Review Open Access
Copyright ©2011 Baishideng Publishing Group Co., Limited. All rights reserved.
World J Cardiol. Jul 26, 2011; 3(7): 230-247
Published online Jul 26, 2011. doi: 10.4330/wjc.v3.i7.230
Implications of discoveries from genome-wide association studies in current cardiovascular practice
Panniyammakal Jeemon, Kerry Pettigrew, Christopher Sainsbury, Sandosh Padmanabhan, Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, G12 8TA, United Kingdom
Panniyammakal Jeemon, Dorairaj Prabhakaran, Centre for Chronic Disease Control, New Delhi, 110016, India
Panniyammakal Jeemon, Public Health Foundation of India, New Delhi, 110070, India
Dorairaj Prabhakaran, Centre for Cardiometabolic Risk Reduction Strategies, Centre of Excellence, Public Health Foundation of India, New Delhi, 110016, India
Author contributions: Jeemon P, Pettigrew K, Sainsbury C, Prabhakaran D and Padmanabhan S solely contributed to this paper; all authors reviewed and approved the final version.
Supported by A Wellcome Trust Capacity Strengthening Strategic Award to the Public Health Foundation of India and a consortium of UK universities (to Jeemon P); Research grants from National Heart Lung and Blood Institute, United States of America (HHSN286200900026C) and National Institute of Health, United States of America (1D43HD065249) (to Prabhakaran D)
Correspondence to: Sandosh Padmanabhan, PhD, Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, G12 8TA, United Kingdom. sandosh.padmanabhan@glasgow.ac.uk
Telephone: +44-141-3308428 Fax: +44-141-3306997
Received: April 29, 2011
Revised: July 2, 2011
Accepted: July 10, 2011
Published online: July 26, 2011

Abstract

Genome-wide association studies (GWAS) have identified several genetic variants associated with coronary heart disease (CHD), and variations in plasma lipoproteins and blood pressure (BP). Loci corresponding to CDKN2A/CDKN2B/ANRIL, MTHFD1L, CELSR2, PSRC1 and SORT1 genes have been associated with CHD, and TMEM57, DOCK7, CELSR2, APOB, ABCG5, HMGCR, TRIB1, FADS2/S3, LDLR, NCAN and TOMM40-APOE with total cholesterol. Similarly, CELSR2-PSRC1-SORT1, PCSK9, APOB, HMGCR, NCAN-CILP2-PBX4, LDLR, TOMM40-APOE, and APOC1-APOE are associated with variations in low-density lipoprotein cholesterol levels. Altogether, forty, forty three and twenty loci have been associated with high-density lipoprotein cholesterol, triglycerides and BP phenotypes, respectively. Some of these identified loci are common for all the traits, some do not map to functional genes, and some are located in genes that encode for proteins not previously known to be involved in the biological pathway of the trait. GWAS have been successful at identifying new and unexpected genetic loci common to diseases and traits, thus rapidly providing key novel insights into disease biology. Since genotype information is fixed, with minimum biological variability, it is useful in early life risk prediction. However, these variants explain only a small proportion of the observed variance of these traits. Therefore, the utility of genetic determinants in assessing risk at later stages of life has limited immediate clinical impact. The future application of genetic screening will be in identifying risk groups early in life to direct targeted preventive measures.

Key Words: Genome-wide association studies, Cardiovascular disease, Lipids, Blood pressure



INTRODUCTION

Cardiovascular disease (CVD) is the leading cause of morbidity and mortality globally[1,2]. There is a concerted effort to reduce this disease burden, particularly that of coronary heart disease (CHD) and cerebrovascular disease in developed countries[3-5]. These range from primary preventive strategies targeted at risk factors through acute management and secondary prevention strategies[6-8]. Kahn et al[9] estimated that aggressive application of nationally recommended prevention activities for CVD would potentially add approximately 224 million quality adjusted life-years to the US adult population over the next 30 years and improve the average lifespan by at least 1.3 years.

CHD is the result of a combination of genetic and environmental factors. More than 200 risk factors have been associated with CHD and, among these low-density lipoprotein cholesterol (LDL-c) and blood pressure (BP) have been shown through randomized controlled trials to be causally related to CHD. A key factor in reducing the global burden of CVD is early prediction of disease to target preventive interventions. More personalised approaches to CVD prevention are attracting increasing interest. Whilst biomarkers and quantitative traits have been extremely useful in targeting primary prevention, the recent advances in genomics offer a smart option for predicting future risk of disease very early in life using the invariant nature of a genotype throughout an individual’s life-span. For example, Cohen et al[10] demonstrated that a genetic variant resulting in a modest 28% reduction in LDL-c from birth results in an 88% reduction in the risk of CHD. Over the last 5 years, genome-wide association studies (GWAS) have revolutionised the discovery of common genetic variants associated with a range of diseases and traits.

There are three key characteristics of a genetic variant that determine its impact on the phenotype studied - (1) the frequency of the variant; (2) the effect size of the variant on the phenotype; and (3) the number of genetic variants acting on the phenotype. The “common disease common variant” hypothesis (CD:CV) is the model invoked to explain how genes influence common traits such as lipids, coronary artery disease (CAD) and BP[11]. This model proposes, using an evolutionary paradigm, that common disease is due to allelic variants with a frequency greater than 5% in the general population and small individual effect size[12]. The CD:CV framework requires population-wide genotyping of very large numbers of common genetic variants (Single Nucleotide Polymorphisms/SNPs) to determine which variants show significant association with the phenotype studied. Technological advances now allow reliable and high-throughput genotyping of hundreds of thousands of SNPs on a genome-wide scale[13]. Such studies employ large scale association mapping using SNPs, making no assumptions about the genomic location or function of the causal variant, and test the hypothesis that allele frequency differs between individuals with differences in phenotype. In most GWAS, emphasis is given to the “P value” for the association of genotype with disease risk, to reduce the potential for false positive association that arises when the association of hundreds of thousands to millions of markers are tested across the whole genome. The current popular method for multiple-test correction is the frequentist approach of adjusting for a number of independent tests - based on this, a significance level of 5 × 10-8 is commonly used, in populations of European ancestry for an overall genome-wide significance threshold of 0.05, adjusted for an estimated 1 million independent SNPs in the genome by the Bonferroni method[14]. It should be noted that the Bonferroni method is a fairly conservative correction method that may increase false negative rate. Other corrections like the False Discovery Rate or permutation testing can be used to set a different threshold. In this context, it is pertinent to recognise that the P-value is an index of a true positive signal and does not in any way reflect the predictive potential of the associated variant. The current gold standard of validity is multiple replication in independent samples. We review the implications of positive GWAS findings in current cardiovascular practice.

GWAS AND CHD

We summarise the GWAS results of CHD from nine case-control studies and three cohort studies[15-26] (Figure 1 and Table 1). The effect sizes (OR) of susceptibility alleles were modest and ranged from 1.05-2.0. Common variants in chromosome 9p21 were implicated in nine independent case-control studies[16-23,25] and in two cohort studies[15,25]. The most replicated SNPs at chromosome 9p21 were rs0757278 and rs13333049. The loci corresponding to MTHFD1L, initially identified in the Wellcome Trust Case Control Consortium (WTCCC) study[17], were later replicated in the German Family MI study[18] with genome-wide statistical significance. However, it did not reach genome-wide statistical significance in the combined analysis of ten different data sets in the study by Kathiresan et al[21]. Genetic loci corresponding to CELSR2, PSRC1 and SORT1 on chromosome 1p13.3 are identified in three independent studies[18,20,21].

Table 1 Single Nucleotide Polymorphisms associated with coronary heart disease in genome-wide association studies.
ChromosomeSNPPositionSample sizeMAF (%)OR (95% CI)PvalueProximal geneRef.
1rs646776109 620 0539746/974681.01.17 (1.11-1.24)CELSR2[18,20,21]
rs599839109 623 6892801/4582-1.29 (1.18-1.40)4.05 × 10-9PSRC1
rs599839109 623 6891926/293880.81.20 (1.10-1.31)1.30 × 10-5SORT1
1rs1120651055 268 62725 538181.01.15 (1.10-1.21)PCSK9[21]
1rs17465637220 890 1529746/974672.01.13 (1.08-1.18)MIA3[18,21]
2801/4582-1.20 (1.12-1.30)1.27 × 10-6
2rs6725887203 454 1309746/974614.01.17 (1.11-1.23)WDR12[21]
2rs2943634226 776 3242801/458237/321.21 (1.03-1.30)1.60 × 10-7Intergenic[18]
3rs9818870139 604 81219 407/21 36617.3/15.41.15 (1.11-1.19) 7.44 × 10-13MRAS[23]
6rs1252645313 035 53025 538165.01.12 (1.08-1.17)PHACTR1[21]
6rs6922269151 294 6782801/458230.0/26.01.23 (1.15-1.33)2.90 × 10-8MTHFD1L[18]
rs6922269151 294 6781926/293829.4/25.31.17 (1.04-1.32)1.50 × 10-5
62rs2048327160 783 5224976/43834.1/2.11.82 (1.57-2.12) 4.20 × 10-15SLC22A3[22]
rs3127599160 827 124LPAL2
rs7767084160 882 493LPA
rs10755578160 889 728
9rs1075727822 114 4771607/672851.7/45.31.28 (1.22-1.35) 3.60 × 10-14CDKN2A[15,16,18,19,21,22,23,25]
rs1075727422 086 055-25.3/20.41.33 (1.23-1.47)CDKN2B
rs133304922 115 503875/164454.0/48.01.33 (1.18-1.51)3.40 × 10-6
rs133304922 115 5031926/293855.4/47.41.47 (1.27-1.70) 1.16 × 10-13MTAP
rs133304922 115 50312 004/28 949-1.24 (1.20-1.28)
-9746/974656.01.28 (1.23-1.33)
rs497757422 088 574---
-19 407/21 366--
-33 282-1.20 (1.08-1.34)3
rs133304922 115 503
10rs174604844 095 8309746/974684.01.14 (1.08-1.21)CXCL12[18,21]
rs50112044 073 8732801/4582-/-1.33 (1.20-1.48)9.46 × 10-8
12rs2259816119 919 97019 407/21 36637.4/35.81.08 (1.05-1.11)HNF1A-C12 or f43[23]
16rs432991355 462 93318 245-1.29 (1.02-1.63)3CETP[26]
rs720236455 342 8910.76 (0.59-0.99)3
19rs112260811 024 60125 538175.01.15 (1.10-1.20)LDLR[20,21]
rs651172011 063 3061926/293890.21.29 (1.10-1.52)6.70 × 10-4
19rs442063850 114 7861926/293820.91.17 (1.08-1.28)1.00 × 10-4APOE/C1/C4[20,21]
rs442063850 114 78614 365/30 576
21rs998260134 520 99825 538113.01.28 (1.23-1.33)MRPS6[21]
SLC5A3
KCNE2
Figure 1
Figure 1 Significant genome-wide association study findings in coronary heart disease. CELSR2: Cadherin EGF LAG seven-pass G-type receptor 2; PSRC1: Proline/serine-rich coiled-coil 1; SORT1: Sortilin 1; PCSK9: Proprotein convertase subtilisin/kexin type 9; MRAS: Ras-related protein M-Ras; MTHFD1L: Methylenetetrahydrofolate dehydrogenase (NADP+ dependent) 1-like; SLC22A3: Solute carrier family 22 (extraneuronal monoamine transporter), member 3; LPAL2: Lipoprotein, Lp(a)-like 2 pseudogene; LPA: Lipoprotein Lp(a); CDKN2A: Cyclin-dependent kinase inhibitor 2A; CDKN2B: Cyclin-dependent kinase inhibitor 2B; MTAP: Methylthioadenosine phosphorylase; CXCL12: Chemokine (C-X-C motif) ligand 12.
GWAS AND LIPIDS

