Genome-wide association study of genetic determinants of LDL-c response to atorvastatin therapy: importance of Lp(a)
2012; Elsevier BV; Volume: 53; Issue: 5 Linguagem: Inglês
10.1194/jlr.p021113
ISSN1539-7262
AutoresHarshal Deshmukh, Helen M. Colhoun, Toby Johnson, Paul McKeigue, D. J. Betteridge, Paul N. Durrington, John Fuller, Shona Livingstone, Valentine Charlton-Menys, Andrew Neil, Neil R Poulter, Peter Sever, Denis C. Shields, Alice Stanton, Aurobindo Chatterjee, Craig Hyde, Roberto A. Calle, David A. DeMicco, Stella Trompet, Iris Postmus, Ian Ford, J. Wouter Jukema, Mark J. Caulfield, G. A. Hitman,
Tópico(s)Cholesterol and Lipid Metabolism
ResumoWe carried out a genome-wide association study (GWAS) of LDL-c response to statin using data from participants in the Collaborative Atorvastatin Diabetes Study (CARDS; n = 1,156), the Anglo-Scandinavian Cardiac Outcomes Trial (ASCOT; n = 895), and the observational phase of ASCOT (n = 651), all of whom were prescribed atorvastatin 10 mg. Following genome-wide imputation, we combined data from the three studies in a meta-analysis. We found associations of LDL-c response to atorvastatin that reached genome-wide significance at rs10455872 (P= 6.13 × 10−9) within the LPA gene and at two single nucleotide polymorphisms (SNP) within the APOE region (rs445925; P= 2.22 × 10−16 and rs4420638; P= 1.01 × 10−11) that are proxies for the ∊2 and ∊4 variants, respectively, in APOE. The novel association with the LPA SNP was replicated in the PROspective Study of Pravastatin in the Elderly at Risk (PROSPER) trial (P= 0.009). Using CARDS data, we further showed that atorvastatin therapy did not alter lipoprotein(a) [Lp(a)] and that Lp(a) levels accounted for all of the associations of SNPs in the LPA gene and the apparent LDL-c response levels. However, statin therapy had a similar effect in reducing cardiovascular disease (CVD) in patients in the top quartile for serum Lp(a) levels (HR = 0.60) compared with those in the lower three quartiles (HR = 0.66; P= 0.8 for interaction). The data emphasize that high Lp(a) levels affect the measurement of LDL-c and the clinical estimation of LDL-c response. Therefore, an apparently lower LDL-c response to statin therapy may indicate a need for measurement of Lp(a). However, statin therapy seems beneficial even in those with high Lp(a). We carried out a genome-wide association study (GWAS) of LDL-c response to statin using data from participants in the Collaborative Atorvastatin Diabetes Study (CARDS; n = 1,156), the Anglo-Scandinavian Cardiac Outcomes Trial (ASCOT; n = 895), and the observational phase of ASCOT (n = 651), all of whom were prescribed atorvastatin 10 mg. Following genome-wide imputation, we combined data from the three studies in a meta-analysis. We found associations of LDL-c response to atorvastatin that reached genome-wide significance at rs10455872 (P= 6.13 × 10−9) within the LPA gene and at two single nucleotide polymorphisms (SNP) within the APOE region (rs445925; P= 2.22 × 10−16 and rs4420638; P= 1.01 × 10−11) that are proxies for the ∊2 and ∊4 variants, respectively, in APOE. The novel association with the LPA SNP was replicated in the PROspective Study of Pravastatin in the Elderly at Risk (PROSPER) trial (P= 0.009). Using CARDS data, we further showed that atorvastatin therapy did not alter lipoprotein(a) [Lp(a)] and that Lp(a) levels accounted for all of the associations of SNPs in the LPA gene and the apparent LDL-c response levels. However, statin therapy had a similar effect in reducing cardiovascular disease (CVD) in patients in the top quartile for serum Lp(a) levels (HR = 0.60) compared with those in the lower three quartiles (HR = 0.66; P= 0.8 for interaction). The data emphasize that high Lp(a) levels affect the measurement of LDL-c and the clinical estimation of LDL-c response. Therefore, an apparently lower LDL-c response to statin therapy may indicate a need for measurement of Lp(a). However, statin therapy seems beneficial even in those with high Lp(a). Statin therapy is now widely accepted for the primary and secondary prevention of cardiovascular disease (CVD) in certain patient groups. However, there is considerable variation in response to statin therapy that remains poorly understood. For example, in the Collaborative Atorvastatin Diabetes Study (CARDS) trial (1Colhoun H.M. Betteridge D.J. Durrington P.N. Hitman G.A. Neil H.A. Livingstone S.J. Thomason M.J. Mackness M.I. Charlton-Menys V. Fuller J.H. Primary prevention of cardiovascular disease with atorvastatin in type 2 diabetes in the Collaborative Atorvastatin Diabetes Study (CARDS): multicentre randomised placebo-controlled trial.Lancet. 