Association between attention-deficit hyperactivity disorder (ADHD) and the 10-repeat allele of the dopamine transporter gene (DAT1) has been reported in independent clinical samples using a categorical clinical definition of ADHD. The present study adopts a quantitative trait loci (QTL) approach to examine the association between DAT1 and a continuous measure of ADHD behaviours in a general-population sample, as well as to explore whether there is an independent association between DAT1 and performance on neuropsychological tests of attention, response inhibition, and working memory. From an epidemiological sample of 872 boys aged 6–11 years, we recruited 58 boys scoring above the 90th percentile for teacher reported ADHD symptoms (SWAN ADHD scale) and 68 boys scoring below 10th percentile for genotyping and neuropsychological testing. A significant association was found between the DAT1 homozygous 10/10-repeat genotype and high-scoring boys (χ2square=4.6, P<0.03; odds ratio=2.4, 95% CI 1.1–5.0). Using hierarchical linear regression, a significant independent association was found between the DAT1 10/10-repeat genotype and measures of selective attention and response inhibition after adjusting for age, IQ, and ADHD symptoms. There was no association between DAT1 and any component of working memory. Furthermore, performance on tasks of selective attention although associated with DAT1 was not associated with SWAN ADHD high scores after controlling for age and IQ. In contrast, impairment on tasks that tapped sustained attention and the central executive component of working memory were found in high-scoring boys after adjusting for age and IQ. The results suggest that DAT1 is a QTL for continuously distributed ADHD behaviours in the general population and the cognitive endophenotype of response inhibition.
Attention-deficit hyperactivity disorder (ADHD) is the most common neurodevelopmental disorder of childhood with prevalence ranging from 4 to 12% in the general population of 6–12 year olds.1, 2, 3 ADHD has a strong genetic influence, with estimates of heritability (h2) ranging from 0.7 to 0.9.4, 5, 6, 7, 8, 9, 10 The heritability appears to be the same for extreme cases of ADHD as it does for individual differences in ADHD behaviours in the general population,6, 11 suggesting that the categorical diagnosis of ADHD represents the extreme of a genetically influenced continuous trait. The implication is that both categorical (diagnostic) and dimensional (quantitative trait) approaches are valid molecular genetic strategies when studying ADHD. The search for susceptibility genes that might contribute to the ADHD phenotype has been the subject of intense investigation. One view is that several genes with independent effects, each contributing a small amount to the total genetic variance, are implicated in ADHD.12 Another view is that a few common variants of genes interact.13 If ADHD is understood as a complex genetic disorder, influenced by multiple genes of small effect, then its expression is most likely to fit a dimensional (quantitative trait) rather than categorical disease model. It is likely that ADHD has multiple causes, both genetic and environmental.
Despite the attractions of a quantitative trait loci (QTL) approach to ADHD, to date, most molecular genetic studies have confined themselves to associations between candidate polymorphisms and a categorically defined clinical diagnosis of ADHD. The principal focus has been on candidate genes within the dopaminergic system. Genetic variation in the dopamine transporter gene (DAT1 or SLC6A3) is of particular interest given that the transporter is the target of stimulant action. The DAT1 gene is located on chromosome 5p15.3.14, 15 The gene contains a 40 base pair variable number of tandem repeats in the 3′-untranslated region (3′UTR-VNTR). Alleles from 3 to 13 repeats have been described, but the alleles with 9 and 10 repeats are the most frequently reported.16, 17 Molecular genetic studies have reported an association between the 10-repeat DAT1 allele and clinically defined ADHD.18, 19, 20, 21, 22, 23, 24 However, other studies have failed to replicate this association.25, 26, 27, 28, 29 Heterogeneity in findings may arise from differences in ascertainment and assessment and/or because DAT1 is in linkage disequilibrium (LD) with the true QTL and that the LD varies between samples, or simply chance variance. The mechanism for this association is not well understood, although several lines of evidence suggest that the number of repeats is associated with variation in DAT1 gene expression. Work by Mill et al,30 Fuke et al,31 Miller and Madras,32 have found that increased levels of DAT1 expression were associated with the number of 10-repeat alleles with greatest DAT1 expression in the homozygous 10/10 genotype.
Genetic studies to date have focused on the allelic rather than genotypic association between the DAT1 polymorphisms and ADHD. However, several recent lines of evidence provide some support for our prediction of a genotypic association with ADHD by suggesting an association between the homozygous 10/10 DAT1 genotype and endophenotypic markers of ADHD. First, in a study investigating the DAT1 polymorphism, inhibitory function, and electrophysiology, Loo et al33 reported an association between the 10/10 DAT1 genotype (contrasted with combined 9/9 and 9/10 genotypes) and increased CPT commission errors and impulsive response style. They also found reduced frontal theta EEG power (increased arousal) with stimulant medication in the DAT1 10/10-repeat group relative to 9/10 or 9/9 repeat genotypes. Second, caudate volume (a marker of ADHD) was found to be significantly reduced in homozygous 10/10-repeat vs heterozygous 9/10 DAT1 subjects with ADHD.34 Third, poor response to methylphenidate has been linked to homozygosity of the 10-repeat allele.35 Given these findings, it seems reasonable to hypothesise a genotypic association between DAT1 (homozygous 10-repeat), ADHD, and neurocognitive markers. One possible reason for inconsistent findings in DAT1 allelic association studies may be due to the lack of a linear relationship between the number of 10-repeat alleles (0, 1, or 2) and ADHD symptoms, with risk discontinuity seen between the 10/10 and 9/10 genotype but possibly not between the 9/9 and 9/10 genotype. The finding of Mill et al30 shows increased mRNA expression for the 10/10 vs 9/10 genotype (there was only one sample with the 9/9 genotype). Hence, this study provides rather stronger evidence of a genotypic rather than linear allelic association with DAT1 expression.