Aulchenko et al[27] studied total cholesterol (TC)-associated genetic markers and identified 11 loci significantly associated with the trait (Figure 2 and Table 2): these corresponded to TMEM57, DOCK7, CELSR2, APOB, ABCG5, HMGCR, TRIB1, FADS2/S3, LDLR, NCAN and TOMM40-APOE. Many of these genes are also implicated in other lipid traits. After screening the genome for common variants associated with plasma lipids in > 100 000 individuals of European ancestry, Teslovich et al[28] identified 39 novel loci associated with TC and replicated several other loci found to be associated with lipid traits in the previous GWAS.

Table 2 Single nucleotide polymorphisms associated with total cholesterol identified through genome-wide association studies.
ChromosomeStrongest SNPChromosome positionSample sizeMAF (average)βPvalueProximal geneRef.
1rs1090312925 641 52422 550540.0615.4 × 10-10TMEM57[27]
1rs116799862 704 22017 34632-0.0736.4 × 10-10DOCK7[27]
rs10888935319 09932-0.0793.7 × 10-12[27]
1rs646776109 620 05317 44122-0.1288.5 × 10-22CELSR2[27]
1rs1202713525 648 320> 100 00045-1.224.0 × 10-11LDLRAP1[28]
1rs751557792 782 026> 100 00021-1.183.0 × 10-8EVI5[28]
1rs2642442219 040 186> 100 00048-1.365.0 × 10-14IRF2BP2[28]
2rs69321 085 70022 50052-0.0968.7 × 10-23APOB[27]
2rs675662943 918 59417 472920.1451.5 × 10-11ABCG5[27]
2rs7570971135 554 376> 100 000341.252.0 × 10-8RAB3GAP1[28]
3rs229015912 603 920> 100 00022-1.424.0 × 10-9RAF1[28]
5rs38466235 421 42920 873440.0922.5 × 10-19HMGCR[27]
rs1291674 692 295> 100 000392.849.0 × 10-47[28]
5rs6882076156 322 875> 100 00035-1.987.0 × 10-28TIMD4[28]
6rs317792832 520 413> 100 000162.314.0 × 10-19HLA[28]
6rs281498234 654 538> 100 00011-1.865.0 × 10-11C6orf106[28]
6rs9488822116 419 586> 100 00035-1.182.0 × 10-10FRK[28]
7rs1267079821 573 877> 100 000231.439.0 × 10-10DNAH11[28]
7rs207218344 545 705> 100 000252.013.0 × 10-11NPC1L1[28]
8rs6987702126 573 90817 413290.0733.3 × 10-9TRIB1[27]
8rs208168759 551 119> 100 000351.232.0 × 10-12CYP7A1[28]
8rs2737229116 717 740> 100 00030-1.112.0 × 10-8TRPS1[28]
10rs2255141113 923 876> 100 000301.142.0 × 10-10GPAM[28]
11rs17457061 353 78820 916830.0881.5 × 10-10FADS2/3[27]
11rs1012871118 589 560> 100 00028-1.043.0 × 10-8SPTY2D1[28]
11rs7941030122 027 585> 100 000380.972.0 × 10-10UBASH3B[28]
12rs11065987110 556 807> 100 00042-0.967.0 × 10-12BRAP[28]
12rs1169288119 901 033> 100 000331.421.0 × 10-14HNF1A[28]
16rs200099970 665 594> 100 000202.343.0 × 10-24HPR[28]
19rs222867111 071 91220 910880.1589.3 × 10-24LDLR[27]
19rs230413019 650 52820 9147-0.1532.0 × 10-15NCAN[27]
19rs207565050 087 45917 463150.1382.9 × 10-19TOMM40-APOE[27]
rs15758050 087 10620 90333-0.095.1 × 10-17[27]
19rs1040196919 268 718> 100 0007-4.743.0 × 10-38CILP2[28]
19rs49260253 898 229> 100 000491.272.0 × 10-10FLJ36070[28]
20rs2277862> 100 00015-1.194.0 × 10-10ERGIC3[28]
20rs290294038 524 901> 100 00029-1.386.0 × 10-11MAFB[28]
Figure 2
Figure 2 Significant genome-wide association study findings in total cholesterol. TMEM57: Transmembrane protein 57; DOCK7: Dedicator of cytokinesis 7; CELSR2: Cadherin, EGF LAG seven-pass G-type receptor 2; LDLRAP1: Low-density lipoprotein receptor adaptor protein 1; EVI5: Ecotropic viral integration site 5; IRF2BP2: Interferon regulatory factor 2 binding protein 2; APOB: Apolipoprotein B; ABCG5: ATP-binding cassette sub-family G member 5; RAB3GAP1: RAB3 GTPase activating protein subunit 1 (catalytic); RAF1: V-raf-1 murine leukemia viral oncogene homolog 1; HMGCR: 3-hydroxy-3-methylglutaryl-CoA reductase; TIMD4: T-cell immunoglobulin and mucin domain containing 4; HLA: Human leukocyte antigen (HLA) complex; C6orf106: Chromosome 6 open reading frame 106; FRK: Fyn-related kinase; DNAH11: Dynein, axonemal, heavy chain 11; NPC1L1: NPC1 (Niemann-Pick disease; type C1, gene)-like 1; TRIB1: Tribbles Homolog-1 (Trib1); CYP7A1: Cytochrome P450, family 7, subfamily A, polypeptide 1; TRPS1: Trichorhinophalangeal syndrome 1; GPAM: Glycerol-3-phosphate acyltransferase, mitochondrial; FADS: Fatty acid desaturase; SPTY2D1: Suppressor of Ty, domain containing 1 (S. cerevisiae); UBASH3B: Ubiquitin associated and SH3 domain containing B; BRAP: BRCA1 associated protein; HNF1A: Hepatocyte nuclear factor-1 α; HPR: Haptoglobin-related protein; LDLR: Low-density lipoprotein receptor; NCAN: Neurocan; TOMM40: Translocase of outer mitochondrial membrane 40 homolog; CILP2: Cartilage intermediate layer protein 2; ERGIC3: Endoplasmic reticulum-Golgi intermediate compartment protein 3; MAFB: V-maf musculoaponeurotic fibrosarcoma oncogene homolog B.

Prior to the publication of the meta-analysis of blood lipids conducted by Teslovich et al[28], 29 loci had been found to be associated with variation in high-density lipoprotein cholesterol (HDL-c) levels[20,27-39]. Teslovich et al[28] identified 31 novel loci associated with HDL-c with genome-wide significance. The most commonly-replicated loci are LPL, LIPC, CETP, ABCA1, LIPG, APOA1/C3/A4/A5 and GALNT2 (Figure 3 and Table 3). The LIPC locus has a set of common variants nearly 50 kb upstream of the gene, strongly associated with HDL-c and appearing to be independent of previously described variants that overlap the transcribed sequence of the gene. SNPs close to the mevalonate kinase-methylmalonic aciduria cblB type (MMAB) locus were found to be associated with HDL-c initially by Willer et al[20] and later confirmed by Kathiresan et al[29].

