Literature DB >> 21977987

Replication of LDL GWAs hits in PROSPER/PHASE as validation for future (pharmaco)genetic analyses.

Stella Trompet1, Anton J M de Craen, Iris Postmus, Ian Ford, Naveed Sattar, Muriel Caslake, David J Stott, Brendan M Buckley, Frank Sacks, James J Devlin, P Eline Slagboom, Rudi G J Westendorp, J Wouter Jukema.   

Abstract

BACKGROUND: The PHArmacogenetic study of Statins in the Elderly at risk (PHASE) is a genome wide association study in the PROspective Study of Pravastatin in the Elderly at risk for vascular disease (PROSPER) that investigates the genetic variation responsible for the individual variation in drug response to pravastatin. Statins lower LDL-cholesterol in general by 30%, however not in all subjects. Moreover, clinical response is highly variable and adverse effects occur in a minority of patients. In this report we first describe the rationale of the PROSPER/PHASE project and second show that the PROSPER/PHASE study can be used to study pharmacogenetics in the elderly.
METHODS: The genome wide association study (GWAS) was conducted using the Illumina 660K-Quad beadchips following manufacturer's instructions. After a stringent quality control 557,192 SNPs in 5,244 subjects were available for analysis. To maximize the availability of genetic data and coverage of the genome, imputation up to 2.5 million autosomal CEPH HapMap SNPs was performed with MACH imputation software. The GWAS for LDL-cholesterol is assessed with an additive linear regression model in PROBABEL software, adjusted for age, sex, and country of origin to account for population stratification.
RESULTS: Forty-two SNPs reached the GWAS significant threshold of p = 5.0e-08 in 5 genomic loci (APOE/APOC1; LDLR; FADS2/FEN1; HMGCR; PSRC1/CELSR5). The top SNP (rs445925, chromosome 19) with a p-value of p = 2.8e-30 is located within the APOC1 gene and near the APOE gene. The second top SNP (rs6511720, chromosome 19) with a p-value of p = 5.22e-15 is located within the LDLR gene. All 5 genomic loci were previously associated with LDL-cholesterol levels, no novel loci were identified. Replication in WOSCOPS and CARE confirmed our results.
CONCLUSION: With the GWAS in the PROSPER/PHASE study we confirm the previously found genetic associations with LDL-cholesterol levels. With this proof-of-principle study we show that the PROSPER/PHASE study can be used to investigate genetic associations in a similar way to population based studies. The next step of the PROSPER/PHASE study is to identify the genetic variation responsible for the variation in LDL-cholesterol lowering in response to statin treatment in collaboration with other large trials.

Entities:  

Mesh:

Substances:

Year:  2011        PMID: 21977987      PMCID: PMC3207930          DOI: 10.1186/1471-2350-12-131

Source DB:  PubMed          Journal:  BMC Med Genet        ISSN: 1471-2350            Impact factor:   2.103


Background

Cardiovascular disease is the leading cause of death in industrialized countries at old age. Advancing age is one of the most important risk factors for cardiovascular disease [1]. With the rising number of elderly people in our society cardiovascular disease has a major impact on healthcare [2]. The prevention of cardiovascular disease is critically dependent on lipid lowering therapy including the 3-hydroxymethyl-3-methylglutaryl coenzyme A (HMG-CoA) reductase inhibitors (statins). Statins are the most prescribed class of drugs worldwide and therapy is generally associated with a reduction of cardiovascular events by 20-30%. However, clinical response is highly variable and adverse effects occur in a minority of patients [3]. Recent research provides evidence that genetic variation contributes importantly to this variable drug response [4]. Pharmacogenomics focuses on unraveling the genetic determinants of such variable drug responses, both in intended, beneficial effects and unintended, adverse effects [5]. Therefore, we here present the PHArmacogenetic study of Statin in the Elderly at risk (PHASE) a genome wide association study (GWAS) in the PROspective Study of Pravastatin in the Elderly at Risk for vascular disease (PROSPER)[6] investigating the genetic variation responsible for the individual variation in drug response funded by the European Union's Seventh Framework Programme. To validate the GWAS performed in the PHASE study, we executed a proof-of-principle study to investigate the underlying genetic variation in LDL cholesterol levels. Recent GWA studies have identified several new loci that influence circulating levels of blood lipids with around 95 loci showing statistical associations with circulating total cholesterol levels, HDL cholesterol, LDL cholesterol, and triglycerides [7]. These GWA studies are executed in population based studies with various age groups, however the elderly (age > 75 years) are rarely represented in these studies. With this proof-of-principle study we provide a testing frame to show that the PROSPER/PHASE study has sufficient statistical power to find genome wide statistical significant associations in quantitative traits such as LDL cholesterol in an elderly population. We replicated our findings from the PROSPER/PHASE study in two independent cohorts to validate that our results contain no false positive findings.