2004; 364: 685-696Abstract Full Text Full Text PDF PubMed Scopus (3261) Google Scholar), among self-reported and pill count-validated compliant recipients of atorvastatin 10 mg daily, the absolute change in LDL-c at one month post-randomization varied from −2 to −0.6 mmol/l, (5th and 95th centiles of the range), and the percentage lowering from baseline varied from 67% to 22%. Understanding the pathways and determinants involved in this variation in response to therapy could lead to improved treatments. Even without understanding the pathways, identifying predictors of poorer response could identify those most in need of additional or alternative therapeutic strategies. Two genome-wide association studies (GWAS) of statin response and several candidate gene association studies have been reported (2Thompson J.F. Hyde C.L. Wood L.S. Paciga S.A. Hinds D.A. Cox D.R. Hovingh G.K. Kastelein J.J. Comprehensive whole-genome and candidate gene analysis for response to statin therapy in the Treating to New Targets (TNT) cohort.Circ. Cardiovasc. Genet. 2009; 2: 173-181Crossref PubMed Scopus (164) Google Scholar–3Barber M.J. Mangravite L.M. Hyde C.L. Chasman D.I. Smith J.D. McCarty C.A. Li X. Wilke R.A. Rieder M.J. Williams P.T. et al.Genome-wide association of lipid-lowering response to statins in combined study populations.PLoS ONE. 2010; 5: e9763Crossref PubMed Scopus (186) Google Scholar, 4Thompson J.F. Man M. Johnson K.J. Wood L.S. Lira M.E. Lloyd D.B. Banerjee P. Milos P.M. Myrand S.P. Paulauskis J. et al.An association study of 43 SNPs in 16 candidate genes with atorvastatin response.Pharmacogenomics J. 2005; 5: 352-358Crossref PubMed Scopus (184) Google Scholar, 5Donnelly L.A. Palmer C.N. Whitley A.L. Lang C.C. Doney A.S. Morris A.D. Donnan P.T. Apolipoprotein E genotypes are associated with lipid-lowering responses to statin treatment in diabetes: a Go-DARTS study.Pharmacogenet. Genomics. 2008; 18: 279-287Crossref PubMed Scopus (51) Google Scholar). From these, the only consistent finding is that variants in the APOE gene region are associated with variation in LDL response. Here, we report a genome-wide analysis of LDL-c response from two randomized clinical trials of atorvastatin, CARDS and the Anglo-Scandinavian Outcomes Trial (ASCOT) (6Sever P.S. Dahlof B. Poulter N.R. Wedel H. Beevers G. Caulfield M. Collins R. Kjeldsen S.E. Kristinsson A. McInnes G.T. Prevention of coronary and stroke events with atorvastatin in hypertensive patients who have average or lower-than-average cholesterol concentrations, in the Anglo-Scandinavian Cardiac Outcomes Trial–Lipid Lowering Arm (ASCOT-LLA): a multicentre randomised controlled trial.Lancet. 2003; 361: 1149-1158Abstract Full Text Full Text PDF PubMed Scopus (3351) Google Scholar), to investigate genetic effects on LDL-c response to atorvastatin. We chose to model genetic determinants of LDL-c response to atorvastatin among those assigned to atorvastatin in these trials. An alternative approach would be to model the interaction of genotype on the effect of atorvastatin on LDL-c using data from both placebo and active treatment groups. However, we did not consider this latter approach as optimal as testing for interactions is much less powerful than direct tests of association and as, in any case, we did not consider genetic effects on change LDL-c in the placebo groups to be plausible. Both trials were conducted with Ethics Committee/IRB approval, under good clinical practice guidelines and in accordance with the Declaration of Helsinki principles. Patients gave consent for genetic studies. Methods in CARDS have been described previously. In brief, 2,838 patients with type 2 diabetes and no previous CVD were randomized to receive either placebo or atorvastatin 10 mg once daily and followed for a median of 3.7 years. Allocation was double blinded. Mean serum LDL-c concentration during baseline visits prior to randomization had to be ≤ 4.14 mmol/l (160 mg/dl) and serum triglycerides ≤ 6.78 mmol/l (600 mg/dl). After randomization, total