Search tips
Search criteria 


Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
J Am Acad Child Adolesc Psychiatry. Author manuscript; available in PMC 2011 April 1.
Published in final edited form as:
PMCID: PMC2888980

A Candidate Gene Analysis of Methylphenidate Response in Attention-Deficit/Hyperactivity Disorder



This study examines the potential role of candidate genes in moderating treatment effects of methylphenidate (MPH) in Attention-Deficit/Hyperactivity Disorder (ADHD).


Eighty two subjects with ADHD aged 6 to 17 participated in a prospective, double-blind, placebo-controlled, multiple-dose, crossover titration trial of immediate release MPH three times daily. Subjects were assessed on a variety of parent and clinician ratings, and a laboratory math test. Data reduction based on principal components analysis identified statistically derived efficacy and side effect outcomes.


ADHD symptom response was predicted by polymorphisms at the serotonin transporter (SLC6A4) intron 2 VNTR (p=.01), with a suggested trend for catechol-O-methyltransferase (COMT) (p=.04). Gene × dose interactions were noted on math test outcomes for the dopamine D4 receptor (DRD4) promoter (p=.008), DRD4 exon 3 VNTR (p=.006), and SLC6A4 promoter insertion/deletion polymorphism (5HTTLPR) (p=.02). Irritability was predicted by COMT (p=.02). Vegetative symptoms were predicted by 5HTTLPR (p=.003). No significance effects were noted for the dopamine transporter (SLC6A3) or synaptosomal-associated protein 25 (SNAP25).


This report confirms and expands previous studies suggesting that genes moderate ADHD treatment response. ADHD outcomes are not unitary, but reflect both behavioral and learning domains that are likely influenced by different genes. Future research should emphasize candidate gene and genome wide association studies in larger samples, symptom reduction as well as side effects outcomes, and responses over full therapeutic dose ranges to assess differences in both gene and gene × dose interactive effects.

Keywords: ADHD, pharmacogenetics, candidate genes, methylphenidate


There is increasing interest in genetic variability as a potential moderator of medication response in Attention-Deficit/Hyperactivity Disorder (ADHD).1-4 Although stimulants are regarded as effective in ADHD management, there is considerable individual variation in both short- and long-term treatment outcomes5 and most patients fail to maintain therapy over time.6 The potential of ADHD pharmacogenetics lies with the possibility that knowledge of patients' genetic profiles will inform better medication choices and maximize individual treatment outcomes, as well as identify optimal targets for future drug development. 1,3,4 Early candidate gene investigations of ADHD susceptibility were directed by an understanding of stimulant activity at the dopamine transporter7 and other catecholamine targets.8-10 There is increased probability, therefore, that candidate genes associated with disorder risk are likely to predict treatment response.1,3

Numerous investigations have described evidence of genetic influences on methylphenidate treatment outcomes, as summarized in several reviews.1-4 Previous studies employed heterogeneous methodologies and yielded conflicting results. Most are retrospective 11-14 or used open-label methylphenidate titrations to “optimal” benefit.15-18 Randomized, placebo-controlled studies are few,19-23 as are studies that assessed outcomes at multiple doses.21,23,24 Only one explored gene × dose interactions in addition to gene effects.21 Four examined side effects.22-25 Methylphenidate doses ranged from 0.3-2.4 mg/kg/day, typically divided, with all but five studies18,20-23 using less than 1.0 mg/kg/day. Outcomes are both categorical and quantitative. Little attention was given to problems from multiple testing or estimating genetic effect sizes.1 Of prospective reports, almost half had fewer than 50 subjects.15,18,20,23,26-28

The majority of studies examined the variable number of tandem repeat (VNTR) polymorphism located in the 3′ untranslated region (UTR) of the dopamine transporter (SLC6A3). These suggested decreased15,20,25,27,29 or increased11 response with the homozygous 10-repeat; decreased response with the homozygous 9-repeat,19,23 or no gene effect.12,14,18,21,22,30,31 Several investigated the 48-base pair (bp) VNTR polymorphism in exon 3 of the dopamine D4 receptor (DRD4), and demonstrated enhanced response with the homozygous 4-repeat,16,18 any 7-repeat allele,13 or no effect.14,20,21,27,30,31 One described an association between improved response and the homozygous 240-bp polymorphism in the DRD4 promoter.21 A study of the single nucleotide polymorphism (SNP) G1287A at the norepinephrine transporter (SLC6A2) suggested decreased response with the homozygous A genotype.28 Two studies of the 44-bp insertion/deletion polymorphism in the promoter region of the serotonin transporter (SLC6A4) were negative.30,31 Differences in side effects risk have been reported for polymorphisms in synaptosomal-associated protein 25 (SNAP25),21 SLC6A3,23 and DRD4, 21 while other investigations found no effects. 24,25 Taken together, these reports and others suggest that the enormous response variability in ADHD treatment remains unexplained, and initial pharmacogenetic studies, while intriguing, lack consistent replication.