Methods

Study population

PROSPER was an investigator-driven, prospective multi-national randomized placebo-controlled trial to assess whether treatment with pravastatin diminishes the risk of major vascular events in the elderly [6;8]. Between December 1997 and May 1999, we screened and enrolled subjects in Scotland, Ireland, and the Netherlands. Men and women aged 70-82 years were recruited if they had pre-existing vascular disease or were at increased risk of such disease because of smoking, hypertension, or diabetes. A total number of 5804 subjects, of whom more than 50% was female, were randomly assigned to pravastatin or placebo. Various clinical laboratory measurements were carried out like inflammatory markers (CRP and various cytokines) and other biochemical substrates (e.g. glucose, leptin) at baseline and during follow-up. The protocol of the PROSPER study meets the criteria of the Declaration of Helsinki and was approved by the Medical Ethics Committees of each participating institution. Written informed consent was obtained from all participating subjects.

LDL cholesterol

Plasma lipids and lipoproteins were measured twice during the screening phase, i.e. at the beginning and end of the single-blind, placebo "run-in" phase according to the standardized Lipid Research Clinics protocol. Baseline LDL cholesterol levels were taken as the average of these 2 determinations prior to randomization to statin treatment. Total cholesterol (TC), HDL cholesterol, and triglycerides were assessed after an overnight fast, LDL cholesterol was calculated by the Friedewald formula, as previously described [8].

Genotyping

The genotyping was conducted using the Illumina 660-Quad beadchips following manufacturer's instructions. These beadchips contain 657,366 single nucleotide polymorphism (SNP) and copy number variants (CNV) probes. After genotyping, samples and genetic markers were subjected to a stringent quality control protocol. From the 5763 samples with DNA available that underwent genotyping, 519 samples (9%) were excluded during the quality control (Figure 1). Excluded were 18 duplicated samples, 219 samples with a call rate < 97.5%, 11 samples with an excess for heterozygosity, 40 samples of non-caucasian origin, 170 samples with familiar relationships (IBD > 0.35), and 61 samples with a gender mismatch. From the 657,366 probes on the beadchips, 95,876 probes were filtered based on CNV intensity. Moreover, 4,298 SNPs were excluded with a call rate < 95%, leaving us with 557,192 SNPs for analysis. To maximize the availability of genetic data and coverage of the genome, imputation up to 2.5 million autosomal CEPH HapMap SNPs was performed with MACH imputation software based on the Hapmap built II release 23. To assess accuracy of the imputed genotypes, we compared the imputation output with SNPs that had been previously genotyped on other platforms.
Figure 1

Flow chart of the Quality Control of the PROSPER/PHASE study.

Flow chart of the Quality Control of the PROSPER/PHASE study.

Statistical Analysis

Genome wide association analysis was performed with PROBABEL software specialized in genetic association analysis with imputed data taking the probability of the genotype into account (http://www.genabel.org/). With analyzing imputed genotypes, the observed allele count is replaced by the imputation's estimated dosage. For the continuous trait, baseline LDL cholesterol levels, an additive linear regression model was used to assess estimates and standard errors. The model was adjusted for sex and age, and country to correct for the within-study population structure. Standard errors for the regression estimates were calculated with model-robust methods. The analysis of 2.5 million SNPs at once poses a multiple testing problem. After the use of a Bonferroni correction, the threshold for genome wide significant results was set at 5.0e-08.

Replication

Associations with a genome-wide significant p-value of 5.0e-08 were replicated in two independent cohorts, the West of Scotland Coronary Prevention Study (WOSCOPS)[9] and the Cholesterol and Recurrent Events (CARE) trial [10]. The WOSCOPS study was a double blind randomized placebo-controlled clinical trial in which 6595 men (age range 45-64 years)with hypercholesterolemia and no history of myocardial infarction were treated with 40 mg pravastatin (N = 3302) or placebo (N = 3293). GWAS data and baseline LDL cholesterol levels were available for 431 subjects. The CARE study was a double blind randomized placebo-controlled clinical trial in which 4159 patients (age range 21-75 years) were treated with 40 mg pravastatin (N = 2081) or placebo (N = 2078). GWAS data and baseline LDL cholesterol levels were available for 751 subjects. The significance level for the replication SNPs was set at p-value < 0.05.

