Original article 119
Association between the dopamine transporter gene (DAT1) and attention deficit hyperactivity disorder-related traits in healthy adults Seong Hoon Jeonga, Keong-Sook Choia, Kyu Young Leeb, Eui-Joong Kimb, Yong-Sik Kimc and Eun-Jeong Joob Background The dopamine transporter gene (DAT1) is an established genetic risk factor for attention deficit hyperactivity disorder (ADHD). Therefore, we hypothesized that DAT1 may also influence the manifestation of ADHDrelated traits in the normal population. Methods A quantitative association study was carried out on 1289 healthy adults. ADHD-related traits were measured using the 25-item Wender Utah Rating Scale (WURS-25). This scale is a self-administered instrument intended to retrospectively measure features of childhood ADHD. Previous studies typically identified three component factors: (i) impulsivity, (ii) inattention, and (iii) mood instability. Our group found that these factors were associated with various diagnoses, such as bipolar disorder and major depression. Six polymorphic markers within the DAT1 gene (rs27072, rs11133767, rs429699, rs27048, rs2937639), including the 3′-untranslated region variable number of tandem repeats, were used as genetic markers. Results The WURS-25 total score was not associated with any of the markers that we examined. However, the mood instability trait was associated significantly with rs2937639 in male participants (P = 0.008); this result was supported
Introduction Attention deficit hyperactivity disorder (ADHD) is one of the most common psychiatric disorders diagnosed in children (Faraone et al., 2003). In over half of all cases, symptoms persist into adulthood, causing significant complications (Kessler et al., 2006). Meanwhile, inattention and impulsivity problems are not only observed in adult ADHD patients but also in a variety of psychiatric patients and in the otherwise normal population. These problems can lead to serious functional impairments and subjective distress, even in adults who do not fulfill the criteria for an ADHD diagnosis. There is no clear demarcation between clinical and nonclinical populations. ADHD symptoms are believed to exist along a continuum, with no natural boundary (Marcus et al., 2012; Balázs and Keresztény, 2014). The long-known concept of ‘subthreshold ADHD’ corroborates this dimensional nature (Scahill et al., 1999; Hong et al., 2014). Genetic etiology analyses using large numbers of twins also confirmed a strong genetic link 0955-8829 Copyright © 2015 Wolters Kluwer Health, Inc. All rights reserved.
by several haplotype-wise findings among the surrounding markers (P = 0.00001–0.004). Conclusion Our study results suggest that DAT1 polymorphisms may modulate mood instability traits in the normal population. Considering that mood instability tends to persist through the entire course of ADHD and is highly prevalent in many diseases that are comorbid with ADHD, this trait may be a core endophenotype that defines the role of the DAT1 gene in various psychiatric conditions. Psychiatr Genet 25:119–126 Copyright © 2015 Wolters Kluwer Health, Inc. All rights reserved. Psychiatric Genetics 2015, 25:119–126 Keywords: association study, attention deficit hyperactivity disorder, DAT1 gene, mood instability, Wender Utah Rating Scale a Department of Psychiatry, Eulji University Hospital, Eulji University School of Medicine, Daejeon, bDepartment of Psychiatry, Eulji General Hospital, Eulji University School of Medicine, Seoul and cDepartment of Psychiatry, Dongguk University International Hospital, Goyang-si, Korea
Correspondence to Eun-Jeong Joo, MD, PhD, Eulji General Hospital, Eulji University School of Medicine, Seoul 139-711, Republic of Korea Tel: + 82 2970 8011; fax: + 82 2949 2356; e-mail: [email protected] Received 10 October 2014 Revised 4 January 2015 Accepted 19 March 2015
between symptoms that manifest in both clinical and nonclinical populations (Larsson et al., 2012). ADHDrelated symptoms are best explained as continuous traits that genetically vary throughout the entire population (Levy et al., 1997). Therefore, there are two ways to approach ADHD: (i) a dimensional approach and (ii) a categorical approach. The former highlights the continuity between clinical and nonclinical conditions, whereas the latter focuses on the demarcation defining the clinical population (Elton et al., 2014). The exceptionally high rate of other psychiatric comorbidities is another prominent feature of ADHD. Substance abuse as well as mood, anxiety, and conduct disorders are commonly associated with ADHD (McGough et al., 2005). For example, ADHD co-occurs in up to 85% of bipolar children, and bipolar disorder is present in almost 22% of children with ADHD (Singh et al., 2006). In addition to the high comorbidity observed between ADHD and bipolar disorder, the extent of familial overlap is also remarkable. Greater rates of DOI: 10.1097/YPG.0000000000000086
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120 Psychiatric Genetics 2015, Vol 25 No 3
bipolar disorder are found in relatives of ADHD patients and vice versa (Faraone et al., 1997). Overall, epidemiological, clinical, and genetic findings indicate a genetic overlap between ADHD and mood disorders. This high comorbidity suggests that certain features of ADHD that are not specific to the disorder may underlie all of ADHD, mood disorders, and substance abuse. These shared features may also be the basis for the continuous nature of ADHD. If this is so, investigation of these features in a nonclinical population is warranted. The dopamine transporter gene (DAT1 or SLC6A3) is located on chromosome 5p15.33. The dopamine transporter plays a principal role in regulating dopamine neurotransmission, and numerous independent studies have implicated DAT1 as an ADHD susceptibility gene (Gizer et al., 2009). The 3′-untranslated region (3′-UTR) variable number of tandem repeats (VNTR) 10-repeat allele has been investigated actively as a risk allele for ADHD (Cook et al., 1995; Lim et al., 2006). Other alleles from the same VNTR or newer markers in the same gene have also been suggested to be risk-conferring alleles. Recently, the 5′ end of the gene, including the promoter region, has received considerable attention (Doyle et al., 2009; Xu et al., 2009; De Azeredo et al., 2014). However, attempts to replicate incidental findings have not yielded consistent results, possibly because of the arbitrary nature of its categorical diagnosis (Bidwell et al., 2011). DAT1 has also been implicated in the genetic etiology of bipolar disorder and substance abuse (Keikhaee et al., 2005; Greenwood et al., 2006; David and Munafò, 2008; Van der Zwaluw et al., 2009). As these disorders frequently overlap with ADHD, we postulate that the pathway from DAT1 to ADHD-related features may be the common basis of the heightened risk of various psychiatric conditions that frequent overlap with ADHD. This postulation can be verified by an association between DAT1 and ADHD-related features not only in ADHD patients but also in other diagnostic groups and even in the nonclinical population. In this vein, Greenwood et al. (2013) found an association between DAT1 and childhood ADHD features in bipolar disorder patients. On the basis of a previous study (Greenwood et al., 2013), we investigated the association between DAT1 and ADHD-related traits in nonclinical individuals. ADHDrelated features were measured using the 25-item Wender Utah Rating Scale (WURS-25). Three WURS-25 factors established from previous factor studies were used as surrogate variables for ADHD-related traits: (i) impulsivity and defiant behavior, (ii) inattention, and (iii) mood instability and anxiety (McCann et al., 2000; Caci et al., 2009; Jeong et al., 2014). This study aimed to clarify whether ADHD-related features prevalent in the nonclinical population were associated with a genetic background, specifically the DAT1 gene. The result obtained
may suggest that DAT1 gene variation and the associated ADHD-related features are worth investigating not only in clinical ADHD patients but also in patients experiencing a diverse range of psychiatric conditions.