To date, we are unaware of any studies that have demonstrated association between the 10-repeat DAT1 allele or 10/10 genotype and ADHD using a QTL approach with ADHD measured as a behavioural dimension in the general population.
Genetic endophenotypes of ADHD
Cognitive endophenotypes are cognitive abilities that may represent a more direct and proximal expression of genetic variation than symptomatic behaviour, and they can be measured on a dimension and predict disorder probabilistically.36 At the cognitive level, studies of executive function in ADHD show a fairly consistent pattern of impairment on tasks measuring behavioural response inhibition, selective attention, planning, and cognitive flexibility/set shifting.37, 38 An impaired ability to sustain one's attention to task forms an important component in the diagnostic criteria for the disorder. However, perhaps due to the widely different measures of this capacity and variations in inclusion criteria, psychometric evidence has been variable in this respect.39 Impairments assessed using time-on-task decline and sustained attention measures that emphasise slow, tedious tasks have, however, been reported across a number of studies—notably including tasks from the Test of Everyday Attention for Children (TEA-Ch) battery, employed in this study.40, 41, 42, 43, 44, 45, 46 The largest effect size in ADHD is found on tests of response inhibition such as the Go-NoGo and Stop Signal tasks47 and CPT commission errors—which show a characteristic pattern of impulsive motor responses in ADHD.37 These findings suggest that response inhibition may represent a cognitive endophenotype of ADHD and may possibly be associated with variation in genes affecting the dopaminergic system such as DAT1. In addition to a primary response inhibition deficit, alternative explanations for poor performance in ADHD on inhibitory tasks includes motivational factors leading to delay aversion48 and deficits in regulating the state of activiation while on task.49, 50 We recognise that ADHD has multiple causes, both genetic and environmental. The contribution of the DAT1 gene as one of multiple genetic factors is the focus of this study, but we do not discount other genetic factors (such as the contribution of the DRD4 gene) or nongenetic factors, such as the effects of fetal distress on brain anatomy and function, as proposed by Lou51 and others. These factors may differentially affect the various components of attention.
One component of executive functioning less well studied in ADHD is working memory (WM). Impairments in this system could possibly underlie ‘inattentive’ symptoms of ADHD such as poor organisation, failure to follow through with tasks, and forgetfulness. In fact Barkley52 has suggested that a deficit in WM is one of the core deficits predicted by this theory of behavioural inhibition. Not surprisingly, such impairments are prominent in current psychological models of ADHD (see Castellanos and Tannock36 for a review). However, the empirical evidence implicating primary WM deficits in ADHD remains equivocal with some studies reporting an association between ADHD and WM, notably visuo-spatial memory (eg, Kempton et al53), and other studies reporting no association.54, 55 The reason for this discrepancy is unclear and requires further investigation. Elliott et al56 showed that methylphenidate can enhance performance on a spatial WM task, and there is evidence that dopamine modulation in the prefrontal cortex influences WM performance in primates.57 Polymorphisms in the COMT gene (responsible for metabolism of dopamine in the prefrontal cortex) have been associated with WM performance in adults with schizophrenia and normal controls.58 Therefore, it is possible that variations in the DAT1 gene may also influence WM performance.
In the present study, we have adopted a QTL approach to study the link between polymorphisms of the DAT1 gene and ADHD symptoms using a truncated case–control association design. In an epidemiological sample of children, we have sampled extreme high and low teacher ratings on a new ADHD scale (adapted from the SNAP-IV)—the Strengths and Weaknesses of ADHD symptoms and Normal behaviour scale (SWAN).59 In the same sample, we have also investigated the link between DAT1 and the cognitive endophenotypes of ADHD including response inhibition, selective and sustained attention, and working memory. We hypothesised that the DAT 10/10 genotype would represent a QTL for ADHD symptoms in the general population as well as deficits in response inhibition and working memory. We also predicted that the genetic association with these cognitive endophenotypes would be independent of ADHD symptoms.