Results

Table 1 shows the baseline characteristics of the subjects participating in the PROSPER and the PROSPER/PHASE study. This table shows that the genotyped subjects in the PROSPER/PHASE study are representative of the total study population of the PROSPER study, since no major discrepancies exist between the two study sets. The mean age of all subjects at study entry was 75.3 years and about 50% of the participants were female.
Table 1

Baseline characteristics of the PROSPER/PHASE study

PROSPER study (n = 5804)PROSPER/PHASE study (n = 5244)
Continuous variables (mean, SD)
 Age (years)75.3 (3.3)75.3 (3.4)
 Education (years)15.1 (2.0)15.1 (2.0)
 Systolic blood pressure (mmHg)154.7 (21.8)154.6 (21.9)
 Diastolic blood pressure (mmHg)83.8 (11.5)83.7 (11.4)
 Height (cm)165.2 (9.4)165.2 (9.4)
 Weight (kg)73.4 (13.4)73.3 (13.4)
 Body mass index (kg/m2)26.8 (4.2)26.8 (4.2)
 Total cholesterol (mmol/L)5.7 (0.9)5.7 (0.9)
 LDL cholesterol (mmol/L)3.8 (0.8)3.8 (0.8)
 HDL cholesterol (mmol/L)1.3 (0.3)1.3 (0.4)
 Triglycerides (mmol/L)1.5 (0.7)1.5 (0.7)

Categorical variables (n, %)
 Males2804 (48.3)2524 (48.1)
 Current smoker1558 (26.8)1392 (26.5)
 History of diabetes623 (10.7)544 (10.4)
 History of hypertension3592 (61.9)3257 (62.1)
 History of angina1559 (26.9)1424 (27.2)
 History of claudication390 (6.7)354 (6.8)
 History of myocardial infarction776 (13.4)708 (13.5)
 History of stroke or TIA649 (11.2)586 (11.2)
 History of vascular disease*2565 (44.2)2336 (44.5)

*Any of stable angina, intermittent claudication, stroke, transient ischemic attack, myocardial infarction, peripheral artery disease surgery, or amputation for vascular disease more than 6 months before study entry.

Baseline characteristics of the PROSPER/PHASE study *Any of stable angina, intermittent claudication, stroke, transient ischemic attack, myocardial infarction, peripheral artery disease surgery, or amputation for vascular disease more than 6 months before study entry. In Figure 2 the QQ-plot of the genome-wide association study with baseline LDL levels within the PROSPER/PHASE study is shown. In this plot it is shown that no genomic inflation has occurred in this analyses (lambda = 1.077) and that population stratification is sufficiently controlled for. In Figure 3 the results of the genome-wide association study with baseline LDL cholesterol levels within the PROSPER/PHASE study are depicted in a Manhattan plot. Forty-two SNPs in five genomic loci, APOE/APOC1, LDLR, FADS2/FEN1, HMGCR, and PSRC1/CELSR5, reached the genome-wide significant p-value of 5.0e-08. In table 2 a summary of the five genomic loci and their corresponding SNPs is given. The top SNP (rs445925, Chr. 19) with a p-value of p = 2.8e-30 is located within the APOC1 gene and near the APOE gene. Sixteen other SNPs in the same genomic region were also found to be associated with LDL cholesterol levels. The second top SNP (rs6511720, Chr. 19) with a p-value of p = 5.22e-15 is located within the LDLR gene. The three other genomic regions included the HMGCR (Chr.5), FADS2/FEN1 (Chr. 11), PSRC1/CELSR5 (Chr. 1) genes. All 5 genomic loci were previously found in association with LDL cholesterol levels and no novel loci were identified.
Figure 2

QQ-plot for the GWAS on baseline LDL cholesterol in the PROSPER/PHASE study.

Figure 3

Manhattan plot for the GWAS on baseline LDL cholesterol in the PROSPER/PHASE study.

Table 2

Genomic loci with a genome wide significant p-value < = 5

Chr.GeneNumber of SNPsTopSNPVariantMAFBetaSEp-valueRef*
19APOEAPOC117rs445925G > A0.11-0.330.032.8e-30(7;11-14;18;19)]
19LDLR5rs6511720G > T0.13-0.190.025.2e-15(7;11;13;14;19)]
5HMGCR5rs258494G > C0.380.100.021.3e-09(7;11;13;14;19)]
11FADS2FEN114rs174541C > T0.38-0.100.021.1e-08(7;11;13;19)]
1PSRC1CELSR51rs602633G > T0.23-0.110.025.0e-08(7;11-14;16-19)]

Abbreviations: SNP, Single Nucleotide Polymorphism; Chr, Chromosome; MAF, minor allele frequency; SE, standard error. * A list of references in which the same loci were found.

