymal hypothesis). More recently, GWASs of ischemic stroke have identified novel loci that were not previously identified through GWASs of other CVDs. Variation in HDAC9 (codes for histone deacetylase 9) has been associated with large-vessel atherosclerotic stroke.51 In addition to confirming ischemic stroke loci initially identified in nonstroke GWASs (PITX2, ZFHX3, and the 9p21 locus), a meta-analysis that involved 12 389 cases of ischemic stroke confirmed the HDAC9 locus.56 HDAC9 variants seem to relate to the predisposition to atherosclerosis.57 As with HDAC9, a novel locus associated with large-vessel ischemic stroke was first discovered in a stroke GWAS.58 Mitochondrial DNA has been shown to harbor variants associated with stroke risk. Mitochondrial DNA is another important part of the human genome, separate from nuclear chromosomal DNA. Mitochondrial DNA is inherited directly from mother to child because of the presence of mitochondria in the oocyte, which gives rise to all mitochondria in the developing tissues. Because of the association of mitochondrial DNA variants with the MELAS (mitochondrial encephalopathy, lactic acidosis, and stroke-like episodes) syndrome, a monogenic cause of ischemic stroke,59 the role of mitochondrial variants in the common form of stroke has been

investigated. A mitochondrial genetic risk profiling study that included all common variants has also shown that common variants are associated with ischemic stroke and with white matter hyperintensity volume.60 Consistent with the GWASs for CAD and MI, the effect sizes of the associated mitochondrial variants for stroke are modest, impeding our ability to discern which particular loci are most useful for profiling in the clinical setting.60 Family studies continue to provide new insights into monogenetic causes of cerebrovascular disease and novel disease mechanisms and pathways. Variants in ACTA2, the vascular smooth muscle cell–specific isoform of α-actin, smooth muscle myosin gene (MYH11),61–63 and multiple transforming growth factor-β signaling genes, including TGFBR1,64–67 TGFBR2,68-70 TGFB2,71,72 and SMAD3,73,74 have been shown to cause familial aortic aneurysms and dissection and to have cerebrovascular manifestations.64–70,75 More recently and similar to the pleiotropic effects of the 9p21 locus, some of the same variants associated with premature CAD are also associated with premature ischemic stroke, including moyamoya disease, which is also associated with other variants such as RNF213.76,77 Laboratory studies demonstrate that these mutations are associated with increased proliferation of smooth muscle cells.78 Although these mutations were found not to be associated with sporadic premature CAD and stroke, these findings suggest a new pathway to target in future genetic research. Global gene expression profiling has recently been studied as a potential tool for distinguishing ischemic stroke from other conditions79 and for identifying the underlying mechanism of ischemic stroke.80 Gene expression profiles from RNA isolated from sequential early blood samples were able to distinguish large-artery atherosclerotic stroke from cardioembolic stroke and to distinguish cardioembolic stroke related to atrial fibrillation from non–atrial fibrillation cardioembolic

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2818  Circulation  December 24/31, 2013 causes. If validated in large independent studies, this approach would be useful for guiding the evaluation of stroke of undetermined origin, currently estimated at 35% to 42% of all ischemic strokes.81

Coronary Calcium, Carotid Intima-Media Thickness, and Carotid Plaque There has also been strong interest in targeting subclinical vascular disease phenotypes as additional traits for GWAS investigations. The Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium82 conducted a metaanalysis of GWASs for coronary artery calcium measured by thoracic computed tomography in 9961 men and women from 5 community-based cohorts and replicated the most promising candidates in 3 independent cohorts (n=6032).83 Several of the loci significantly associated with coronary artery calcium had previously been shown to be associated with MI. These included loci in the 9p21 region (CDKN2B) and SNPs associated with PHACTR1 and COL4A2. Interestingly, other established MI loci showed considerable statistical evidence (although they failed to meet GWAS significance) for association with subclinical coronary disease, including the SORT1/ CELSR2/PSRC1 locus, MRAS, CXCL12, the COL4A1/ COL4A2 locus, and ADAMST7. The convergence of GWAS evidence for both clinical events and anatomic coronary disease provides even more compelling validation of many of the genetic variants and strongly suggests that their apparent associations with MI are likely attributable to influences on the pathogenesis of atherosclerosis rather than other mechanisms that contribute to the development of acute coronary syndromes. In a much larger GWAS meta-analysis involving 31 211 273 subjects in replication subjects in discovery and 11  cohorts, the Cohorts for Heart and Aging Research in Genomic Epidemiology consortium also sought genetic variants associated with common carotid intima-media thickness and carotid plaque.84 However, despite the larger samples sizes, only 2 of the 8 loci with genome-wide or nearly genome-wide SNPs associated with intima-media thickness or plaque were linked to CAD in the CARDIoGRAM consortium (LDLR and EDNRA), supporting the concept that these carotid phenotypes likely share some pathogenic mechanisms that are distinct from those contributing to CAD. In the aggregate, these GWAS meta-analyses targeting subclinical vascular disease traits provide important additional validation for a number of loci related to the pathogenesis of atherosclerosis. Although some of these loci have biologically plausible links to CVD risk factors or known cellular mechanisms involved with atherogenesis, others implicate genes with no currently recognized link to vascular disease. These novel discoveries are now motivating further hypothesis-driven research that may ultimately lead to fundamentally important new discoveries concerning genetic determinants of CVD.