We examined the potential role of candidate genes in moderating ADHD treatment outcomes using a prospective, randomized, blinded, placebo-controlled trial of multiple methylphenidate doses in a relatively large sample. Both efficacy and side effects ratings were obtained, and data reduction techniques were used to derive primary outcome measures. Genotype classifications were defined a priori to any analyses to minimize potential problems from multiple comparisons. We considered both gene effects and gene × dose interactions. Primary hypotheses were that individual candidate genes contribute to variability in symptom changes and side effects profiles during short-term methylphenidate treatment of ADHD.


Participants and Procedures

Male and female subjects aged 6 to 17 years met DSM-IV criteria for ADHD, any subtype. Additional inclusion criteria included 1) the absence of a clinically adequate methylphenidate trial comparable to the dose range and treatment duration employed in this study, and 2) normal intelligence (IQ > 70) as assessed by the Kaufman Brief Intelligence Test.32 Exclusion criteria were 1) clinical need for other medication with central nervous system effects, 2) current conditions that might require ongoing concomitant medication, or 3) evidence of autism, psychosis, or suicidality. Inclusion and exclusion criteria were determined following clinical evaluation and review of screening data by the study physician.

After receiving a full verbal explanation of study procedures, parents and subjects provided written informed consent/assent approved by the UCLA Institutional Review Board. Psychiatric diagnoses were based on the Schedule for Affective Disorders and Schizophrenia for School Aged Children (KSADS-PL)33 administered to parent and child, and confirmed by clinical interview. Blood samples were obtained for subsequent genotyping.

Subjects participated in a four- or five-week, double-blind, placebo-controlled, crossover titration trial of immediate-release methylphenidate modeled after other ADHD studies.5,34,35 Subjects weighing less than 25 kg received one week each of 5mg, 10mg, 15mg, or placebo three times daily (TID) in random order. Subjects weighing 25kg or more had an additional randomized week of 20mg TID. The first dose in each period was given in the research laboratory and followed after 45 minutes with the Permanent Product Measure of Performance (PERMP) – a 10 minute age-appropriate math test sensitive to stimulant dose effects and scored to provide numbers of problems attempted and correct.36 The ADHD Rating Scale IV (ADHD-RS) measured ADHD symptoms in each previous week, based on the investigator's interview with parent and subject.37,38 Parents also rated each prior week's behavior with the Strengths and Weaknesses of ADHD-Symptoms and Normal Behavior (SWAN) scale, an instrument designed to assess population variability in attention, impulsivity, and hyperactivity based on a 7-point scale (-3…0…+3) of DSM-IV ADHD related behaviors ranging from far below to far above average.39 Potential side effects on each dose were evaluated via parent completed Side Effects Rating Scales, consisting of 4-point (none, mild, moderate, or severe) ratings on 11 adverse events that commonly occur with stimulants. 40-42


To optimize use of study measures while minimizing risks of multiple testing, we used principal components analyses with varimax rotation to derive composite outcome measures for efficacy and side effects, retaining components with eigenvalues ≥ 1. Six efficacy variables were initially considered – the ADHD-RS-inattentive subscale, ADHD-RS-hyperactive/impulsive subscale, parent SWAN-inattentive subscale, parent SWAN-hyperactive/impulsive subscale, PERMP-number attempted, and PERMP-number correct. Inattentive and hyperactive subscales of the ADHD-RS and parent SWAN were used in anticipation of their loading on separate components, but they did not. They were replaced respectively by ADHD-RS total and SWAN total scores. A two component solution explained 92% of variance. The first, ADHD Symptoms (eigenvalue 2.1, 54% variability explained), comprised equally weighted standardized values from the ADHD-RS and SWAN total scores. The second was comprised of a single item Math Problems Correct (eigenvalue 1.5, 38% variability explained). In consideration of potential ethnic heterogeneity, secondary analyses of positive outcomes were conducted limited to white and non-Hispanic white subject subgroups.

Principal components analysis was also employed to suggest logical side effects groupings. Four components explained 61% of variance. These were Irritability, comprised of picking, worried/anxious, crabby/irritable, and tearful/sad/depressed (eigenvalue 3.2, 30% variability explained); Vegetative Symptoms, comprised of dull/tired/listless, sleep problems, and appetite loss (eigenvalue 1.4, 13% variability explained); Abnormal Movements, comprised of motor tics and buccal-lingual movements (eigenvalue 1.1; 10% variability explained), and Somatic Symptoms, comprised of headache and stomachache (eigenvalue 1.0; 9% variability explained). Since distributions of side effects ratings within each component were skewed towards “none” or “mild”, we were unable to derive component scores based on weighted rankings of individual items. Instead, consistent with earlier work,21 we derived binary outcomes (absent/present) for each component by assigning a value of “0” if ratings for all items within the component were coded “none” or “1” if any item within the component received a rating of “mild”, “moderate”, or “severe.”