Materials and methods
Stage 1: population sample ascertainment and selection
In the first stage of the study, we anonymously screened an epidemiological sample of 6–11-year-old children from Central England (UK) using teacher ratings on the SWAN.59 Children were recruited to this study by contacting principle teachers from all elementary schools (UK Primary Schools) in the county of Nottinghamshire, UK. Of the 402 schools listed, a total of 157 agreed to participate. Each school was then asked to complete the SWAN questionnaire for one class in a given year group (across year groups 2–6, chronological age 6–11 year olds). This yielded teacher questionnaires on 1776 children. In all, 92 questionnaires were excluded from further analysis due to missing or incomplete responses. There were complete questionnaires on 872 boys and 812 girls. SWAN summary (total) scores were normally distributed: Boys: mean=4.7, SD=23.1, skewness=−0.11, kurtosis=−0.30; Girls: mean=−9.9, SD=20.4, skewness=−0.24, kurtosis=−0.30. The distribution of SWAN total scores in boys was shifted to the right (higher scores) compared to the distribution of scores for girls. Boys represented 86% of the highest 10% of summary (total) SWAN scores in the population.
The SWAN ADHD scale
The SWAN scale is based on the 18 ADHD symptoms listed in DSM-IV,60 as is the SNAP-IV scale,61 which are divided into two subsets of nine items corresponding to the domains of Inattention (items 0–9) and Hyperactivity/Impulsivity (items 10–18). For the SNAP-IV, the 18 symptoms are stated exactly as listed in DSM-IV60 ADHD diagnostic criteria. Thus, the items are defined as abnormal behaviour considered to represent psychopathology (ie, Does your child have a problem with…), and they are rated on a scale from 0 (psychopathology not present) to 3 (1–3 indicating the degree of presence of psychopathology). For the SWAN, the items were reworded to reflect normal behaviour (ie, How does your child…), and the scale was also revised. Instead of using the four-point SNAP-IV scale to evaluate psychopathology, a seven-point scale was used for the SWAN to allow for ratings of relative strengths (better than average) as well as weaknesses (worse than average). The seven-point scale was anchored in the middle by 0 representing average behaviour for each item, with deviations from average on either side (far above average=−3, above average=−2, slightly above average=−1, average=0, slightly below average=1, below average=2, and far below average=3). Children's total scores ranged from a minimum of −27 to a maximum of 27 for each subscale. Dividing by the number of items produced a summary measure reflecting average-rating-per-item, which ranged from −3 to +3. The SWAN scale differs from the SNAP-IV in its definition of items and in its scoring system, which give a closer approximation to a normal distribution for each item and of summary scores of overall ADHD and its two domains (Inattention and Hyperactivity-Impulsivity) in the general population than does the SNAP-IV or any other ADHD scale that evaluates psychopathology (which by definition will not be present in most individuals in the population). The SWAN total scale score also approximates to a normal distribution, which has an advantage when selecting extreme high and low scorers in genetic association studies investigating QTL. The heritability of the SWAN scale has been demonstrated in the Australian Twin ADHD Project (ATAP).7, 59
Stage 2: selection of children for neuropsychological testing and genotyping
We decided to follow the QTL association mapping approach of sampling at the extreme ends of the ADHD behavioural dimension. This truncated association case–control design increases the likelihood of detecting associations between candidate genetic polymorphisms that may account for only 1–2% of the variance in ADHD symptoms.62 Hence, we selected cases for DNA and further phenotypic analysis from the top and bottom 10% of the SWAN distribution. As boys represented 86% of the highest 10% of SWAN summary (total) scores in the population, we limited our neuropsychological testing and genotyping to boys only.
Boys were eligible for inclusion in the second stage of the study if they scored either above the 90th percentile or below the 10th percentile on the sex-specific distribution of boy's scores on the Inattentive or Hyperactivity/Impulsivity subscales of the SWAN. In total, 123 boys were scored by teachers above the 90th percentile and 119 below the 10th percentile on the SWAN. All schools participating in the second stage wrote to parents inviting their children to participate in the study. This produced two groups of participants (total 126): (1) 58 boys who were rated by teachers above the 90th percentile for Inattentive and/or Hyperactivity/Impulsivity subscale items on the SWAN questionnaire (age range: 6–11 years; mean age: 8 years 6 months); (2) 68 boys who were rated by teachers as below the 10th percentile for Inattentive and/or Hyperactivity/Impulsivity subscale items on the SWAN questionnaire (age range: 6–11 years; mean age: 9 years 5 months). None of the children were receiving stimulant medication (eg, methylphenidate or dexamfetamine). In all, 98% (N=124) of the sample was Caucasian (indigenous white British). One child was African Caribbean, and one child was Cypriot (Mediterranean Caucasian).
