Attention deficit hyperactivity disorder (ADHD) is a highly heritable psychiatric condition of early childhood onset characterised by marked inattention, hyperactivity and impulsiveness. Molecular genetic investigations of ADHD have found positive associations with the 480-bp allele of a VNTR situated in the 3′ untranslated region of DAT1 and allele 7 of a VNTR in exon 3 of DRD4. A number of independent studies have attempted to replicate these findings but the results have been inconsistent. We used both family-based and case control approaches to examine these polymorphisms in a sample of 137 children diagnosed with ICD-10, DSM-IV or DSM-III-R ADHD. We found no evidence of association with the DAT1 polymorphism, despite a sample size that has up to 80% power to detect a previously reported effect size. We observed a significant increase in the DRD4 7 repeat allele amongst ADHD probands (21.7%) and their parents (18.9% in mothers, 22.3% in fathers), compared to ethnically matched controls (12.8%). However TDT analysis showed no preferential transmission of allele 7 to ADHD probands.
Attention Deficit Hyperactivity Disorder (ADHD) is a highly disabling childhood-onset psychiatric condition, characterised by marked inattention, hyperactivity and impulsiveness. There is now considerable evidence from family, twin and adoption data to suggest that ADHD is familial and highly heritable.1
Molecular genetic investigations of ADHD are currently underway. Genes coding for enzymes and receptors involved in dopamine neurotransmitter pathways represent attractive candidates, given that around 70% of children with ADHD show a symptomatic improvement when treated with methylphenidate, a psychostimulant which acts primarily to inhibit dopamine re-uptake.2 To date, genetic studies of ADHD have primarily focused on two candidate genes; the dopamine transporter gene (DAT1) and the dopamine 4 receptor gene (DRD4).
Positive associations with the 480-bp allele of a VNTR situated in the 3′ untranslated region of DAT13, 4, 5, 6 and allele 7 of a VNTR in exon 3 of DRD47, 8, 9, 10 have now been independently replicated. However, other groups have failed to find support for these findings.10, 11, 12 There are a number of possible reasons for these conflicting results.
First, some studies have adopted a case-control design where positive findings may have reflected population stratification, while others have used family-based designs to avoid this problem. Second, researchers have differed in their methods of sample ascertainment and in how they have measured and defined ADHD. For example, some studies have defined ADHD on the basis of a clinical diagnosis, whereas others have used structured diagnostic interviews or questionnaire-based diagnoses. Subsequently, these variations may have resulted in sample differences (eg symptom severity, co-morbidity) which could account for conflicting findings. Finally, non-replication of findings may reflect a lack of statistical power, given that some studies have been based on relatively small sample sizes.13
In this study, we examine the previously studied polymorphisms in DAT1 and DRD4 in a well-characterised sample of children with ADHD using both family-based and case control designs. We have defined ADHD according to ICD-10, DSM-IV and DSM-III-R criteria and also included phenotypic definitions used in other studies, in order to allow for a comparison of results.
Subjects and methods
Families with children who had suspected ADHD were recruited from Child and Adolescent Psychiatry Clinics throughout Greater Manchester and Cheshire, UK. Those families who gave consent to participate in the study were approached by the research team and underwent the assessment process. Potential subjects were first assessed using the Wechsler Intelligence Scale for Children, version III (WISC-III). Only those children with a full scale IQ score above 70 (mean = 91.2, SD = 13.1) were included in the study. Other exclusion criteria were: any evidence of major medical or neurological conditions, Tourette's syndrome, pervasive developmental disorders or laboratory evidence of fragile X syndrome.
The sample included 137 British Caucasian children (126 males, 11 females) aged between 6 and 12 years (mean = 9.3, SD = 1.8). These children were derived from 133 families (included four affected sibling pairs), with 132 mothers and 107 fathers also participating in the study.
In addition to a family-based design, we also incorporated a control group to allow for comparisons with previous case-control findings. The control group (n = 295) was comprised of British Caucasian healthy blood donors and healthy individuals selected at random from general practice registers in East Anglia, UK. An additional random sample of controls (n = 147) was taken from St Mary's Hospital in Manchester, UK and genotyped for the DRD4 polymorphism. Socio-demographic data for the control sample were not available.