Methods Study participants
The original study was designed to investigate the ADHD-related traits present in the normal population, and possible determinants of these features, whether genetic or environmental (Jeong et al., 2014). The work described here was a subordinate study that focused on genetic determinants, specifically the DAT1 gene. Participants were recruited from several sources, including nurses and other workers in university-affiliated general hospitals, students in medical colleges, and administrative officers and firefighters in various fire departments. The purpose and procedures were of the study advertised by means of posters and leaflets or direct presentation by investigators in the various workplaces. All participants were provided with a detailed description of the study before signing an informed consent form. Trained psychiatric nurses conducted brief psychiatric interviews for screening purposes. We excluded participants who had a lifelong history of major psychiatric illness, including schizophrenia, bipolar disorder, obsessive-compulsive disorder, or history of brain trauma. We did not implement a systematic ADHD evaluation using Diagnostic and statistical manual of mental disorders, 4th ed. (DSM-IV) criteria; however, participants who reported severe attention or behavioral problems in their childhood were excluded to ensure that the sample was representative of the nonclinical population. A total of 1289 participants were included in the analysis. All participants were Korean and genetically unrelated. This study was carried out according to the Declaration of Helsinki guidelines for human research, and was approved by the Ethics Committees of Eulji General Hospital and Eulji University Hospital.
Measurement of childhood attention deficit hyperactivity disorder features
Past ADHD features were measured using the WURS-25, which was developed to retrospectively measure ADHD-related symptoms experienced before 12 years of age (Ward et al., 1993). Although the original WURS contained 61 items, the shortened version (WURS-25) consists of 25 items selected from the original. The Korean version of the WURS-25 was developed and standardized using normal female Korean adults (Koo et al., 2009). According to the original authors, a cutoff score of 36 shows 96% sensitivity and specificity for identifying childhood ADHD among the general population (Ward et al., 1993).
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Association between DAT1 and ADHD-related traits Jeong et al. 121
Table 1 Six genetic markers in the DAT1 gene that were subjected to association analysis
rs nos
Gene location
Chromosomal position
Alleles
MAF
HWE P-value
VNTR (= rs28363170) rs27072 rs11133767 rs429699 rs27048 rs2937639
3′-UTR 3′-UTR Intron 13 Intron 11 Intron 8 Intron 1
1393862 1394522 1401580 1409127 1412645 1443728
T/C A/G A/G T/C G/A
0.254 0.086 0.236 0.125 0.154
0.011 0.594 0.589 0.448 0.086
Minor allele frequencies and HWE analyses are also included. rs nos indicates single nucleotide polymorphism (SNP) identification in SNP site of NCBI. Chromosomal position was based on GRCh37.p10 referenced from NCBI dbSNP. A, adenine; C, cytosine; G, guanine; HWE, Hardy–Weinberg equilibrium; MAF, minor allele frequency; T, thymine; 3′-UTR, 3′-untranslated region; VNTR, variable number of tandem repeats.
Age and total WURS-25 score were compared between the sexes using Student’s t-tests. An exploratory factor analysis was carried out to extract factors representing the WURS-25 component factors. The principal component method was used for factor extraction. Factor number was decided using eigenvalue criteria (≥1). Extracted factors were rotated by promax rotation with Kaiser normalization. After rotation, the factor scores of each participant were calculated by regression analysis. Genetic analysis was carried out using PLINK software (Purcell et al., 2007). The linkage disequilibrium structure was investigated using Haploview software (Barrett et al., 2005), and separate haplotype blocks were determined on the basis of the four-gamete rule (Fig. 1; Wang
Block 1 (7 kb) 1
2
Block 2 (3 kb)
3 56
rs2937639
Fig. 1
rs27048
SNP genotyping was performed using the TaqMan method (Applied Biosystems). Standard PCR was carried out in a 5 µl volume containing 50 ng genomic DNA, 2.5 µl 2 × TaqMan Master Mix, 0.9 µmol/l of each primer, and 0.2 µl each of FAM dye (Applied Biosystems, Foster City, California, USA), and VIC dye probe (Applied Biosystems). After the initial 2-min denaturation at 50°C, followed by 10 min at 95°C, 40 thermal cycles consisting of 15 s at 95°C and 1 min at 60°C were carried out. Automated reading was performed using an ABI Prism 7900HT Sequence Detection System (Applied Biosystems).
Statistical analysis
rs429699
DNA was extracted from blood samples using a DNA Isolation Kit (Roche, Mannheim, Germany). The five SNPs selected were present throughout the DAT1 gene and a VNTR was located near the 3′-UTR (rs28363170) according to a literature search; these were our designated association markers (Table 1). Relevant information was obtained using the Entrez SNP database [National Center for Biotechnology Information (NCBI), Bethesda, Maryland, USA). Genotyping was performed according to the TaqMan method (Applied Biosystems, Foster City, California, USA; McGuigan and Ralston, 2002). SNP chromosomal location was referenced from the NCBI dbSNP dataset in February 2014 based on the GRCh37.p.10 assembly.
rs11133767
Genotyping
For VNTR genotyping, standard PCR was carried out in a 15 µl volume containing 50 ng genomic DNA, 10 mmol/ l of each deoxynucleotide triphosphate (dNTP), 20 pmol/ l of each primer, and 0.3 µl i-MAX II DNA polymerase (iNtRON Biotechnology, Inc., Seongnam, Korea). After the initial 5-min denaturation at 94°C, 40 thermal cycles consisting of 30 s at 94°C and 50 s at 72°C were carried out, and the reaction was incubated for 10 min at 72°C for final extension. The amplification products were resolved on 3% agarose gels and bands were read using an UV illumination transmitter.
rs27072
We selected five single nucleotide polymorphisms (SNPs) on the basis of DAT1 gene structural information published by Greenwood et al. (2006, 2013). We primarily selected haplotype-tagging SNPs (htSNPs) with published Asian population allele distribution information (rs2937639, rs11133767, rs27072). If information was unavailable, we selected other SNPs that were very close to the htSNPs and had reasonable minor allele frequencies in the Asian population (rs27048 and rs429699).