Table 3 Single nucleotide polymorphisms associated with high-density lipoprotein cholesterol identified through genome-wide association studie.
ChromosomeStrongest SNPChromosome positionSample sizeMAF (average)Change in HDLc/βPvalueProximal geneRef.
1rs2144300228 361 539865640- 6.6 × 10-7GALNT2[20]
rs4846914228 362 31419 79440-0.05 SD 4.0 × 10-8[30]
1rs466029339 800 767> 100 00023-0.48 4.0 × 10-10PABPC4[28]
1rs1689800180 435 508> 100 00035-0.47 3.0 × 10-10ZNF648[28]
2rs126032627 584 44416 682410.93%< 5 × 10-8GCKR[31]
2rs675429521 059 68817 915252.63 (z-sc)1 4.4 × 10-8APOB[27]
2rs2972146226 808 942> 100 000370.46 3.0 × 10-9IRS1[28]
2rs1049096451 926 90818 245121.35 mg/dL 3.9 × 10-9COBLL1, GRB14[26]
rs12328675165 249 046> 100 000130.68 3.0 × 10-10COBLL1[28]
4rs13107325103 407 732> 100 0007-0.84 7.0 × 10-11SLC39A8[28]
5rs645017653 333 782> 100 00026-0.49 5.0 × 10-8ARL15[28]
6rs281494434 660 775> 100 00016-0.49 4.0 × 10-9C6orf106[28]
6rs605066139 871 359> 100 00042-0.39 3.0 × 10-8CITED2[28]
6rs1084651161 009 807> 100 000161.95 3.0 × 10-8LPA[28]
7rs4731702130 083 924> 100 000480.59 1.0 × 10-15KLF14[28]
8rs208363719 909 45517 922264.14 (z-sc)1 5.5 × 10-18LPL[27]
rs1050366919 891 970865610 3.2 × 10-10[20]
rs33119 864 6856382281.5 mg/dL 9.1 × 10-7[32]
rs1748275319 876 9268180- 2.8 × 10-11[33]
rs32619 863 71910 53622-30 1.8 × 10-8[35]
rs33119 864 6856382281.5 mg/dL 9.1 × 10-7[32]
rs1267891919 888 50219 794100.23 SD 2.0 × 10-34[30]
rs30119 861 2145592250.04 9.3 × 10-11[36]
8rs391602719 869 1485592270.04 5.4 × 10-10SLC18A1[36]
8rs33119 864 68516 809270.43%< 5 × 10-8Intergenic, PPP1R3B, LPL[31]
rs99872899 220 768> 100 0009-1.21 6.0 × 10-25PPP1R3B[25]
8rs2293889116 668 374> 100 0000.41-0.44 6.0 × 10-11TRPS1[28]
9r1323432103 402 7588656121.93 mg/dL 2.5 × 10-8GRIN3A[20]
9rs3905000106 696 89117 91314-4.37 (z-sc)1 8.6 × 10-13ABCA1[27]
rs4149268106 687 041865636 3.3 × 10-7[20]
rs3890182106 687 47621 312- 3.0 × 10-10[29]
rs9282541106 660 65610 5360-9 4.8 × 10-8[35]
rs2515614106 724 13916 798340.20%< 5 × 10-8[31]
rs1883025106 704 12219 37126-0.08 SD 1.0 × 10-9[30]
9rs47136415 279 57840 41412-0.08 SD 3.0 × 10-10TTC39B[29]
rs58108015 295 378> 100 00018-0.65 3.0 × 10-12[28]
9rs1883025106 704 122> 100 00025-0.94 2.0 × 10-33ABCA1[28]
11rs12225230116 233 8406382181.5 mg/dL 5.3 × 10-5APOA1/C3/A4/A5[32]
rs618923116 159 36912 111250.30%< 5 × 10-8[31]
rs964184116 154 12719 79414-0.17 SD 1.0 × 10-12[30
rs7350481116 091 4938993280.62% 8.8 × 10-10[36]
rs7350481116 091 49318 245 2.8 × 10-12[26]
11rs292308410 345 358> 100 00017-0.41 5.0 × 10-8AMPD3[28]
11rs313644146 699 823> 100 000150.78 3.0 × 10-18LRP4[28]
11rs739566217 917392.82 (z-sc)1 6.0 × 10-11MADD-FOLH1[27]
11rs17454761 327 35940 33033-0.09 SD 2.0 × 10-12FADS1-S3[30]
11rs6589565116 145 4475592-0.05 4.4 × 10-7BUD13[36]
11rs2075290116 158 50655927-0.05 4.2 × 10-7ZNF259[36]
12rs2338104108 379 551865645 1.9 × 10-6MVK/MMAB[20]
rs2338104108 379 55119 79345-0.07 SD 1.0 × 10-10[30]
rs7134594108 484 574> 100 00047-0.44 7.0 × 10-15[28]
12rs713437520 365 025> 100 000420.40 4.0 × 10-8PDE3A[28]
12rs4759375122 362 191> 100 00060.86 7.0 × 10-9SBNO1[28]
12rs4765127123 026 120> 100 000340.44 3.0 × 10-10ZNF664[28]
12rs838880123 827 546> 100 000310.61 3.0 × 10-14SCARB1[28]
12rs1818702102 047 68516 844290.22%< 5 × 10-8Intergenic, ASCL1, PAH[31]
15rs153208556 470 65819 736415.03 (z-sc)1 9.7 × 10-36LIPC[27]
rs4115041121 186 681865633 2.8 × 10-9[20]
rs153208556 470 6586382371.8 mg/dL 1.3 × 10-10[32]
rs180058856 510 96721 312- 2.0 × 10-32[29]
rs1185816456 530 02310 53627-55 7.0 × 10-8[35]
rs153208556 470 6586382371.8 mg/dL 1.3 × 10-10[32]
rs180058856 510 96716 811220.60%< 5 × 10-8[31]
rs1046801756 465 80419 794300.10 SD 8.0 × 10-23[30]
rs107783456 510 7715987491.00% 1.3 × 10-14[36]
rs107783456 510 77118 245 1.4 × 10-23[26]
rs26134256 518 4455592220.03 6.3 × 10-8[36]
rs153208556 470 658> 100 000391.45 3.0 × 10-96[28]
15rs265283461 183 920> 100 00020-0.39 9.0 × 10-9LACTB[28]
16rs180077555 552 737262347 2.5 × 10-13CETP[28]
rs153262455 562 98019 674438.24 (z-sc)1 9.4 × 10-94[27]
rs376426155 550 8258656312.42 mg/dL 2.8 × 10-19[20]
rs376426155 550 8256382314.0 mg/dL 1.0 × 10-41[32]
rs180077555 552 7372758492.6 mg/dL 3.0 × 10-13[29]
rs180077555 552 7371643473.99 mg/dL 6.1 × 10-15[33]
rs998941955 542 6408216- 8.5 × 10-27[33]
rs720580455 562 39010 53637-50 4.7 × 10-47[36]
rs376426155 550 8256382314.0 mg/dL 1.0 × 10-41[32]
rs180077555 552 73716 779492.50%< 5 × 10-8[31]
rs376426155 550 825322-6.2 mg/dL 3.4 × 10-12[39]
rs376426155 550 82518 245 3.7 × 10-93[26]
rs17353955 545 54519 794320.25 SD 4.0 × 10-75[30]
rs376426155 550 8255987212.11% 4.8 × 10-29[37]
rs376426155 550 82518 24530-48 3.7 × 10-93[27]
rs1723150655 552 0295592320.07 2.3 × 10-36[36]
rs376426155 550 825> 100 000323.39  7.0 × 10-380[28]
16rs25505266 582 496865617 1.5 × 10-6LCAT[20]
rs25505266 582 4968656 + 4534- 1.2 × 10-7[20]
rs227129366 459 57131 946110.07 SD 9.0 × 10-13[30]
rs1694288766 485 543> 100 000121.27 8.0 × 10-33[28]
16rs227129366 459 57117 910134.99 (z-sc)1 8.3 × 10-16CTCF-PRMT8[27]
16rs28974355 575 295592310.03 8.6 × 10-9NLRC5[36]
16rs292597980 092 291> 100 00030-0.45 2.0 × 10-11CMIP[28]
17rs1186928635 067 382> 100 00034-0.48 1.0 × 10-13STARD3[28]
17rs414800864 386 889> 100 00032-0.42 2.0 × 10-10ABCA8[28]
17rs412976773 915 579> 100 00049-0.39 8.0 × 10-9PGS1[28]
18rs493988345 421 21216 25817-3.98 (z-sc)1 1.6 × 10-11LIPG[27]
rs215655245 435 666865616 8.4 × 10-7[20]
rs215655245 435 66621 312- 2.0 × 10-7[29]
rs493988345 421 21216 648160.22%< 5 × 10-8[31]
rs493988345 421 21219 78517-0.14 SD 7.0 × 10-15[30]
rs493988345 421 21218 245 1.4 × 10-9[26]
rs724191845 414 951> 100 00017-1.31 3.0 × 10-49[28]
18rs1296713556 000 003> 100 00023-0.42 7.0 × 10-9MC4R[28]
19rs76944950 101 84216 728120.30%< 5 × 10-8APOC1-APOE[31]
18 245 2.6 × 10-11[26]
19rs29676058 375 73835 15116-0.12 SD 1.0 × 10-8ANGPTL3[30]
rs72554368 339 196> 100 00047-0.45 3.0 × 10-8[28]
19rs73733711 208 493> 100 0008-0.64 3.0 × 10-9LOC55908[28]
19rs38600059 484 573> 100 000200.83 4.0 × 10-16LILRA3[28]
20rs606590643 987 42216 810480.40%< 5 × 10-8PLTP[31]
18 245 1.9 × 10-14[26]
20rs180096142 475 77830 7143-0.19 SD 8.0 × 10-10HNF4A[30]
rs180096142 475 778> 100 0003-1.88 1.0 × 10-15[28]
20rs767944 009 90940 24819-0.07 SD 4.0 × 10-9PLTP[30]
rs606590643 987 422> 100 00018-0.93 2.0 × 10-22[28]
22rs18136220 262 068> 100 00020-0.46 1.0 × 10-8UBE2L3[28]
Figure 3
Figure 3 Significant genome-wide association study findings in high-density lipoprotein cholesterol. GALNT2: N-acetylgalactosaminyltransferase 2; PABPC4: Poly(A) binding protein; cytoplasmic 4 (inducible form); ZNF648: Zinc finger protein 648; GCKR: Glucokinase (hexokinase 4) regulator; APOB: Apolipoprotein B; IRS1: Insulin receptor substrate 1; COBLL1: COBL-like 1; GRB14: Growth factor receptor-bound protein 14; SLC39A8: Solute carrier family 39 (zinc transporter) member 8; ARL15: ADP-ribosylation factor-like 15; C6orf106: Chromosome 6 open reading frame 106; CITED2: Cbp/p300-interacting transactivator, with Glu/Asp-rich carboxy-terminal domain 2; LPA: Lipoprotein, Lp(a); KLF14: Kruppel-like factor 14; LPL: Lipoprotein lipase; SLC18A1: Solute carrier family 18 (vesicular monoamine) member 1; PPP1R3B: Protein phosphatase 1, regulatory (inhibitor) subunit 3B; TRPS1: Trichorhinophalangeal syndrome 1; GRIN3A: Glutamate receptor, ionotropic, N-methyl-D-aspartate 3A; ABCA1: ATP-binding cassette; sub-family A (ABC1) member 1; TTC39B: Tetratricopeptide repeat domain 39B; ABCA1: ATP-binding cassette, sub-family A (ABC1) member 1; APOA1: Apolipoprotein A-I; AMPD3: Adenosine monophosphate deaminase 3; LRP4: Low-density lipoprotein receptor-related protein 4; MADD-FOLH1: MAP-kinase activating death domain- folate hydrolase (prostate-specific membrane antigen) 1; FADS1-S3: Fatty acid desaturase 1; BUD13: BUD13 homolog; ZNF259: Zinc finger protein 259; MVK: Mevalonate kinase; MMAB: Methylmalonic aciduria (cobalamin deficiency) cblB type; PDE3A: Phosphodiesterase 3A; SBNO1: Strawberry notch homolog 1; ZNF664: Zinc finger protein 664; SCARB1: Scavenger receptor class B member 1; ASCL1: Achaete-scute complex homolog 1; PAH: Phenylalanine hydroxylase; LIPC: Hepatic lipase; LACTB: Lactamase β; CETP: Cholesteryl ester transfer protein plasma; LCAT: Lecithin-cholesterol acyltransferase; CTCF: CCCTC-binding factor (zinc finger protein); PRMT8: Protein arginine methyltransferase 8; NLRC5: NLR family CARD domain containing 5; STARD3: StAR-related lipid transfer (START) domain containing 3; ABCA8: ATP-binding cassette; sub-family A (ABC1) member 8; PGS1: Phosphatidylglycerophosphate synthase 1; LIPG: Lipase endothelial; MC4R: Melanocortin 4 receptor; APOC1: Apolipoprotein C-I; APOE: Apolipoprotien E; ANGPTL3: Angiopoietin-like 3; LILRA3: Leukocyte immunoglobulin-like receptor, subfamily A (without TM domain) member 3; PLTP: Phospholipid transfer protein; HNF4A: Hepatocyte nuclear factor 4 α; PLTP: Phospholipid transfer protein; UBE2L3: Ubiquitin-conjugating enzyme E2L 3.