cholesterol (TC), HDL-C, and triglycerides were measured at one, two, and three months, and then every six months. Patients attended after an overnight fast. LDL-c was calculated with the Friedewald formula (7Friedewald W.T. Levy R.I. Fredrickson D.S. Estimation of the concentration of low-density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge.Clin. Chem. 1972; 18: 499-502Crossref PubMed Scopus (64) Google Scholar), or if serum triglycerides exceeded 4.0 mmol/l, by removing VLDL by ultracentrifugation and then measuring the change in infranatant cholesterol content when LDL was removed by precipitation of apolipoprotein B-containing lipoproteins. For this genome-wide study, the analyses were restricted to those randomized to atorvastatin, and the mean of two pretreatment LDL-c measurements was used as the baseline LDL-c and a weighted average of five post-randomization values within the first year post-randomization was the outcome measure or "on treatment LDL-c," with weights (0.6 for month 1 and then 0.1 for measurements at 2, 3, 6, and 12 months). Lipoprotein(a) concentrations were determined by an immunoturbidimetric assay with Immuno LEIA® reagents from Technoclone Ltd., Dorking, UK (now www.PathwayDiagnostics.com), which is calibrated against the IFCC standard preparation PRM02. Of 19,342 hypertensive patients (40–79 years of age with at least three other cardiovascular risk factors) who were randomized to one of two antihypertensive regimens in ASCOT, 10,305 with nonfasting TC concentrations of 6.5 mmol/l or less (measured at the nonfasting screening visit) had been randomly assigned additional atorvastatin 10 mg or placebo. These patients formed the lipid-lowering arm of the study. For this genome-wide study, two subpopulations from ASCOT were included. The first subpopulation included individuals randomized to 10 mg atorvastatin in whom pretreatment LDL-c was measured at the (fasting) randomization visit and on-treatment LDL-c was calculated as the simple average of measures at the (fasting) visits 6 months and 12 months post-randomization. LDL-c was estimated using the Friedewald equation as in CARDS. Following the end of the randomization phase, there was an observational period. The second subpopulation included all individuals not originally randomized to 10 mg atorvastatin (i.e., those randomized to placebo and those not eligible for the LLA) who were subsequently prescribed atorvastatin 10 mg. For these individuals, pretreatment LDL-c was defined as the measurement on the last visit before or equal to date of starting atorvastatin, and on-treatment LDL-c was defined as the measurement taken from the first visit after date of starting atorvastatin. All data were from the PROspective Study of Pravastatin in the Elderly at Risk (PROSPER) (8Shepherd J. Blauw G.J. Murphy M.B. Cobbe S.M. Bollen E.L. Buckley B.M. Ford I. Jukema J.W. Hyland M. Gaw A. et al.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.Am. J. Cardiol. 1999; 84: 1192-1197Abstract Full Text Full Text PDF PubMed Scopus (257) Google Scholar). PROSPER was a prospective multicenter randomized placebo-controlled trial to assess whether treatment with pravastatin diminishes the risk of major vascular events in elderly. Between December 1997 and May 1999, we screened and enrolled subjects in Scotland (Glasgow), Ireland (Cork), and the Netherlands (Leiden). Men and women 70–82 years of age were recruited if they had preexisting vascular disease or increased risk of such disease because of smoking, hypertension, or diabetes. A total number of 5,804 subjects were randomly assigned to pravastatin or placebo, of which 2,550 subjects assigned to the Pravastatin arm of the trial were included in the present study. TC, HDL-C, and triglycerides were assessed after an overnight fast, at baseline, and at 3, 6, 12, 24, and 36 months post-randomization. LDL-C was calculated by the Friedewald formula. The pretreatment measurement was at baseline before randomization, and the posttreatment was the mean of the lipid measurements after randomization. To maximize power to detect associations and to improve test statistic behavior under the null for low minor allele frequency (MAF) single nucleotide polymorphisms (SNP), we transformed measured LDL-c levels to conform to the distributional assumptions made by our association