Candidate Genes and Genotypes

Selection of candidate genes was guided by previous pharmacogenetic studies, and known roles in catecholamine pathways or as putative stimulant targets. We examined polymorphisms at catechol-O-methyltransferase (COMT),3,4 the DRD4 promoter region (DRD4PROM)28 and exon 3 VNTR (DRD4VNTR),3,4 SLC6A2,3,4 SLC6A3,3,4 the SLC6A4 promoter region insertion/deletion (5HTTLPR)3,4 and intron 2 VNTR (SLC6A4VNTR),43 and SNAP25 T1065G and T1069C.3,4 To minimize multiple testing of non-independent genotypes, allele frequencies were computed and subjects were categorized by a 3-level variable - homozygous (+/+), heterozygous (+/-), or absent (-/-), with reference to the most frequently occurring allele.

PCR Amplification and DNA Sequencing

The COMT Val158Met polymorphism was genotyped by PCR, restriction digest, and electrophoresis. PCR amplification of 50ng of genomic DNA was carried out in a volume of 25μL reactions containing 0.8mM of dNTP, 0.8μM of forward and reverse primers (F- 5′-CTC ATC ACC ATC GAG ATC AA-3′; R- 5′-CCA GGT CTG ACA ACG CCT CA-3′), 1× PCR Buffer, 1× Q-Solution, 0.6U of HotStarTaq DNA polymerase (Qiagen, California). The DNA was denatured at 95°C for 15 minutes, followed by 35 cycles of 95°C (30 secs), 50°C (45 secs), and 72°C (45 secs), and completed with a 10 min final extension at 72°C. Aliquots of 7.5μL of PCR product were digested for 3 hours in 10μL reactions consisting of 1μg BSA, 1× manufacture buffer #4, and 5U NlaIII restriction enzyme (New England Biolab).

The DRD4PROM 120/240-bp allele was genotyped using published methods and the following primers: F- 5′-GTT GTC TGT CTT TTC TCA TTG TTT CCA TTG-3′; R 5′-GAA GAA GCA GGC ACC GTG AGC-3′.44 The DRD4VNTR was genotyped with the following modified primers: F- 5′-CTA CCC TGC CCG CTC ATG-3′; R- 5′-CCG GTG ATC TTG GCA CGC-3′ using previously described methods.45 The SLC6A2 G1287A SNP was genotyped following previously published methods.46 The SLC6A3 480-bp VNTR in the 3′ UTR was genotyped using published methods and the following primers: F- 5′-TGT GGT GTA GGG AAC GGC CTG AG-3′; R- 5′-CTT CCT GGA GGT CAC GGC TCA AGG-3′.7 The SLC6A4 promoter region 44-bp insertion/deletion short/long variant (5HTTLPR) and intron 2 VNTR were genotyped according to published protocols.43 SNAP25 T1065G and T1069C were genotyped following published methods and using the following primers: F- 5′-TTC TCC TCC AAA TGC TGT CG-3′; R- 5′-CCA CCG AGG AGA GAA AAT G-3′.47

PCR products were electrophoresed in 2% gold agarose (BMA) gels in 1× TBE and imaged with ethidium bromide under fluorescent Kodak digital camera. Alleles were determined by comparison with molecular weight standards and control individuals with previously determined genotypes. Samples were double-scored by two technicians independent of clinical information. All assays included positive and negative control samples.

Data Analysis

Analyses used SAS version 9.1.48 Descriptive statistics were derived for demographic and genetic characteristics. Genotypes were assessed for Hardy-Weinberg equilibrium (HWE) using SAS PROC ALLELE. Predictors of efficacy were tested using repeated measures ANOVA, fit using SAS PROC MIXED to account for of missing data. The fixed effects terms in each model were gene, dose, and gene × dose interaction. Effect size estimates for efficacy were based on Cohen's f2, the ratio of variance explained to unexplained variance for the main and interactive effects.49 For side effects, generalized estimating equations based on logistic link functions were conducted using SAS PROC GENMOD to assess genotype contributions on binary outcomes.21

Given the pilot nature of this investigation, tests of each candidate gene were considered independently. However, due to our wish to minimize false positive findings and consideration of two primary efficacy measures, α (Type I error rate) was set at 2.5% based on the Bonferroni correction. Since very few previous studies have considered genetic risk for side effects, statistical significance for side effects outcomes remained at 5%. All tests were two tailed. The sample was sufficiently powered to detect small gene and gene × dose effects at p<.025 and medium effects at p<.0005 based on Cohen's f2.


