hyperactivity disorder

Progress in Neuro-Psychopharmacology & Biological Psychiatry 35 (2011) 466–472

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Progress in Neuro-Psychopharmacology & Biological Psychiatry j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / p n p

Genome-wide association study of blood pressure response to methylphenidate treatment of attention-deficit/hyperactivity disorder Eric Mick a,⁎, James J. McGough b, Frank A. Middleton c, Benjamin Neale d,e, Stephen V. Faraone c a

Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School; 55 Fruit St – Warren 705, Boston, MA 02114, United States UCLA Semel Institute for Neuroscience & Human Behavior; 300 UCLA Medical Plaza – Suite 1414, Los Angeles, CA 90095, United States Departments of Psychiatry and Neuroscience & Physiology, SUNY Upstate Medical University; 750 East Adams St, Syracuse, NY 13210, United States d Center for Human Genetic Research, Massachusetts General Hospital; 185 Cambridge St – CPZN, Boston, MA 02114, United States e The Broad Institute of Harvard and MIT; 7 Cambridge Center, Cambridge, MA 02142, United States b c

a r t i c l e

i n f o

Article history: Received 2 June 2010 Received in revised form 23 November 2010 Accepted 23 November 2010 Available online 2 December 2010 Keywords: ADHD Blood pressure Genome-wide association study Methylphenidate Pharmacogenomic

a b s t r a c t Objective: We conducted a genome-wide association study of blood pressure in an open-label study of the methylphenidate transdermal system (MTS) for the treatment of attention-deficit/hyperactivity disorder (ADHD). Method: Genotyping was conducted with the Affymetrix Genome-Wide Human SNP Array 6.0. Multivariate association analyses were conducted using the software package PLINK. After data cleaning and quality control we tested 316,934 SNPs in 140 children with ADHD. Results: We observed no genome-wide statistically significant findings, but a SNP in a K+-dependent Na+/Ca2+ exchanger expressed in vascular smooth muscle (SLC24A3) was included in our top associations at p b 1E-04. Genetic enrichment analyses of genes with ≥1 SNP significant at p b 0.01, implicated several functional categories (FERM domain, p = 5.0E-07; immunoglobulin domain, p = 8.1E-06; the transmembrane region, p = 4.4E-05; channel activity, p = 2.0E-04; and type-III fibronectins, p = 2.7E-05) harboring genes previously associated with related cardiovascular phenotypes. Conclusions: The hypothesis generating results from this study suggests that polymorphisms in several genes consistently associated with cardiovascular diseases may impact changes in blood pressure observed with methylphenidate pharmacotherapy in children with ADHD. © 2010 Elsevier Inc. All rights reserved.

Abbreviations: ADHD, attention-deficit/hyperactivity disorder; MTS, methylphenidate transdermal system; SNP, Single Nucleotide Polymorphism; FERM, Protein 4.1, Ezrin, Radixin, Moesin; bpm, beats per minute; bp, Base Paur; VNTR, Variable Number Tandem Repeat; DSM-IV-TR, Diagnostic and Statistical Manual for Mental Disorders-IV-Text Revision; ADHD-RS-IV, ADHD Rating Scale-IV; BMI, Body Mass Index; CNS, Central Nervous System; HWE, Hardy–Weinberg Equilibrium; SD, Standard Deviation; MDS, multidimensional scaling; DAVID, Database for Annotation, Visualization and Integrated Discovery; UTR, Untranslated Region; EASE, Expression Analysis Systematic Explorer; GWAS, genome-wide association study; MAF, Minor Allele Frequency. Gene names: C5orf46, chromosome 5 open reading frame 46; C7orf36, chromosome 7 open reading frame 36; CALCR, calcitonin receptor; CLCN6, chloride channel 6; DOCK9, dedicator of cytokinesis 9; EDAR, ectodysplasin A receptor; EPB41L2, erythrocyte membrane protein band 4.1-like 2; F13A1, coagulation factor XIII, A1 polypeptide; FAM124A, family with sequence similarity 124A; FGF14, fibroblast growth factor 14; FGFR1, fibroblast growth factor receptor 1; FRMD4A, FERM domain containing 4A; FRMD4B, FERM domain containing 4B; FSTL4, follistatin-like 4; FSTL5, follistatin-like 5; GALNT13, UDP-N-acetyl-alpha-D-galactosamine:polypeptide N-acetylgalactosaminyltransferase 13; HDGFRP3, hepatoma-derived growth factor, related protein 3; KALRN, kalirin, RhoGEF kinase; KCNQ1, potassium voltage-gated channel, KQT-like subfamily, member 1; KCNQ5, potassium voltage-gated channel, KQT-like subfamily member 5; KLRD1, killer cell lectin-like receptor subfamily D, member 1; LENG8, leukocyte receptor cluster (LRC) member 8; LRP1B, low density lipoprotein-related protein 1B; LSAMP, limbic system-associated membrane protein; MALL, mal, T-cell differentiation protein-like; MARK2, MAP/microtubule affinityregulating kinase 2; MGST1, microsomal glutathione S-transferase 1; MYBPC3, myosin binding protein C, cardiac; MYLK, myosin light chain kinase; MYO1E, myosin IE; NRG1, neuregulin 1; NUMB, numb homolog (Drosophila); OBSCN, obscurin; PALLD, palladin, cytoskeletal associated protein; PKD2L2, polycystic kidney disease 2-like 2; PLXNA2, plexin A2; PRUNE2, prune homolog 2 (Drosophila); PTCD3, Pentatricopeptide repeat domain 3; PTMA, prothymosin, alpha; PTPRD, protein tyrosine phosphatase, receptor type, D; PTPRJ, protein tyrosine phosphatase, receptor type, J; PTPRM, protein tyrosine phosphatase, receptor type, M; RASGRF2, Ras protein-specific guanine nucleotide-releasing factor 2; RHOU, ras homolog gene family, member U; RHOV, ras homolog gene family, member V; RYR2, ryanodine receptor 2 (cardiac); RYR3, ryanodine receptor 3; SAMD3, sterile alpha motif domain containing 3; SCN10A, sodium channel, voltage-gated, type X, alpha subunit; SCN5A, sodium channel, voltage-gated type V, alpha subunit; SDK1, sidekick homolog 1; SEMA3C, semaphorin 3C; SLC24A3, solute carrier family 24 (sodium/potassium/calcium exchanger), member 3; ST6GAL1, ST6 beta-galactosamide alpha-2,6-sialyltranferase 1; WIPI1, WD repeat domain, phosphoinositide interacting 1; XKR4, XK, Kell blood group complex subunit-related family, member 4; ZNF165, zinc finger protein 165; ZPBP, zona pellucida binding proteinKeywords, ADHD, Blood pressure, Genome-wide association study, Methylphenidate, Pharmacogenomic. ⁎ Corresponding author. Tel.: + 1 617 724 0956; fax: + 1 617 943 8983. E-mail address: [email protected] (E. Mick). 0278-5846/$ – see front matter © 2010 Elsevier Inc. All rights reserved. doi:10.1016/j.pnpbp.2010.11.037