QQ-plot for the GWAS on baseline LDL cholesterol in the PROSPER/PHASE study. Manhattan plot for the GWAS on baseline LDL cholesterol in the PROSPER/PHASE study. Genomic loci with a genome wide significant p-value < = 5 Abbreviations: SNP, Single Nucleotide Polymorphism; Chr, Chromosome; MAF, minor allele frequency; SE, standard error. * A list of references in which the same loci were found. We replicated the positive associations with genome-wide significant p-values in two independent cohorts, the WOSCOPS study and the CARE trial (table 3). Of our five genomic loci that were significantly associated with baseline LDL cholesterol levels we selected the top SNP for replication in both replication cohorts. If the SNP was not genotyped in their GWAS analysis, we chose a proxy in high linkage disequilibrium (r2 > 0.5%) for that SNP. These SNPs were associated with baseline LDL levels before randomisation to statin treatment in both studies. Three out of the five loci (APOE/APOC1; HMGCR; PSRC1/CELSR5) replicated in one or two replication cohorts (p < 0.05). The two other loci (LDLR and FADS2/FEN1) showed similar trends as shown in the discovery cohort, although they did not reach statistical significance (table 3).
Table 3

Replication of the 5 significant loci in the WOSCOPS trial and CARE study in association with baseline LDL cholesterol levels

WOSCOPSN = 431CAREN = 751

SNPGeneChr.betasep-valuebetasep-value
rs445925APOE APOC1190.070.050.164-0.100.040.006
rs6511720LDLR19-0.030.050.657-0.030.030.411
rs258494*1HMGCR50.060.030.0440.030.020.147
rs174541*2FADS2FEN111-0.040.030.264-0.030.020.134
rs602633*3PSRC1CELSR51-0.090.040.026-0.050.020.035

* A proxy for this SNP was used in both replication cohorts, for 1 the proxy SNP was rs7715806 with a r2 of 0.93, for 2 the proxy SNP was rs174545 with a r2 of 0.90, and for 3 the proxy SNP was rs660240 with a r2 of 0.88.

Abbreviations: SNP, Single Nucleotide Polymorphism; Chr, Chromosome.

Replication of the 5 significant loci in the WOSCOPS trial and CARE study in association with baseline LDL cholesterol levels * A proxy for this SNP was used in both replication cohorts, for 1 the proxy SNP was rs7715806 with a r2 of 0.93, for 2 the proxy SNP was rs174545 with a r2 of 0.90, and for 3 the proxy SNP was rs660240 with a r2 of 0.88. Abbreviations: SNP, Single Nucleotide Polymorphism; Chr, Chromosome.

Discussion

With this first proof-of principle study we show that the PROSPER/PHASE GWAS can confirm previously found genetic associations with LDL-cholesterol levels. This proof-of-principle study indicates that the PROSPER/PHASE study is likely to be capable of detecting genomic regions responsible for the variation in various other quantitative traits. With almost 6000 samples in the PROSPER/PHASE study and access to various replication studies, the PROSPER/PHASE study can provide a good testing frame to identify the genetic variation responsible for the variation in LDL-cholesterol lowering in response to statin treatment. The main locus responsible for the person-to-person variation in LDL-cholesterol levels is the chromosome 19 locus, which contains the APOE, APOC1, and LDLR genes. Other important loci included the HMGCR locus on chromosome 5, FADS2/FEN1 locus on chromosome 11, and the PSRC1/CELSR5 locus on chromosome 1. The five genomic loci that were associated with variation in LDL-cholesterol levels in the PHASE GWAS study were all genomic regions that were previously reported with LDL cholesterol variation [7;11-19]. Three out of the five loci were replicated in the WOSCOPS study and the CARE trial. The LDLR and FADS2/FEN1 loci were not replicated, however these loci were repeatedly found to be associated with LDL cholesterol levels in various other studies with large number of participants [7;11-14;16;19]. Moreover, both the WOSCOPS and CARE studies had genotype data available in a small number of subjects. Therefore, the lack of replication of these loci in WOSCOPS and CARE was most likely due to lack of statistical power. Finally, since we used in the replication studies a proxy SNP for some of the topSNPs, this may have diluted the effect.

Conclusions

With this proof-of-principle study we show that the PROSPER/PHASE study can be used to investigate genetic associations in a similar way to population based studies. Moreover, we can also assume from these results that the PROSPER/PHASE study is likely to have sufficient power to detect genome-wide significant hits with large effects for other quantitative traits. The next step of the PROSPER/PHASE study is to identify the genetic variation responsible for the variation in LDL-cholesterol lowering in response to statin treatment.