Subject characteristics appear in Table 1. Candidate genes, allele frequencies, and analyzed genotype groupings appear in Table 2. There were no differences in the frequencies of psychiatric comorbidities across genotypes (p<.05). Genotypes met criteria for Hardy Weinberg equilibrium (p>.01). Allele frequencies did not differ from established population norms (ALFRED database: http//

Table 1
Sample Demographic and Clinical Characteristics
Table 2
Allele Frequencies and Genotype Classifications

Active medication was superior to placebo for both ADHD Symptoms (F80=43.01, p<.0001) and Math Problems Correct (F81=50.91, p<.0001). Sex, weight, and age were not significant as covariates for any outcome. For all efficacy models, dose was a positive predictor (p<.01). Gene effects and gene × dose interactions are summarized in Table 3, with significant outcomes further illustrated in Figures 1 and and2.2. A significant effect on ADHD Symptoms was detected for the SLCA64VNTR, with a trend for COMT. Gene × dose interactions were detected for Math Problems Correct and the DRD4PROM, DRD4VNTR, and 5HTTLPR. There were no significant effects for SLC6A2, SLC6A3, or SNAP25.

Figure 1
Significant gene effects on ADHD symptoms
Figure 2
Significant gene × dose effects on Math Problems Correct
Table 3
Gene and Gene × Dose (GXD) Effects

When restricted to white subjects (N=60), significant gene × dose interactions remained for Math Problems Correct with DRD4PROM (F(8,227)=3.90, p=.0002, f2=.14), DRD4VNTR (F(8,227)=2.91, p=.004, f2=.10), and 5HTTLPR (F(8,227)=2.04, p=0.04, f2=.07). When restricted to white/non-Hispanic subjects (N=42), a significant gene × dose interaction remained with the DRD4PROM (F(8,148)=3.93, p=.0003, f2=.21).

The majority of side effects ratings were “none” or “mild”, accounting for an average of 87% of ratings across individual items. Irritability, Vegetative Symptoms, Somatic Symptoms, and Abnormal Movements occurred in 75%, 67%, 38%, and 23% of subjects respectively. There were several suggestive predictors of side effects (Table 4). Irritability was predicted by COMT, with trends noted for DRD4PROM and SNAP25. Similarly, there were trends for both COMT and DRD4PROM on Somatic Symptoms. 5HTTLPR predicted Vegetative Symptoms. There were no significant predictors of Abnormal Movements.

Table 4
Genetic Predictors of Side Effects (P value <.15)


This study confirms and expands previous investigations suggesting individual genes moderate response variability in ADHD treatment. Unlike most previous reports, we prospectively examined an expanded set of candidate genes, used blinded ratings of both efficacy and side effects, assessed responses over a range of doses, analyzed statistically derived components as dependent variables rather than arbitrarily choosing outcome measures, and defined genotypes empirically based on allele frequencies without a priori consideration of potential effects. While recognizing that sample sizes for certain genotypes are too small to sustain population inferences, the overall study sample is larger than a majority of currently published ADHD pharmacogenetic studies. Apart from any significant findings, study methods described provide a strong basis for future investigations.

For efficacy outcomes, our most striking finding is the observation that differences in response profiles across genotypes, i.e. gene × dose interactions, are potentially more significant than genotype effects alone. Conflicting results in prior reports might be due in part to failures to titrate medication over a sufficient range to elicit differences in dose-response by genotype. Earlier investigations described both linear50 and curvilinear51 stimulant dose-response profiles. Our results, while requiring replication, suggest that genotype might moderate dose-response outcomes depending on the specific gene and outcome of interest. Assessing response over a full range of doses and consideration of both gene effects and gene × dose interactions are essential for future ADHD pharmacogenetic studies.

Selection of appropriate outcome measures remains a major issue. Correlations between various ADHD efficacy measures are notably weak,52 and there is particular risk for spurious findings in pharmacogenetic studies of outcome variables selected post hoc. Our data reduction strategy allowed us to select outcome measures objectively and reduced the risk of false positive findings from multiple exploratory tests. Although our primary results did not meet a level for genome wide significance (p≤1×10-8), the high prior probability that selected candidates are involved in stimulant medication response and the strength of demonstrated associations support the validity of our findings. Interestingly, our two component solution for efficacy outcomes is consistent with earlier observations describing two domains of ADHD treatment response, namely learning performance and behavior.51 Our results support the idea that ADHD outcomes are not unitary, but reflect multiple domains of functioning. Furthermore, our data suggest that different genes differentially influence response on these separate domains.

Our results are largely consistent with previous speculations about the neurobiological significance of these genes. The possibility remains that selected candidates are merely linked with other genes that influence medication response, or are interacting with other unidentified genes to yield observed findings. As such, attempts to provide mechanistic explanations significant effects are speculative.