E. Mick et al. / Progress in Neuro-Psychopharmacology & Biological Psychiatry 35 (2011) 466–472

1. Introduction Psychostimulants are a mainstay in the treatment of attentiondeficit/hyperactivity disorder (ADHD) ((for a review, see Spencer, 2007; Biederman and Spencer, 2008)) due to their well-documented efficacy in children, adolescents and adults (Faraone and Buitelaar, 2009; Faraone and Glatt, 2009). Although the most common side effects of stimulants are benign, concerns have been raised about the misuse and diversion (Faraone and Wilens, 2007), effects on growth (Faraone et al., 2008) and cardiovascular risks (Samuels et al., 2006) associated with stimulant therapy. Although prior reviews of the literature suggested that blood pressure changes seen with stimulants were mostly transient (Rapport and Moffitt, 2002), recent research suggests that increased systolic blood pressure associated with methylphenidate therapy persisted for up to 6 months in adolescents with ADHD (Hammerness et al., 2009). The American Heart Association's Scientific Statement on the topic concluded that stimulant treatment is associated with average increases in heart rate of 1–2 beats per minute (bpm) and systolic/diastolic blood pressures of 3–4 mm Hg (Vetter et al., 2008). Given that the heritability of hypertension has been recognized for several decades (Samani, 2003), pharmacogenetics could provide a means for identifying genes that control the blood pressure response to stimulant medications. In fact, linkage studies of blood pressure and hypertension have identified multiple genome-wide significant loci (chromosomes 2p, 4p, 17q, and 18q) (Samani, 2003) and large genome-wide association studies have identified and replicated several loci strongly associated blood pressure (Levy et al., 2009; Newton-Cheh et al., 2009b). Advances in our understanding of the genetic architecture underlying normative changes in blood pressure on the population level should be helpful in understanding the variation in blood pressure associated with therapeutic stimulants. Despite a recent focus on pharmacogenetic studies of treatment emergent side effects (Gruber et al., 2009; McGough et al., 2006; McGough et al., 2009), the potential genomic contribution to blood pressure response to stimulant therapy for ADHD has received little attention. Two studies have assessed the impact of a 40-bp VNTR in exon 15 of the dopamine transporter gene (SLC6A3) on change in blood pressure or heart rate associated with psychostimulants (Lott et al., 2005; Mick et al., 2006). Lott et al. (2005) found that subjects homozygous for the 9-repeat allele demonstrated a statistically significant blunting of diastolic blood pressure increases with increasing amphetamine dose. We also observed a decrease in diastolic blood pressure with methylphenidate treatment of 9-repeat homozygous ADHD adults but our findings were not statistically significant (Mick et al., 2006). A third study examined additional markers in SLC6A3 (4 Single Nucleotide Polymorphisms (SNPs) not in linkage disequilibrium with the exon 15 VNTR) in healthy individuals treated with d-amphetamine and found no association with blood pressure changes (Hamidovic et al., 2010). We recently published a genome-wide association study of acute efficacy with a methylphenidate transdermal system (MTS) in 187 children with ADHD (Mick et al., 2008). Using that sample, we have now conducted a secondary genome-wide association study of changes in systolic and diastolic blood pressure and focus on markers under loci associated with essential hypertension (Levy et al., 2009; Newton-Cheh et al., 2009b). 2. Methods 2.1. Study sample As previously reported (Warshaw et al., 2010), this was a phase IV, multi-center, open-label, dose optimization study designed to characterize the dermal response of MTS in 309 children with ADHD (clinicaltrials.gov number NCT00434213). Subjects were

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eligible for participation if they were 6–12 years of age, met full diagnostic criteria for DSM-IV-TR ADHD based on a detailed psychiatric evaluation, and had a clinician completed ADHD-RS-IV score of ≥26 at baseline. All research was conducted in accordance with the Declaration of Helsinki and informed consent was obtained from each subject's parent or legally authorized representatives in addition to documentation of assent by the subject indicating that he or she was aware of the investigational nature of the study and protocol requirements under procedures approved by each participating site's Institutional Review Board. Subjects were not eligible for participation if they had a current comorbid psychiatric disorder, a known history of a structural cardiac abnormality, a BMI N95th percentile for age, a blood pressure measurement N95th percentile for age, sex, and height at screening and baseline, a history of seizures or tics, conduct disorder, substance use disorder, abnormal thyroid function, skin disease, sensitive-skin syndrome, clinical skin irritation, or other medical illness that could confound the assessment of response or tolerability. Subjects were also excluded if they had taken any investigational drug, had treatment with any known hepatic and/or P450 enzyme altering agents, or had taken any other drugs with CNS effects within 30 days prior to screening. Subjects with a prior history of nonresponse to psychostimulants (i.e. reported no clinical improvement following separate trials of two psychostimulant medications taken for ADHD at appropriate doses for at least four weeks each) were also excluded from the trial. Subjects were screened for approximately two weeks prior to a one-week washout period. Subjects were then initiated on 10 mg MTS and were titrated to an acceptable dose over five weekly visits (maximum dose was 30 mg). For the purposes of dose titration, an acceptable response was defined by at least a 25% change in ADHDRS-IV score with tolerable side effects. Once subjects demonstrated a 25% decrease in symptoms, the dose could be further increased to achieve optimal dosing if the current dose was well tolerated, and in the investigator's opinion, the subject would potentially receive additional symptom reduction. Measurements of vital signs (oral temperature, systolic and diastolic blood pressure, resting heart rate, and sitting respiratory rate) were performed at all study visits after subjects had remained seated for a minimum of five minutes. Blood pressure was determined by cuff (either manual or automated with the same method used each visit) in the same arm and was performed by the same site personnel throughout the study. 2.2. Genotyping methods After obtaining a separate consent and assent from interested subjects, DNA was obtained via buccal swab (Catch-All Sample Collection SwabTM). All laboratory assays were blinded to the subject's identity and clinical data. All DNA extraction and genotyping (Affymetrix® Genome-Wide Human SNP Nsp/Sty assay kit 6.0) was conducted at SUNY Upstate Medical University per manufacturer protocols. Details are available in (Mick et al., 2008) and in supplemental material available online (methods appendix). 2.2.1. Subject exclusions Two-hundred thirty-six children (76% of the 309 enrolled in the parent study) participated in the optional genetic study. The reasons for refusal to provide a DNA sample were not recorded and data were not available to compare those refusing participation with those in the current pharmacogenomic sub-study. Two subjects who provided DNA but did not return for post treatment assessment were dropped. An additional 47 subjects were dropped due to low genotyping rate (N10% missing) resulting in 187 subjects available for analysis. There were no differences in response between those dropped due to low

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genotyping rate and those included in the analyses presented here (26.9 ± 11.2 vs. 26.1 ± 12.5, p = 0.7), however.