GWAS have identified several genetic loci associated with LDL-c (Figure 4 and Table 4)[20,27-32,34,36,40], such as the study by Teslovich et al[28] which identified 22 novel and 25 previously implicated loci. CELSR2-PSRC1-SORT1 and PCSK9 loci on chromosome 1, APOB, HMGCR, NCAN-CILP2-PBX4, LDLR, TOMM40-APOE, and APOC1-APOE were the most commonly-replicated loci in LDL-c. Several of these loci were also associated with CHD in the WTCCC study[17].

Table 4 Single nucleotide polymorphisms associated with low-density lipoprotein cholesterol identified through genome-wide association studies.
ChromosomeStrongest SNPChromosome positionSample sizeMAF (average)βPvalueProximal geneRef.
1rs116799862 704 22012 68522-0.155 7.8 × 10-23PSRC1, CELSR2, SORT1[27]
rs646776109 620 053638222- 4.9 × 10-19[32]
rs599839109 623 689163624-1.1 × 10-7[34]
rs602633109 623 034858920- 4.8 × 10-14[20]
rs646776109 620 05316 79122-0.04< 5 × 10-8CELSR2[31]
rs646776109 620 05321 31224-0.16 5 × 10-42PSRC1[29]
rs12740374109 619 11319 64821-0.23 2.0 × 10-42[30]
rs599839109 623 68911 68521-0.05 1.7 × 10-15[40]
rs646776109 620 053433721-0.164.3 × 10-9[40]
rs12740374109 619 113559221-0.151.8 × 10-9SORT1[36]
rs646776109 620 053559222-0.143.8 × 10-8[36]
rs629301109 619 829> 100 00022-5.65  1.0 × 10-170[28]
1rs1159114755 278 23516 8262-0.12< 5 × 10-8PCSK9[31]
rs1159114755 278 23512 1672-0.13< 5 × 10-8[31]
rs1159114755 278 23521 3121-0.26 2.0 × 10-24[30]
rs1120651055 268 62719394 3.5 × 10-11[20]
rs1120651055 268 62719 62919-0.094.0 × 10-8[30]
rs1159114755 278 2355592-0.55 9.3 × 10-12[36]
rs247940955 277 238> 100 000302.01 2.0 × 10-28[28]
2rs69321 085 7002601227.1 × 10-7APOB[38]
rs56233821 141 8261636 + 263117 8.6 × 10-13[34]
rs51513521 139 562858983 3.1 × 10-14[20]
rs56233821 141 8268589 + 10 849 5.6 × 10-22[20]
rs69321 085 70016 11252-0.098 3.6 × 10-17[28]
rs50658521 250 687638220-4.69.3 × 10-9[32]
rs50658521 250 68716 84220-0.04< 5 × 10-8[31]
rs69321 085 70021 312480.12 1.0 × 10-21[29]
rs51513521 139 56219 64820-0.16 5.0 × 10-29[30]
rs56233821 141 82611 68520-0.041.4 × 10-9[40]
rs171322221 124 828433716-0.171.0 × 10-8[40]
rs56233821 141 826559218-0.18 1.2 × 10-11[36]
rs136711721 117 405> 100 000304.05 4.0 × 10-114[28]
2rs675662943 918 59412 706920.157 2.6 × 10-10ABCG5[27]
2rs654471343 927 38523 456320.15  2 × 10-20ABCG8[30]
rs429937643 926 080> 100 000302.75 2.0 × 10-47ABCG5/8[28]
2rs78009427 594 74116 841400.03< 5 × 10-8GCKR[31]
5rs1501908156 330 74727 28037-0.07  1 × 10-11TIMD4-HAVCR1[30]
5rs384666274 686 84016 135440.079 1.5 × 10-11HMGCR[27]
rs1265426474 684 3592758 + 18 554390.1 1.0 × 10-20[29]
rs384666374 691 48219 648380.07 8.0 × 10-12[30]
rs1265426474 684 3595592380.115.8 × 10-8[36]
6rs375735488 570 980> 100 00022-1.43 1.0 × 10-11MYLIP[28]
6rs180056226 201 120> 100 0006-2.22 6.0 × 10-10HFE[28]
6rs1564348160 498 850> 100 00017-0.56 2.0 × 10-17LPA[28]
7rs1267079821 573 87712 695240.0896.1 × 10-9DNAH11[27]
7rs4731702130 083 92416 74749-0.02< 5 × 10-8KLF14[31]
8rs6982636126 548 49716 79847-0.02< 5 × 10-8TRIB1[31]
8rs11136341145 115 531> 100 000401.4 4.0 × 10-13PLEC1[28]
9rs9411489135 144 821> 100 000202.24 6.0 × 10-13ABO[28]
11rs17457061 353 78816 153830.11 4.4 × 10-13FADS2/3[31]
11rs3135506116 167 61716 8376-0.13< 5 × 10-8APOA1-A5[31]
rs2072560116 167 036559260.222.4 × 10-7[36]
11rs11220462125 749 162> 100 000141.95 1.0 × 10-15ST3GAL4[28]
12rs7307277123 041 10916 80434-0.02< 5 × 10-8CCDC92/DNAH10/ZNF664[31]
12rs2650000119 873 34539 340360.072.0 × 10-8HNF1A[30]
14rs801737751 667 587> 100 000471.14 5.0 × 10-1NYNRIN[28]
16rs70827255 553 78916 84343-0.04< 5 × 10-8CETP[31]
rs1723150655 552 029559232-0.115.0 × 10-7[36]
17rs720697142 780 114> 100 000490.782.0 × 10-8OSBPL7[28]
19rs169961419 519 47221 31210-0.13 × 10-8NCAN, CILP2, PBX4[29]
rs222860319 190 924858971.8 × 10-7[20]
rs1699614819 519 47219 3942.7 × 10-9[20]
rs1040196919 268 71819 6486-0.052.0 × 10-8[30]
19rs68811 088 6024267457.3 × 10-7LDLR[34]
rs651172011 063 30685899 6.8 × 10-10[20]
rs651172011 063 30619 394 4.2 × 10-23[20]
rs222867111 071 91216 148820.136 4.2 × 10-14[27]
rs651172011 063 306638212-7.7 5.2 × 10-15[32]
rs651172011 063 30616 84312-0.04< 5 × 10-8[31]
rs651172011 063 30621 31210-0.26  2 × 10-51[29]
rs651172011 063 30619 64810-0.26 2.0 × 10-26[30]
rs222867111 071 912433712-0.181.1 × 10-8[40]
rs1724872011 059 187559213-0.31 7.8 × 10-25[36]
rs651172011 063 306> 100 000116.99 4.0 × 10-117[28]
19rs207565050 087 45912 697150.16 9.3 × 10-19TOMM40-APOE[27]
rs15758050 087 10616 16068-0.111 2.1 × 10-19[27]
rs207565050 087 4594337130.23 7.1 × 10-14[40]
rs207565050 087 4595592140.23 1.1 × 10-14[36]
19rs442063850 114 786260122 3.4 × 10-13APOC1-APOE[38]
rs442063850 114 786426719 8.3 × 10-14[34]
rs442063850 114 786858912 1.5 × 10-21[20]
rs442063850 114 78619 394 3.0 × 10-43[20]
rs480375049 939 46763827 -9.6 3.6 × 10-14[32]
rs480375049 939 46716 6167 -9.3< 5 × 10-8[32]
rs442063850 114 78621 312200.19 1.0 × 10-60[29]
rs442063850 114 78611 881160.29 4.0 × 10-27[30]
rs442063850 114 78611 685180.06 1.2 × 10-20APOC2[40]
rs1272104650 113 0945592150.21 7.6 × 10-14[36]
rs1272110950 139 06155922-0.54 5.1 × 10-14[36]
rs442063850 114 786> 100 000177.14  9.0 × 10-147APOE[28]
19rs1040227150 021 05411 685330.034.1 × 10-8BCAM[40]
rs460527550 030 333433731-0.134.7 × 10-8[40]
19rs480375049 939 46743377-0.28 2.4 × 10-11BCL3[40]
rs153151749 934 01355927-0.225.3 × 10-8[36]
19rs1040227150 021 0545592330.15 2.1 × 10-12PVRL2[36]
20rs606590643 987 42216 843480.02< 5 × 10-8PLPT[31]
20rs610205938 662 19828 89532-0.064.0 × 10-9MAFB[30]
20rs602952639 106 032> 100 000471.39 4.0 × 10-19TOP1[28]
Figure 4
Figure 4 Significant genome-wide association study findings in low-density lipoprotein cholesterol. CELSR2: Cadherin EGF LAG seven-pass G-type receptor 2; PSRC1: Proline/serine-rich coiled-coil 1; SORT1: Sortilin 1; PCSK9: Proprotein convertase subtilisin/kexin type 9; APOB: Apolipoprotein B; ABCG: ATP-binding cassette sub-family G; GCKR: Glucokinase (hexokinase 4) regulator; TIMD4: T-cell immunoglobulin and mucin domain containing 4; HAVCR1: Hepatitis A virus cellular receptor 1; HMGCR: 3-hydroxy-3-methylglutaryl-CoA reductase; MYLIP: Myosin regulatory light chain interacting protein; HFE: Human hemochromatosis; LPA: Lipoprotein, Lp(a); DNAH11: Dynein axonemal heavy chain 11; KLF14: Kruppel-like factor 14; TRIB1: Tribbles homolog 1; PLEC1: Plectin; ABO: ABO blood group (transferase A, α 1-3-N-acetylgalactosaminyltransferase, transferase B, α 1-3-galactosyltransferase); FADS: Fatty acid desaturase; APOA1: Apolipoprotien A1; ST3GAL4: ST3 β-galactoside α-2;3-sialyltransferase 4; CCDC92: Coiled-coil domain containing 92; DNAH10: Dynein axonemal heavy chain 10; ZNF664: Zinc finger protein 664; HNF1A: HNF1 homeobox A; NYNRIN: NYN domain and retroviral integrase containing; CETP: Cholesteryl ester transfer protein plasma; OSBPL7: Oxysterol binding protein-like 7; NCAN: Nucleoporin 214kDa; CILP2: Cartilage intermediate layer protein 2; PBX4: Pre-B-cell leukemia homeobox 4; LDLR: Low-density lipoprotein receptor; TOMM40: Translocase of outer mitochondrial membrane 40 homolog; APOE: Apolipoprotien E; APOC2: Apolipoprotein C-II; APOE: Apolipoprotien E; BCAM: Basal cell adhesion molecule; BCL3: B-cell CLL/lymphoma 3; PVRL2: Poliovirus receptor-related 2 (herpesvirus entry mediator B); PLPT: Proteolipid protein; MAFB: V-maf musculoaponeurotic fibrosarcoma oncogene homolog B; TOP1: Topoisomerase (DNA) 1.