Mothers were interviewed using the Child and Adolescent Psychiatric Assessment (CAPA),14 a semi-structured, interviewer-administered, diagnostic interview. Information on ADHD symptoms in school (which was required to determine whether children fulfilled the diagnostic criterion of symptom pervasiveness) was obtained from teachers using a semi-structured teacher telephone interview that has been shown to have good test-retest reliability and criterion validity (Holmes et al, unpublished data). This telephone interview was typically conducted with the child's class teacher, as the majority of children who participated in the study were in primary school. However for those children in secondary school, the teacher interview was conducted with the teacher who had the most extensive knowledge of the child's behaviour. Hence, one teacher was interviewed for each child participating in the study.
Diagnoses were made according to ICD-10, DSM-IV and DSM-III-R criteria. On the basis of parent and teacher reports, the following diagnoses were assigned to 137 probands: ICD-10 hyperkinetic disorder (65.0%), DSM-IV ADHD combined type (71.5%), DSM-IV ADHD hyperactive-impulsive type (10.9%), DSM-IV ADHD inattentive type (7.3%), and DSM-III-R ADHD (94.9%). High co-morbidity rates were observed amongst the sample: specifically 58.4% of the sample also met criteria for oppositional defiant disorder, 12.4% conduct disorder, 13.1% tic disorders, 2.9% anxiety disorders and 1.5% depressive disorders.
Interviews were conducted by a psychologist (JH) and two child psychiatrists (ALT, HF) who were trained to use the CAPA by attending a 3-day intensive training workshop (conducted by AT and RCH). Furthermore, interviews were audiotaped and supervised on a weekly basis by an experienced child and adolescent psychiatrist (AT). Inter-rater reliability kappa coefficients were calculated for ICD-10 Hyperkinetic Disorder (0.83), DSM-IV ADHD (0.88), DSM-III-R ADHD (0.88) and for an overall diagnosis of ADHD (ie fulfilling ICD-10, DSM-IV or DSM-III-R criteria) (kappa = 1.0).
Sub-groups for statistical analysis
Initial statistical analysis was carried out on the total sample of ADHD probands. To allow for a comparison with a previous finding for ADHD and the DRD4 polymorphism, a further subgroup was defined: namely ‘Swanson's refined phenotype’ which refers to DSM-IV diagnosed ADHD, without serious co-morbidity, with the exception of oppositional defiant disorder.10 Given that the most compelling argument for the involvement of the dopamine system in ADHD is based on the therapeutic effects of methylphenidate, we also defined an additional sub-group of ‘methylphenidate responders’. This group was classified as those children on methylphenidate who were rated as ‘very improved’ (by parent and referring psychiatrist) on the global improvement item of the Clinical Global Impression Scale.15 Seventy-seven children were classified as definite methylphenidate responders.
Collection of DNA and laboratory procedures
DNA was obtained from venous blood for 125 children and from cheek swabs for 12 children (where it was not possible to obtain venous blood). DNA was also requested from both parents. Biological mothers and fathers who were not living at home were also contacted where possible and DNA samples were requested. For families where parental DNA was not available, DNA was obtained from unaffected full biological siblings (n = 5) aged between 5 and 15 years who were screened for ADHD using the CAPA. Additional control DNA was extracted from 442 UK controls. All genotypes for DAT1 and DRD4 for ADHD cases, parents and controls were checked for Mendelian inheritance.
DNA extraction from blood
DNA was extracted from 5 ml of blood using the Bioline DNAce MaxiBlood Purification System. DNA was resuspended in 200 μl of water and stored at 4°C (concentration range 25–100 ng L−1).
DNA extraction from cheek scrapes
Buccal cells were harvested using three cytology brushes per child. Brushes were air dried and stored at −20°C. To extract DNA the brushes were incubated in 200 μl 50 mM NaOH for 5 min at 95°C and neutralised with 30 μl 1 M Tris pH 8.0. Samples were stored at −20°C. The success rate of DNA extraction from cheek scrape samples was over 90%.