SWAN and Conners' ADHD scores
The 58 boys in the ‘High SWAN ADHD’ group had a mean total SWAN score of 40.8. (mean+1.56 SD; 94th percentile; SWAN T score=65.6), and the 65 boys in the ‘Low SWAN ADHD’ group had a mean total SWAN score of −33.1 (mean−1.63 SD; 5th percentile; SWAN T score=33.7). The 126 boys selected at the extreme ends of the SWAN scale were also rated by the same class teacher using the Conners' Teacher Rating Scale-Revised: Short version (CTRS-R:S)63, 64 and by parents using the Conners' Parent Rating Scale-Revised: Short version (CPRS-R:S).63, 65 The ADHD index of the CPRS-R:S and CTRS-R:S contains 12 items that discriminate well between ADHD clinical cases and controls.63 Boys scoring >90th percentile on the SWAN had CTRS-R:S and CPRS-R:S ADHD Index T scores of 67.5 (SD=8.2) and 69.1 (SD=9.1), respectively. Boys scoring <10th percentile on the SWAN had CTRS-R:S and CPRS-R:S ADHD Index T scores of 43.7 (SD=4.7) and 44.5 (SD=5.4), respectively. There were high correlations between the CTRS-R:S and SWAN Inattentive subscale (r=0.87; P<0.01), and Hyperactivity/Impulsivity subscale (r=0.91; P<0.01); and the CPRS-R:S and SWAN Inattentive subscale (r=0.75; P<0.01), and Hyperactivity/Impulsivity subscale (r=0.82; P<0.01).
DNA extraction and genotyping
Buccal cells were harvested in 10 ml sterile saline and DNA extracted by alkaline lysis of the cells, as described by Ferrie et al.66. The DAT1 polymorphism was amplified on an MJ DNA engine thermal cycler (MJ Research) with an initial denaturation at 94°C for 4 min, followed by 32 cycles of 45 s at 94°C, 45 s at 68°C, and 60 s at 72°C, and a final elongation of 5 min at 72°C. The 25 μl reaction mixture consisted of 50 mM Tris (pH 9.0), 20 mM NH4SO4, 3 mM MgCl2, 200 μM dNTPs, 0.5 μM primers,15 and 1 U Taq polymerase (Invitrogen). Products were electrophoresed on 2% agarose gel and visualised with ethidium bromide. The oligo primer sequences used to amplify the VNTR are DAT1-F: 5′-TgT ggT gTA ggg AAC ggC CTg Ag-3′ DAT1-R: 5′-CTT CCT ggA ggT CAC ggC TCA Agg, as originally described in Waldman et al.23 Each individual was genotyped twice.
Genotyping was conducted on 120 out of 126 of the eligible participants (boys scoring >90th and <10th percentile on the SWAN scale). DNA samples were unobtainable from six participants and these children were excluded from any further analysis. One participant was excluded from the analyses because the repeat sequence was only available for one allele. Genotype frequencies of the remaining 119 samples were as follows: 10/10 (n=62; 52.1%), 9/10 (n=41; 34.4%), 9/9 (n=12; 10.1%), 3/10 (n=2; 1.7%), 8/10 (n=1; 0.8%), and 11/10 (n=1; 0.8%). The population frequencies were 0.71 for the 10-repeat allele and 0.27 for the 9-repeat allele. Initial analyses were conducted using the three most common genotype groups: 10/10 (n=62), 9/10 (n=41), and 9/9 (n=12). Further analyses were carried out combining the 9/10 genotype with 3/10, 8/10, and 11/10 genotypes to create a heterozygous 10-repeat allele group (n=45). See Figure 1.
Intellectual level was assessed using the Wechsler Abbreviated Scale of Intelligence (WASI),67 which produced a full-scale IQ (FSIQ) for each participant. A battery of tasks specifically designed for use with children was selected to assess the different cognitive aspects of attention, inhibition, and working memory. The test battery consists of recently published standardised neuropsychological tests: The Test of Everyday Attention for Children (TEA-Ch)68 and the Working Memory Test Battery for Children (WMTB-C).69
Selective attention (TEA-Ch)—Sky Search Task. This is a speeded visual search task. Following practice, participants were presented with a 20.3 cm × 29.0 sheet showing 20 targets (spaceships) hidden among 108 distractors, and asked to mark all of the targets as quickly as possible. Participants were then presented with a subsequent sheet showing the same 20 targets in identical locations. In this trial, however, no distractors were present. Subtraction of the time required to find the targets in part 2 from part 1 provides a measure of the attentional costs of the distractors (the ‘attention score’) that is relatively free from variance due to the motor demands of the task.