In total, 43 different loci have been found to be associated with triglycerides (TAG) in GWAS (Figure 5 and Table 5). SNPs in proximity to ANGPTL3, APOB, GCKR, MLXIPL, LPL, TRIB1, APOA1/A4/A5/C3, and NCAN-CILP2-PBX4 have been associated with TAG in several GWAS.

Table 5 Single nucleotide polymorphisms associated with triglycerides identified through genome-wide association studies.
ChromosomeStrongest SNPStudiesSample sizeMAF (average)βPvalueProximal geneRef.
1rs116799862 704 22014 26832-0.091 2.0 × 10-12DOCK7[27]
rs1088935362 890 78414 33732-0.085 8.2 × 10-11[27]
1rs1159114755 278 23516 8262-0.09< 5 × 10-8PCSK9[31]
1rs1204231962 822 4074267343.2 × 10-7ANGPTL3[34]
rs1088935362 890 78416 83133-0.03< 5 × 10-8[31]
rs1088935362 890 784899314-0.132.0 × 10-9[37]
rs1213033362 964 36521 31222-0.112.0 × 10-8[29]
rs1088935362 890 78419 83433-0.053.0 × 10-7[30]
rs174819562 822 18118 243 1.7 × 10-10[20]
rs213192562 798 530> 100 00032-4.94 9.0 × 10-43[28]
1rs4846914228 362 31421 312400.08 7.0 × 10-15GALNT2[28]
2rs675429521 059 68814 33825-0.0772.5 × 10-8APOB[27]
rs67354821 091 04912 694760.0861.1 × 10-8[27]
rs67354821 091 04916 79721-0.04< 5 × 10-8[31]
rs69321 085 70021 312480.12 1.0 × 10-21[29]
rs755706721 061 71719 84022-0.08 9.0 × 10-12[30]
rs104203421 078 786> 100 00022-5.99 1.0 × 10-45[28]
2rs78009427 594 7412659353.7 × 10-8GCKR[38]
rs78009427 594 741426739 8.1 × 10-14[34]
rs126032627 584 444868440 1.5 × 10-15[20]
rs78009427 594 74118 243 6.1 × 10-32[20]
rs78009427 594 74117 79063-0.103 3.1 × 10-20[27]
rs126032627 584 4446382410.07 1.3 × 10-16[32]
rs126032627 584 44416 650410.07< 5 × 10-8[31]
rs126032627 584 444899345-0.101 1.1 × 10-11[37]
rs78009427 594 74121 312340.13 3.0 × 10-14[29]
rs126032627 584 44419 840450.12 2.0 × 10-31[30]
rs126032627 584 4445592400.061.8 × 10-7[36]
rs126032627 584 444> 100 000418.76 6.0 × 10-133[28]
2rs10195252165 221 337> 100 00040-2.01 2.0 × 10-10COBLL1[28]
3rs645040137 409 312> 100 00022-2.223.0 × 10-8MSL2L1[28]
4rs44217788 249 285> 100 00041-2.25 9.0 × 10-12KLHL8[28]
5rs968666155 897 543> 100 000202.57 1.0 × 10-10MAP3K1[28]
6rs207653032 471 79416 829430.03< 5 × 10-8BTNL2[31]
6rs224705631 373 469> 100 00025-2.99 2.0 × 10-15HLA[28]
7rs1323820371 767 603> 100 0004-7.911.0 × 10-9TYW1B[28]
7rs1714573872 620 8102758 + 18 55413-0.14 7.0 × 10-22BCL7B, TBL2, MLXIPL[29]
TBL2
rs1197440972 627 326559220-0.085.7 × 10-9MLXIPL[36]
rs10551921107 998 852559220-0.081.3 × 10-8[36]
7rs224046672 494 20512 680870.137 1.1 × 10-12MLXIPL[27]
rs1197440972 627 32616 83919-0.04< 5 × 10-8[31]
rs71405272 502 80519 84012-0.16 3.0 × 10-15[30]
rs1714573872 620 81018 243 2.0 × 10-12[20]
rs1714573872 620 810> 100 00012-9.32 6.0 × 10-58[28]
7rs4731702130 083 92416 71449-0.03< 5 × 10-8KLF14[31]
7rs1714571372 542 746559220-0.09 5.3 × 10-10BAZ1B[36]
8rs1177676710 721 339> 100 000372.011.0 × 10-8PINX1[28]
8rs149574118 317 161> 100 000222.85 5.0 × 10-14NAT2[28]
8rs208363719 909 45514 34426-0.107 1.0 × 10-14LPL[27]
rs1009663319 875 20112 708880.174 1.9 × 10-18[27]
rs1267891919 888 50219 84010-0.25 2.0 × 10-41[30]
rs1009663319 875 201899312-0.169 9.3 × 10-14[37]
rs33119 864 685559225-0.08 1.7 × 10-11[36]
rs1267891919 888 502> 100 00012-13.64  2.0 × 10-115[28]
8rs1748275319 876 9262652114.9 × 10-7LPL[38]
rs1748275319 876 9261636101.2 × 10-9[34]
rs1748275319 876 9261636 + 2631 5.2 × 10-15[34]
rs699341419 947 198868446 1.4 × 10-13[20]
rs1050366919 891 9704267 3.9 × 10-22[20]
rs32819 864 004638211-0.09 4.7 × 10-11Intergenic, PPP1R3B, LPL[32]
rs33119 864 685638228-0.061.7 × 10-9[32]
rs32819 864 68516 81211-0.09< 5 × 10-8LPL[31]
rs32819 864 00421 2429-0.19 2.0 × 10-28[29]
8rs6982636126 548 49716 76547-0.03< 5 × 10-8TRIB1[31]
rs1732151512 655 59121 24249-0.08 4.0 × 10-17[29]
rs2954029126 560 1548684562.8 × 10-8[20]
rs1732151512 655 59114 176 7.0 × 10-13[20]
rs2954029126 560 15419 84044-0.11 3.0 × 10-19[30]
rs2954029126 560 154> 100 00047-5.64 3.0 × 10-55[28]
8rs391602719 869 148559227-0.08 1.0 × 10-10SLC18A1[36]
8rs781941211 082 57133 33648-0.043.0 × 10-8XKR6-AMAC1L2[30]
10rs1076173164 697 616> 100 00043-2.38 3.0 × 10-12JMJD1C[28]
10rs206888894 829 632> 100 00046-2.282.0 × 10-8CYP26A1[28]
11rs12272004116 108 93412 6227-0.181 5.4 × 10-13APO (A1/A4/A5/C3)[27]
rs6589566116 157 63316366 1.5 × 10-11[34]
rs6589566116 157 6331636 + 2631 3.7 × 10-12[34]
rs964184116 154 127868412 1.5 × 10-16[20]
rs12286037116 157 41718 422 1.0 × 10-26[20]
rs3135506116 167 617638260.13 5.5 × 10-12[32]
rs662799116 168 917638260.14 2.9 × 10-15[32]
rs3135506116 167 61716 80460.14< 5 × 10-8[31]
rs7350481116 091 4938993430.24 1.4 × 10-49[37]
rs28927680116 124 28321 31270.26 2.0 × 10-17[29]
rs964184116 154 12719 840140.3 4.0 × 10-62[30]
rs651821116 167 789559260.21 8.8 × 10-21APOA1[36]
rs964184116 154 127> 100 0001316.95  7.0 × 10-240[28]
11rs17454761 327 35938 846330.06 2.0 × 10-14FADS1-S3[30]
rs17454661 326 406> 100 000343.82 5.0 × 10-24[28]
11rs6589565116 145 447559270.19 4.5 × 10-20BUD13[36]
11rs2075290116 158 506559270.19 6.6 × 10-20ZNF259[36]
12rs7307277123 041 10916 77134-0.04< 5 × 10-8CCDC92/DNAH10/[31]
ZNF664
12rs11613352-> 100 00023  -2.7 4.0 × 10-10LRP1[28]
15rs241271040 471 079> 100 000272.0 × 10-8CAPN3[28]
15rs292928242 033 223> 100 00055.13 2.0 × 10-11FRMD5[28]
15rs477504156 461 9878684677.3 × 10-5LIPC[20]
rs477504156 461 98717 1041.6 × 10-8[20]
16Rs1164965330 825 988> 100 00040-2.133.0 × 10-8CTF1[28]
16rs180077555 552 73716 77949-0.03< 5 × 10-8CETP[31]
19rs15758050 087 10616 16033-0.0691.2 × 10-8TOMM40-APOE[27]
rs43940150 106 29111 885680.0861.8 × 10-9[27]
19rs1699614819 519 47221 31210  -0.14.0 × 10-9NCAN, CILP2, PBX4[29]
rs1040196919 268 718868482.3 × 10-7[20]
rs1699614819 519 47218 3912.5 × 10-9[20]
rs1721652546 471 51619 8407-0.11 4.0 × 10-11[30]
rs1261018519 582 72255929  -0.15.6 × 10-7[36]
19rs43940150 106 29116 63835-0.04< 5 × 10-8APOC1-APOE[31]
rs43940150 106 291> 100 00036  -5.5 1.0 × 10-30APOE[28]
19rs230412819 607 15155929  -0.13.2 × 10-7GMI[36]
20rs606590643 987 42216 810480.04< 5 × 10-8PLPT[31]
rs767944 009 90938 561190.07 7.0 × 10-11[30]
22rs575693136 875 979> 100 00040-1.544.0 × 10-8PLA2G6[28]
Figure 5
Figure 5 Significant genome-wide association study findings in triglycerides. DOCK7: Dedicator of cytokinesis 7; PCSK9: Proprotein convertase subtilisin/kexin type 9; GALNT2: N-acetylgalactosaminyltransferase 2; ANGPTL3: Angiopoietin-like 3; APOB: Apolipoprotien B; GCKR: Glucokinase (hexokinase 4) regulator; COBLL1: COBL-like 1; MSL2L1: Male-specific lethal 2 homolog; KLHL8: Kelch-like 8; MAP3K1: Mitogen-activated protein kinase kinase kinase 1; BTNL2: Butyrophilin-like 2 (MHC class II associated); HLA: Major histocompatibility complex; TYW1B: tRNA-yW synthesizing protein 1 homolog B; TBL2: Transducin (β)-like 2; BCL7B: B-cell CLL/lymphoma 7B; TBL2: Transducin (β)-like 2; MLXIPL: MLX interacting protein-like; KLF14: Kruppel-like factor 14; BAZ1B: Bromodomain adjacent to zinc finger domain 1B; PINX1: PIN2/TERF1 interacting, telomerase inhibitor 1; NAT2: N-acetyltransferase 2 (arylamine N-acetyltransferase); LPL: Lipoprotein lipase; PP1R3B: Protein phosphatase 1, regulatory (inhibitor) subunit 3B; TRIB1: Tribbles homolog 1; SLC18A1: Solute carrier family 18 (vesicular monoamine) member 1; XKR6: XK Kell blood group complex subunit-related family member 6; AMAC1L2: Acyl-malonyl condensing enzyme 1-like 2; JMJD1C: Jumonji domain containing 1C; CYP26A1: Cytochrome P450 family 26 subfamily A polypeptide 1; APOA1: Apolipoprotein A-I; FADS: Fatty acid desaturase; CCDC92: Coiled-coil domain containing 92; DNAH10: Dynein axonemal heavy chain 10; ZNF664: Zinc finger protein 664; LRP1: Low density lipoprotein receptor-related protein 1; CAPN3: Calpain 3, (p94); FRMD5: FERM domain containing 5; LIPC: Hepatic lipase; CTF1: Cardiotrophin 1; CETP: Cholesteryl ester transfer protein plasma; TOMM40: Translocase of outer mitochondrial membrane 40 homolog; APOE: Apolipoprotien E; NCAN: Nucleoporin 214kDa; CILP2: Cartilage intermediate layer protein 2; PBX4: Pre-B-cell leukemia homeobox 4; GMIP: GEM interacting protein; PLPT: Palmitoyl-protein thioesterase 1; PLA2G6: Phospholipase A2, group VI (cytosolic; calcium-independent).
GWAS AND BP