analysis model using the same transformation for off- and on-treatment measures to preserve the relationship between the two. We maximized the fit of the residuals in a regression of on-treatment on the pretreatment value to a Gaussian distribution. We used a 2-parameter Box-Cox transform of the form applied to baseline and on-treatment LDL-c values. The parameter values α and β were chosen by maximizing the likelihood of a model with linear regressions of the transformed pretreatment and response (transformed pretreatment minus transformed pretreatment) values on the covariates (age and sex), with the joint distribution of the residuals from the two regression models being bivariate Gaussian. The parameter values obtained were α= 0.156, β=−0.505 mmol/l in CARDS. In ASCOT, the parameters were α= 0.6807, β= 0.8850 mmol/l in the randomized dataset, and α= 0.4805, β= 0.5813 mmol/l in the observational dataset. This transformation has the same motivation as the inverse normal transform used in some GWAS applications (9Lindgren C.M. Heid I.M. Randall J.C. Lamina C. Steinthorsdottir V. Qi L. Speliotes E.K. Thorleifsson G. Willer C.J. Herrera B.M. et al.Genome-wide association scan meta-analysis identifies three loci influencing adiposity and fat distribution.PLoS Genet. 2009; 5: e1000508Crossref PubMed Scopus (429) Google Scholar, 10Willer C.J. Speliotes E.K. Loos R.J. Li S. Lindgren C.M. Heid I.M. Berndt S.I. Elliott A.L. Jackson A.U. Lamina C. et al.Six new loci associated with body mass index highlight a neuronal influence on body weight regulation.Nat. Genet. 2009; 41: 25-34Crossref PubMed Scopus (1378) Google Scholar), but the use of a parametric transform preserves the relationship between pre and on-treatment measures, thereby allowing the difference between the two, adjusted for pretreatment value, to be used as a response variable as was done in ASCOT or as simply the on-treatment adjusted for pretreatment value as in CARDS (these are equivalent). The effect sizes in discovery cohorts (CARDS and ASCOT) and the replication cohort (PROSPER) were scaled so that the residuals had unit variance, thereby allowing studies using different transforms to be combined. DNA was extracted from whole-blood EDTA samples. DNA was isolated from 10 ml of frozen blood using the Gentra Puregene DNA Isolation Kit from Qiagen (Cat. no. 158389). Briefly, RBC was lysed with an anionic detergent in the presence of a DNA stabilizer that limits the activity of intracellular DNases. White blood cells were collected by centrifugation at 2,000 g for 2 min. RNA was removed by treatment with RNase A. Protein was removed by salt precipitation (centrifugation at 2000 g for 5 min). Genomic DNA was recovered by precipitation with isopropanol and centrifugation at 2,000 g for 5 min, the DNA pellet was washed in 70% ethanol, air dried, and dissolved in hydration solution (1 mM EDTA, 10 mM Tris·CI, pH 7.5). Purified DNA was stored at −20°C. DNA aliquots were genotyped at Perlegen Sciences using a proprietary SNP set comprising 599,164 SNPs. Of these, 243 SNPs that had discrepant map positions between HapMap and Perlegen were dropped. We set a minimum SNP call rate threshold of 80% for including SNPs in the analysis, and we required that the P-value for a test of deviation from Hardy-Weinberg equilibrium (HWE) was not < 10−5. This gave 517,746 SNPs for analysis. The average call rate was 98%, with 86.25% SNPs with a call rate of greater than 90%. SNP annotation was based on build 36 of the Human Genome Sequence. All SNPs were used in the analysis regardless of allele frequency, but the allele frequency was considered when evaluating putative associations. Allele frequency was below 1% at 6% of SNPs. We selected samples from those people who had been allocated atorvastatin 10 mg daily, had given consent for genotyping, and had a sample SNP call rate > 80%. After applying the exclusions of HWE, we estimated relatedness with PLINK, and those individuals with Pi_HAT > 0.25(excluding first- and second-degree relatives) were removed (n = 0). Only LDL-c values from time points at which the person was compliant with atorvastatin (based on pill count > 80%) were used. Genotyping was carried out on HumanCNV370 (Illumina) array on 3,868 individuals at Centre National de Génotypage (CNG) in two batches. Samples were excluded if they had ≥ 5% missing data (two samples). SNPs were excluded based on the following criteria: i) they had been mapped to different chromosomes or positions in the different releases (two SNPs), or ii) they were polymorphic A/T or C/G in either release or in the combined dataset, or iii) they had call rate ≤ 97% in either release or in the combined dataset (47,744 SNPs), or iv) they had HWE P-value ≤ 10−7 in either release or in the combined dataset (8,502 SNPs). After applying the above exclusions, samples were excluded if they had estimated relatedness > 0.1875 (halfway cut point between second- and third-degree relatives), estimated using a using a subset of 101,954 SNPs obtained by linkage disequalibrium (LD)-based pruning (87 duplicates, 15 first-degree relatives and 4 presumed second-degree relatives removed. Then SNPs were excluded if they showed significant differences in allele frequency between the different batches at P < 10−7 (20 SNPs), if they were monomorphic in the combined dataset (3,838 SNPs), if they were not in HapMap r22 (12,817 SNPs) or had different alleles to HapMap r22 (6 SNPs), or if they showed significant differences (P < 10−7 using Fisher's exact test) in allele frequency between the combined dataset and HapMap r22 (308 SNPs). After applying all the above exclusions, ancestry outliers were excluded (n = 143) by using ancestry principal component analysis (11Patterson N. Price A.L. Reich D. Population structure and eigenanalysis.PLoS Genet. 2006; 2: e190Crossref PubMed Scopus (3066) Google Scholar) on a subset of 100,905 SNPs selected by LD-based pruning, and ancestry principal components (PCs) were calculated for the remaining 3,804 individuals. A whole genome-wide screening was performed in the sequential PHASE project. DNA was available for genotyping 5,763 subjects. Genotyping was performed with the Illumina 660K beadchip. After QC (call rate < 95%), 5,244 subjects and 557,192 SNPs were left for analysis (12Trompet S. de Craen A.J. Postmus I. Ford I. Sattar N. Caslake M. Stott D.J. Buckley B.M. Sacks F. Devlin J.J. et al.Replication of LDL GWAs hits in PROSPER/PHASE as validation for future (pharmaco)genetic analyses.BMC Med. Genet. 2011; 12: 131Crossref PubMed Scopus (33) Google Scholar). The CARDS genotype data were combined with phased haplotypes from HapMap phase II CEU r22 to compute posterior probability distribution of genotype at all HapMap loci using the IMPUTE program (13Marchini J. Howie B. Myers S. McVean G. Donnelly P. A new multipoint method for genome-wide association studies by imputation of genotypes.Nat. Genet. 2007; 39: 906-913Crossref PubMed Scopus (1970) Google Scholar). For ASCOT and PROSPER, genotypes at unmeasured SNPs were imputed using MACH (14Li Y. Willer C. Sanna S. Abecasis G. Genotype imputation.Annu. Rev. Genomics Hum. Genet. 2009; 10: 387-406Crossref PubMed Scopus (806) Google Scholar) and phased haplotypes from HapMap CEU r22. For ASCOT, a randomly chosen subset of 400 individuals was used to estimate transition and emission probabilities (i.e., to estimate recombination rates between SNPs and per-SNP genotyping error rates) using MACH options "-greedy -r 100" for each (entire) chromosome in turn. Using these estimated rates (the .rec and .erate files), genotypes were imputed for the whole sample of 3,804 individuals using MACH options "-greedy-mle-mldetails" for each (entire) chromosome in turn. The EIGENSTRAT program (15Price A.L. Patterson N.J. Plenge R.M. Weinblatt M.E. Shadick N.A. Reich D. Principal components analysis corrects for stratification in genome-wide association studies.Nat. Genet. 2006; 38: 904-909Crossref PubMed Scopus (6859) Google Scholar) was used to adjust for population structure. Using PLINK (16Purcell S. Neale B. Todd-Brown K. Thomas L. Ferreira M.A. Bender D. Maller J. Sklar P. de Bakker P.I. Daly M.J. et al.PLINK: a tool set for whole-genome association and population-based linkage analyses.Am. J. Hum. Genet. 