Sustained attention (TEA-Ch). Three tests of sustained attention from the TEA-Ch were used. The first, Score!, is adapted from adult neuropsychological and functional imaging studies.70, 71, 72 Here, children were asked to count the number of identical tones (between three and 15) presented in each item. The demands of the test lie in maintaining active attention to the dull task across the long, silent intervals between each sound (up to 5 s). Even brief attentional lapses during the course of an item are likely to show up in an erroneous total (see Manly et al42). The second task, Score DT, keeps the same form with the addition of built-in distraction in the form of a news story being read in the background. The third task is derived from the Sustained Attention to Response Test.73, 74 Here, children were given a pen and asked to mark each successive square on a paper ‘path’ as they heard successive regularly presented tones. They were told that one tone in each item would end in the cartoon-like exclamation—meaning that the next step should not be taken. The score is the number of items on which the response was correctly withheld. In common with other ‘hidden’ constructs argued to influence behaviour, sustained attention is probably best measured in the shared variance between different tasks that are thought to require it, rather than in the performance of a single task, which inevitably makes other demands. To reduce the contribution of factors such as counting or response inhibition in this respect, the average score across the three tasks was used as the measure of sustained attention here. The sensitivity of these relatively new measures to ADHD first reported in Manly et al42 has been supported in subsequent studies.45, 47, 75
Response inhibition. Impairments in the capacity to inhibit prepotent responses has been argued to be central to ADHD.76 The Opposite World task from the TEA-Ch was derived from similar measures of verbal inhibition described by Gerstadt et al77 and Passler et al.78 In each of the four trials of the test, children are shown a stimulus array of 24 boxes, each containing either the digit 1 or 2. Following practice, in the first part of the test the examiner points to each digit in the array in turn, while the child names that digit aloud. In the second part, the child is asked to say the opposite for each digit (ie, ‘one’ for 2 and ‘two’ for 1). The remaining two sections of the test repeat the conventional and opposite conditions. Each trial is timed. The standardised instructions are for the examiner to move on to the next digit in the array only when the correct response has been given. Errors therefore contribute to the overall time score. The final score was the total time taken to complete the two opposite response trials. The sensitivity of this measure to ADHD is reported in Manly et al42 and is correlated with ratings of ADHD.79
Working memory measures
Phonological memory. (1) The Digit Recall task required participants to recall a series of digits. Digit sequences were read out by the experimenter at a rate of about one digit per second. The maximum span tested was nine. Total number of correct trials was recorded. (2) The Word List Matching task required participants to judge whether a word list was presented in the same or different order to the original presentation. The number of correct trials was recorded. (3) The Word List Recall task was administered in exactly the same way as digit recall, except words rather than digits were used. The maximum span tested was seven. (4) The Nonword List Recall task was administered in exactly the same way as digit recall, except nonwords (eg, meck) rather than digits were used. The maximum span tested was six. The final score is a composite score of the three tasks.
Visual-spatial memory. (1) The Visual Patterns test required participants to reproduce a checkerboard pattern that was presented to them for 3 s, onto a blank grid of the same size and shape. Visual span was recorded from the level of complexity of the largest grid with at least one of the three patterns correctly recalled. (2) The Corsi Blocks task required participants to copy the exact sequence of block ‘tapping’ demonstrated by the experimenter. A board with nine blocks attached was used. The maximum span tested was nine. The number of correct trials was recorded. (3) The Mazes task required participants to reproduce a maze solution that was presented to them onto an empty maze of the same shape and size. The maze was presented for the length of time it took the participant to trace the route with their finger. The maximum span tested was seven. The number of correct trials was recorded. The final score is a composite score of the three tasks.
Central executive memory. (1) The Listening Span task required participants to listen to a series of sentences, judge whether they were true or false, and recall the final word from each sentence, that is, ‘Fish have long hair’: correct response is ‘false, hair’. The maximum span tested was six. The number of correct trials was recorded. (2) The Counting Recall task required participants to count the number of dots per card on a series of cards and recall the number of dots per card. The maximum span tested was seven. The number of correct trials was recorded. (3) The Backwards Digit Recall task required participants to recall a series of digits in the reverse order to which they were presented, for example, 2 8 5 would be recalled as 5 8 2. The task was administered in exactly the same way as the digit recall task. The final score is a composite score of the three tasks.
DAT1 gene–behaviour associations
DAT1 genotype and SWAN ADHD high and low scorers
DAT1 genotype frequencies for high and low scorers are presented in Table 1 and Figure 1. A comparison of SWAN scores between the three commonly occurring DAT1 genotype groups (10/10, 9/10, and 9/9) revealed a significant overall association between DAT1 genotype and high and low subgroups based on SWAN summary score for ADHD (χ2=6.2; df=2; P=0.04). In the subgroup defined by the high SWAN cutoff, a greater percentage of individuals had the 10/10 genotype than the subgroup defined by the low SWAN cutoff (χ2 with continuity correction=5.1; df=1; P=0.02). Comparison between samples grouped into those with two 10-repeat alleles (10/10) and one 10-repeat allele (9/10, 3/10, 8/10, and 11/10) showed a similar over-representation of individuals with the 10/10 homozygous genotype in children with high SWAN (ADHD) scores (χ2 with continuity correction=4.7; df=1; P=0.03).
A 2 × 2 cross tabulation of SWAN ADHD scores (high vs low) in cases with the 10/10 genotype (N=62) against all other cases (N=57) showed a significant association of the 10/10 genotype with high SWAN ADHD score (χ2 with continuity correction=4.6; df=1; P=0.03). There was a greater than two-fold relative risk of a 10/10 genotype in those with a high SWAN ADHD score compared to those with a low SWAN ADHD score; odds ratio=2.4 (95% CI 1.1–5.0). The adjusted R2 statistic indicates that the DAT1 10/10 genotype contributes 3.8% of the phenotypic variance in ADHD symptoms. See Table 1.