VNTR
Selection of genetic markers
4 85
39 44
5
6 22
22
78 9
84 13
Genomic structure, polymorphic sites, and linkage disequilibrium (LD) structure of the DAT1 gene in all participants. The number in each cell represents the LD parameter, D′. An LD block was defined using the four-gamete rule. VNTR, variable number of tandem repeats.
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122 Psychiatric Genetics 2015, Vol 25 No 3
et al., 2002). Allele-based and haplotype-based association analyses were carried out as implemented in the PLINK software. Diverse alleles of the 3′-UTR VNTR (3–11 repeats) were recoded as ‘0’ or ‘1’. The most common allele (10 repeats) was coded as ‘1’ and other alleles were coded as ‘0’. The statistical framework was basically a linear regression analysis. The WURS-25 total score and factor scores were entered as response variables, and the respective SNP markers or reconstructed haplotypes were entered as explanatory variables. Possible covariates that could influence the effect of each allele on WURS-25 scores were included in the model using the ‘–covar’ option in the ‘–linear’ command. The sliding window approach was used for haplotype construction (Huang et al., 2007). From the five SNP markers within the DAT1 gene (VNTR was not included in the haplotype analysis), four 2-adjacent-marker haplotypes, three 3-adjacent-marker haplotypes, two 4-adjacent-marker haplotypes, and one 5-marker haplotype were constructed. Nonadjacent marker haplotypes were not considered. When carrying out haplotype-based association analyses, the ‘-hap-omnibus’ option in PLINK was used to jointly estimate all haplotype effects simultaneously. All reported P-values represent nominal P-values without any adjustments. However, as multiple markers and phenotypes were analyzed simultaneously, inflation of the family-wise error rate was expected. To control for inflation of the type-I error rate, the false discovery rate (FDR) method proposed by Storey and Tibshirani (2003) was used. FDR controls the expected proportion of null results that are falsely identified as significant in a set of predictions. To set an acceptable significance threshold, the FDR was calculated at each P-value obtained. The FDR values obtained represent the expected family-wise error rate when the associated P-value was set to the significance threshold. FDR values were calculated using R statistical software for Windows version 2.15.1 (R Development Core Team, 2008) with the ‘QVALUE’ add-on package (Storey and Tibshirani, 2003). All other statistical analyses were also carried out using the R statistical software (R Development Core Team, 2008).
Characteristics of the study participants and the total 25-item Wender Utah Rating Scale scores in male and female participants
Table 2
Number of participants Age WURS-25
Whole
Male
Female
1289
476
813
25.1 ± 6.1 19.4 ± 14.8
27.4 ± 8.3 19.5 ± 15.7
23.7 ± 3.5 19.3 ± 14.2
Student’s t-test (male vs. female)
t = 11.0 t = 0.201
P < 0.001 P = 0.840
Age and WURS-25 total scores were compared between the sexes using Student’s t-tests. Values are represented as mean ± SD. WURS-25, 25-item Wender Utah Rating Scale.
Results Demographic features and total 25-item Wender Utah Rating Scale scores
Table 2 presents the number, age, and sex of the participants. Approximately two-thirds of the participants were women, who were significantly younger than their male counterparts [23.70 vs. 27.37 years, t(1,287) = 10.988, P < 0.001]. The mean WURS-25 total score was 19.38 ± 14.76 (range 0–87). However, no significant difference was noted between the sexes [t(1,287) = 0.201, P = 0.840]. 25-Item Wender Utah Rating Scale factor extraction and comparison of male and female participants
Exploratory factor analysis was carried out to identify relevant factors from the WURS-25. Three factors were extracted using eigenvalue criteria. The combined extracted factors explained 55.22% of the total variance (first factor, 40.17%; second factor, 8.34%; third factor, 6.71%). According to the loading pattern, the three factors were tentatively named as follows: ‘impulsivity and defiant behavior’, first factor (IMP); ‘inattention’, second factor (INATT); and ‘mood instability and anxiety’, third factor (MOOD). It is important to note that these factors are compatible with similar factor analyses carried out in other studies (Caci et al., 2009; Joo et al., 2010). The mean values of each factor score for male and female participants were then compared. Although there was no significant difference with respect to the IMP factor, the INATT factor was significantly higher in male participants [t(1,287) = 5.032, P < 0.001], whereas the MOOD factor was significantly higher in female participants [t(1,287) = − 4.147, P < 0.001]. Allele frequency, Hardy–Weinberg equilibrium, and linkage disequilibrium structure
All markers showed a minor allele frequency greater than 10% and did not deviate significantly from Hardy–Weinberg equilibrium (P = 0.01) (Table 1). The overall D′ values between the marker-pairs were relatively low, and ranged from 0.09 to 0.85. Only the rs429699 marker had a higher D′ value with some SNP markers (D′ = 0.85 with rs11133767; D′ = 0.78 with rs27072; and D′ = 0.84 with 3′-UTR VNTR). However, no definitive haplotype block could be discerned according to the criteria suggested by Gabriel et al. (2002). Quantitative trait association analysis of 25-item Wender Utah Rating Scale total scores