Degenerate Oligonucleotide-Primed PCR (DOP-PCR)
DOP-PCR16 was used to increase the number of priming sites of the DNA extracted from buccal cells prior to specific amplification. PCRs were carried out in 96-well microtitre plates with a final reaction volume of 100 μl containing: 2 μM DOP primer (5′ CCG ACT CGA GNN NNN NAT GTG G, where N = A, C, G or T), 0.2 mM dNTPs, NH4 buffer (Bioline, London, UK), 1.5 mM MgCl2, 2 U Taq (Bioline, BioTaq), 10 μl DNA sample. Cycle conditions: 5 min 95°C, followed by five cycles 95°C for 60 s, 30°C for 90 s, 72°C for 3 min and then 30 cycles of 95°C for 60 s, 60°C for 60 s and 72°C for 3 min, and a final extension of 72°C for 10 min. PCR amplification was performed on the PTC-225 Peltier Thermal Cycler (MJ Research, Essex, UK).
DRD4 specific amplification
A 2–7 48-bp VNTR in exon 3 was amplified using primers 5′ GCG ACT ACG TGG TCT ACT CG 3′ and 5′ AGG ACC CTC ATG GCC TTG 3′. PCRs were carried out in 96-well microtitre plates with a final reaction volume of 20 μl containing: NH4 buffer (Bioline), 1.5 mM MgCl2, 0.1 mM dNTPs, 0.2 units Taq (Bioline, BioTaq), 0.6 μM primers, 2 μl DOP product or 2 μl of genomic DNA and 1 M Betaine. Cycle conditions: 35 cycles at 95°C for 40 s, 59°C for 30 s, 72°C for 30 s followed by a 5-min extension using a PTC-225 Peltier Thermal Cycler (MJ Research). Anecdotally genotyping of the DRD4 VNTR has been found to be difficult with occasional differential amplification observed. To address this potential problem, special attention was given to the conditions for PCR amplification. The addition of Betaine (1 M) was found to significantly improve the consistency and rate of successful amplifications. In order to check genotyping accuracy, approximately 30% of samples were genotyped on two separate occasions and on every plate of samples, five of known genotype were included. The amplified products were separated on a 3% agarose gel stained with ethidium bromide and visualised under UV light.
DAT1 specific amplification
A 40-bp VNTR was amplified using primers 5′ TGT GGT GTA GGG AAC GGC CTG AG 3′ and 5′ CTT CCT GGA GGT CAC GGC TCA AGG 3′. PCR amplification and visualisation was performed as described for DRD4 with the exception of 0.5 M Betaine and a 65°C annealing temperature.
Association of DRD4 and DAT1 with ADHD was examined by: (i) comparing cases and controls; and (ii) using family controls in an extension of the transmission disequilibrium test.17 In this paper, we have used the abbreviated term TDT to refer to the logistic regression TDT described below.
Allele frequencies in different groups of subjects were compared using Fisher's exact test. Odds ratios (and 95% confidence intervals) were calculated to estimate the risk of disease associated with possession of the candidate allele (allele 10 (480 bp) for DAT1 and allele 7 for DRD4).
For the family-based analysis, a conditional logistic regression framework was used to combine data on ADHD children and either their parents or siblings.18 Three types of case-control sets were constructed depending on the availability of family members.
(1) Parent-child trios
Where both parents were available, the case (with genotype m1f1) was compared with three ‘pseudo-controls’. If the two parents have genotypes m1m2 and f1f2, the pseudocontrols have genotypes m1f2, m2f1, m2f2, representing the other three genotypes which could have been passed to the child. Families are only included in this analysis if one or both parents are heterozygous, since homozygous parents must transmit the allele they have two copies of, and thus contribute no information about transmission.
(2) Parent-child pairs
If only one parent was available, the case (with genotype m1f1) and parent (m1m2) was analysed as a pair, providing m1, f1 and m2 were distinct alleles. (Inclusion of case-parent pairs where the case is homozygous can introduce bias.)19 In this situation one ‘pseudo-control’ was created with genotype m2f1.
Where no parents were genotyped, but information on an unaffected sibling was available, the case and unaffected sibling were analysed as a matched pair.