2.2.2. Additional marker filtering and pruning As described in (Mick et al., 2008) and in the online supplemental material, there were 327,867 marker genotypes available in the sample after data cleaning and filtering in all the subjects. After eliminating the low performing subject samples, we subsequently recleaned the SNP data based on high rates of missing data (N10% were missing, N = 667 eliminated), Minor Allele Frequency b0.01 (N = 6,433 eliminated), or HWE p ≤ 0.001 (N = 1051 eliminated). After all of these data cleaning steps, we obtained a set of 319,722 high performing SNPs that were suitable for association testing.

2.3. Association analyses We performed a quantitative trait analysis of the change in systolic and diastolic blood pressure from baseline to endpoint. Genome-wide association analyses were conducted with the PLINK software package (Purcell et al., 2007) on the Genetic Cluster Computer (http://www. geneticcluster.org) which is financially supported by the Netherlands Scientific Organization (NWO 480-05-003). To control for the number of SNPs tested we adopted the conservative recommendation of Dudbridge (2006) and Pe'er et al. (2008) and considered p-values less than 5.0E-08 to be statistically significant genome-wide. To account for the multiple correlated phenotypes (r2 = 0.42 between systolic and diastolic blood pressure) we employed the MQFAM multivariate extension of PLINK (Ferreira and Purcell, 2009). This multivariate test uses canonical correlation analysis to identify the linear combination of phenotypes that maximizes the covariation between each SNP and the phenotypes. This test is conducted only once for each SNP so there is no need for further adjustment of the study-wide alpha level as would be required if each phenotype was tested separately. Because we enrolled singleton cases from diverse ethnic and geographic groups, it is unlikely that these subjects share homogenous genetic background. To identify any population stratification in our cleaned dataset we applied the multi-dimensional scaling algorithm (MDS) in PLINK to a linkage disequilibrium pruned sample of 125,671 independent SNPs (Price et al., 2006; Purcell et al., 2007). The first two dimensions representing shared covariance due to, in part, population stratification are plotted against one another in Fig. 1. We observed a clear relationship with extreme scores (beyond 1SD) on either the first (N= 23) or second (N= 24) dimension of genomic background and both self-reported race/ethnicity and (χ2(4) = 178.2, p b 0.001; Fig. 1— shaded areas) and change in blood pressure (F(4179) = 3.26, p = 0.01; Fig. 1 panel inset). We considered three analytic approaches to address population stratification in these data: 1) a “crude analysis” in all subjects (N = 187) with observed phenotypes; 2) an “adjusted analysis” in all subjects (N = 187) with phenotypes adjusted for population stratification (i.e., model residuals generated for each phenotype regressed on the MDS dimensions representing genetic substructure); and 3) a “restricted analysis” in 140 subjects within 1SD of MDS dimensions 1 and 2. We then conducted multivariate tests of association in the cleaned dataset under each of these conditions and compared the λ statistic (defined as the observed median F statistic divided by the expected median F statistic) between analytic methods. We found both the “crude analysis” λ statistic (1.105) and the “adjusted analysis” λ statistic (0.896) were deviant from the expected λ value of 1.0. The “restricted analysis” conducted in 140 subjects resulted in a λ statistic of 0.986 in 316,934 SNPs and , therefore, most effectively accounted for genetic background in this study of blood pressure response.

Fig. 1. Association with genetic substructure, race/ethnicity, and blood pressure response to methylphenidate. Caption. SBP: Systolic Blood Pressure. DBP Diastolic Blood Pressure. D1: 1st Dimension. D2: 2nd Dimension. The first two dimensions from the multi-dimensional scaling algorithm (MDS) in PLINK to a linkage disequilibrium pruned are plotted against one another in the main figure. We observed a clear relationship with extreme scores (beyond 1SD) on either the first (N=23) or second (N=24) dimension of genomic background and both self-reported race/ethnicity and (χ2(4)=178.2, pb 0.001; main figure shaded areas) and change in blood pressure (F(4179)=3.26, p =0.01; panel inset).

2.4. Functional annotation Functional annotation and gene enrichment tests were conducted online with the Database for Annotation, Visualization and Integrated Discovery (DAVID v6.7; http://david.abcc.ncifcrf.gov/home.jsp) clustering algorithm (Dennis et al., 2003; Huang et al., 2009). Genes were submitted to DAVID for functional annotation clustering if at least one SNP located within that gene (i.e., 3′ UTR, 5′ UTR, intron or exon) was significant at p b 0.01. Each cluster is assigned an EASE score representing the statistical significance of gene enrichment in the selected gene list (i.e., − log10 of the p-value). This p-value is calculated by comparing the percent of submitted genes associated with a particular functional category against the percent of genes associated with that functional category in the relevant genomic background (i.e., the Affymetrix 6.0 SNP array). Although any EASE score ≥ 1.3 is statistically significant at p b 0.05, we applied a sequential Bonferroni correction (Holm, 1979) to the EASE scores to account for the number of clusters identified. 3. Results As described in the methods, 140 of the 187 available subjects are the focus of this report (47 subjects were excluded to avoid confounding our analyses with genomic background). Subjects were predominantly male (N = 98, 70%), 9.3 ± 2.0 years of age, and had a mean ADHD-RS-IV score of 42.3 ± 7.8 at baseline. Of the subjects with a prior history of ADHD pharmacotherapy (N = 87, 67%), the average number of previous ADHD medications was 1.8 ± 0.9 (range 1–4). The majority of the subjects completed the study (88%, N = 123) and the mean ADHD-RS-IV score at endpoint was 15.5 ± 11.7 with an average change score of − 26.6 ± 11.3 (i.e., 63.9 ± 19.9% improvement). The dose at the end of the 5-week dose optimization period was 10 mg in 16% (N = 22) of the subjects, 15 mg in 30% (N = 42) of the subjects, 20 mg in 29% (N = 40) of the subjects, and 30 mg in 26% (N = 36) of the subjects. In the subsample analyzed for this GWAS (N= 140), change in systolic blood pressure was 2.0 ±9.5 mm Hg (from 102.5 ± 9.0 to 104.5 ± 8.4, p = 0.01) and in diastolic blood pressure was 3.0 ±7.8 mm Hg (from 61.6±7.7 to 64.6±7.5, p b 0.0001) from baseline

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to endpoint. These changes are slightly, but not significantly, larger than that observed in the complete sample (see (Warshaw et al., 2010), 3.2 ± 12.9 beats per minute for pulse and 1.1 ± 10.3 mm Hg and 1.8 ±8.2 mm Hg and for systolic and diastolic blood pressure, respectively) because the subjects excluded to decrease genetic heterogeneity (see Fig. 1 in methods) demonstrated reductions in blood pressure at endpoint. No subjects met clinical criteria for pediatric hypertension (≥95th percentile for ≥3 repeated visits) according to the National Working Group on High Blood Pressure in Children and Adolescents (2004) for systolic or diastolic blood pressure. Table 1 presents the most significant SNPs. We found no evidence of genome-wide statistical significance (pb 5E-08), but three of the top 50 SNPs at p b 1E-04 are in genes previously associated with cardiovascular phenotypes. Ryanodine receptor 3 (RYR3) is a calcium release channel gene expressed in the right sinoatrial node (Masumiya et al., 2003). UDP-N-acetyl-alpha-D-galactosamine:polypeptide N-acetylgalactosaminyltransferase 13 (GALNT13) is involved in the glycosylation of mucins and has been associated with left ventricular dimension in