In 2007, the Framingham Heart Study[41] reported on 1327 individuals whose BP had been sampled longitudinally in the Framingham Community project. In the same year, the WTCCC[17] reported results from 2000 Northern European subjects with HTN. Although a few SNPs did reach a statistical significance of P < 10-5, none of them achieved genome-wide significance (P < 5 × 10-8). The most significant GWAS findings in blood pressure are summarized in Table 6 and Figure 6 [42-50].

The global BPgen consortium[42] studied 34 433 subjects of European ancestry, subsequently followed up the findings with direct genotyping of 71 225 individuals of European ancestry and 12 889 individuals of Indian Asian ancestry and conducted a joint analysis. They identified an association between systolic or diastolic BP (SBP/DBP) and common variants in eight regions near the CYP17A1 (intergenic CNNM2/NT5C2), CYP1A2 (intron CSK), FGF5, SH2B3 (intron ATXN2), MTHFR, c10orf107, ZNF652 and intron PLCD3. Furthermore, three of these common variants (MTHFR, CYP17A1 and CYP17A2 or CSK) were associated with HTN (P < 5 × 10-8). The CHARGE consortium study (n = 29 136) identified 13, 20 and 10 SNPs for SBP, DBP and HTN respectively[43].

In a joint meta-analysis of CHARGE consortium data with BPgen consortium data (n = 34 433)[43], four CHARGE loci attained genome-wide significance for SBP (ATP2B1, CYP17A1, PLEKHA7, SH2B3), six for DBP (ATP2B1, CACNB2, CSK-ULK3, SH2B3, TBX3-TBX5, ULK4) and one for HTN (ATP2B1). The KORA study by Org et al[48] in a South German Cohort identified a SNP upstream of T-cadherin adhesion molecule (CDH13) gene on chromosome 16 (rs11646213) as significantly associated with HTN at a genome-wide level. Finally, in a population of African origin, Adeyemo et al[44] identified four common variants (MYLIP, chr 6; YWHAZ, chr 8; IPO7, chr 11 and SLC24A4, chr 14) associated with SBP with genome-wide significance.

Wang et al[47] identified STK39, SPAK (STE20/SPS1-related proline and alanine rich kinase; a serine/threonine kinase) with a P value of 1.6 × 10-7 in an Amish cohort. Several other studies also identified potentially important genetic loci associated with BP traits with borderline genome-wide significance. These include ATP2B1[43,51] (ATPase, Ca++ transporting, plasma membrane 1) on chromosome 12, FOXD3[41] (fork head box D3) on chromosome 1, CCNG1 (cyclin G1)[48] on chromosome 5, BCAT1 (branched chain aminotransferase 1, cytosolic)[17] on chromosome 12, ATXN2 (ataxin 2)[42,43] on chromosome 12 and TBX3 (T-box3)[43] on chromosome 12 (Figure 6 and Table 6). However, none of these loci were replicated in other studies. Using an extreme case-control design, Padmanabhan et al[50] identified a novel HTN locus on chromosome 16 in the promoter region of uromodulin (UMOD; rs13333226, combined P value 3.6 × 10-11). The minor G allele of this SNP is associated with a lower risk of HTN [OR (95% CI): 0.87 (0.84-0.91)], reduced urinary UMOD excretion and increased estimated glomerular filtration rate (3.6 mL/min per minor-allele, P = 0.012), and borderline association with renal sodium balance.

Table 6 Single nucleotide polymorphisms associated with hypertension and blood pressure in genome-wide association studies.
ChrSNPPositionAncestryN (discovery)PhenotypeRisk alleleRisk allele frequencyOR/βPNearest geneRef.
1rs1736750411 785 365E34 433SBPG0.14-0.852 × 10-13MTHFR, CLCN6, NPPA, NPPB, AGTRAP[42,43]
2rs6749447168 749 632E542SBPG0.281.908 × 10-5STK39[47]
3rs981535441 887 655E29 136DBPA0.170.493 × 10-9ULK4[42,43]
4rs1699807381 403 365E34 433DBPT0.210.501 × 10-21FGF5, PRDM8, C4orf22[42,43]
4rs991316100 541 468AA1017SBPT0.451.625 × 10-6ADH7[44]
10rs1101416618 748 804E29 136DBPA0.660.371 × 10-8CACNB2[42,43]
10rs153044063 194 597E34 433DBPT0.19-0.391 × 10-9C10orf107, TMEM26, RTKN2, RHOBTB1, ARID5B, CYP17A1[42,43]
10rs1004467104 584 497E29 136SBPA0.901.051 × 10-10TMEM26, RTKN2, RHOBTB1, ARID5B, CYP17A1[42,43]
10rs11191548104 836 168E34 433SBPT0.911.163 × 10-7CYP17A1, AS3MT, CNNM2, NT5C2[42,43]
11rs38181516 858 844E29 136SBPT0.260.652 × 10-9PLEKHA7[42,43]
12rs1724975488584EA8842SBP, DBPA0.371.069 × 10-7ATP2B1[49]
12rs268147288 533 090E29 136SBP, DBP, HTNA0.830.502 × 10-9ATP2B1[42,43]
12rs2681492,88 537 220E29 136SBP, DBP, HTNT0.800.854 × 10-11ATP2B1[42,43]
12rs3184504110 368 991E29 136SBP, DBPT0.490.483 × 10-14ATXN2, SH2B3[42,43]
12rs653178110 492 139E34 433DBPT0.53-0.463 × 10-18ATXN2, SH2B3[42,43]
12rs2384550113 837 114E29 136DBPA0.350.434 × 10-8TBX3, TBX5[42,43]
15rs155057656 000 706AA1017SBPC0.861.923 × 10-6ALDH1A2[44]
15rs137894272 865 396E34 433DBPC0.360.431 × 10-23CSK, CYP1A1, CYP1A2, LMAN1L, CPLX3, ARID3B, ULK3[42,43]
15rs649512272 912 698E29 136DBPA0.420.402 × 10-10CSK, CYP1A1, CYP1A2, LMAN1L, CPLX3, ARID3B, ULK3[42,43]
16rs1333322620 273 155E3320HTNA0.811.154 × 10-11UMOD[50]
16rs1164621381 200 152E1977HTNT0.601.288 × 10-6CDH13[48]
17rs1294645440 563 647E34 433SBPT0.280.571 × 10-8PLCD3, ACBD4, HEXIM1, HEXIM2[42,43]
17rs1694804844 795 465E34 433DBPG0.390.315 × 10-9ZNF652, PHB[42,43]
Figure 6
Figure 6 Significant genome-wide association study findings in blood pressure. ACBD4: Acyl-CoA binding domain containing 4; ADH7: Alcohol dehydrogenase 7 (class IV), mu or sigma polypeptide; AGTRAP: Angiotensin II receptor-associated protein; ALDH1A2: Aldehyde dehydrogenase 1 family, member A2; ARID5B: AT rich interactive domain 5B (MRF1-like); AS3MT: Arsenic (+3 oxidation state) methyltransferase; ATP2B1: ATPase, Ca++ transporting; plasma membrane 1; ATXN2: Ataxin 2; C10orf107: Chromosome 10 open reading frame 107; C4orf22: Chromosome 4 open reading frame 22; CACNB2: Calcium channel, voltage-dependent, β 2 subunit; CDH13: Cadherin 13, H-cadherin (heart); CLCN6: Chloride channel 6; CNNM2: Cyclin M2; CPLX3: Complexin 3; CSK: C-src tyrosine kinase; CYP17A1: Cytochrome P450, family 17, subfamily A; polypeptide 1; CYP1A1: Cytochrome P450, family 1, subfamily A, polypeptide 1; CYP1A2: Cytochrome P450, family 1, subfamily A, polypeptide 2; FGF5: Fibroblast growth factor 5; HEXIM1: Hexamethylene bis-acetamide inducible 1; HEXIM2: Hexamthylene bis-acetamide inducible 2; LMAN1L: Lectin, mannose-binding, 1 like; MTHFR: Methylenetetrahydrofolate reductase (NAD(P)H); NPPA: Natriuretic peptide A; NPPB: Natriuretic peptide B; NT5C2: 5'-nucleotidase, cytosolic II; PHB: Prohibitin; PLCD3: Phospholipase C, Δ 3; PLEKHA7: Pleckstrin homology domain containing, family A member 7; PRDM8: PR domain containing 8; RHOBTB1: Rho-related BTB domain containing 1; RTKN2: Rhotekin 2; SH2B3: SH2B adaptor protein 3; STK39: Serine threonine kinase 39; TBX3: T-box 3; TBX5: T-box 5; TMEM26: Transmembrane protein 26; ULK3: Unc-51-like kinase 3 (C. elegans); ULK4: Unc-51-like kinase 4 (C. elegans); UMOD: Uromodulin; ZNF652: Zinc finger protein 652.
CLINICAL IMPLICATIONS

GWAS are a useful tool in the identification of new and unexpected genetic loci of common diseases and traits, thus providing key novel insights into disease biology. But the clinical utility of these discoveries is negligible at this stage. The comparatively small numbers of variants which have been successfully replicated in several independent studies explain only a small proportion of the observed variation of these traits and explain in aggregate less than 20% of disease heritability. For example, the loci underpinning LDL-C levels[28] and BP account for < 20% of the variance of these quantitative traits. The variants associated with CHD increase disease risk by up to 20% per allele[51,52]. Next generation sequencing is now used to study low-frequency and rare variants that may potentially explain some of the missing heritabilities; however it is likely that studies designed to test for gene-environment interactions and gene-gene interactions may hold the answer. There were attempts to develop genetic profiles using the results from GWAS studies, but these have very limited value in personalised risk prediction as the genotype-phenotype effect sizes are very small. In the few studies that have evaluated the ability of a panel of genetic markers to discriminate CHD cases, the area under the receiver operating characteristic curve has been small indicating that conventional risk factors and family history are better at predicting risk and the incremental advantage of adding genetic markers is negligible. A few studies have attempted reclassification based on incorporation of SNPs from GWAS of CAD, lipids, etc.[52-58], and while they showed some improvement in net reclassification, the interpretation of these are still controversial and not translatable into general use[59]. Many companies are providing direct-to-consumer genetic tests that provide a “genetic risk profile” for an individual using risk alleles of small-to-moderate effects despite the clinical utility of genetic screening not being established. None of the major healthcare providers in Europe and USA have adopted these tests for CHD risk prediction, and the FDA has advised that direct-to-consumer genetic tests should be considered to be medical devices requiring FDA approval for commercial use. The future application of genetic screening will be in identifying risk groups early in life to direct targeted preventive measures and potentially pharmacogenetic tests to identify those at higher risk for adverse events. While technology is not a barrier to achieving this, the discovery, evaluation and deployment of these tests will require the same standards as non-genetic tests[60].