The first allele was arbitrarily chosen as the baseline, and variables a2, . . ., ak were created, recording the count (0, 1, or 2) for each of the remaining K-1 alleles. The parameters bi (i = 1, 2, . . ., K; b1 = 0) associated with each allele are such that bi–bj represents the log odds of transmitting allele ai to an affected child given a parental genotype aiaj. This model allows for any number of alleles (rather than any one of the alleles) to be preferentially transmitted. If only the parent-child groupings are used, this method is equivalent to the extended transmission disequilibrium test (ETDT) for multiallelic markers.20
The data from the three types of case-control sets defined above were combined in one conditional logistic regression analysis using the statistical software package Stata.21 Departures from Hardy–Weinberg equilibrium were tested using exact tests as implemented in the software Genepop.22 This program estimates the P-value using a Markov chain method23 when the number of alleles makes complete enumeration infeasible.
(1) Case-control analysis
For DAT1, 133 ADHD children were genotyped successfully and allele frequencies for these cases were compared with a set of 295 controls. There was no evidence of an overall difference in distribution in allele frequencies (P = 0.82, Table 1). Possession of allele 10 (480 bp) confers no additional risk of ADHD (odds ratio 0.96 (95% confidence interval 0.7, 1.3)). There is no evidence against Hardy–Weinberg equilibrium for DAT1 in the controls (P = 0.41), ADHD cases (P = 0.72) or parents (P = 1.00).
(2) TDT analysis
For the TDT analysis of DAT1, genotype information was available from both parents for 103 children and from one unaffected sibling for a further five children. Thus 108 cases and their relatives were used in the analysis. Eighty-six parent-child transmissions were informative for TDT (Table 2). There was no overall evidence of association between DAT1 and ADHD (likelihood ratio chi-squared statistic 0.56, 2 df, P = 0.75). Previous studies have shown an association between allele 10 (480 bp) and ADHD.3, 4 Thus, to investigate this previously reported association, an additional analysis was carried out grouping together alleles 9 and 11. This gave an estimated odds ratio of 0.89 (95% confidence interval 0.6, 1.4) for allele 10 (480 bp). Thus we detected no evidence of association with DAT1 using either TDT or case control analysis (Table 2). Previous research suggests that allele 10 (480 bp) may be a high risk allele for hyperactive-impulsive symptoms (questionnaire-rated),5 however the number of children diagnosed in our sample with DSM-IV hyperactive-impulsive subtype (n = 15) was too few to allow for separate analysis.
(1) Case-control analysis
For DRD4, 129 ADHD cases were successfully genotyped and compared with a set of 442 controls (Table 3). There was strong evidence of a difference in distribution (P = 0.01, using Fisher's exact test). This difference was observed even more strongly between parents of cases and the controls (P = 0.001). (There was no significant difference between mothers and fathers (P = 0.1).) Allele frequencies for ADHD cases, their relatives and other case and control groups are given in Table 3. Possession of the candidate allele 7 is significantly more common in ADHD cases than in the controls (odds ratio = 1.9 (1.3, 2.7), P = 0.001). It is also similarly more common in mothers (odds ratio = 1.6 (1.1, 2.5), P = 0.02) and fathers (odds ratio = 2.5 (1.6, 3.8), P<0.0001) of ADHD cases than in controls. We could detect no difference between the frequency of allele 7 in our controls and those of Rowe et al8 (P = 0.96). There was some evidence against Hardy–Weinberg equilibrium in controls (P = 0.02), weaker evidence in cases (P = 0.08) and some evidence in parents (P = 0.03), although the differences between the observed and expected distribution of genotypes was small.
(2) TDT analysis
For DRD4, genotype information was available from both parents for 98 children, from one parent for seven children and from one unaffected sibling for a further five cases. Thus 110 ADHD children and their relatives were included in the analysis. Six DRD4 alleles were observed in these families. One hundred and fifteen parent-child transmissions were informative for TDT (Table 4). From the combined regression analysis, there was no overall evidence of association between DRD4 and ADHD (likelihood ratio chi-squared statistic 5.97, 5 df, P = 0.31). Given the previously reported association findings for the 7 repeat allele and ADHD,7, 8, 9, 10 a further analysis grouping together all alleles other than allele 7 was carried out. This gave an estimated odds ratio of 0.95 (95% confidence interval 0.6, 1.5) for allele 7. This allele was transmitted 38 times and not transmitted 39 times by a heterozygous parent (Table 4).