Competing interests

The authors declare that they have no competing interests.

Authors' contributions

ST performed statistical analysis, interpretation of data, and drafted the manuscript. AdC performed statistical analysis, interpretation of data, and drafted the manuscript. IP performed statistical analysis and drafted the manuscript. IF, NS, DS, BB, JD, FS participated in design of the study and collected the data. MC carried out genotyping analyses. PS supervised the laboratory analysis and manuscript editing. RW participated in the design of the study and manuscript editing. JWJ participated in the design of the study, interpretation of the data, and manuscript editing. All authors read and approved the final manuscript.

Pre-publication history

The pre-publication history for this paper can be accessed here: http://www.biomedcentral.com/1471-2350/12/131/prepub
  19 in total

1.  The design of a prospective study of Pravastatin in the Elderly at Risk (PROSPER). PROSPER Study Group. PROspective Study of Pravastatin in the Elderly at Risk.

Authors:  J Shepherd; G J Blauw; M B Murphy; S M Cobbe; E L Bollen; B M Buckley; I Ford; J W Jukema; M Hyland; A Gaw; A M Lagaay; I J Perry; P W Macfarlane; A E Meinders; B J Sweeney; C J Packard; R G Westendorp; C Twomey; D J Stott
Journal:  Am J Cardiol       Date:  1999-11-15       Impact factor: 2.778

Review 2.  Clinical review 141: lipids and atherosclerosis: lessons learned from randomized controlled trials of lipid lowering and other relevant studies.

Authors:  Robert A Kreisberg; Albert Oberman
Journal:  J Clin Endocrinol Metab       Date:  2002-02       Impact factor: 5.958

3.  Genome-wide association analysis of total cholesterol and high-density lipoprotein cholesterol levels using the Framingham heart study data.

Authors:  Li Ma; Jing Yang; H Birali Runesha; Toshiko Tanaka; Luigi Ferrucci; Stefania Bandinelli; Yang Da
Journal:  BMC Med Genet       Date:  2010-04-06       Impact factor: 2.103

4.  Genome-wide association of lipid-lowering response to statins in combined study populations.

Authors:  Mathew J Barber; Lara M Mangravite; Craig L Hyde; Daniel I Chasman; Joshua D Smith; Catherine A McCarty; Xiaohui Li; Russell A Wilke; Mark J Rieder; Paul T Williams; Paul M Ridker; Aurobindo Chatterjee; Jerome I Rotter; Deborah A Nickerson; Matthew Stephens; Ronald M Krauss
Journal:  PLoS One       Date:  2010-03-22       Impact factor: 3.240

5.  Recent trends in acute coronary heart disease--mortality, morbidity, medical care, and risk factors. The Minnesota Heart Survey Investigators.

Authors:  P G McGovern; J S Pankow; E Shahar; K M Doliszny; A R Folsom; H Blackburn; R V Luepker
Journal:  N Engl J Med       Date:  1996-04-04       Impact factor: 91.245

6.  Six new loci associated with blood low-density lipoprotein cholesterol, high-density lipoprotein cholesterol or triglycerides in humans.

Authors:  Sekar Kathiresan; Olle Melander; Candace Guiducci; Aarti Surti; Noël P Burtt; Mark J Rieder; Gregory M Cooper; Charlotta Roos; Benjamin F Voight; Aki S Havulinna; Björn Wahlstrand; Thomas Hedner; Dolores Corella; E Shyong Tai; Jose M Ordovas; Göran Berglund; Erkki Vartiainen; Pekka Jousilahti; Bo Hedblad; Marja-Riitta Taskinen; Christopher Newton-Cheh; Veikko Salomaa; Leena Peltonen; Leif Groop; David M Altshuler; Marju Orho-Melander
Journal:  Nat Genet       Date:  2008-01-13       Impact factor: 38.330