Footnotes

Peer reviewer: Boris Z Simkhovich, MD, PhD, The Heart Institute, Good Samaritan Hospital, 1225 Wilshire Boulevard, Los Angeles, CA 90017, United States

S- Editor Tian L L- Editor O’Neill M E- Editor Zheng XM

References
1.  World Health Organization The World Health Report, 2002: reducing risks, promoting healthy life. Geneva, 2002. .  [PubMed]  [DOI]  [Cited in This Article: ]
2.  Ezzati M, Lopez AD, Rodgers A, Vander Hoorn S, Murray CJ. Selected major risk factors and global and regional burden of disease. Lancet. 2002;360:1347-1360.  [PubMed]  [DOI]  [Cited in This Article: ]
3.  Unal B, Critchley JA, Capewell S. Explaining the decline in coronary heart disease mortality in England and Wales between 1981 and 2000. Circulation. 2004;109:1101-1107.  [PubMed]  [DOI]  [Cited in This Article: ]
4.  Ford ES, Capewell S. Coronary heart disease mortality among young adults in the U.S. from 1980 through 2002: concealed leveling of mortality rates. J Am Coll Cardiol. 2007;50:2128-2132.  [PubMed]  [DOI]  [Cited in This Article: ]
5.  Peeters A, Nusselder WJ, Stevenson C, Boyko EJ, Moon L, Tonkin A. Age-specific trends in cardiovascular mortality rates in the Netherlands between 1980 and 2009. Eur J Epidemiol. 2011;26:369-373.  [PubMed]  [DOI]  [Cited in This Article: ]
6.  Ford ES, Capewell S. Proportion of the decline in cardiovascular mortality disease due to prevention versus treatment: public health versus clinical care. Annu Rev Public Health. 2011;32:5-22.  [PubMed]  [DOI]  [Cited in This Article: ]
7.  Unal B, Critchley JA, Fidan D, Capewell S. Life-years gained from modern cardiological treatments and population risk factor changes in England and Wales, 1981-2000. Am J Public Health. 2005;95:103-108.  [PubMed]  [DOI]  [Cited in This Article: ]
8.  Kabir Z, Bennett K, Shelley E, Unal B, Critchley J, Feely J, Capewell S. Life-years-gained from population risk factor changes and modern cardiology treatments in Ireland. Eur J Public Health. 2007;17:193-198.  [PubMed]  [DOI]  [Cited in This Article: ]
9.  Kahn R, Robertson RM, Smith R, Eddy D. The impact of prevention on reducing the burden of cardiovascular disease. Circulation. 2008;118:576-585.  [PubMed]  [DOI]  [Cited in This Article: ]
10.  Cohen JC, Boerwinkle E, Mosley TH, Hobbs HH. Sequence variations in PCSK9, low LDL, and protection against coronary heart disease. N Engl J Med. 2006;354:1264-1272.  [PubMed]  [DOI]  [Cited in This Article: ]
11.  Lander ES. The new genomics: global views of biology. Science. 1996;274:536-539.  [PubMed]  [DOI]  [Cited in This Article: ]
12.  McCarthy MI, Abecasis GR, Cardon LR, Goldstein DB, Little J, Ioannidis JP, Hirschhorn JN. Genome-wide association studies for complex traits: consensus, uncertainty and challenges. Nat Rev Genet. 2008;9:356-369.  [PubMed]  [DOI]  [Cited in This Article: ]
13.  Ku CS, Loy EY, Pawitan Y, Chia KS. The pursuit of genome-wide association studies: where are we now? J Hum Genet. 2010;55:195-206.  [PubMed]  [DOI]  [Cited in This Article: ]
14.  Pe'er I, Yelensky R, Altshuler D, Daly MJ. Estimation of the multiple testing burden for genomewide association studies of nearly all common variants. Genet Epidemiol. 2008;32:381-385.  [PubMed]  [DOI]  [Cited in This Article: ]
15.  McPherson R, Pertsemlidis A, Kavaslar N, Stewart A, Roberts R, Cox DR, Hinds DA, Pennacchio LA, Tybjaerg-Hansen A, Folsom AR. A common allele on chromosome 9 associated with coronary heart disease. Science. 2007;316:1488-1491.  [PubMed]  [DOI]  [Cited in This Article: ]
16.  Helgadottir A, Thorleifsson G, Manolescu A, Gretarsdottir S, Blondal T, Jonasdottir A, Jonasdottir A, Sigurdsson A, Baker A, Palsson A. A common variant on chromosome 9p21 affects the risk of myocardial infarction. Science. 2007;316:1491-1493.  [PubMed]  [DOI]  [Cited in This Article: ]
17.  Wellcome Trust Case Control Consortium. Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature. 2007;447:661-678.  [PubMed]  [DOI]  [Cited in This Article: ]
18.  Samani NJ, Erdmann J, Hall AS, Hengstenberg C, Mangino M, Mayer B, Dixon RJ, Meitinger T, Braund P, Wichmann HE. Genomewide association analysis of coronary artery disease. N Engl J Med. 2007;357:443-453.  [PubMed]  [DOI]  [Cited in This Article: ]
19.  Schunkert H, Götz A, Braund P, McGinnis R, Tregouet DA, Mangino M, Linsel-Nitschke P, Cambien F, Hengstenberg C, Stark K. Repeated replication and a prospective meta-analysis of the association between chromosome 9p21.3 and coronary artery disease. Circulation. 2008;117:1675-1684.  [PubMed]  [DOI]  [Cited in This Article: ]
20.  Willer CJ, Sanna S, Jackson AU, Scuteri A, Bonnycastle LL, Clarke R, Heath SC, Timpson NJ, Najjar SS, Stringham HM. Newly identified loci that influence lipid concentrations and risk of coronary artery disease. Nat Genet. 2008;40:161-169.  [PubMed]  [DOI]  [Cited in This Article: ]
21.  Kathiresan S, Voight BF, Purcell S, Musunuru K, Ardissino D, Mannucci PM, Anand S, Engert JC, Samani NJ, Schunkert H. Genome-wide association of early-onset myocardial infarction with single nucleotide polymorphisms and copy number variants. Nat Genet. 2009;41:334-341.  [PubMed]  [DOI]  [Cited in This Article: ]
22.  Trégouët DA, König IR, Erdmann J, Munteanu A, Braund PS, Hall AS, Grosshennig A, Linsel-Nitschke P, Perret C, DeSuremain M. Genome-wide haplotype association study identifies the SLC22A3-LPAL2-LPA gene cluster as a risk locus for coronary artery disease. Nat Genet. 2009;41:283-285.  [PubMed]  [DOI]  [Cited in This Article: ]
23.  Erdmann J, Grosshennig A, Braund PS, König IR, Hengstenberg C, Hall AS, Linsel-Nitschke P, Kathiresan S, Wright B, Trégouët DA. New susceptibility locus for coronary artery disease on chromosome 3q22.3. Nat Genet. 2009;41:280-282.  [PubMed]  [DOI]  [Cited in This Article: ]
24.  Elliott P, Chambers JC, Zhang W, Clarke R, Hopewell JC, Peden JF, Erdmann J, Braund P, Engert JC, Bennett D. Genetic Loci associated with C-reactive protein levels and risk of coronary heart disease. JAMA. 2009;302:37-48.  [PubMed]  [DOI]  [Cited in This Article: ]
25.  Karvanen J, Silander K, Kee F, Tiret L, Salomaa V, Kuulasmaa K, Wiklund PG, Virtamo J, Saarela O, Perret C. The impact of newly identified loci on coronary heart disease, stroke and total mortality in the MORGAM prospective cohorts. Genet Epidemiol. 2009;33:237-246.  [PubMed]  [DOI]  [Cited in This Article: ]
26.  Ridker PM, Paré G, Parker AN, Zee RY, Miletich JP, Chasman DI. Polymorphism in the CETP gene region, HDL cholesterol, and risk of future myocardial infarction: Genomewide analysis among 18 245 initially healthy women from the Women's Genome Health Study. Circ Cardiovasc Genet. 2009;2:26-33.  [PubMed]  [DOI]  [Cited in This Article: ]
27.  Aulchenko YS, Ripatti S, Lindqvist I, Boomsma D, Heid IM, Pramstaller PP, Penninx BW, Janssens AC, Wilson JF, Spector T. Loci influencing lipid levels and coronary heart disease risk in 16 European population cohorts. Nat Genet. 2009;41:47-55.  [PubMed]  [DOI]  [Cited in This Article: ]
28.  Teslovich TM, Musunuru K, Smith AV, Edmondson AC, Stylianou IM, Koseki M, Pirruccello JP, Ripatti S, Chasman DI, Willer CJ. Biological, clinical and population relevance of 95 loci for blood lipids. Nature. 2010;466:707-713.  [PubMed]  [DOI]  [Cited in This Article: ]
29.  Kathiresan S, Melander O, Guiducci C, Surti A, Burtt NP, Rieder MJ, Cooper GM, Roos C, Voight BF, Havulinna AS. Six new loci associated with blood low-density lipoprotein cholesterol, high-density lipoprotein cholesterol or triglycerides in humans. Nat Genet. 2008;40:189-197.  [PubMed]  [DOI]  [Cited in This Article: ]
30.  Kathiresan S, Willer CJ, Peloso GM, Demissie S, Musunuru K, Schadt EE, Kaplan L, Bennett D, Li Y, Tanaka T. Common variants at 30 loci contribute to polygenic dyslipidemia. Nat Genet. 2009;41:56-65.  [PubMed]  [DOI]  [Cited in This Article: ]
31.  Chasman DI, Paré G, Mora S, Hopewell JC, Peloso G, Clarke R, Cupples LA, Hamsten A, Kathiresan S, Mälarstig A. Forty-three loci associated with plasma lipoprotein size, concentration, and cholesterol content in genome-wide analysis. PLoS Genet. 2009;5:e1000730.  [PubMed]  [DOI]  [Cited in This Article: ]