Other phenotypic definitions of ADHD
We further examined four other phenotypic definitions, namely: ICD-10 Hyperkinetic disorder with or without oppositional defiant disorder (n = 67); any DSM-IV ADHD diagnosis with co-morbidity (n = 123); ‘Swanson's refined phenotype’ (n = 92); and ‘methylphenidate responders’ (n = 77). Case-control analysis for these four phenotypes revealed similar results to those for our total sample. However when using the TDT, there was no evidence of association for ICD-10 Hyperkinetic Disorder (odds ratio 0.88 (0.5, 1.6)), DSM-IV ADHD (odds ratio 0.97 (0.6, 1.6)), or ‘Swanson's refined phenotype’ (odds ratio 0.87 (0.5, 1.5)) with allele 7 of DRD4. There was marginal evidence of association in the subgroup of ‘methylphenidate responders’ (likelihood ratio chi-squared statistic 10.85, 5 df, P = 0.05). However there was no preferential transmission of allele 7 (odds ratio 0.81 (0.5, 1.4)). The allele which showed most evidence of preferential transmission was allele 4 (odds ratio 1.86 (1.1, 3.1)) compared with all other alleles. This finding should be regarded with caution, given that the confidence interval does not allow for multiple testing, and allele 4 has not previously been associated with ADHD. Separate analysis for DSM-IV ADHD subtypes was not carried out because of the small number of cases in each diagnostic subgroup.
In a well-characterised sample of children with ADHD we found no evidence of association with DAT1 whether examined by case-control analysis, or by the family-based TDT method. There have now been three independent reports of a positive association of the DAT1 480-bp allele and ADHD. In the first study, Cook et al3 used the haplotype-based haplotype relative risk (HHRR) method and showed a significant association of the DAT1 480-bp allele with DSM-III-R diagnosed ADHD in a sample of 49 patients. (It should be noted that the HHRR method ignores matching within families, because it is based on comparing the overall frequencies of transmitted and untransmitted alleles, including homozygous parents in the analysis. The validity of this method requires the independence of all four parental alleles.)24 These findings were replicated in a sample of 40 Irish ADHD patients4 and confirmed when the sample was extended to 118 children.6 Waldman et al5 also found an association for DAT1 and DSM-IV ADHD symptoms, however this study was somewhat different, as ADHD symptoms were questionnaire-rated in a sample of children referred to psychiatry clinics for learning and behavioral problems. However our own and other family-based studies have reported negative findings.10, 11 It may be that the failure to replicate findings for DAT1 in some instances has been due to an inadequate sample size. However, depending on the underlying disease model, our sample size gives up to 80% power25 to detect the effect size previously reported by Gill et al.4, 13 Non-replication may also be explained by sample and assessment differences across the studies which are shown in Table 5. The main difference being that the three negative studies used structured diagnostic interviews (this study; Swanson et al,10 Palmer et al11), whereas Cook et al3 and Daly et al6 relied on clinical consensus opinion to determine an ADHD diagnosis, and Waldman et al5 examined ADHD symptoms and categories on the basis of parent questionnaire ratings. Furthermore, Cook et al3 and Daly et al6 included children of a wider age range. Given these differences across studies, one possibility for conflicting findings may be that the DAT1 480-bp allele is related to a broader phenotype. However genetic heterogeneity is also likely to be an important factor that may account for this non-replication.
Our results for DRD4 are interesting. We found strong evidence of association with the DRD4 7 repeat allele using case-control analysis, which is in keeping with results from three other studies7, 8, 9, 10 and a preliminary meta-analysis (which includes unpublished results).26 The frequency of the 7 repeat allele was found to be high amongst affected children and their parents. However TDT analysis showed that there was no preferential transmission of the 7 repeat allele to the affected child. These findings are strikingly similar to those of Rowe and his colleagues,8 who, although defining ADHD using different methods (questionnaire-based), found a positive association for the DRD4 7 repeat allele using case-control analysis, but not when using the TDT.