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systole (Vasan et al., 2007). Solute carrier family 24 (sodium/ potassium/calcium exchanger), member 3 (SLC24A3) regulates calcium homeostasis and is expressed in vascular smooth muscle tissue (Dong et al., 2006), the basolateral layer of distal convoluted tubules in the kidney (Lee et al., 2009), and in substantia nigral dopamine neurons (Zhou et al., 2009). We next attempted to replicate association with genome-wide statistically significant SNPs from the CHARGE and Global BPgen consortia studies of blood pressure and essential hypertension (Levy et al., 2009; Newton-Cheh et al., 2009b). None of the SNPs identified in those samples was present in our study, but using the online SNAP tool (http://www.broadinstitute.org/mpg/snap) we found 35 SNPs in linkage disequilibrium with 11 of the CHARGE and BPgen sentinel SNPs (Supplementary Table 2). Of these, one was nominally statistically significant (rs12567136-A, p = 0.02) at the 1p36 loci located in the neuronally expressed chloride channel 6 (CLCN6) gene. Of the 316,934 SNPs tested for association, 4079 (1.28% 95%CI [1.24%–1.32%]) in 997 genes were statistically significant at p b 0.01.

Table 1 SNPs associated with change in blood pressure at p b 1E-04. Chromosome 1 1 1 1 2 2 2 2 2 2 2 2 3 3 3 5 5 6 6 6 7 7 7 8 8 8 9 9 10 11 12 12 13 13 13 13 14 15 15 15 15 15 16 17 17 17 17 18 19 20

q31.1 q32.2 q42.13 q42.13 p25.2 p22.3 p11.2 q13 q13 q22.1 q24.1 q37.1 q13.31 q13.31 q27.3 q14.1 q33.1 p22.1 q13 q23.1 p14.1 p12.2 q21.3 p22 q12.1 q21.3 p21.1 q21.13 p12.33 q13.1 p13.2 p12.3 q14.3 q21.1 q32.3 q33.1 q24.2 q14 q15.1 q21.1 q22.2 q25.2 p13.2 q11.1 q11.1 q24.2 q24.2 p11.22 q13.42 p11.23

Gene

SNP

Allele

Position

MAF

HWE p-value

MQFAM p-value

– PLXNA2 – RHOU – – PTCD3 EDAR MALL LRP1B GALNT13 PTMA – – ST6GAL1 RASGRF2 C5orf46 ZNF165 – SAMD3 C7orf36 ZPBP CALCR – XKR4 – – PRUNE2 – MARK2 KLRD1 MGST1 FAM124A – DOCK9 FGF14 NUMB RYR3 RHOV – MYO1E HDGFRP3 – – – WIPI1 WIPI1 – LENG8 SLC24A3

rs3010654 rs591305 rs10157777 rs2748092 rs920185 rs6543818 rs2241437 rs13428296 rs6705801 rs17477276 rs16836453 rs13398149 rs7432616 rs1432364 rs6778436 rs17228156 rs6580511 rs16893573 rs6453739 rs6909415 rs7777434 rs7782801 rs41496647 rs4487765 rs6988356 rs4734739 rs10970695 rs700809 rs2451504 rs12099085 rs2537782 rs34725238 rs7318961 rs17054878 rs12859979 rs544434 rs10141031 rs2467565 rs7176208 rs6493206 rs16941421 rs17841157 rs13336012 rs1975980 rs11080056 rs12943181 rs1002446 rs487755 rs3813149 rs942994

G G T A T T C G A C T C C T A C C A T T A A G G C C A T T A A C T A A C G T A T A A C C T C T T A T

186,515,272 206,264,505 226,134,766 227,035,380 5,596,487 34,298,662 86,214,084 109,015,826 110,206,673 140,796,328 154,873,487 232,273,125 117,908,124 117,928,014 188,167,058 80,538,659 147,314,138 28,107,400 74,927,540 130,684,608 39,572,599 50,178,540 92,925,088 15,274,490 56,165,070 92,707,424 32,017,297 78,429,870 19,132,993 63,423,520 10,345,182 16,469,864 50,634,084 52,625,473 98,260,478 101,647,266 72,843,362 31,679,876 38970733 44,356,758 57,484,838 81,643,624 9,469,583 22,419,818 22,428,176 63,935,936 63,936,518 10,501,148 59,655,364 19,547,829

0.328 0.031 0.014 0.015 0.059 0.014 0.016 0.019 0.011 0.025 0.014 0.134 0.277 0.422 0.007 0.094 0.481 0.007 0.207 0.352 0.011 0.018 0.051 0.237 0.015 0.011 0.011 0.025 0.172 0.007 0.015 0.185 0.171 0.027 0.072 0.014 0.201 0.460 0.015 0.011 0.295 0.047 0.019 0.324 0.384 0.303 0.307 0.011 0.395 0.036

1.00 1.00 1.00 0.02 0.37 1.00 1.00 1.00 1.00 1.00 1.00 0.47 1.00 0.11 1.00 0.61 0.12 1.00 0.60 0.06 1.00 1.00 1.00 0.48 0.02 1.00 1.00 1.00 1.00 1.00 1.00 1.00 0.12 1.00 0.52 1.00 0.44 0.50 1.00 1.00 0.11 1.00 1.00 0.32 1.00 0.69 0.55 1.00 0.03 1.00

7.27E-05 1.23E-05 1.15E-05 2.42E-05 9.69E-05 9.16E-05 2.99E-06 5.71E-05 2.03E-05 9.27E-05 2.72E-05 2.15E-05 6.07E-05 4.20E-06 5.23E-06 7.54E-06 4.54E-05 8.93E-05 2.43E-05 7.14E-05 1.75E-05 5.78E-05 5.39E-05 8.74E-05 1.41E-05 9.44E-05 7.35E-05 2.61E-05 7.69E-05 5.13E-06 4.59E-05 8.77E-05 3.12E-05 4.12E-05 1.52E-05 5.68E-06 8.93E-05 5.82E-05 9.88E-05 4.47E-06 8.20E-05 4.45E-05 7.62E-05 9.34E-05 6.08E-05 4.63E-05 2.05E-05 3.22E-05 5.71E-05 9.14E-05

SNP: Single Nucleotide Polymorphism. MAF: Minor Allele Frequency HWE: Hardy–Weinberg Equilibrium. MQFAM: Multivariate test of association.

