Jeemon P, Pettigrew K, Sainsbury C, Prabhakaran D, Padmanabhan S. Implications of discoveries from genome-wide association studies in current cardiovascular practice. World J Cardiol 2011; 3(7): 230-247 [PMID: 21860704 DOI: 10.4330/wjc.v3.i7.230]
Corresponding Author of This Article
Sandosh Padmanabhan, PhD, Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, G12 8TA, United Kingdom. sandosh.padmanabhan@glasgow.ac.uk
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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
ORCID number: $[AuthorORCIDs]
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.
Citation: Jeemon P, Pettigrew K, Sainsbury C, Prabhakaran D, Padmanabhan S. Implications of discoveries from genome-wide association studies in current cardiovascular practice. World J Cardiol 2011; 3(7): 230-247
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.
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.
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.
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.
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.
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.
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
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