One prior study of DRD4VNTR reported that subjects with the 7-repeat required higher doses to respond on overt ADHD behaviors.18 The apparent deterioration on Math Problems Correct at higher doses in subjects without the homozygous +4/+4 genotype is consistent with older observations that the higher stimulant doses required to control behavior lead to concomitant impairments in learning measures.51,53 For the SLC6A4VNTR, findings that individuals with the -12/-12 genotype are less sensitive to symptom improvement are consistent with one report describing increased euphoria from dextro-amphetamine.43 The poorer Math response at higher doses in individuals with the +L allele at 5HTTLPR is consistent with knowledge of the interactions between serotonergic and dopaminergic systems, where serotonin is generally found to constrain dopaminergic signaling.26 Although our results only demonstrated trend effects for COMT, several previous reports described a potential role in moderating medication response in both depression and schizophrenia.54,55 Our lack of significant findings with SLC6A3, in spite of overall power to detect small effects, reflects the inconsistencies in earlier reports and provides further evidence suggesting that this gene plays less of a role in treatment response than previously proposed.

Examination of genetic moderators of tolerability might ultimately prove more useful than predictions of efficacy.1,3,21 Side effects findings for the DRD4PROM and SNAP25 are consistent with previous work.21 However, relative to investigations of symptom reduction, optimal approaches to assess genetic contributions to side effects are much less developed. For example, the current report describes a brief titration trial and used a low severity threshold to generate sufficient side effect frequencies for reasonable analysis. More clinically relevant information would be derived from a longer term study in larger numbers of patients. Long term investigations of potential genetic moderators of side effects such as growth deceleration, appetite loss, irritability, and tic emergence would, if positive and replicated, have significant impact on clinical practice.

There is an increasing emphasis in ADHD pharmacogenetics on estimating gene effect sizes,1,19-22 often based on Cohen's d. While we view the estimation of gene effects as laudable, we are now of the opinion that using Cohen's d for this purpose is a misapplication of that statistic that leads to an overestimation of genetic effects. Cohen's d generally serves to estimate the differences in effect size between two treatments.49 However, given the large stimulant treatment effects demonstrated in ADHD, effects of medication dose are likely to outweigh any genetic effects. The proper question is not whether different genotypes should be considered as different treatments, but how much variance in treatment outcome is explained by genetic as opposed to medication effects. Cohen f2 provides a signal to noise ratio, indicating how much variability is attributable to the model's fixed effects and thus provides a dimensionless measure of the magnitude of gene and gene × dose signals.49 Based on convention, whereby small effects are defined by f2>.02, moderate effects as >.15, and large effects >.35, none of the statistically significant effects demonstrated in primary analyses are likely to represent more than small clinical effects. This is consistent with results from meta-analyses of these same candidate genes for ADHD risk.8,10

Unlike the majority of published ADHD pharmacogenetic studies, we adjusted significance levels for multiple efficacy outcomes based on the Bonferroni correction. We regarded tests of each genotype as independent, but chose to report these within a single study rather than describe each finding separately. Nonetheless, some might argue that a stricter correction should have been applied, i.e. that further corrections should have been made when examining two polymorphisms within a single gene, or that testing two outcomes for each of nine genotypes suggests adjusting for eighteen tests (p<.003). Although this pilot investigation was underpowered to detect small effects at this very conservative level of significance, the fact that over 20% of primary outcomes were positive at p<.025 is highly suggestive of true findings. Interestingly, our most robust suggested finding was in the restricted sample of white/non-Hispanic subjects which demonstrated a medium sized effect of DRD4PROM × dose on Math Problems Correct, at a significance level corrected for 100 tests (p<.0005). This highlights the need to conduct future research in more homogeneous samples.

This study has several limitations. Although there is increased probability that candidate genes in the catecholamine system are involved in methylphenidate response, candidate gene investigations have high false positive rates. Our results require replication in independent samples prior to any consideration of clinical relevance. Although the majority of our positive findings remained significant when the sample was restricted to whites only, we were unable, given the size and heterogeneity of the sample, to make further assessments of potential population stratification. We did not correct for comparison of multiple polymorphisms within single genes, but treated these as independent tests. This might prove meaningful as our four positive findings are found within two genes. Future research should consider the possibility of linkage disequilibrium between these variants. While we attempted to reduce problems associated with multiple testing, our decision to define genotypes a priori based on allele frequencies negated our ability to assess other previously described genotypes of interest, such as the homozygous 9-repeat allele at SLC6A3, or the 7-repeat allele at DRD4 as primary dependent variables. However, it may prove advantageous to assess the role of these alleles directly after removing minor alleles in future studies of larger samples. Our work did not consider recent findings suggesting that 5HTTLPR is functionally triallelic, with the +L polymorphism exhibiting a G versus A substitution with nearly equivalent expression as the S allele.56 However, whereas this variant was not reported until after we completed genotyping and was not considered in earlier studies involving this deletion/insertion polymorphism, full elucidation of the role of polymorphisms at 5HTTLPR will require ongoing examination. Finally, we did not explore potential gene × gene interactions as these would have required additional sets of hypotheses and analyses. These are more suitable for a separate report.