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Because the 95% confidence interval does not cover 1.0, this suggests that there is an enrichment of SNPs at the lower level of the p-value distribution. This is also true if we prune the SNP list for linkage disequilibrium. (1833 / 125,671; 1.46%; 95%CI [1.39%–1.52%]). Functional annotation clustering of this list of genes identified 61 nonmutually exclusive functional categories statistically significant at p b 0.05 (i.e. EASE score ≥1.3) of which 19 were statistically significant at p b 0.05 after correction for the number of categories identified (Supplementary Table 3). The top 5 biological processes implicated from our genetic enrichment analyses were FERM domains (N = 16 genes, p = 5.0E-07), immunoglobulin domains (N = 43 genes, p = 8.1E-06), the transmembrane region (N = 360 genes, p = 4.4E05), channel activity (N = 45 genes, p = 2.0E-04) and type-III fibronectins (N = 28 genes, p = 2.7E-05).

Excluding genes located only in the very large and non-specific “transmembrane region” category, 155 genes were associated with the four remaining top enrichment categories. Of these, 27 (see Table 2) have been associated with additional cardiovascular phenotypes: hypertension (FSTL5, SDK1, PTPRJ, CLCN6) (Dmitrieva et al., 2009; Newton-Cheh et al., 2009b; Oguri et al., 2009; Yang et al., 2009), vascular contractility (PTPRM, KCNQ5) (Brueggemann et al., 2009; Koop et al., 2005; Shapiro, 2009), coronary artery disease (LSAMP, MYLK, KALRN) (Horne et al., 2009; Krug et al., 2010; Wang et al., 2007; Wang et al., 2008), advanced heart failure (FRMD4B) (Cappola et al., 2010), myocardial infarction (PALLD) (Koch et al., 2009), stroke (FSTL4, KALRN, F13A1) (Bersano et al., 2008; Krug et al., 2010; Luke et al., 2009), hypertrophic cardiomyopathy (OBSCN, MYBPC3) (Arimura et al., 2007; Girolami et al., 2009), arrythmogenic right ventricular cardiomyopathy

Table 2 SNPs from genes assigned to top DAVID enrichment categories demonstrating prior evidence of association with cardiovascular phenotypes. Chromosome

Gene

SNP

Allele

Position

MAF

HWE p-value

MQFAM p-value

FERM domain 3 p14.1 6 q23.2

FRMD4B EPB41L2

10

FRMD4A

rs900668 rs9492764 rs6569716 rs1887241

G T G G

69,508,309 131,308,759 131,354,422 14,201,563

0.074 0.320 0.304 0.109

1.000 0.844 0.319 0.662

9.76E-03 2.58E-03 4.20E-03 5.66E-03

rs16848404 rs865358 rs2168439 rs619460 rs640945 rs10265031 rs6973319 rs1897789 rs10977624 rs10816190 rs10121247 rs1052373 rs449971 rs8083556 rs10163540 rs12961323

T C C G T C T T C C G A A G A A

226,479,160 124,901,603 124,939,122 125,828,567 4,149,376 4,188,615 4,263,840 8,805,681 9,260,120 9,811,895 9,868,687 47,311,363 7,811,378 8,083,288 8,132,163 8,151,470

0.007 0.011 0.018 0.351 0.232 0.095 0.175 0.190 0.181 0.056 0.021 0.399 0.015 0.007 0.130 0.122

1.000 1.000 1.000 0.128 1.000 1.000 0.555 1.000 0.768 1.000 1.000 0.485 1.000 1.000 0.698 0.692

4.90E-04 1.51E-03 6.44E-03 2.38E-03 7.48E-03 4.23E-03 9.54E-03 6.53E-03 8.86E-03 3.68E-03 5.50E-03 4.93E-03 4.19E-03 1.37E-03 6.88E-03 7.07E-03

p13

Immunoglobulin domain/fibronectin type-III 1 q42.13 OBSCN 3 q21.1 MYLK 3 7

q21.2 p22.2

KALRN SDK1

9

p24.1

PTPRD

11 18

p11.2 p11.23

MYBPC3 PTPRM

Immunoglobulin domain 3 q13.31 4 q32.2 4 q32.3 5 q31.1 7 q21.11 8 p12 8 p12

LSAMP FSTL5 PALLD FSTL4 SEMA3C NRG1 FGFR1

rs16824268 rs17041022 rs11721718 rs2162768 rs1058425 rs28667343 rs2288696

G G G C A A T

117,118,003 162,547,861 169,748,080 132,659,558 80,225,702 32,338,224 38,405,382

0.004 0.025 0.014 0.370 0.223 0.123 0.200

1.000 1.000 1.000 0.715 0.311 0.431 0.407

2.27E-03 2.73E-03 4.88E-03 5.49E-03 4.41E-03 8.85E-03 1.30E-03

Fibronectin type-III 6 p25.1

F13A1

11

PTPRJ

rs35010441 rs9392757 rs1742930 rs1234515 rs12285789

A T T G T

6,100,136 6,149,340 6,173,405 47,962,636 48,129,077

0.208 0.020 0.132 0.011 0.014

0.287 1.000 1.000 1.000 1.000

9.37E-04 3.60E-03 5.43E-03 5.44E-03 8.20E-03

rs17037429 rs961120 rs4659491 rs790888 rs7373819 rs7374004 rs11129803 rs6802294 rs6809264 rs17601227 rs6938067 rs12294861 rs2217807 rs682639 rs2467565

C G G C C A G C C G G C G T T

11,796,374 235,489,830 235,499,862 236,044,624 38,594,316 38,600,713 38,727,972 38,734,826 38,775,767 137,257,313 73,763,308 2,645,884 31,570,503 31,653,570 31,679,876

0.141 0.473 0.474 0.412 0.456 0.418 0.194 0.132 0.212 0.16 0.018 0.004 0.408 0.124 0.46

0.47 0.379 0.301 0.214 0.301 0.164 0.105 0.467 0.318 1 1 1 0.861 1 0.497

7.58E-03 4.08E-03 3.38E-03 7.61E-03 8.92E-04 5.96E-04 9.60E-04 1.07E-03 4.89E-04 6.92E-03 6.25E-03 1.67E-03 6.53E-03 5.96E-03 5.82E-05

p11.2

Channel activity 1 p36.22 1 q43

CLCN6 RYR2

3

p22.2

SCN5A

3

p22.2

SCN10A

5 6 11 15

q31.2 q13 p15.5 q14

PKD2L2 KCNQ5 KCNQ1 RYR3

SNP: Single Nucleotide Polymorphism. MAF: Minor Allele Frequency HWE: Hardy–Weinberg Equilibrium. MQFAM: Multivariate test of association.