Hypertension BP genetics have been largely illuminated by gene identification studies in mendelian hypertension and hypotension

syndromes with specific electrolyte and other biochemical abnormalities.85 These studies show that functional defects in renal sodium reabsorption or activation of mineralocorticoid activity in the distal nephrons of the kidney and their consequent effects on the renin-angiotensin-aldosterone system lead to biochemical and physiological imbalance in hypertension syndromes and confirm the “renal hypothesis” of Arthur Guyton.86 However, these mendelian mutations are rare, have negligible population-attributable risk, and, even collectively, constitute a small fraction of secondary hypertension cases. Recent studies have attempted to assess the role of rare variants in renal salt-handling genes in essential hypertension and interindividual BP variability. However, once again, these “mendelian” genes make very small contributions to hypertension risk in the population.87 In 2004, Wilson and colleagues88 published data from an unusual white kindred, segregating hypomagnesemia, hypertension, and hypercholesterolemia, which they traced to a mitochondrial isoleucine tRNA mutation. Although the mechanistic basis of this genotype-phenotype connection is not understood, other mutations in this same gene have been identified in a large Chinese kindred.89 Thus, mitochondrial dysfunction, as a consequence of aging, may be a general mechanism for essential hypertension; however, this remains an unproven but exciting hypothesis. The association of mitochondrial variants with hypertension in the population, as has been done for stroke, has yet to be explored. There are physiological reasons to suspect that many other genes affecting functions of the adrenal gland, brain, blood, or vasculature play critical roles in hypertension. BP is a classic heritable quantitative trait, with 30% to 60% heritability in humans and many different genes contributing to this variation.90 The difficulty in making firm conclusions about the role of specific genes in hypertension is that the constituent studies are very heterogeneous with respect to the trait measured (BP versus hypertension versus postural hypertension, etc), measurement technique, treatment, comorbidities, ascertainment, sample size, and genetic methodology. Few findings, except for the roles of genetic variation in the renin-angiotensin-aldosterone system pathway in BP and hypertension, have consistently been replicated because of study differences and low statistical power.90–92 However, in the past 3 years, carefully designed GWASs based on phenotype harmonization and high statistical power have convincingly identified numerous loci involved in BP control and hypertension. Two large GWASs of individuals of European ancestry were published in 2009 and reported 13 loci for systolic BP, diastolic BP, and hypertension.92,93 The 2 consortia that reported these results then combined cohort-level association results into a single consortium effort, the International Consortium of Blood Pressure, for a single combined “mega-meta-analysis,” comprising >79 000 samples for the discovery phase and >200 000 individuals from diverse ancestral populations in the discovery and replication phases.94 In total, 29 loci were found to be associated with systolic BP, diastolic BP, and hypertension (Table 3); however, in total, these loci account for 80 000 subjects identifies multiple loci for C-reactive protein levels. Circulation. 2011;123:731–738. 202. Reiner AP, Barber MJ, Guan Y, Ridker PM, Lange LA, Chasman DI, Walston JD, Cooper GM, Jenny NS, Rieder MJ, Durda JP, Smith JD, Novembre J, Tracy RP, Rotter JI, Stephens M, Nickerson DA, Krauss RM. 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Genetics and genomics for the prevention and treatment of cardiovascular disease: update: a scientific statement from the American Heart Association.

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