7.  Genetic variants influencing circulating lipid levels and risk of coronary artery disease.

Authors:  Dawn M Waterworth; Sally L Ricketts; Kijoung Song; Li Chen; Jing Hua Zhao; Samuli Ripatti; Yurii S Aulchenko; Weihua Zhang; Xin Yuan; Noha Lim; Jian'an Luan; Sofie Ashford; Eleanor Wheeler; Elizabeth H Young; David Hadley; John R Thompson; Peter S Braund; Toby Johnson; Maksim Struchalin; Ida Surakka; Robert Luben; Kay-Tee Khaw; Sheila A Rodwell; Ruth J F Loos; S Matthijs Boekholdt; Michael Inouye; Panagiotis Deloukas; Paul Elliott; David Schlessinger; Serena Sanna; Angelo Scuteri; Anne Jackson; Karen L Mohlke; Jaako Tuomilehto; Robert Roberts; Alexandre Stewart; Y Antero Kesäniemi; Robert W Mahley; Scott M Grundy; Wendy McArdle; Lon Cardon; Gérard Waeber; Peter Vollenweider; John C Chambers; Michael Boehnke; Gonçalo R Abecasis; Veikko Salomaa; Marjo-Riitta Järvelin; Aimo Ruokonen; Inês Barroso; Stephen E Epstein; Hakon H Hakonarson; Daniel J Rader; Muredach P Reilly; Jacqueline C M Witteman; Alistair S Hall; Nilesh J Samani; David P Strachan; Philip Barter; Cornelia M van Duijn; Jaspal S Kooner; Leena Peltonen; Nicholas J Wareham; Ruth McPherson; Vincent Mooser; Manjinder S Sandhu
Journal:  Arterioscler Thromb Vasc Biol       Date:  2010-09-23       Impact factor: 8.311

8.  Biological, clinical and population relevance of 95 loci for blood lipids.

Authors:  Tanya M Teslovich; Kiran Musunuru; Albert V Smith; Andrew C Edmondson; Ioannis M Stylianou; Masahiro Koseki; James P Pirruccello; Samuli Ripatti; Daniel I Chasman; Cristen J Willer; Christopher T Johansen; Sigrid W Fouchier; Aaron Isaacs; Gina M Peloso; Maja Barbalic; Sally L Ricketts; Joshua C Bis; Yurii S Aulchenko; Gudmar Thorleifsson; Mary F Feitosa; John Chambers; Marju Orho-Melander; Olle Melander; Toby Johnson; Xiaohui Li; Xiuqing Guo; Mingyao Li; Yoon Shin Cho; Min Jin Go; Young Jin Kim; Jong-Young Lee; Taesung Park; Kyunga Kim; Xueling Sim; Rick Twee-Hee Ong; Damien C Croteau-Chonka; Leslie A Lange; Joshua D Smith; Kijoung Song; Jing Hua Zhao; Xin Yuan; Jian'an Luan; Claudia Lamina; Andreas Ziegler; Weihua Zhang; Robert Y L Zee; Alan F Wright; Jacqueline C M Witteman; James F Wilson; Gonneke Willemsen; H-Erich Wichmann; John B Whitfield; Dawn M Waterworth; Nicholas J Wareham; Gérard Waeber; Peter Vollenweider; Benjamin F Voight; Veronique Vitart; Andre G Uitterlinden; Manuela Uda; Jaakko Tuomilehto; John R Thompson; Toshiko Tanaka; Ida Surakka; Heather M Stringham; Tim D Spector; Nicole Soranzo; Johannes H Smit; Juha Sinisalo; Kaisa Silander; Eric J G Sijbrands; Angelo Scuteri; James Scott; David Schlessinger; Serena Sanna; Veikko Salomaa; Juha Saharinen; Chiara Sabatti; Aimo Ruokonen; Igor Rudan; Lynda M Rose; Robert Roberts; Mark Rieder; Bruce M Psaty; Peter P Pramstaller; Irene Pichler; Markus Perola; Brenda W J H Penninx; Nancy L Pedersen; Cristian Pattaro; Alex N Parker; Guillaume Pare; Ben A Oostra; Christopher J O'Donnell; Markku S Nieminen; Deborah A Nickerson; Grant W Montgomery; Thomas Meitinger; Ruth McPherson; Mark I McCarthy; Wendy McArdle; David Masson; Nicholas G Martin; Fabio Marroni; Massimo Mangino; Patrik K E Magnusson; Gavin Lucas; Robert Luben; Ruth J F Loos; Marja-Liisa Lokki; Guillaume Lettre; Claudia Langenberg; Lenore J Launer; Edward G Lakatta; Reijo Laaksonen; Kirsten O Kyvik; Florian Kronenberg; Inke R König; Kay-Tee Khaw; Jaakko Kaprio; Lee M Kaplan; Asa Johansson; Marjo-Riitta Jarvelin; A Cecile J W Janssens; Erik Ingelsson; Wilmar Igl; G Kees Hovingh; Jouke-Jan Hottenga; Albert Hofman; Andrew A Hicks; Christian Hengstenberg; Iris M Heid; Caroline Hayward; Aki S Havulinna; Nicholas D Hastie; Tamara B Harris; Talin Haritunians; Alistair S Hall; Ulf Gyllensten; Candace Guiducci; Leif C Groop; Elena Gonzalez; Christian Gieger; Nelson B Freimer; Luigi Ferrucci; Jeanette Erdmann; Paul Elliott; Kenechi G Ejebe; Angela Döring; Anna F Dominiczak; Serkalem Demissie; Panagiotis Deloukas; Eco J C de Geus; Ulf de Faire; Gabriel Crawford; Francis S Collins; Yii-der I Chen; Mark J Caulfield; Harry Campbell; Noel P Burtt; Lori L Bonnycastle; Dorret I Boomsma; S Matthijs Boekholdt; Richard N Bergman; Inês Barroso; Stefania Bandinelli; Christie M Ballantyne; Themistocles L Assimes; Thomas Quertermous; David Altshuler; Mark Seielstad; Tien Y Wong; E-Shyong Tai; Alan B Feranil; Christopher W Kuzawa; Linda S Adair; Herman A Taylor; Ingrid B Borecki; Stacey B Gabriel; James G Wilson; Hilma Holm; Unnur Thorsteinsdottir; Vilmundur Gudnason; Ronald M Krauss; Karen L Mohlke; Jose M Ordovas; Patricia B Munroe; Jaspal S Kooner; Alan R Tall; Robert A Hegele; John J P Kastelein; Eric E Schadt; Jerome I Rotter; Eric Boerwinkle; David P Strachan; Vincent Mooser; Kari Stefansson; Muredach P Reilly; Nilesh J Samani; Heribert Schunkert; L Adrienne Cupples; Manjinder S Sandhu; Paul M Ridker; Daniel J Rader; Cornelia M van Duijn; Leena Peltonen; Gonçalo R Abecasis; Michael Boehnke; Sekar Kathiresan
Journal:  Nature       Date:  2010-08-05       Impact factor: 49.962