The initial analysis sought to probe the association between each of the used markers and the WURS-25 total scores, but the results showed no significant association. However, when the reanalysis was carried out with sex included within the interaction term, we found that the allelic effect of the rs2937639 marker on the WURS-25 total score was significantly influenced by sex
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Association between DAT1 and ADHD-related traits Jeong et al. 123
Table 3 Results of the allele-wise quantitative trait association analysis between the six markers in the DAT1 gene and the 25-item Wender Utah Rating Scale total score with each component factor (impulsivity, inattention, instability) WURS-25
Male VNTR rs27072 rs11133767 rs429699 rs27048 rs2937639 Female VNTR rs27072 rs11133767 rs429699 rs27048 rs2937639
Impulsivity
Inattention
Instability
Group size
β
T
P
β
t
P
β
t
P
β
t
P
476 476 476 476 476 476
− 0.497 − 0.180 0.083 0.858 − 0.705 2.630
− 0.309 − 0.166 0.046 0.725 − 0.491 2.060
0.758 0.868 0.963 0.469 0.624 0.040
− 0.055 0.050 − 0.028 0.076 0.042 0.117
− 0.522 0.714 − 0.242 0.989 0.444 1.411
0.602 0.476 0.809 0.323 0.657 0.159
− 0.014 − 0.086 0.006 0.028 − 0.150 0.141
− 0.122 − 1.110 0.047 0.332 − 1.454 1.533
0.903 0.268 0.963 0.740 0.147 0.126
− 0.024 − 0.003 0.034 0.031 0.000 0.202
− 0.250 − 0.050 0.319 0.447 0.000 2.675
0.803 0.958 0.750 0.655 1.000 0.008
813 813 813 813 813 813
1.863 − 0.140 2.155 − 0.847 0.500 − 1.595
1.491 − 0.176 1.729 − 1.005 0.459 − 1.608
0.136 0.861 0.084 0.315 0.646 0.108
0.032 − 0.013 0.033 − 0.094 0.059 − 0.067
0.375 − 0.243 0.380 − 1.616 0.783 − 0.972
0.708 0.808 0.704 0.107 0.434 0.331
0.194 − 0.038 0.217 0.032 − 0.021 − 0.086
2.430 − 0.746 2.724 0.601 − 0.304 − 1.343
0.015 0.456 0.007 0.548 0.761 0.180
0.090 0.035 0.127 − 0.062 0.038 − 0.130
0.994 0.602 1.400 − 1.008 0.476 − 1.806
0.321 0.547 0.162 0.314 0.634 0.071
We carried out an analysis of covariance with age as a covariate and genotype as the predictor. Results from male and female participants were obtained separately (β, regression coefficient; t, Wald t-test statistic). WURS-25, 25-item Wender Utah Rating Scale; VNTR, variable number of tandem repeats.
P-values obtained from the haplotype-based (sliding window approach) quantitative trait association analysis between the five single nucleotide polymorphism markers in the DAT1 gene and the 25-item Wender Utah Rating Scale total score with each component factor Table 4
Male P-values rs27072–rs11133767 rs27072–rs11133767–rs429699 rs27072–rs11133767–rs429699–rs2704 rs27072–rs11133767–rs429699–rs27048–rs2937639 rs11133767–rs429699 rs11133767–rs429699–rs2704 rs11133767–rs429699–rs27048–rs2937639 rs429699–rs2704 rs429699–rs27048–rs2937639 rs27048–rs2937639
Female
WURS-25
Impulsivity
Inattention
Instability
WURS
Impulsivity
Inattention
Instability
0.693 0.853 0.705 0.105 0.819 0.845 0.032 0.717 0.020 0.005
0.876 0.703 0.659 0.540 0.473 0.722 0.417 0.590 0.340 0.238
0.398 0.752 0.654 0.256 0.981 0.549 0.174 0.341 0.083 0.024
0.630 0.898 0.798 0.004 0.832 0.896 0.0002 0.863 0.00001 0.00002
0.221 0.443 0.502 0.550 0.137 0.271 0.198 0.592 0.402 0.290
0.927 0.405 0.623 0.664 0.422 0.589 0.437 0.265 0.381 0.535
0.024 0.078 0.152 0.378 0.026 0.070 0.290 0.852 0.721 0.322
0.270 0.616 0.516 0.416 0.086 0.217 0.165 0.598 0.394 0.280
The reported values were global haplotypic P-values. Haplotype frequencies were estimated with the EM-algorithm using PLINK software. Data from male and female participants were analyzed separately. WURS-25, 25-item Wender Utah Rating Scale.
(P = 0.008). For this reason, subsequent analyses were carried out on men and women separately. Upon analysis of male and female data separately, we found no evidence of a significant association, with the exception of nominally significant signals in the haplotype analyses of male participants (haplotype rs27048–rs2937639, P = 0.005, FDR = 0.09; rs429699–rs27048–rs2937639, P = 0.02, FDR = 0.17; Tables 3 and 4). Quantitative trait association analysis of 25-item Wender Utah Rating Scale factor scores
Next, an association analysis was carried out using each factor score as the response variable (Tables 3 and 4). According to this analysis, rs2937639 showed a potential association with the MOOD factor in male participants (P = 0.008). Furthermore, all haplotypes containing rs2937639 showed a significant association with the same factor in male participants (P = 0.00001–0.004). Although the allele-wise association between rs2937639 and the
MOOD factor did not fulfill the FDR criteria (FDR = 0.08), three haplotype-wise associations (rs11133767–rs429699–rs27048–rs2937639, rs429699–rs27048–rs2937639, and rs27048–rs2937639) fulfilled the FDR criteria (FDR = 0.0007–0.004). Meanwhile, no evidence of an association with any of the investigated markers (either allele-wise or haplotype-wise) was found in female participants.
Discussion In the study described here, a potential genetic association between several markers within the DAT1 gene and ADHD-related features was investigated in 1289 nonclinical adult participants. The WURS-25 was used to measure ADHD-related features in each individual. Qualitative trait genetic association was then assessed using the WURS-25 score as the qualitative trait, and each marker and the constructed haplotypes as predictor variables.