Our investigation assessed short-term outcomes. Large scale studies document that short-term response rates, cited as high as 75-80%, often fall to 55-60% over time. Short-term data are incomplete assessments of treatment response. Long-term outcomes might prove to be much more clinically relevant for investigation. Ultimately, definitive findings in ADHD pharmacogenetics are likely to depend on a combination of candidate gene and genome wide association approaches with extended treatment outcomes in much larger samples.


This study was supported by grants K23MH01966 (to Dr. McGough) and K24MH01805 (to Dr. McCracken) from the National Institute of Mental Health.


Disclosure: Dr. McGough has served as a consultant to and received research support from Eli Lilly & Company and Shire Pharmaceuticals. Dr. McCracken has served as a consultant to and received research support from Aspect, Bristol-Meyers Squib, Eli Lilly & Company, and Seaside Therapeutics. The other authors report no conflicts of interest.

Portions of this manuscript were previously presented at the 55th Annual Meeting of the American Academy of Child and Adolescent Psychiatry, Chicago, IL, October 30, 2008 and the 56th Annual Meeting of the American Academy of Child and Adolescent Psychiatry, Honolulu, HI, October 30, 2009.

This article is the subject of an editorial by Dr. David Mrazek in this issue.


1. McGough JJ. Attention-deficit/hyperactivity disorder pharmacogenomics. Biol Psychiatry. 2005;57:1367–1373. [PubMed]
2. Polanczyk G, Zeni C, Genro JP, Roman T, Hutz MH, Rohde LA. Attention-deficit/hyperactivity disorder: advancing on pharmacogenomics. Pharmacogenomics. 2005;6:225–234. [PubMed]
3. Stein MA, McGough JJ. The pharmacogenomic era: promise for personalizing attention deficit hyperactivity disorder therapy. Child Adolesc Psychiatr Clin N Am. 2008;17:475–490. [PMC free article] [PubMed]
4. Froehlich TE, McGough JJ, Stein MA. Progress and promise of ADHD pharmacogenetics. CNS Drugs. 2009;23 in press.
5. Greenhill L, Abikoff H, Arnold LE, et al. Medication treatment strategies in the MTA: relevance to clinicians and researchers. J Am Acad Child Adolesc Psychiatry. 1996;35:1304–1313. [PubMed]
6. Charach A, Ickowicz A, Schachar R. Stimulant treatment over five years; Adherence, effectiveness, and adverse events. J Am Acad Child Adolesc Psychiatry. 2004;43:559–567. [PubMed]
7. Cook EH, Stein MA, Krasowski MD, et al. Association of attention-deficit disorder and the dopamine transporter gene. Am J Hum Genet. 1995;56:993–998. [PubMed]
8. Faraone SV, Perlis RH, Doyle AE, et al. Molecular genetics of attention-deficit/hyperactivity disorder. Biol Psychiatry. 2005;57:1313–1323. [PubMed]
9. Ogdie MN, Macphie IL, Minassian SL, et al. A genome-wide scan for attention-deficit/hyperactivity disorder: suggestive linkage on 17p11. Am J Hum Genet. 2003;72:1268–1279. [PubMed]
10. Mick E, Faraone S. Genetics of attention deficit hyperactivity disorder. Child Adolesc Psychiatric Clin N Am. 2008;17:261–284. [PubMed]
11. Kirley A, Lowe N, Hawi Z, et al. Association of the 480 bp DAT1 allele with methylphenidate response in a sample of Irish children with ADHD. Am J Med Genet B Neuropsychiatr Genet. 2003;121B:50–54. [PubMed]
12. Langley K, Turic D, Peirce TR, et al. No support for association between the dopamine transporter (DAT1) gene and ADHD. Am J Med Genet B Neuropsychiatr Genet. 2005;139B:7–10. [PubMed]
13. Tahir E, Yazgan Y, Cirakoglu B, Ozbay F, Waldman I, Asherson PJ. Association and linkage of DRD4 and DRD5 with attention deficit hyperactivity disorder (ADHD) in a sample of Turkish children. Mol Psychiatry. 2000;5:396–404. [PubMed]
14. Van der meulen EM, Bakker SC, Pauls DL, et al. High sibling correlation on methylphenidate response but no association with DAT1-10R homozygosity in Dutch sibpairs with ADHD. J Child Psychol Psychiatry. 2005;46:1074–1080. [PubMed]
15. Cheon KA, Ryu YH, Kim JW, Cho DY. The homzygosity for 10-repeat allele at dopamine transporter gene and dopamine transporter density in Korean children with attention deficit hyperactivity disorder: relating to treatment response to methylphenidate. Eur Neuropsychopharmacol. 2005;15:95–101. [PubMed]