2007; 81: 559-575Abstract Full Text Full Text PDF PubMed Scopus (19732) Google Scholar), we generated a pruned subset of 152,587 SNPs that are in approximate linkage equilibrium with each other in the CARDS dataset. Principal components analysis was undertaken using this subset of SNPs. Thirty-seven individuals identified as outliers in the initial principal components analysis were excluded from the subsequent computation of principal components, leaving 1174 persons evaluable for statin response. The first three principal components were retained and included as covariates in all tests of association. On-treatment values for LDL-c for each individual at 1, 2, 3, 6, and 12 months post-randomization were available. We initially used the first available post-randomization LDL-c and established that the previously reported APOE genotype at rs445925 was the strongest association in a genome-wide analysis of response at P= 1.1 × 10−13. To maximize the power to detect any further new associations, we trained the weighting of post-randomization LDL-c time points to maximize the strength of the association of LDL response with APOE genotype at rs445925. Based on this, the nonmissing values for each individual were combined in a weighted average, with the one-month value allocated a weight of 0.6 and the four subsequent values, weights of 0.1 each (P-value for rs445925 with these weights = 2.2 × 10−16). SNPTEST (13Marchini J. Howie B. Myers S. McVean G. Donnelly P. A new multipoint method for genome-wide association studies by imputation of genotypes.Nat. Genet. 2007; 39: 906-913Crossref PubMed Scopus (1970) Google Scholar) was used to test for association of LDL response with genotype in a linear regression with the weighted average post-randomization LDL value as dependent variable and with covariates, including transformed pretreatment LDL-c, age, sex, and scores on the first three principal components of population stratification. The missing-data likelihood option was used to allow for uncertainty of genotypes at each imputed locus. In practice, the use of several weighted post-randomization LDL-c values rather than a single first value made very little difference to the results (see supplementary table II). We used the conditional analysis test in PLINK (16Purcell S. Neale B. Todd-Brown K. Thomas L. Ferreira M.A. Bender D. Maller J. Sklar P. de Bakker P.I. Daly M.J. et al.PLINK: a tool set for whole-genome association and population-based linkage analyses.Am. J. Hum. Genet. 2007; 81: 559-575Abstract Full Text Full Text PDF PubMed Scopus (19732) Google Scholar) to test for independence of SNP associations over short regions within the same gene; a null model based on equating the effects of haplotypes that differed only at the SNP under test was compared with a more general model in which the effects of these haplotypes were unconstrained. The null hypothesis is that the SNP under test accounts for all associations of haplotypes with response. Other analyses included those carried out to explore initial associations, including a test of whether LPA genotype modifies the effect of atorvastatin on CVD. This was carried out by estimating the hazard ratio associated with allocation to atorvastatin in a Cox regression model of time to first CVD event and using a likelihood ratio test comparing a model with this main treatment effect and one including a term for interaction of genotype × treatment effect. We regressed the response variable (transformed on-treatment minus transformed pretreatment LDL-c) onto imputed expected genotype dosage as implemented in ProbABEL (14Li Y. Willer C. Sanna S. Abecasis G. Genotype imputation.Annu. Rev. Genomics Hum. Genet. 2009; 10: 387-406Crossref PubMed Scopus (806) Google Scholar, 17Aulchenko Y.S. Struchalin M.V. van Duijn C.M. ProbABEL package for genome-wide association analysis of imputed data.BMC Bioinformatics. 2010; 11: 134Crossref PubMed Scopus (320) Google Scholar). This is asymptotically equivalent to score test for taking into account uncertainty in imputed genotypes (as in SNPTEST) but with improved finite sample size operating characteristics (18Kutalik Z. Johnson T. Bochud M. Mooser V. Vollenweider P. Waeber G. Waterworth D. Beckmann J.S. Bergmann S. Methods for testing association between uncertain genotypes and quantitative traits.Biostatistics. 