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124 Psychiatric Genetics 2015, Vol 25 No 3
In the initial analysis, no meaningful association was found between any of the investigated markers and WURS-25 total scores. However, qualitative differences between men and women in the epidemiologic and symptomatic characteristics suggested that we might proceed with further analyses on men and women separately (Biederman et al., 2008; Cho et al., 2010). After this separation, a weak association was found between rs2937639 and WURS-25 total scores in male participants (P = 0.04). This large P-value does not seem meaningful in and of itself; however, when the haplotypes containing rs2937639 and nearby markers were examined, the haplotype rs2937639–rs429699 pair yielded more convincing evidence of an association between the DAT1 gene and WURS-25 score (P = 0.005). These discoveries warranted further analysis. Subsequent analysis using the multifactor structure of WURS-25 showed that the mood instability factor (MOOD) was significantly associated with rs2937639 and some haplotypes, but that inattention (INATT) and impulsivity (IMP) were not. Although the allelic association with rs2937639 did not pass the FDR test (nominal P = 0.008, FDR = 0.08), haplotype analysis showed a convincing association with this factor (FDRs = 0.0007–0.004). This suggests that the weak signal between rs2937639 and the WURS-25 total score may actually result from the effect of the mood instability factor. However, an alternative explanation is that the WURS-25 total score and each specific factor score reflect different genetic effects. In a linkage study with bipolar families, a stronger signal with the inattention factor was found in a different region (10p14, LOD = 3.35), although a linkage signal with the WURS-25 total score was found in the 12q24 region (LOD =2.05; Joo et al., 2010). Some studies also support the latter explanation. In one study with Korean mood disorder patients, impulsivity and inattention factors were associated significantly with bipolar disorder, but mood instability was associated with a major depressive disorder (Joo et al., 2012). Another study that investigated the association between DAT1 and ADHD-related features in bipolar patients found that only the mood instability factor had a noteworthy association with markers in DAT1 (Greenwood et al., 2013). Together, these results suggest that ADHDrelated features have a multidimensional structure, and that individual dimensions may be differentially influenced by distinct genetic factors. Recently, a study of Taiwanese ADHD patients found a significant association between the rs27048–rs429699 haplotype and the inattentive subtype of ADHD (Shang et al., 2011). This haplotype is situated much closer to the VNTR than rs2937639. Considering the accumulated literature on the association between the VNTR polymorphism and attention in clinical populations, it may be that the attentional dimension is associated with the 3′-end
haplotypic block and that mood instability is associated with the 5′-end block (Rommelse et al., 2008). It was estimated that an adult with ADHD has an at least 2–8-fold higher risk of another psychiatric disorder in his/ her lifespan (Kessler et al., 2006). This risk is not much different in subthreshold ADHD individuals (Cho et al., 2009). It was hypothesized that more fundamental abnormalities than mere hyperactivity may underlie various psychiatric conditions (Brown, 2008). Until now, the inability to regulate attention or a broader deficit in executive function was most commonly highlighted (Brown et al., 2010). On the basis of the assumption that a deep connection exists between ADHD and mood disorders, many studies have attempted to identify attentional deficits in mood disorders as a common denominator. According to one meta-analysis, sustained attention deficit persisted even during remission in bipolar patients (Bora et al., 2008). However, mood instability and emotional hyper-reactivity are also problems in ADHD. Moreover, ADHD is frequently misdiagnosed as bipolar disorder because of severe mood dysregulation. Therefore, it is possible that the link between ADHD and mood disorders involves not only cognitive impairment but also mood disruption. We cannot consider the two dimensions of cognitive impairment and mood dysregulation separately because both are influenced by deficits in executive function. However, relatively little attention has been paid to the mood dysregulation aspect to date. The fundamental problem in ADHD patients is likely not a single overarching abnormality, but rather a group of interconnected but separate abnormalities. In addition, this diversity of problems may explain the high frequency of a variety of psychiatric and/or behavioral comorbidities. In summary, we attempted to identify the genetic influence of the DAT1 gene on ADHD-related features in healthy adults. Although we found no convincing evidence for this connection, we found that one of the DAT1 polymorphisms (rs2937639) was associated with the WURS-25 mood instability factor. As only a handful of markers were used, and these did not cover all the possible genetic variations in a gene as large as DAT1, it was unclear whether the DAT1 gene haplotype block was associated or a specific marker was haphazardly associated with the mood instability factor. However, the haplotype-wise analysis results supported the former possibility. Our finding supports the concept that the genetic association between DAT1 and the mood instability component typically found in ADHD patients may be the basis of the extensive comorbidities among ADHD, mood disorders, and other conditions. Further studies involving other clinical conditions that are frequently comorbid with ADHD will be necessary for a more comprehensive understanding of the genetic role of DAT1 in both the clinical and general populations. In addition, this study supports the idea that mood
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Association between DAT1 and ADHD-related traits Jeong et al. 125
instability is a meaningful subphenotype of ADHD, which differs in its genetic background from other clinical features, such as inattention and impulsivity. Studies using mood instability as a subphenotype of ADHD may help dissect ADHD in a new perspective, and will contribute toward a more comprehensive understanding of DAT1 in ADHD-related features in a broader population that includes both diagnosed ADHD patients and the general population.
Acknowledgements This work was supported by a National Research Foundation of Korea Grant funded by the Korean government (KRF-2008-531-E00045, 2011-0003164). Conflicts of interest
There are no conflicts of interest.
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Association between the dopamine transporter gene (DAT1) and attention deficit hyperactivity disorder-related traits in healthy adults Seong Hoon Jeonga, Keong-Sook Choia, Kyu Young Leeb, Eui-Joong Kimb, Yong-Sik Kimc and Eun-Jeong Joob Background The dopamine transporter gene (DAT1) is an established genetic risk factor for attention deficit hyperactivity disorder (ADHD). Therefore, we hypothesized that DAT1 may also influence the manifestation of ADHDrelated traits in the normal population. Methods A quantitative association study was carried out on 1289 healthy adults. ADHD-related traits were measured using the 25-item Wender Utah Rating Scale (WURS-25). This scale is a self-administered instrument intended to retrospectively measure features of childhood ADHD. Previous studies typically identified three component factors: (i) impulsivity, (ii) inattention, and (iii) mood instability. Our group found that these factors were associated with various diagnoses, such as bipolar disorder and major depression. Six polymorphic markers within the DAT1 gene (rs27072, rs11133767, rs429699, rs27048, rs2937639), including the 3′-untranslated region variable number of tandem repeats, were used as genetic markers. Results The WURS-25 total score was not associated with any of the markers that we examined. However, the mood instability trait was associated significantly with rs2937639 in male participants (P = 0.008); this result was supported
Introduction Attention deficit hyperactivity disorder (ADHD) is one of the most common psychiatric disorders diagnosed in children (Faraone et al., 2003). In over half of all cases, symptoms persist into adulthood, causing significant complications (Kessler et al., 2006). Meanwhile, inattention and impulsivity problems are not only observed in adult ADHD patients but also in a variety of psychiatric patients and in the otherwise normal population. These problems can lead to serious functional impairments and subjective distress, even in adults who do not fulfill the criteria for an ADHD diagnosis. There is no clear demarcation between clinical and nonclinical populations. ADHD symptoms are believed to exist along a continuum, with no natural boundary (Marcus et al., 2012; Balázs and Keresztény, 2014). The long-known concept of ‘subthreshold ADHD’ corroborates this dimensional nature (Scahill et al., 1999; Hong et al., 2014). Genetic etiology analyses using large numbers of twins also confirmed a strong genetic link 0955-8829 Copyright © 2015 Wolters Kluwer Health, Inc. All rights reserved.