9.  LDL-cholesterol concentrations: a genome-wide association study.

Authors:  Manjinder S Sandhu; Dawn M Waterworth; Sally L Debenham; Eleanor Wheeler; Konstantinos Papadakis; Jing Hua Zhao; Kijoung Song; Xin Yuan; Toby Johnson; Sofie Ashford; Michael Inouye; Robert Luben; Matthew Sims; David Hadley; Wendy McArdle; Philip Barter; Y Antero Kesäniemi; Robert W Mahley; Ruth McPherson; Scott M Grundy; Sheila A Bingham; Kay-Tee Khaw; Ruth J F Loos; Gérard Waeber; Inês Barroso; David P Strachan; Panagiotis Deloukas; Peter Vollenweider; Nicholas J Wareham; Vincent Mooser
Journal:  Lancet       Date:  2008-02-09       Impact factor: 79.321

10.  Loci influencing lipid levels and coronary heart disease risk in 16 European population cohorts.

Authors:  Yurii S Aulchenko; Samuli Ripatti; Ida Lindqvist; Dorret Boomsma; Iris M Heid; Peter P Pramstaller; Brenda W J H Penninx; A Cecile J W Janssens; James F Wilson; Tim Spector; Nicholas G Martin; Nancy L Pedersen; Kirsten Ohm Kyvik; Jaakko Kaprio; Albert Hofman; Nelson B Freimer; Marjo-Riitta Jarvelin; Ulf Gyllensten; Harry Campbell; Igor Rudan; Asa Johansson; Fabio Marroni; Caroline Hayward; Veronique Vitart; Inger Jonasson; Cristian Pattaro; Alan Wright; Nick Hastie; Irene Pichler; Andrew A Hicks; Mario Falchi; Gonneke Willemsen; Jouke-Jan Hottenga; Eco J C de Geus; Grant W Montgomery; John Whitfield; Patrik Magnusson; Juha Saharinen; Markus Perola; Kaisa Silander; Aaron Isaacs; Eric J G Sijbrands; Andre G Uitterlinden; Jacqueline C M Witteman; Ben A Oostra; Paul Elliott; Aimo Ruokonen; Chiara Sabatti; Christian Gieger; Thomas Meitinger; Florian Kronenberg; Angela Döring; H-Erich Wichmann; Johannes H Smit; Mark I McCarthy; Cornelia M van Duijn; Leena Peltonen
Journal:  Nat Genet       Date:  2008-12-07       Impact factor: 38.330

View more
  18 in total

1.  Association of common variants in TOMM40/APOE/APOC1 region with human longevity in a Chinese population.

Authors:  Rong Lin; Yunxia Zhang; Dongjing Yan; Xiaoping Liao; Gu Gong; Junjie Hu; Yunxin Fu; Wangwei Cai
Journal:  J Hum Genet       Date:  2015-12-10       Impact factor: 3.172

Review 2.  A review of pharmacogenetics of adverse drug reactions in elderly people.