16. Cheon KA, Kim BN, Cho SC. Association of 4-repeat allele of the dopamine D4 receptor gene exon III polymorphism and response to methylphenidate treatment in Korean ADHD children. Neuropsychopharmacology. 2007;32:1377–1383. [PubMed]
17. da Silva TL, Pianca TG, Roman T, et al. Adrenergic [omicron]2A receptor gene and response to methylphenidate in attention-deficit/hyperactivity disorder-predominately inattentive type. J Neural Transm. 2008;115:341–345. [PubMed]
18. Hamarman S, Fossella J, Ulger C, Brimacombe M, Dermody J. Dopamine receptor 4 (DRD4) 7-repeat allele predicts methylphenidate dose response in children with attention-deficit/hyperactivity disorder: a pharmacogenetic study. J Child Adolesc Psychopharmacol. 2004;14:564–574. [PubMed]
19. Joober R, Grizenko N, Sengupta S, et al. Dopamine transporter 3′-UTR VNTR genotype and ADHD: a pharmaco-behavioural genetic study with methylphenidate. Neuropsychopharmacology. 2007;32:1370–1376. [PubMed]
20. Kooij JS, Boonstra A, Vermeulen SH, et al. Response to methylphenidate in adults with ADHD is associated with a polymorphism in SLC6A3 (DAT1) Am J Med Genet B Neuropsychiatr Genet. 2008;147B:201–208. [PubMed]
21. McGough JJ, McCracken JT, Swanson J, et al. Pharmacogenetics of methylphenidate response in preschoolers with ADHD. J Am Acad Child Adolesc Psychiatry. 2006;45:1314–1322. [PubMed]
22. Mick E, Biederman J, Spencer T, Faraone SV, Sklar P. Absence of association with DAT1 polymorphism and response to methylphenidate in a sample of adults with ADHD. Am J Med Genet B Neuropsychiatr Genet. 2006;141B:890–894. [PMC free article] [PubMed]
23. Stein MA, Waldman ID, Sarampote CS, et al. Dopamine transporter genotype with methylphenidate dose response in children with ADHD. Neuropsychopharmacology. 2005;30:1374–1382. [PubMed]
24. Polanczyk G, Zeni C, Genro JP, et al. Association of the adrenergic α2A receptor gene with methylphenidate improvement of inattentive symptoms in children and adolescent with attention-deficit/hyperactivity disorder. Arch Gen Psychiatry. 2007;64:218–224. [PubMed]
25. Purper-Ouakil D, Wohl M, Orejarena S, et al. Pharmacogenetics of methylphenidate response in attention deficit/hyperactivity disorder: association with the dopamine transporter gene (SLC6A3) Am J Med Genet B Neuropsychiatr Genet. 2008;147B:1425–1430. [PubMed]
26. Seeger G, Schloss P, Schmidt MH. Marker gene polymorphisms in hyperkinetic disorder – predictors of clinical response to treatment with methylphenidate? Neurosci Lett. 2001:45–48. [PubMed]
27. Winsberg BG, Comings DE. Association of the dopamine transporter gene (DAT1) with poor methylphenidate response. J Am Acad Child Adolesc Psychiatry. 1999;38:1474–1477. [PubMed]
28. Yang L, Wang YF, Li J, Faraone SV. Association of norepinephrine transporter gene with methylphenidate response. J Am Acad Child Adolesc Psychiatry. 2004;43:1154–1158. [PubMed]
29. Roman T, Szobot C, Martins S, Biederman J, Rohde LA, Hutz MH. Dopamine transporter gene and response to methylphenidate in attention-deficit/hyperactivity disorder. Pharmacogenetics. 2000;12:497–499. [PubMed]
30. Zeni SP, Guimarães AP, Polanczyk GV, et al. No significant association between response to methylphenidate and genes of the dopaminergic and serotonergic systems in a sample of Brazilian children with attention-deficit/hyperactivity disorder. Am J Med Genet B Neuropsychiatr Genet. 2007;144B:391–393. [PubMed]
31. Tharoor H, Lobos EA, Todd RD, Reiersen AM. Association of dopamine, serotonin, and nicotine gene polymorphisms with methylphenidate response in ADHD. Am J Med Genet B Neuropsychiatr Genet. 2008;147B:527–530. [PubMed]
32. Kaufman AS, Kaufman NL. Kaufman brief intelligence test. 1st. Circle Pines, MN: American Guidance Service; 1990.
33. Kaufman J, Birmaher B, Brent D, et al. Schedule for Affective Disorders and Schizophrenia for School-Age Children – Present and Lifetime Version (KSADS-PL): initial reliability and validity data. J Am Acad Child Adolesc Psychiatry. 1997;36:980–988. [PubMed]