E. Mick et al. / Progress in Neuro-Psychopharmacology & Biological Psychiatry 35 (2011) 466–472

(FRMD4A) (Matolweni et al., 2006), cardiac conduction (RYR2, RYR3, SCN5A, SCN10A, PKD2L2) (Betzenhauser and Marks, 2010; Holm et al., 2010; Lu and Kass, 2010; Masumiya et al., 2003; Newton-Cheh et al., 2009a; Pfeufer et al., 2009; Pfeufer et al., 2010; Volk et al., 2003), coagulation factors associated with venous thromboembolic disease (F13A1) (Reiner et al., 2009), total plasma homocysteine concentration (PTPRD) (Malarstig et al., 2009), haemostatic factors associated with cardiovascular disease (EPB41L2) (Yang et al., 2007), cardioprotective mechanisms (NRG1) (De Keulenaer et al., 2010), and cardiac development (SEMA3C, FGFR1) (Fantin et al., 2009; Pennisi and Mikawa, 2009). 4. Discussion As discussed previously (Mick et al., 2008), there are several limitations to be considered in interpreting results from this data set. The clinical trial was an uncontrolled open-label single arm study conducted in a heterogeneous sample, the sample was small for genome-wide association studies, multiple correlated phenotypes exacerbated multiple statistical comparison problems, and genotyping success was reduced by inconsistent DNA quality collected via buccal swabs. To counteract these threats to validity we cleaned the genotype data to exclude poorly performing SNPs, identified and accounted for genetic heterogeneity in the sample, and conducted a multivariate test of association to reduce the number of tests conducted. Given these limitations and the conservative analytic approach it is not surprising that we did not identify a genome-wide statistically significant association between methylphenidate and change in blood pressure. It is noteworthy, however, that we observed several interesting signals suggesting association with genes functionally related to blood pressure regulation and other cardiovascular phenotypes. Although these findings are useful for hypothesis generation, they should be interpreted cautiously until confirmed by additional studies. We attempted to replicate known genome-wide significant associations with blood pressure and essential hypertension from the CHARGE and BPgen cohorts (Levy et al., 2009; Newton-Cheh et al., 2009b) but only found nominal association with one SNP in LD with a sentinel SNP on chromosome 1p36 (rs12567136, p = 0.02) in the CLCN6 gene. Two additional genes, SDK1 and FSTL5, in the immunoglobulin domain functional cluster have been associated with hypertension but only in Japanese (Oguri et al., 2009) and Han Chinese (Yang et al., 2009) samples. Gene enrichment analyses also suggested a role of type-III fibronectin genes one of which, PTPRJ, was differentially expressed in four strains of the spontaneously hypertensive rat compared to normotensive control rat strains (Dmitrieva et al., 2009). Thus, our study failed to document association with an expansive list of genes implicated specifically with hypertension. In contrast to the underwhelming results in genes associated with variation in blood pressure, we found stronger evidence of association with genes involved in regulation of vascular tone. The most significant association directly related to blood pressure regulation was with SLC24A3, a gene harboring one of the 50 most significant associations. The K+-dependent Na+/Ca2+ exchangers are predominantly expressed in the brain, but SLC24A3 is unique in that it is functionally expressed in vascular smooth muscle tissue and regulates vascular tone via calcium homeostasis (Dong et al., 2006). In addition, gene enrichment analyses suggested association with the protein tyrosine phosphatase mu-receptor gene (PTPRM) expressed in arterial endothelium that is key to mesenteric artery flow-induced dilation (Koop et al., 2005) and potassium voltage-gated channel gene (KCNQ5) also expressed in vascular smooth muscle cells (Mackie et al., 2008; Shapiro, 2009; Yeung et al., 2007). Recent research suggests that drug-specific actions on the ion channel encoded by KCNQ5 partially explain the differential cardiovascular risks associated with COX-2 inhibiter analgesics (Brueggemann et al., 2009).

471

Although our results should be viewed cautiously, this research serves an important role as a pilot, hypothesis generating study. For example, we observed more associations at p b 0.01 than expected by chance in the analyzed SNPs (4079 / 316,934; 1.29%; 95%CI [1.24%– 1.32%]) and when accounting for linkage disequilibrium among SNPs (1833 / 125,671; 1.46%; 95%CI [1.39%–1.52%]). These results indicate that the number of SNPs at the low end of the p-value distribution is larger than expected by chance. We attempted to distinguish falsely negative from falsely positive findings with functional annotation clustering via DAVID and found evidence of enrichment with domains harboring channel activity genes involved in cardiac conduction. We found suggestive evidence of association with genes coding ryanodine receptors (RYR2 and RYR3) that regulate intracellular calcium release required for myocyte contraction. RYR2 is exclusively expressed in cardiac myocytes and harbors several mutations that cause catecholaminergic polymorphic ventricular tachycardia (Betzenhauser and Marks, 2010). Likewise, we found nominal association with methylphenidate induced change in blood pressure and genes affecting PR interval (SCN5A and SCN10) (Chambers et al., 2010; Holm et al., 2010; Pfeufer et al., 2010) and QT interval (SCN5A and KCNQ1) (Newton-Cheh et al., 2009a; Pfeufer et al., 2009) duration in genome-wide association studies. Future studies would benefit from addressing the limitations of the current hypothesis generating study. A more robust sample size would not only increase the chances of observing statistically significant genome-wide associations but would also afford the opportunity to follow-up on subgroups of interest. Blood pressure response to methylphenidate varied with differences in genetic background that were correlated with self-reported race/ethnicity but our limited sample size required exclusion of those subject clusters so that inferences about SNP blood pressure associations were not confounded by ethnicity. The small number of subjects in these clusters prohibited any valid or clinically relevant inferences to be made regarding these understudied populations of children with ADHD. Finally, more comprehensive genotyping arrays employed with higher quality DNA are needed to better evaluate both common and rare variants that may influence changes in blood pressure associated with methylphenidate treatment in children with ADHD. Nonetheless, these preliminary associations point in a logical direction and should encourage more research into genetic factors affecting the cardiovascular safety of methylphenidate therapy of ADHD. Appendix A. Supplementary data Supplementary data to this article can be found online at doi:10.1016/j.pnpbp.2010.11.037. References Working Group on High Blood Pressure in Children and Adolescents. The fourth report on the diagnosis, evaluation, and treatment of high blood pressure in children and adolescents. Pediatrics 2004s;114:555–76 (2 Suppl 4th Report). Arimura T, Matsumoto Y, Okazaki O, Hayashi T, Takahashi M, Inagaki N, et al. Structural analysis of obscurin gene in hypertrophic cardiomyopathy. Biochem Biophys Res Commun 2007;362(2):281–7. Bersano A, Ballabio E, Bresolin N, Candelise L. Genetic polymorphisms for the study of multifactorial stroke. Hum Mutat 2008;29(6):776–95. Betzenhauser MJ, Marks AR. Ryanodine receptor channelopathies. Pflugers Arch 2010. Biederman J, Spencer TJ. Psychopharmacological interventions. Child Adolesc Psychiatr Clin N Am 2008;17(2):439–58 xi. Brueggemann LI, Mackie AR, Mani BK, Cribbs LL, Byron KL. Differential effects of selective cyclooxygenase-2 inhibitors on vascular smooth muscle ion channels may account for differences in cardiovascular risk profiles. Mol Pharmacol 2009;76(5):1053–61. Cappola TP, Li M, He J, Ky B, Gilmore J, Qu L, et al. Common variants in HSPB7 and FRMD4B associated with advanced heart failure. Circ Cardiovasc Genet 2010. Chambers JC, Zhao J, Terracciano CM, Bezzina CR, Zhang W, Kaba R, et al. Genetic variation in SCN10A influences cardiac conduction. Nat Genet 2010;42(2):149–52. De Keulenaer GW, Doggen K, Lemmens K. The vulnerability of the heart as a pluricellular paracrine organ: lessons from unexpected triggers of heart failure in targeted ErbB2 anticancer therapy. Circ Res 2010;106(1):35–46.