by several haplotype-wise findings among the surrounding markers (P = 0.00001–0.004). Conclusion Our study results suggest that DAT1 polymorphisms may modulate mood instability traits in the normal population. Considering that mood instability tends to persist through the entire course of ADHD and is highly prevalent in many diseases that are comorbid with ADHD, this trait may be a core endophenotype that defines the role of the DAT1 gene in various psychiatric conditions. Psychiatr Genet 25:119–126 Copyright © 2015 Wolters Kluwer Health, Inc. All rights reserved. Psychiatric Genetics 2015, 25:119–126 Keywords: association study, attention deficit hyperactivity disorder, DAT1 gene, mood instability, Wender Utah Rating Scale a Department of Psychiatry, Eulji University Hospital, Eulji University School of Medicine, Daejeon, bDepartment of Psychiatry, Eulji General Hospital, Eulji University School of Medicine, Seoul and cDepartment of Psychiatry, Dongguk University International Hospital, Goyang-si, Korea
Correspondence to Eun-Jeong Joo, MD, PhD, Eulji General Hospital, Eulji University School of Medicine, Seoul 139-711, Republic of Korea Tel: + 82 2970 8011; fax: + 82 2949 2356; e-mail: [email protected] Received 10 October 2014 Revised 4 January 2015 Accepted 19 March 2015
between symptoms that manifest in both clinical and nonclinical populations (Larsson et al., 2012). ADHDrelated symptoms are best explained as continuous traits that genetically vary throughout the entire population (Levy et al., 1997). Therefore, there are two ways to approach ADHD: (i) a dimensional approach and (ii) a categorical approach. The former highlights the continuity between clinical and nonclinical conditions, whereas the latter focuses on the demarcation defining the clinical population (Elton et al., 2014). The exceptionally high rate of other psychiatric comorbidities is another prominent feature of ADHD. Substance abuse as well as mood, anxiety, and conduct disorders are commonly associated with ADHD (McGough et al., 2005). For example, ADHD co-occurs in up to 85% of bipolar children, and bipolar disorder is present in almost 22% of children with ADHD (Singh et al., 2006). In addition to the high comorbidity observed between ADHD and bipolar disorder, the extent of familial overlap is also remarkable. Greater rates of DOI: 10.1097/YPG.0000000000000086
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120 Psychiatric Genetics 2015, Vol 25 No 3
bipolar disorder are found in relatives of ADHD patients and vice versa (Faraone et al., 1997). Overall, epidemiological, clinical, and genetic findings indicate a genetic overlap between ADHD and mood disorders. This high comorbidity suggests that certain features of ADHD that are not specific to the disorder may underlie all of ADHD, mood disorders, and substance abuse. These shared features may also be the basis for the continuous nature of ADHD. If this is so, investigation of these features in a nonclinical population is warranted. The dopamine transporter gene (DAT1 or SLC6A3) is located on chromosome 5p15.33. The dopamine transporter plays a principal role in regulating dopamine neurotransmission, and numerous independent studies have implicated DAT1 as an ADHD susceptibility gene (Gizer et al., 2009). The 3′-untranslated region (3′-UTR) variable number of tandem repeats (VNTR) 10-repeat allele has been investigated actively as a risk allele for ADHD (Cook et al., 1995; Lim et al., 2006). Other alleles from the same VNTR or newer markers in the same gene have also been suggested to be risk-conferring alleles. Recently, the 5′ end of the gene, including the promoter region, has received considerable attention (Doyle et al., 2009; Xu et al., 2009; De Azeredo et al., 2014). However, attempts to replicate incidental findings have not yielded consistent results, possibly because of the arbitrary nature of its categorical diagnosis (Bidwell et al., 2011). DAT1 has also been implicated in the genetic etiology of bipolar disorder and substance abuse (Keikhaee et al., 2005; Greenwood et al., 2006; David and Munafò, 2008; Van der Zwaluw et al., 2009). As these disorders frequently overlap with ADHD, we postulate that the pathway from DAT1 to ADHD-related features may be the common basis of the heightened risk of various psychiatric conditions that frequent overlap with ADHD. This postulation can be verified by an association between DAT1 and ADHD-related features not only in ADHD patients but also in other diagnostic groups and even in the nonclinical population. In this vein, Greenwood et al. (2013) found an association between DAT1 and childhood ADHD features in bipolar disorder patients. On the basis of a previous study (Greenwood et al., 2013), we investigated the association between DAT1 and ADHD-related traits in nonclinical individuals. ADHDrelated features were measured using the 25-item Wender Utah Rating Scale (WURS-25). Three WURS-25 factors established from previous factor studies were used as surrogate variables for ADHD-related traits: (i) impulsivity and defiant behavior, (ii) inattention, and (iii) mood instability and anxiety (McCann et al., 2000; Caci et al., 2009; Jeong et al., 2014). This study aimed to clarify whether ADHD-related features prevalent in the nonclinical population were associated with a genetic background, specifically the DAT1 gene. The result obtained
may suggest that DAT1 gene variation and the associated ADHD-related features are worth investigating not only in clinical ADHD patients but also in patients experiencing a diverse range of psychiatric conditions.