34. Abikoff H, McGough J, Vitiello B, et al. Sequential pharmacotherapy for children with comorbid attention-deficit/hyperactivity disorder and anxiety disorders. J Am Acad Child Adolesc Psychiatry. 2005;44:418–427. [PubMed]
35. Greenhill LL, Kollins S, Abikoff H, et al. Efficacy and safety of immediate-release methylphenidate treatment for preschoolers with ADHD. J Am Acad Child Adolesc Psychiatry. 2006;45:1284–1293. [PubMed]
36. Swanson JM, Agler D, Fineberg E. Laboratory school protocol for pharmacokinetic and pharmacodynamic studies. In: Greenhill LL, Osman BB, editors. Ritalin: Theory and Practice. New York, NY: Mary Ann Leibert; 2000.
37. DuPaul GJ, Power TJ, Anastopoulos AD, Reid R. ADHD Rating Scale-IV: Checklists, Norms, and Clinical Interpretation. New York, NY: Guilford Press; 1998.
38. Faries DE, Yalcon I, Harder D, Heilengenstein JH. Validation of the ADHD Rating Scale as a clinician administered and scored instrument. J Atten Disord. 2001;5:107–115.
39. Swanson JM, Schuck S, Mann M, et al. Categorical and dimensional definitions and evaluations of ADHD: the SNAP and SWAN ratings scales. [June 12, 2009].
40. MTA Cooperative Group. 14-Month randomized clinical trial of treatment strategies for attention-deficit/hyperactivity disorder. Arch Gen Psychiatry. 1999;56:1073–1086. [PubMed]
41. Pelham W. Pharmacotherapy for children with attention deficit hyperactivity disorder. School Psychol Rev. 1993;22:199–227.
42. Wigal T, Greenhill L, Chuang S, et al. Safety and tolerability of methylphenidate in preschool children with ADHD. J Am Acad Child Adolesc Psychiatry. 2006;45:1294–1303. [PubMed]
43. Lott DC, Kim SJ, Cook EH, deWit H. Serotonin transporter genotype and acute subjective response to amphetamine. Am J Addict. 2006;15:327–335. [PubMed]
44. McCracken JT, Smalley SL, McGough JJ, et al. Evidence for linkage of a tandem duplication polymorphism upstream of the dopamine D4 receptor gene (DRD4) with attention deficit hyperactivity disorder (ADHD) Mol Psychiatry. 2000;5:531–536. [PubMed]
45. Shaikh S, Collier D, Kerwin RW, et al. Dopamine D4 receptor subtypes and response to clozapine. Lancet. 1993;341:689–690. [PubMed]
46. Stöber G, Nöthen MM, Pörzgen P, et al. Systematic search for variation in the human norepinephrine transporter gene: identification of five naturally occurring missense mutations and study of association with major psychiatric disorders. Am J Med Genet. 1996;67:523–532. [PubMed]
47. Barr CL, Feng Y, Wigg K, et al. Identification of DNA variants in the SNAP-25 gene and linkage study of these polymorphisms and attention-deficit/hyperactivity disorder. Mol Psychiatry. 2000;5:405–409. [PubMed]
48. SAS Version 9.1. Cary, NC: SAS Institute; 2006.
49. Cohen J, Cohen P, West SG, Aiken LS. Applied multiple regression/correlation analysis for the behavioral sciences. 3rd. Mahwah, NJ: Erlbaum; 2003.
50. Rapport MD, Denney C. Titrating methylphenidate in children with attention-deficit/hyperactivity disorder; is body mass predictive of clinical response? J Am Acad Child Adolesc Psychiatry. 1997;36:523–530. [PubMed]
51. Sprague RL, Sleator EK. Methylphenidate in hyperkinetic children; differences in dose effects on learning and social behavior. Science. 1997;198:1274–1276. [PubMed]
52. Pelham WE, Millich R. Individual differences in response to ritalin in class work and social behavior. In: Greenhill LL, Osman BB, editors. Ritalin: Theory and Practice. New York, NY: Mary Ann Leibert; 1991.
53. Gan J, Cantwell DP. Dosage effects of methylphenidate on paired associate learning: positive/negative placebo responders. J Am Acad Child Adolesc Psychiatry. 1982;21:237–242. [PubMed]
54. Baune BT, Hohoff C, Berger K, et al. Association of the COMT val158met variant with antidepressant response in major depression. Neuropsychopharmacology. 2008;33:924–932. [PubMed]
55. Weickert TW, Goldberg TE, Mishara A, et al. Catechol-O-methyltransferase val108/158met genotype predicts working memory response to antipsychotic medications. Biol Psychiatry. 2004;56:677–682. [PubMed]
56. Hu XZ, Lipsky RH, Zhu G, et al. Serotonin transporter promoter gain-of-function genotypes are linked to obsessive-compulsive disorder. Am J Hum Genet. 2006;78:815–826. [PubMed]