2011; 12: 1-17Crossref PubMed Scopus (32) Google Scholar). Age, sex, age*sex, and transformed pretreatment LDL were used as covariates, plus 10 ancestry principal components. The response variable was regressed (natural log of transformed on-treatment minus natural log of pretreatment LDL-c) onto imputed expected genotype dosage as implemented in SNPTEST. Age, sex, transformed pretreatment LDL, and top three principal components were used as covariates. The score and observed information for the effect parameter were summed over studies to obtain a summary score test. This is algebraically equivalent (based on the quadratic approximation of the log-likelihood) to obtaining a weighted average of the maximum likelihood estimates with weights inversely proportional to the squared standard errors, with the useful feature that the ratio of observed to complete information (calculated by summing numerators and denominators over the three studies) is obtained as a summary measure of the efficiency of genotype imputation. For concise presentation, we focus here on showing the results of the meta-analysis rather than each study separately and provide study-specific estimates of effect only at the most extreme significance levels. In the data presentation, those loci at which the overall proportion of information extracted was less than 30% across the studies have been excluded. We have used the P-value threshold of <5 × 10−8 as the threshold for declaring a genome-wide significant association. Effects of genetic variation on treatment response as measured by on-treatment LDL-c could be mediated through effects on the pretreatment LDL-c. To evaluate whether genetic on-treatment LDL-c likely reflects residual effect on pretreatment LDL-c, it is necessary to adjust for the pretreatment LDL-c levels and to correct the maximum likelihood estimate of the adjusted effect of genotype on on-treatment value for the noise in pretreatment values (the noise is both random measurement error and intra-individual variation in usual LDL-c). From the rules of path analysis, we calculated the direct effect γ of genotype on an on-treatment trait value as β−αδ (1 −ρ) / ρ, where β is the coefficient of regression for on-treatment trait value on genotype adjusted for measured pretreatment value, ρ is the intraclass correlation between replicate measurements of pretreatment values, and δ is the coefficient of regression for on-treatment value on observed pretreatment value. For these calculations, we used ρ= 0.8 as a plausible value for the intraclass correlation based on the within-person correlation in LDL-c values taken over two pretreatment visits in CARDS. Table 1 compares baseline characteristics of participants in the three studies. Fig. 1 shows a quantile-quantile plot of the −log10P-values for association of each SNP with LDL-c response to treatment, obtained by meta-analyzing effect size estimates across the CARDS and ASCOT datasets. This plot shows that the cumulative distribution of test statistics approximates the null distribution over most of its range but that there is a tail of extreme results. Fig. 2 shows a Manhattan plot of the −log10P-values by map position. Table 2 shows all loci at which the summary test for association yielded a nominal P-value < 10−6. The estimates of effect (β) are for the transformed response variable (see Materials and Methods). In CARDS, the response variable was transformed on-treatment LDL-c with transformed pretreatment LDL-c entered as a covariate in the model. This is mathematically equivalent to modeling change in LDL-c with pretreatment LDL-c as a covariate (i.e., the difference in transformed on-treatment and adjustment for pretreatment LDL) as was done in ASCOT. A negative β for an allele means that the modeled allele is associated with a bigger reduction in posttreatment LDL-c and a better response to statins.TABLE 1Characteristics of patients and studies included in the meta-analysisCARDSASCOT-RASCOT-Obsn = 1194n = 895n = 691Age (mean years ± SD)61.6 ± 8.264.1 ± 8.064.2 ± 8.6EthnicityCaucasian (UK and Ireland)Caucasian (UK and Ireland)Caucasian (UK and Ireland)Women (%)4711.013.1Diabetes (%)1002121Follow-up years (median IQR)3.9 years (3.0–4.7)First year was usedFirst year was usedHypertension (%)87100100LDL-c level at baseline (mean mmol/l ± SD)3.04 ± 0.713.47 ± 0.703.75 ± 0.85aIn N = 656 with nonmissing LDL-c at baseline; the missingness is nonrandom because these are individuals with baseline triglycerides too high for Friedewald formula.Lipid entry criterionFasting LDL-c ≤ 4.14 mmol/lNon-fasting TC ≤ 6.5 mmol/lNoneFasting status for lipidsbFasting status for LDL-c at baseline (see previous row) and for r
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