Methods Study participants
The original study was designed to investigate the ADHD-related traits present in the normal population, and possible determinants of these features, whether genetic or environmental (Jeong et al., 2014). The work described here was a subordinate study that focused on genetic determinants, specifically the DAT1 gene. Participants were recruited from several sources, including nurses and other workers in university-affiliated general hospitals, students in medical colleges, and administrative officers and firefighters in various fire departments. The purpose and procedures were of the study advertised by means of posters and leaflets or direct presentation by investigators in the various workplaces. All participants were provided with a detailed description of the study before signing an informed consent form. Trained psychiatric nurses conducted brief psychiatric interviews for screening purposes. We excluded participants who had a lifelong history of major psychiatric illness, including schizophrenia, bipolar disorder, obsessive-compulsive disorder, or history of brain trauma. We did not implement a systematic ADHD evaluation using Diagnostic and statistical manual of mental disorders, 4th ed. (DSM-IV) criteria; however, participants who reported severe attention or behavioral problems in their childhood were excluded to ensure that the sample was representative of the nonclinical population. A total of 1289 participants were included in the analysis. All participants were Korean and genetically unrelated. This study was carried out according to the Declaration of Helsinki guidelines for human research, and was approved by the Ethics Committees of Eulji General Hospital and Eulji University Hospital.
Measurement of childhood attention deficit hyperactivity disorder features
Past ADHD features were measured using the WURS-25, which was developed to retrospectively measure ADHD-related symptoms experienced before 12 years of age (Ward et al., 1993). Although the original WURS contained 61 items, the shortened version (WURS-25) consists of 25 items selected from the original. The Korean version of the WURS-25 was developed and standardized using normal female Korean adults (Koo et al., 2009). According to the original authors, a cutoff score of 36 shows 96% sensitivity and specificity for identifying childhood ADHD among the general population (Ward et al., 1993).
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Association between DAT1 and ADHD-related traits Jeong et al. 121
Table 1 Six genetic markers in the DAT1 gene that were subjected to association analysis
rs nos
Gene location
Chromosomal position
Alleles
MAF
HWE P-value
VNTR (= rs28363170) rs27072 rs11133767 rs429699 rs27048 rs2937639
3′-UTR 3′-UTR Intron 13 Intron 11 Intron 8 Intron 1
1393862 1394522 1401580 1409127 1412645 1443728
T/C A/G A/G T/C G/A
0.254 0.086 0.236 0.125 0.154
0.011 0.594 0.589 0.448 0.086
Minor allele frequencies and HWE analyses are also included. rs nos indicates single nucleotide polymorphism (SNP) identification in SNP site of NCBI. Chromosomal position was based on GRCh37.p10 referenced from NCBI dbSNP. A, adenine; C, cytosine; G, guanine; HWE, Hardy–Weinberg equilibrium; MAF, minor allele frequency; T, thymine; 3′-UTR, 3′-untranslated region; VNTR, variable number of tandem repeats.
Age and total WURS-25 score were compared between the sexes using Student’s t-tests. An exploratory factor analysis was carried out to extract factors representing the WURS-25 component factors. The principal component method was used for factor extraction. Factor number was decided using eigenvalue criteria (≥1). Extracted factors were rotated by promax rotation with Kaiser normalization. After rotation, the factor scores of each participant were calculated by regression analysis. Genetic analysis was carried out using PLINK software (Purcell et al., 2007). The linkage disequilibrium structure was investigated using Haploview software (Barrett et al., 2005), and separate haplotype blocks were determined on the basis of the four-gamete rule (Fig. 1; Wang
Block 1 (7 kb) 1
2
Block 2 (3 kb)
3 56
rs2937639
Fig. 1
rs27048
SNP genotyping was performed using the TaqMan method (Applied Biosystems). Standard PCR was carried out in a 5 µl volume containing 50 ng genomic DNA, 2.5 µl 2 × TaqMan Master Mix, 0.9 µmol/l of each primer, and 0.2 µl each of FAM dye (Applied Biosystems, Foster City, California, USA), and VIC dye probe (Applied Biosystems). After the initial 2-min denaturation at 50°C, followed by 10 min at 95°C, 40 thermal cycles consisting of 15 s at 95°C and 1 min at 60°C were carried out. Automated reading was performed using an ABI Prism 7900HT Sequence Detection System (Applied Biosystems).
Statistical analysis
rs429699
DNA was extracted from blood samples using a DNA Isolation Kit (Roche, Mannheim, Germany). The five SNPs selected were present throughout the DAT1 gene and a VNTR was located near the 3′-UTR (rs28363170) according to a literature search; these were our designated association markers (Table 1). Relevant information was obtained using the Entrez SNP database [National Center for Biotechnology Information (NCBI), Bethesda, Maryland, USA). Genotyping was performed according to the TaqMan method (Applied Biosystems, Foster City, California, USA; McGuigan and Ralston, 2002). SNP chromosomal location was referenced from the NCBI dbSNP dataset in February 2014 based on the GRCh37.p.10 assembly.
rs11133767
Genotyping
For VNTR genotyping, standard PCR was carried out in a 15 µl volume containing 50 ng genomic DNA, 10 mmol/ l of each deoxynucleotide triphosphate (dNTP), 20 pmol/ l of each primer, and 0.3 µl i-MAX II DNA polymerase (iNtRON Biotechnology, Inc., Seongnam, Korea). After the initial 5-min denaturation at 94°C, 40 thermal cycles consisting of 30 s at 94°C and 50 s at 72°C were carried out, and the reaction was incubated for 10 min at 72°C for final extension. The amplification products were resolved on 3% agarose gels and bands were read using an UV illumination transmitter.
rs27072
We selected five single nucleotide polymorphisms (SNPs) on the basis of DAT1 gene structural information published by Greenwood et al. (2006, 2013). We primarily selected haplotype-tagging SNPs (htSNPs) with published Asian population allele distribution information (rs2937639, rs11133767, rs27072). If information was unavailable, we selected other SNPs that were very close to the htSNPs and had reasonable minor allele frequencies in the Asian population (rs27048 and rs429699).
VNTR
Selection of genetic markers
4 85
39 44
5
6 22
22
78 9
84 13
Genomic structure, polymorphic sites, and linkage disequilibrium (LD) structure of the DAT1 gene in all participants. The number in each cell represents the LD parameter, D′. An LD block was defined using the four-gamete rule. VNTR, variable number of tandem repeats.