472

E. Mick et al. / Progress in Neuro-Psychopharmacology & Biological Psychiatry 35 (2011) 466–472

Dennis Jr G, Sherman BT, Hosack DA, Yang J, Gao W, Lane HC, et al. DAVID: Database for Annotation, Visualization, and Integrated Discovery. Genome Biol 2003;4(5):3. Dmitrieva RI, Hinojos CA, Grove ML, Bell RJ, Boerwinkle E, Fornage M, et al. Genomewide identification of allelic expression in hypertensive rats. Circ Cardiovasc Genet 2009;2(2):106–15. Dong H, Jiang Y, Triggle CR, Li X, Lytton J. Novel role for K + -dependent Na+/Ca2+ exchangers in regulation of cytoplasmic free Ca2+ and contractility in arterial smooth muscle. Am J Physiol Heart Circ Physiol 2006;291(3):H1226–35. Dudbridge F. UNPHASED user guide. Technical Report 2006/5. Cambridge, UK, MRC Biostatistics Unit; 2006. Fantin A, Maden CH, Ruhrberg C. Neuropilin ligands in vascular and neuronal patterning. Biochem Soc Trans 2009;37(Pt 6):1228–32. Faraone SV, Biederman J, Morley CP, Spencer TJ. Effect of stimulants on height and weight: a review of the literature. J Am Acad Child Adolesc Psychiatry 2008;47(9): 994-1009. Faraone SV, Buitelaar J. Comparing the efficacy of stimulants for ADHD in children and adolescents using meta-analysis. Eur Child Adolesc Psychiatry 2009. Faraone SV, Glatt SJ. A comparison of the efficacy of medications for adult attentiondeficit/hyperactivity disorder using meta-analysis of effect sizes. J Clin Psychiatry 2009. Faraone SV, Wilens TE. Effect of stimulant medications on later substance use and the potential for misuse, abuse, and diversion. J Clin Psychiatry 2007;68(suppl 11): 15–22. Ferreira MA, Purcell SM. A multivariate test of association. Bioinformatics 2009;25(1): 132–3. Girolami A, Candeo N, Vettore S, Lombardi AM, Girolami B. The clinical significance of the lack of arterial or venous thrombosis in patients with congenital prothrombin or FX deficiency. J Thromb Thrombolysis 2009;29(3):299–302. Gruber R, Joober R, Grizenko N, Leventhal BL, Cook Jr EH, Stein MA. Dopamine transporter genotype and stimulant side effect factors in youth diagnosed with attention-deficit/hyperactivity disorder. J Child Adolesc Psychopharmacol 2009;19 (3):233–9. Hamidovic A, Dlugos A, Palmer AA, de Wit H. Polymorphisms in dopamine transporter (SLC6A3) are associated with stimulant effects of D-amphetamine: an exploratory pharmacogenetic study using healthy volunteers. Behav Genet 2010;40(2): 255–61. Hammerness P, Wilens T, Mick E, Spencer T, Doyle R, McCreary M, et al. Cardiovascular effects of longer-term, high-dose OROS methylphenidate in adolescents with attention deficit hyperactivity disorder. J Pediatr 2009;155(1):84–9 89 e1. Holm H, Gudbjartsson DF, Arnar DO, Thorleifsson G, Thorgeirsson G, Stefansdottir H, et al. Several common variants modulate heart rate, PR interval and QRS duration. Nat Genet 2010;42(2):117–22. Holm S. A simple sequentially rejective multiple test procedure. Scand J Stat 1979;6: 65–70. Horne BD, Hauser ER, Wang L, Muhlestein JB, Anderson JL, Carlquist JF, et al. Validation study of genetic associations with coronary artery disease on chromosome 3q13-21 and potential effect modification by smoking. Ann Hum Genet 2009;73(Pt 6): 551–8. Huang DW, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc 2009;4(1):44–57. Koch W, Hoppmann P, Schomig A, Kastrati A. Variations of specific non-candidate genes and risk of myocardial infarction: a replication study. Int J Cardiol 2009. Koop EA, Gebbink MF, Sweeney TE, Mathy MJ, Heijnen HF, Spaan JA, et al. Impaired flow-induced dilation in mesenteric resistance arteries from receptor protein tyrosine phosphatase-mu-deficient mice. Am J Physiol Heart Circ Physiol 2005;288 (3):H1218–23. Krug TH, Krug T, Manso H, Gouveia L, Sobral J, Xavier JM, et al. Kalirin: a novel genetic risk factor for ischemic stroke. Hum Genet 2010. Lee GS, Choi KC, Jeung EB. K + -dependent Na+/Ca2+ exchanger 3 is involved in renal active calcium transport and is differentially expressed in the mouse kidney. Am J Phys Ren Phys 2009;297(2):F371–9. Levy D, Ehret GB, Rice K, Verwoert GC, Launer LJ, Dehghan A, et al. Genome-wide association study of blood pressure and hypertension. Nat Genet 2009. Lott DC, Kim SJ, Cook Jr EH, de Wit H. Dopamine transporter gene associated with diminished subjective response to amphetamine. Neuropsychopharmacology 2005;30(3):602–9. Lu JT, Kass RS. Recent progress in congenital long QT syndrome. Curr Opin Cardiol 2010. Luke MM, O'Meara ES, Rowland CM, Shiffman D, Bare LA, Arellano AR, et al. Gene variants associated with ischemic stroke: the cardiovascular health study. Stroke 2009;40(2):363–8. Mackie AR, Brueggemann LI, Henderson KK, Shiels AJ, Cribbs LL, Scrogin KE, et al. Vascular KCNQ potassium channels as novel targets for the control of mesenteric artery constriction by vasopressin, based on studies in single cells, pressurized arteries, and in vivo measurements of mesenteric vascular resistance. J Pharmacol Exp Ther 2008;325(2):475–83. Malarstig A, Buil A, Souto JC, Clarke R, Blanco-Vaca F, Fontcuberta J, et al. Identification of ZNF366 and PTPRD as novel determinants of plasma homocysteine in a familybased genome-wide association study. Blood 2009;114(7):1417–22. Masumiya H, Yamamoto H, Hemberger M, Tanaka H, Shigenobu K, Chen SR, et al. The mouse sino-atrial node expresses both the type 2 and type 3 Ca(2+) release channels/ryanodine receptors. FEBS Lett 2003;553(1–2):141–4. Matolweni LO, Bardien S, Rebello G, Oppon E, Munclinger M, Ramesar R, et al. Arrhythmogenic right ventricular cardiomyopathy type 6 (ARVC6): support for the locus assignment, narrowing of the critical region and mutation screening of three candidate genes. BMC Med Genet 2006;7:29.