DAT1 allele frequency and SWAN ADHD high and low scores
The DAT1 allele numbers and frequencies in the high-scoring ADHD group (N=55, total alleles=110) were for the 9-repeat allele n=23 (20.9%) and for the 10-repeat allele n=85 (77.3%). For the low-scoring ADHD group (N=64, total alleles=128), allele numbers and frequencies were for 9-repeat allele n=42 (32.8%) and the 10-repeat allele n=84 (65.6%). The frequencies for the 3-, 8-, and 11-repeat alleles were all <0.5%. Considering just the 9- and 10-repeat alleles, there was a significant association between DAT1 allele frequency and ADHD group (χ2=4.9, df=1, P<0.05).
DAT1 genotype–cognition associations
In order to examine the pattern of any main effects and interactions, we conducted ANOVA with genotype and SWAN membership (high vs low scorers) as the independent variables and the neuropsychological task measures as the dependent variables.
The relation between DAT1 genotype, SWAN high and low scorers, and measures of selective attention, sustained attention, and inhibition
To control for multiple comparisons, the Bonferroni correction test was used only where those results meeting an unadjusted α level of 0.05/3=0.01 were considered statistically significant. Given that the sample size to dependent variable ratio is high, and that we have reduced variance (and error variance) by truncating the sample using the top and bottom 10% on the SWAN, a conservative α (Bonferroni) is therefore appropriate. Analysis revealed a significant main effect of SWAN grouping on performance across all measures: selective attention (F1,102=15.17; P<0.001), sustained attention (F1,102=60.78; P<0.001), and inhibition (F1,102=26.30; P<0.001). In all cases, the low scorers (SWAN score <10th percentile) performed significantly better than high scorers (SWAN score >90th percentile). In a comparison between the DAT1 genotype groupings (two 10-repeat alleles vs one 10-repeat allele), we found a significant main effects on measure of selective attention (F1,102=9.02; P<0.003) and inhibition (F1,102=6.98; P<0.01), with worse performance in children with the 10/10 genotype compared to children with one 10--repeat allele. There was no significant main effect of genotype grouping on the measure of sustained attention (F1,102=0.83; NS) and no significant interactions across any of the measures. See Table 2a for a detailed summary of group means and analyses (P-value <0.01) on all individual attention measures.
The relation between DAT1 genotype, SWAN high and low scorers, and measures of working memory
As above, to control for multiple comparisons, the Bonferroni correction test was used only where those results meeting an unadjusted α level of 0.05/3=0.01 were considered statistically significant. Analysis revealed a significant main effect of SWAN grouping on performance across all measures: phonological memory (F1,102=23.13; P<0.001), visual-spatial memory (F1,102=28.70; P<0.001), and central executive capacity (F1,102=65.88; P<0.001). In all cases, the low scorers (SWAN score <10th percentile) performed significantly better than high scorers (SWAN score >90th percentile). In a comparison between the DAT1 genotype groupings (two 10-repeat alleles vs one 10-repeat allele), we found no significant main effect on any of the three measures: phonological memory (F1,102=0.03; NS), visuo-spatial memory (F1,102=1.08; NS), and central executive capacity (F1,102=0.01; NS). Nor were there any significant interactions across any of the measures. See Table 2b for a detailed summary of group means and analyses (P-value <0.01) on all individual working memory measures.
The relation between SWAN high and low scorers and IQ and chronological age (CA)
In a comparison between high and low scorers, we found a significant group difference on IQ (t=8.38; P<0.001), with the high scorers presenting with a lower mean IQ (89.0 (13.92)) than the low scorers (109 (13.10)). The high scorers were also younger than the low scorers (t=2.58; P<0.011).
Genotype prediction of cognitive function
The extent to which extraneous variables such as IQ and CA affect task performance was examined using correlational analysis. Table 3 shows that IQ was highly correlated with all measures and that CA was correlated predominantly with selective attention.
Thus to establish whether there was a unique relationship between measures of attention, inhibition, and working memory and SWAN group membership (high vs low scorers), independent of CA and IQ, we turned to hierarchical linear regression (HLM) analysis. CA was included in this analysis even though it was not significant in all correlations, in order to account for any small amounts of extraneous variance and also because the SWAN groups did differ significantly on CA. Additionally, we wanted to know to what extent the DAT1 genotype (two 10-repeat alleles vs one 10-repeat allele) was a predictor of performance independent of SWAN group membership. We therefore entered genotype as a fourth step after CA, IQ, and SWAN membership to provide the strongest possible test of its unique prediction. We report six four-step fixed-order multiple regressions. The dependent variable in each was one of the six neuropsychological measures: selective attention, sustained attention, inhibition, phonological memory, visuo-spatial memory, and central executive capacity. The extraneous variables entered in the first two steps of each analysis were (a) IQ (WASI) and (b) CA. The third and fourth step was SWAN group membership and genotype, respectively.
Attention and inhibition measures
Examination of Table 4 reveals, for ADHD scores, that after adjusting for IQ and CA, performance on tasks of sustained attention was strongly related to SWAN group membership (high ADHD score), but the association with tasks of selective attention and inhibition was less consistent. There was also a significant independent association between the 10/10 genotype and tasks of selective attention and inhibition that remained after adjusting for the effects of SWAN group membership (high ADHD score), IQ, and CA.