32.  Chasman DI, Paré G, Zee RY, Parker AN, Cook NR, Buring JE, Kwiatkowski DJ, Rose LM, Smith JD, Williams PT. Genetic loci associated with plasma concentration of low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, triglycerides, apolipoprotein A1, and Apolipoprotein B among 6382 white women in genome-wide analysis with replication. Circ Cardiovasc Genet. 2008;1:21-30.  [PubMed]  [DOI]  [Cited in This Article: ]
33.  Heid IM, Boes E, Müller M, Kollerits B, Lamina C, Coassin S, Gieger C, Döring A, Klopp N, Frikke-Schmidt R. Genome-wide association analysis of high-density lipoprotein cholesterol in the population-based KORA study sheds new light on intergenic regions. Circ Cardiovasc Genet. 2008;1:10-20.  [PubMed]  [DOI]  [Cited in This Article: ]
34.  Wallace C, Newhouse SJ, Braund P, Zhang F, Tobin M, Falchi M, Ahmadi K, Dobson RJ, Marçano AC, Hajat C. Genome-wide association study identifies genes for biomarkers of cardiovascular disease: serum urate and dyslipidemia. Am J Hum Genet. 2008;82:139-149.  [PubMed]  [DOI]  [Cited in This Article: ]
35.  Kooner JS, Chambers JC, Aguilar-Salinas CA, Hinds DA, Hyde CL, Warnes GR, Gómez Pérez FJ, Frazer KA, Elliott P, Scott J. Genome-wide scan identifies variation in MLXIPL associated with plasma triglycerides. Nat Genet. 2008;40:149-151.  [PubMed]  [DOI]  [Cited in This Article: ]
36.  Talmud PJ, Drenos F, Shah S, Shah T, Palmen J, Verzilli C, Gaunt TR, Pallas J, Lovering R, Li K. Gene-centric association signals for lipids and apolipoproteins identified via the HumanCVD BeadChip. Am J Hum Genet. 2009;85:628-642.  [PubMed]  [DOI]  [Cited in This Article: ]
37.  Kamatani Y, Matsuda K, Okada Y, Kubo M, Hosono N, Daigo Y, Nakamura Y, Kamatani N. Genome-wide association study of hematological and biochemical traits in a Japanese population. Nat Genet. 2010;42:210-215.  [PubMed]  [DOI]  [Cited in This Article: ]
38.  Saxena R, Voight BF, Lyssenko V, Burtt NP, de Bakker PI, Chen H, Roix JJ, Kathiresan S, Hirschhorn JN, Daly MJ. Genome-wide association analysis identifies loci for type 2 diabetes and triglyceride levels. Science. 2007;316:1331-1336.  [PubMed]  [DOI]  [Cited in This Article: ]
39.  Hiura Y, Shen CS, Kokubo Y, Okamura T, Morisaki T, Tomoike H, Yoshida T, Sakamoto H, Goto Y, Nonogi H. Identification of genetic markers associated with high-density lipoprotein-cholesterol by genome-wide screening in a Japanese population: the Suita study. Circ J. 2009;73:1119-1126.  [PubMed]  [DOI]  [Cited in This Article: ]
40.  Sandhu MS, Waterworth DM, Debenham SL, Wheeler E, Papadakis K, Zhao JH, Song K, Yuan X, Johnson T, Ashford S. LDL-cholesterol concentrations: a genome-wide association study. Lancet. 2008;371:483-491.  [PubMed]  [DOI]  [Cited in This Article: ]
41.  Levy D, Larson MG, Benjamin EJ, Newton-Cheh C, Wang TJ, Hwang SJ, Vasan RS, Mitchell GF. Framingham Heart Study 100K Project: genome-wide associations for blood pressure and arterial stiffness. BMC Med Genet. 2007;8 Suppl 1:S3.  [PubMed]  [DOI]  [Cited in This Article: ]
42.  Newton-Cheh C, Johnson T, Gateva V, Tobin MD, Bochud M, Coin L, Najjar SS, Zhao JH, Heath SC, Eyheramendy S. Genome-wide association study identifies eight loci associated with blood pressure. Nat Genet. 2009;41:666-676.  [PubMed]  [DOI]  [Cited in This Article: ]
43.  Levy D, Ehret GB, Rice K, Verwoert GC, Launer LJ, Dehghan A, Glazer NL, Morrison AC, Johnson AD, Aspelund T. Genome-wide association study of blood pressure and hypertension. Nat Genet. 2009;41:677-687.  [PubMed]  [DOI]  [Cited in This Article: ]
44.  Adeyemo A, Gerry N, Chen G, Herbert A, Doumatey A, Huang H, Zhou J, Lashley K, Chen Y, Christman M. A genome-wide association study of hypertension and blood pressure in African Americans. PLoS Genet. 2009;5:e1000564.  [PubMed]  [DOI]  [Cited in This Article: ]
45.  Kato N, Miyata T, Tabara Y, Katsuya T, Yanai K, Hanada H, Kamide K, Nakura J, Kohara K, Takeuchi F. High-density association study and nomination of susceptibility genes for hypertension in the Japanese National Project. Hum Mol Genet. 2008;17:617-627.  [PubMed]  [DOI]  [Cited in This Article: ]
46.  Yang HC, Liang YJ, Wu YL, Chung CM, Chiang KM, Ho HY, Ting CT, Lin TH, Sheu SH, Tsai WC. Genome-wide association study of young-onset hypertension in the Han Chinese population of Taiwan. PLoS One. 2009;4:e5459.  [PubMed]  [DOI]  [Cited in This Article: ]
47.  Wang Y, O'Connell JR, McArdle PF, Wade JB, Dorff SE, Shah SJ, Shi X, Pan L, Rampersaud E, Shen H. From the Cover: Whole-genome association study identifies STK39 as a hypertension susceptibility gene. Proc Natl Acad Sci U S A. 2009;106:226-231.  [PubMed]  [DOI]  [Cited in This Article: ]
48.  Org E, Eyheramendy S, Juhanson P, Gieger C, Lichtner P, Klopp N, Veldre G, Döring A, Viigimaa M, Sõber S. Genome-wide scan identifies CDH13 as a novel susceptibility locus contributing to blood pressure determination in two European populations. Hum Mol Genet. 2009;18:2288-2296.  [PubMed]  [DOI]  [Cited in This Article: ]
49.  Cho YS, Go MJ, Kim YJ, Heo JY, Oh JH, Ban HJ, Yoon D, Lee MH, Kim DJ, Park M. A large-scale genome-wide association study of Asian populations uncovers genetic factors influencing eight quantitative traits. Nat Genet. 2009;41:527-534.  [PubMed]  [DOI]  [Cited in This Article: ]
50.  Padmanabhan S, Melander O, Johnson T, Di Blasio AM, Lee WK, Gentilini D, Hastie CE, Menni C, Monti MC, Delles C. Genome-wide association study of blood pressure extremes identifies variant near UMOD associated with hypertension. PLoS Genet. 2010;6:e1001177.  [PubMed]  [DOI]  [Cited in This Article: ]
51.  Ioannidis JP. Prediction of cardiovascular disease outcomes and established cardiovascular risk factors by genome-wide association markers. Circ Cardiovasc Genet. 2009;2:7-15.  [PubMed]  [DOI]  [Cited in This Article: ]
52.  Morrison AC, Bare LA, Chambless LE, Ellis SG, Malloy M, Kane JP, Pankow JS, Devlin JJ, Willerson JT, Boerwinkle E. Prediction of coronary heart disease risk using a genetic risk score: the Atherosclerosis Risk in Communities Study. Am J Epidemiol. 2007;166:28-35.  [PubMed]  [DOI]  [Cited in This Article: ]
53.  Paynter NP, Chasman DI, Buring JE, Shiffman D, Cook NR, Ridker PM. Cardiovascular disease risk prediction with and without knowledge of genetic variation at chromosome 9p21.3. Ann Intern Med. 2009;150:65-72.  [PubMed]  [DOI]  [Cited in This Article: ]
54.  Kathiresan S, Melander O, Anevski D, Guiducci C, Burtt NP, Roos C, Hirschhorn JN, Berglund G, Hedblad B, Groop L. Polymorphisms associated with cholesterol and risk of cardiovascular events. N Engl J Med. 2008;358:1240-1249.  [PubMed]  [DOI]  [Cited in This Article: ]
55.  Talmud PJ, Cooper JA, Palmen J, Lovering R, Drenos F, Hingorani AD, Humphries SE. Chromosome 9p21.3 coronary heart disease locus genotype and prospective risk of CHD in healthy middle-aged men. Clin Chem. 2008;54:467-474.  [PubMed]  [DOI]  [Cited in This Article: ]
56.  Paynter NP, Chasman DI, Paré G, Buring JE, Cook NR, Miletich JP, Ridker PM. Association between a literature-based genetic risk score and cardiovascular events in women. JAMA. 2010;303:631-637.  [PubMed]  [DOI]  [Cited in This Article: ]
57.  Rosenberg S, Elashoff MR, Beineke P, Daniels SE, Wingrove JA, Tingley WG, Sager PT, Sehnert AJ, Yau M, Kraus WE. Multicenter validation of the diagnostic accuracy of a blood-based gene expression test for assessing obstructive coronary artery disease in nondiabetic patients. Ann Intern Med. 2010;153:425-434.  [PubMed]  [DOI]  [Cited in This Article: ]
58.  Ripatti S, Tikkanen E, Orho-Melander M, Havulinna AS, Silander K, Sharma A, Guiducci C, Perola M, Jula A, Sinisalo J. A multilocus genetic risk score for coronary heart disease: case-control and prospective cohort analyses. Lancet. 2010;376:1393-1400.  [PubMed]  [DOI]  [Cited in This Article: ]
59.  Pepe MS, Feng Z, Gu JW. Comments on 'Evaluating the added predictive ability of a new marker: From area under the ROC curve to reclassification and beyond' by M. J. Pencina et al., Statistics in Medicine (DOI: 10.1002/sim.2929). Stat Med. 2008;27:173-181.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in F6Publishing: 1]  [Reference Citation Analysis (0)]
60.  Hlatky MA, Greenland P, Arnett DK, Ballantyne CM, Criqui MH, Elkind MS, Go AS, Harrell FE, Hong Y, Howard BV. Criteria for evaluation of novel markers of cardiovascular risk: a scientific statement from the American Heart Association. Circulation. 2009;119:2408-2416.  [PubMed]  [DOI]  [Cited in This Article: ]