We have considered several possible reasons for this finding. First, the most obvious explanation is that our positive case-control findings are due to population stratification and the negative TDT results demonstrate that the association with the DRD4 7 repeat allele is spurious. Although our control group was ethnically matched and the frequencies for allele 7 in this group were comparable with control data reported by Lahoste7 (12%), Rowe8 (12.9%) and Asherson27 (13%), we observed slight departures from Hardy–Weinberg expectations. However the difference between the observed and expected distribution of genotypes was small and with our relatively large control sample, even small departures from expectations were detected. These slight departures were somewhat concerning, given that such observations may be caused by inaccurate genotyping or population stratification. However, as mentioned previously (see DRD4 specific amplification in Methods section), additional checks on genotyping for DRD4 were made. The test for differences in allele frequencies between cases and controls does not assume Hardy–Weinberg equilibrium, nevertheless it has to be considered that our findings may represent an artifact of population stratification. This provides a strong argument for using a family-based design. However, we should note that two independent US studies have found an association with ADHD and the DRD4 7 repeat allele even when using family-based designs.9, 10
Second, our negative TDT results may raise concerns for the use of the TDT. It has been stated that as the TDT only utilises parent-child trios this may introduce bias.28 Indeed, a proportion of biological fathers in our study could not be traced. Thus the sample used for the TDT analysis (where most of the sample included complete parent-child trios), was not the same as the total sample used in the case control analysis. If the children with missing fathers represent a selected group (eg a more familial subgroup or those with more severe symptoms), this could have affected our results. When we examined for this, we actually found a trend for the 7 repeat allele to be slightly less common in the children excluded from TDT analysis (17% vs 23%), however there was no significant difference between the two groups (P = 0.74). Another potential criticism of the TDT is that it may not be as sensitive as case-control analyses (at least when dealing with quantitative traits).29 However we failed to detect even a trend for association with the DRD4 7 repeat allele using the TDT.
We also have to consider the potential effects of assortative mating. Although, case-control data revealed that both mothers and fathers of ADHD children showed an increased frequency of the DRD4 7 repeat allele, subsequent analysis indicated that spouses of fathers carrying the 7 repeat allele were no more likely to possess this allele than other mothers within the sample. Nevertheless, assortative mating would not affect our family-based data, as the TDT is robust to all possible mating patterns.24
Finally, as we have mentioned previously, different studies have defined the ADHD phenotype in various ways. However we have been able to define ADHD using the same diagnostic criteria as Smalley et al9 (DSM-IV or DSM III-R) and Swanson et al,10 yet still fail to find a positive association using the TDT.
Considering our results in the context of findings from other groups, we suggest that one explanation may be that the DRD4 7 repeat allele increases susceptibility for some other factor that is highly associated or correlated with ADHD, rather than perhaps a susceptibility allele for a clinical diagnosis of ADHD itself. For example, this factor could represent a related phenotypic trait (eg novelty seeking,30 neuro-cognitive deficit, IQ), or an environmental risk factor, that is brought about by the presence of allele 7 in the parents or affected child (ie gene–environment correlation). If the strength of the relationship of this related factor varies in different ADHD samples, this could potentially lead to repeated findings of positive associations, using case-control analysis, as well as positive TDT results in some studies, but not in others. We further examined whether our positive case-control findings were related to IQ or a diagnosis of conduct disorder but these were not found to be confounding factors.
In summary, in a well-characterised sample of children with ADHD we found no evidence for association with DAT1. For DRD4, there is strong evidence of association when using case-control analysis, but not when using the TDT method. When these results are considered together with other emerging findings, overall results for DRD4 and ADHD are suggestive yet inconclusive. Given our findings, we suggest that subsequent research should use family-based designs to examine related phenotypes and potential cognitive, temperamental and environmental risk factors that may be related to this DRD4 polymorphism. Careful attention should be given to the measurement of these phenotypes and risk dimensions, given that susceptibility genes involved in ADHD may be related to very specific and highly refined attributes.
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This study was funded by Action Research and SPARKS. We thank Kay Poulton for initial assistance with laboratory work; Nicole Perrin Trent for administrative support and all our colleagues from Child and Adolescent Psychiatry services in Greater Manchester and Cheshire, UK for referring families to the study. Finally, we are extremely grateful to all the families who participated in this study.
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Cite this article
Holmes, J., Payton, A., Barrett, J. et al. A family-based and case-control association study of the dopamine D4 receptor gene and dopamine transporter gene in attention deficit hyperactivity disorder. Mol Psychiatry 5, 523–530 (2000) doi:10.1038/sj.mp.4000751
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