Working memory measures
Examination of Table 5 reveals a very weak association between performance on tasks of working memory and SWAN group membership with only tasks of central executive capacity significantly related to the high ADHD score group after adjusting for CA and IQ. There were no significant independent genotype associations with any of the working memory measures, suggesting that DAT1 may play a relatively specific role in modulating selective attention and inhibition rather than working memory performance.
In the present study, we adopted a QTL approach to investigate in an epidemiological sample of elementary school children the association between a genotype (polymorphisms of the DAT1 gene) and a qualitative phenotype (dimension of attention and activity based on SWAN ratings). Also, we investigated the relationship of ADHD symptoms and genotype to neuropsychological performance (inhibition, selective and sustained attention, and working memory). We predicted that firstly, the DAT1 10/10 genotype would be associated with high levels of ADHD symptoms. Second, we predicted that DAT1 genotype would be associated with cognitive impairments in response inhibition and working memory. In addition, we predicted that the genotype association with these neurocognitive impairments (cognitive endophenotypes) would be independent of ADHD symptoms.
Our findings can be summarised as follows. At the behavioural level, we found a significant association between DAT1 10/10 genotype and high ADHD scores (>90th percentile) in a general population sample. This association replicates and supports previous studies in the literature that have suggested a link between DAT1 and ADHD in clinical samples.18, 20, 21, 23, 24 To our knowledge, this is the first study to show an association between a DAT1 polymorphism and ADHD symptoms in a nonclinical general population sample. These data are consistent with the QTL hypothesis, but do not yet demonstrate this conclusively due to lack of data for the mid-portions of the distribution. Ideally, demonstration of a QTL effect would require a regression analysis of genotype against ADHD behaviour in the whole sample—or select high-, low-, and mid-scoring groups.
These results are inconsistent with one other population-based twin study that failed to find an association between DAT1 and ADHD subtypes.29 However, Todd et al29 used assessment of items that was categorical and based a dimension on summing ratings reflecting just the weakness characteristic of ADHD (which should be rare in the population). We used an assessment of items that was dimensional, and thus provides a different basis for forming a dimension based on a sum (or average) of the items. Other sources of discrepancies between studies include the possibility that DAT1 is in linkage disequilibrium (LD) with the true QTL, where the degree of LD differs between samples, the assessment measures used, and sample selection. Population-based (in contrast to family-based) association studies run the risk of finding spurious associations when there are differences in ethnic composition between case and control samples, but this is usually not the case.80 Here, the positive association described is unlikely to be due to population stratification effects, given the virtually homogeneous Caucasian ethnic composition of our sample. While heterogeneity of DAT1 allele frequencies cannot be excluded, both the high- and low-scoring ADHD groups were drawn from the same population and were almost entirely (98%) of white indigenous British ethnic extraction from a stable community with minimal inward migration or ethnic mixing.
We believe that our use of the SWAN scale, defined at the item level as a continuous measure of attention and activity rather than a degree of psychopathology (ie, presence of ADHD symptoms), may have increased our power to detect a QTL of relatively small effect (3.8% of phenotypic variance in our sample) in the general population.62 The SWAN scale provides an approximate normal distribution of ADHD symptoms allowing greater contrast between extreme groups at both ends of the population distribution of ADHD scores. In the present study, we used only teacher reports of ADHD symptoms to identify extreme groups. However, the SWAN teacher ratings correlated highly with both parent and teacher Conners' ADHD ratings within the extreme groups, and both parent and teacher Conners' scores were >1.5 SD above the population mean. Parent and teacher correlations are likely to be increased as a result of the groups being restricted to either very low or very high scorers on the SWAN, where agreement between parents and teachers may be expected to be greater than for children scoring in the mid-range for ADHD symptoms. Future studies will require even larger population samples to examine the genotype frequency of candidate polymorphisms across the whole range of the ADHD symptom distribution including separate analysis for both the Inattentive and Hyperactive/Impulsive dimensions.
At a cognitive level, these findings suggest a differential impact of the DAT1 QTL on neuropsychological performance with an independent association (after adjusting for ADHD symptoms, IQ, and age) between the 10/10 DAT1 genotype and impaired performance on tasks of selective attention and response inhibition. These findings support our prediction of an association between the DAT1 ‘high-risk’ homozygous 10/10 genotype and a cognitive endophenotype (response inhibition) independent of ADHD symptoms. To our knowledge, this is the first study to report an independent association between a DAT1 polymorphism and a putative cognitive endophenotype of ADHD in either a clinical or nonclinical population. Although intriguing, this finding clearly needs to be replicated. In the present study, our clinical inhibition task predominantly assessed speed of correct responding (requiring inhibition of incorrect responses). In future studies, sensitive computerised paradigms that allow clearer separation of ‘go’ and ‘stop’ processes (eg Go-NoGo task and Stop signal task) would be of value in clarifying this issue. Furthermore, while the association between ADHD and motor inhibitory control deficits is robust,81 and some results suggest that deficient response inhibition may be a marker for a genetic susceptibility to ADHD,82 inhibitory control deficits are not specific to ADHD and may not be, as proposed,52 a primary deficit in ADHD. Inhibitory control problems could be a secondary consequence of altered motivational processes and aversion to delay,48, 83 an insufficient ability to regulate the state of activation,84, 85 or as a consequence of more fundamental, simpler problems such as a more generalized deficit of processing speed, time processing, and motor response organization.83, 86, 87 These alternative explanations require further investigation.