Authors:  Maurizio Cardelli; Francesca Marchegiani; Andrea Corsonello; Fabrizia Lattanzio; Mauro Provinciali
Journal:  Drug Saf       Date:  2012-01       Impact factor: 5.606

3.  Genetic association analysis and meta-analysis of imputed SNPs in longitudinal studies.

Authors:  Isaac Subirana; Juan R González
Journal:  Genet Epidemiol       Date:  2013-04-17       Impact factor: 2.135

Review 4.  Pharmacogenetics and cardiovascular disease--implications for personalized medicine.

Authors:  Julie A Johnson; Larisa H Cavallari
Journal:  Pharmacol Rev       Date:  2013-05-17       Impact factor: 25.468

Review 5.  Review of the cost effectiveness of pharmacogenetic-guided treatment of hypercholesterolaemia.

Authors:  Michael J Sorich; Michael D Wiese; Rebekah L O'Shea; Brita Pekarsky
Journal:  Pharmacoeconomics       Date:  2013-05       Impact factor: 4.981

6.  PCSK9 SNP rs11591147 is associated with low cholesterol levels but not with cognitive performance or noncardiovascular clinical events in an elderly population.

Authors:  Iris Postmus; Stella Trompet; Anton J M de Craen; Brendan M Buckley; Ian Ford; David J Stott; Naveed Sattar; P Eline Slagboom; Rudi G J Westendorp; J Wouter Jukema
Journal:  J Lipid Res       Date:  2013-02       Impact factor: 5.922

7.  Genome-wide association studies identify genetic loci for low von Willebrand factor levels.

Authors:  Janine van Loon; Abbas Dehghan; Tang Weihong; Stella Trompet; Wendy L McArdle; Folkert W Asselbergs; Ming-Huei Chen; Lorna M Lopez; Jennifer E Huffman; Frank W G Leebeek; Saonli Basu; David J Stott; Ann Rumley; Ron T Gansevoort; Gail Davies; James J F Wilson; Jacqueline C M Witteman; Xiting Cao; Anton J M de Craen; Stephan J L Bakker; Bruce M Psaty; John M Starr; Albert Hofman; J Wouter Jukema; Ian J Deary; Caroline Hayward; Pim van der Harst; Gordon D O Lowe; Aaron R Folsom; David P Strachan; Nicolas Smith; Moniek P M de Maat; Christopher O'Donnell
Journal:  Eur J Hum Genet       Date:  2015-10-21       Impact factor: 4.246

8.  Genome-wide association analysis of blood biomarkers in chronic obstructive pulmonary disease.

Authors:  Deog Kyeom Kim; Michael H Cho; Craig P Hersh; David A Lomas; Bruce E Miller; Xiangyang Kong; Per Bakke; Amund Gulsvik; Alvar Agustí; Emiel Wouters; Bartolome Celli; Harvey Coxson; Jørgen Vestbo; William MacNee; Julie C Yates; Stephen Rennard; Augusto Litonjua; Weiliang Qiu; Terri H Beaty; James D Crapo; John H Riley; Ruth Tal-Singer; Edwin K Silverman
Journal:  Am J Respir Crit Care Med       Date:  2012-11-09       Impact factor: 21.405

9.  Genome-wide association study of genetic determinants of LDL-c response to atorvastatin therapy: importance of Lp(a).

Authors:  Harshal A Deshmukh; Helen M Colhoun; Toby Johnson; Paul M McKeigue; D John Betteridge; Paul N Durrington; John H Fuller; Shona Livingstone; Valentine Charlton-Menys; Andrew Neil; Neil Poulter; Peter Sever; Denis C Shields; Alice V Stanton; Aurobindo Chatterjee; Craig Hyde; Roberto A Calle; David A DeMicco; Stella Trompet; Iris Postmus; Ian Ford; J Wouter Jukema; Mark Caulfield; Graham A Hitman
Journal:  J Lipid Res       Date:  2012-02-24       Impact factor: 5.922

10.  Non-homologous end-joining pathway associated with occurrence of myocardial infarction: gene set analysis of genome-wide association study data.

Authors:  Jeffrey J W Verschuren; Stella Trompet; Joris Deelen; David J Stott; Naveed Sattar; Brendan M Buckley; Ian Ford; Bastiaan T Heijmans; Henk-Jan Guchelaar; Jeanine J Houwing-Duistermaat; P Eline Slagboom; J Wouter Jukema
Journal:  PLoS One       Date:  2013-02-15       Impact factor: 3.240

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.