McGough J, McCracken J, Swanson J, Riddle M, Kollins S, Greenhill L, et al. Pharmacogenetics of methylphenidate response in preschoolers with ADHD. J Am Acad Child Adolesc Psychiatry 2006;45(11):1314–22. McGough JJ, McCracken JT, Loo SK, Manganiello M, Leung MC, Tietjens JR, et al. A candidate gene analysis of methylphenidate response in attention-deficit/hyperactivity disorder. J Am Acad Child Adolesc Psychiatry 2009. Mick E, Biederman J, Spencer T, Faraone SV, Sklar P. Absense of association with DAT1 polymorphism and response to methylphendate in a sample of adults with ADHD. Am J Med Genet Neuropsychiatr Genet 2006;141B(8):890–4. Mick E, Neale B, Middleton FA, McGough JJ, Faraone SV. Genome-wide association study of response to methylphenidate in 187 children with attention-deficit/hyperactivity disorder. Am J Med Genet B Neuropsychiatr Genet 2008;147B(8):1412–8. Newton-Cheh C, Eijgelsheim M, Rice KM, de Bakker PI, Yin X, Estrada K, et al. Common variants at ten loci influence QT interval duration in the QTGEN Study. Nat Genet 2009a;41(4):399–406. Newton-Cheh C, Johnson T, Gateva V, Tobin MD, Bochud M, Coin L, et al. Genome-wide association study identifies eight loci associated with blood pressure. Nat Genet 2009b. Oguri M, Kato K, Yokoi K, Yoshida T, Watanabe S, Metoki N, et al. Assessment of a polymorphism of SDK1 with hypertension in Japanese individuals. Am J Hypertens 2009;23(1):70–7. 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(4):381–5. Pennisi DJ, Mikawa T. FGFR-1 is required by epicardium-derived cells for myocardial invasion and correct coronary vascular lineage differentiation. Dev Biol 2009;328 (1):148–59. Pfeufer A, Sanna S, Arking DE, Muller M, Gateva V, Fuchsberger C, et al. Common variants at ten loci modulate the QT interval duration in the QTSCD study. Nat Genet 2009;41(4):407–14. Pfeufer A, van Noord C, Marciante KD, Arking DE, Larson MG, Smith AV, et al. Genomewide association study of PR interval. Nat Genet 2010;42(2):153–9. Price AL, Patterson NJ, Plenge RM, Weinblatt ME, Shadick NA, Reich D. Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet 2006;38(8):904–9. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 2007;81(3):559–75. Rapport MD, Moffitt C. Attention deficit/hyperactivity disorder and methylphenidate. A review of height/weight, cardiovascular, and somatic complaint side effects. Clin Psychol Rev 2002;22(8):1107–31. Reiner AP, Lange LA, Smith NL, Zakai NA, Cushman M, Folsom AR. Common hemostasis and inflammation gene variants and venous thrombosis in older adults from the Cardiovascular Health Study. J Thromb Haemost 2009;7(9):1499–505. Samani NJ. Genome scans for hypertension and blood pressure regulation. Am J Hypertens 2003;16(2):167–71. Samuels JA, Franco K, Wan F, Sorof JM. Effect of stimulants on 24-h ambulatory blood pressure in children with ADHD: a double-blind, randomized, cross-over trial. Pediatr Nephrol 2006;21(1):92–5. Shapiro MS. An ion channel hypothesis to explain divergent cardiovascular safety of cyclooxygenase-2 inhibitors: the answer to a hotly debated puzzle? Mol Pharmacol 2009;76(5):942–5. Spencer TJ. Pharmacology of adult ADHD with stimulants. CNS Spectr 2007;12(4 Suppl 6):8-11. Vasan RS, Larson MG, Aragam J, Wang TJ, Mitchell GF, Kathiresan S, et al. Genome-wide association of echocardiographic dimensions, brachial artery endothelial function and treadmill exercise responses in the Framingham Heart Study. BMC Med Genet 2007;8(Suppl 1):S2. Vetter VL, Elia J, Erickson C, Berger S, Blum N, Uzark K, et al. Cardiovascular monitoring of children and adolescents with heart disease receiving stimulant drugs: a scientific statement from the American Heart Association Council on Cardiovascular Disease in the Young Congenital Cardiac Defects Committee and the Council on Cardiovascular Nursing. Circulation 2008;117(18):2407–23. Volk T, Schwoerer AP, Thiessen S, Schultz JH, Ehmke H. A polycystin-2-like large conductance cation channel in rat left ventricular myocytes. Cardiovasc Res 2003;58(1):76–88. Wang L, Hauser ER, Shah SH, Pericak-Vance MA, Haynes C, Crosslin D, et al. Peakwide mapping on chromosome 3q13 identifies the kalirin gene as a novel candidate gene for coronary artery disease. Am J Hum Genet 2007;80(4):650–63. Wang L, Hauser ER, Shah SH, Seo D, Sivashanmugam P, Exum ST, et al. Polymorphisms of the tumor suppressor gene LSAMP are associated with left main coronary artery disease. Ann Hum Genet 2008;72(Pt 4):443–53. Warshaw, E. M., L. Squires, Y. Li, R. Civil and A. S. Paller. Methylphenidate transdermal system: a multisite, open-label study of dermal reactions in pediatric patients diagnosed with ADHD. Prim Care Companion J Clin Psychiatry 2010;12(6):e1–9. Yang HC, Liang YJ, Wu YL, Chung CM, Chiang KM, Ho HY, et al. Genome-wide association study of young-onset hypertension in the Han Chinese population of Taiwan. PLoS ONE 2009;4(5):e5459. Yang Q, Kathiresan S, Lin JP, Tofler GH, O'Donnell CJ. Genome-wide association and linkage analyses of hemostatic factors and hematological phenotypes in the Framingham Heart Study. BMC Med Genet 2007(8 Suppl 1):S12. Yeung SY, Pucovsky V, Moffatt JD, Saldanha L, Schwake M, Ohya S, et al. Molecular expression and pharmacological identification of a role for K(v)7 channels in murine vascular reactivity. Br J Pharmacol 2007;151(6):758–70. Zhou Q, Li J, Wang H, Yin Y, Zhou J. Identification of nigral dopaminergic neuronenriched genes in adult rats. Neurobiol Aging 2009.