We also report an association between DAT1 and selective attention that is independent of SWAN ADHD score and IQ. However, if part of the cognitive deficit associated with ADHD is due to nongenetic factors such as perinatal environmental influences,51 then adjusting for IQ might remove some of this nongenetic effect in the ADHD group proportionally more than in the non-ADHD group. This may be especially important for selective attention. Thus, in the non-ADHD subgroup, the genetic effects of DAT on selective attention may statistically be significant independent of IQ and the components of ADHD that are nongenetic, but not in the overall ADHD subgroup, due to other factors that increase the variability of the relationship of DAT and selective attention. Furthermore, our finding is consistent with recent work that has also failed to find a specific ADHD deficit in selective attention.88, 89
Contrary to our hypothesis, we found no evidence of an association between the DAT1 10/10 genotype and performance on working memory measures (irrespective of the subcomponent) and on a measure of sustained attention. Performance on the latter task was predicted only by high ADHD scores. The only aspect of working memory to be associated with high ADHD scores was the ‘central executive’ subcomponent of working memory. This finding that the central executive is a unique predictor even after IQ has been entered first is particularly noteworthy, as the central executive measure was itself strongly associated with IQ (r=0.73). This shows that while partly overlapping, independent effects of central executive measures on ADHD group membership are clearly evident. The frequently reported IQ difference between children with high and low ADHD scores requires a clearer explanation as a result of future studies.
Unexpectedly, we found no association between ADHD and visual-spatial working memory performance, which contrasts with previous reports of reduced performance in this working memory domain (eg, Kempton et al53). However, it is possible that different subtypes of ADHD may be associated with selective working memory impairments and that those individuals whose symptoms predominantly fall within the inattentive subtype, for example, might show greater visual-spatial working memory impairments than those individuals with ADHD combined type who show more generalised, central executive impairments.
Our results provide some support for the view that response inhibition is a possible cognitive endophenotype of ADHD,90 although processes such as delay aversion48 or deficits in state regulation84, 85 could provide alternative endophenotypes. Our findings also suggest a degree of genetic specificity for cognitive endophenotypes in ADHD. We found that response inhibition is influenced by the DAT1 gene polymorphism, with poor performance associated with the 10/10 genotype. In contrast, while we found evidence of WM impairment in the functioning of the central executive associated with ADHD, there was no direct link between WM and the DAT1 genotype. Hence, it is possible that WM deficits in ADHD may be associated with other genetic polymorphisms (eg, DRD4 or COMT), or that these are secondary, ‘downstream,’ deficits resulting from a primary deficit in response inhibition.76 Interestingly, the COMT gene, which is responsible for the clearance of dopamine in the prefrontal cortex, has been associated with WM performance in adults58, 91 and children,92 and with ADHD in one study93 but not in others.94, 95, 96 Of course, it is also possible that different genes could influence the same cognitive endophenotype. For example, Langley et al97 reported a more impulsive response style on the Stop task among ADHD children with the DRD4 7-repeat allele, suggesting an influence of the DRD4 polymorphism in response inhibition. Conversely, a single polymorphism such as DAT1 may influence multiple cognitive processes—with some processes (eg response inhibition) being specifically associated with ADHD while others such as selective attention are not. Further studies should examine the association between a range of candidate genes and candidate cognitive endophenotypes within the same samples (both clinical and general population) and at different developmental stages to examine pleiotropic effects of candidate genes. Functional brain-imaging studies can complement these approaches by isolating specific brain systems associated with cognitive endophenotypes.
In conclusion, we have used a quantitative trait approach to demonstrate that the DAT1 gene influences ADHD symptoms in the general population and is also associated with the cognitive endophenotype of response inhibition independent of ADHD symptoms. This finding supports the view that the genetic influences for ADHD symptoms operate as a QTL throughout the whole population and are not just confined to clinical samples. Furthermore, these results suggest that one pathway from DAT1 to ADHD behaviours may operate through variation in response inhibition.
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This research was supported by a project grant from the Sir Jules Thorn Charitable Trust, UK to C Hollis, K Cornish, G Cross, and N Butler. We express our thanks to the participating schools and students.
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Cornish, K., Manly, T., Savage, R. et al. Association of the dopamine transporter (DAT1) 10/10-repeat genotype with ADHD symptoms and response inhibition in a general population sample. Mol Psychiatry 10, 686–698 (2005) doi:10.1038/sj.mp.4001641
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