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122 Psychiatric Genetics 2015, Vol 25 No 3
et al., 2002). Allele-based and haplotype-based association analyses were carried out as implemented in the PLINK software. Diverse alleles of the 3′-UTR VNTR (3–11 repeats) were recoded as ‘0’ or ‘1’. The most common allele (10 repeats) was coded as ‘1’ and other alleles were coded as ‘0’. The statistical framework was basically a linear regression analysis. The WURS-25 total score and factor scores were entered as response variables, and the respective SNP markers or reconstructed haplotypes were entered as explanatory variables. Possible covariates that could influence the effect of each allele on WURS-25 scores were included in the model using the ‘–covar’ option in the ‘–linear’ command. The sliding window approach was used for haplotype construction (Huang et al., 2007). From the five SNP markers within the DAT1 gene (VNTR was not included in the haplotype analysis), four 2-adjacent-marker haplotypes, three 3-adjacent-marker haplotypes, two 4-adjacent-marker haplotypes, and one 5-marker haplotype were constructed. Nonadjacent marker haplotypes were not considered. When carrying out haplotype-based association analyses, the ‘-hap-omnibus’ option in PLINK was used to jointly estimate all haplotype effects simultaneously. All reported P-values represent nominal P-values without any adjustments. However, as multiple markers and phenotypes were analyzed simultaneously, inflation of the family-wise error rate was expected. To control for inflation of the type-I error rate, the false discovery rate (FDR) method proposed by Storey and Tibshirani (2003) was used. FDR controls the expected proportion of null results that are falsely identified as significant in a set of predictions. To set an acceptable significance threshold, the FDR was calculated at each P-value obtained. The FDR values obtained represent the expected family-wise error rate when the associated P-value was set to the significance threshold. FDR values were calculated using R statistical software for Windows version 2.15.1 (R Development Core Team, 2008) with the ‘QVALUE’ add-on package (Storey and Tibshirani, 2003). All other statistical analyses were also carried out using the R statistical software (R Development Core Team, 2008).
Characteristics of the study participants and the total 25-item Wender Utah Rating Scale scores in male and female participants
Table 2
Number of participants Age WURS-25
Whole
Male
Female
1289
476
813
25.1 ± 6.1 19.4 ± 14.8
27.4 ± 8.3 19.5 ± 15.7
23.7 ± 3.5 19.3 ± 14.2
Student’s t-test (male vs. female)
t = 11.0 t = 0.201
P < 0.001 P = 0.840
Age and WURS-25 total scores were compared between the sexes using Student’s t-tests. Values are represented as mean ± SD. WURS-25, 25-item Wender Utah Rating Scale.
Results Demographic features and total 25-item Wender Utah Rating Scale scores
Table 2 presents the number, age, and sex of the participants. Approximately two-thirds of the participants were women, who were significantly younger than their male counterparts [23.70 vs. 27.37 years, t(1,287) = 10.988, P < 0.001]. The mean WURS-25 total score was 19.38 ± 14.76 (range 0–87). However, no significant difference was noted between the sexes [t(1,287) = 0.201, P = 0.840]. 25-Item Wender Utah Rating Scale factor extraction and comparison of male and female participants
Exploratory factor analysis was carried out to identify relevant factors from the WURS-25. Three factors were extracted using eigenvalue criteria. The combined extracted factors explained 55.22% of the total variance (first factor, 40.17%; second factor, 8.34%; third factor, 6.71%). According to the loading pattern, the three factors were tentatively named as follows: ‘impulsivity and defiant behavior’, first factor (IMP); ‘inattention’, second factor (INATT); and ‘mood instability and anxiety’, third factor (MOOD). It is important to note that these factors are compatible with similar factor analyses carried out in other studies (Caci et al., 2009; Joo et al., 2010). The mean values of each factor score for male and female participants were then compared. Although there was no significant difference with respect to the IMP factor, the INATT factor was significantly higher in male participants [t(1,287) = 5.032, P < 0.001], whereas the MOOD factor was significantly higher in female participants [t(1,287) = − 4.147, P < 0.001]. Allele frequency, Hardy–Weinberg equilibrium, and linkage disequilibrium structure
All markers showed a minor allele frequency greater than 10% and did not deviate significantly from Hardy–Weinberg equilibrium (P = 0.01) (Table 1). The overall D′ values between the marker-pairs were relatively low, and ranged from 0.09 to 0.85. Only the rs429699 marker had a higher D′ value with some SNP markers (D′ = 0.85 with rs11133767; D′ = 0.78 with rs27072; and D′ = 0.84 with 3′-UTR VNTR). However, no definitive haplotype block could be discerned according to the criteria suggested by Gabriel et al. (2002). Quantitative trait association analysis of 25-item Wender Utah Rating Scale total scores
The initial analysis sought to probe the association between each of the used markers and the WURS-25 total scores, but the results showed no significant association. However, when the reanalysis was carried out with sex included within the interaction term, we found that the allelic effect of the rs2937639 marker on the WURS-25 total score was significantly influenced by sex
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Association between DAT1 and ADHD-related traits Jeong et al. 123
Table 3 Results of the allele-wise quantitative trait association analysis between the six markers in the DAT1 gene and the 25-item Wender Utah Rating Scale total score with each component factor (impulsivity, inattention, instability) WURS-25
Male VNTR rs27072 rs11133767 rs429699 rs27048 rs2937639 Female VNTR rs27072 rs11133767 rs429699 rs27048 rs2937639
Impulsivity
Inattention
Instability
Group size
β
T
P
β
t
P
β
t
P
β
t
P
476 476 476 476 476 476
− 0.497 − 0.180 0.083 0.858 − 0.705 2.630
− 0.309 − 0.166 0.046 0.725 − 0.491 2.060
0.758 0.868 0.963 0.469 0.624 0.040
− 0.055 0.050 − 0.028 0.076 0.042 0.117
− 0.522 0.714 − 0.242 0.989 0.444 1.411
0.602 0.476 0.809 0.323 0.657 0.159
− 0.014 − 0.086 0.006 0.028 − 0.150 0.141
− 0.122 − 1.110 0.047 0.332 − 1.454 1.533
0.903 0.268 0.963 0.740 0.147 0.126
− 0.024 − 0.003 0.034 0.031 0.000 0.202
− 0.250 − 0.050 0.319 0.447 0.000 2.675
0.803 0.958 0.750 0.655 1.000 0.008
813 813 813 813 813 813
1.863 − 0.140 2.155 − 0.847 0.500 − 1.595
1.491 − 0.176 1.729 − 1.005 0.459 − 1.608
0.136 0.861 0.084 0.315 0.646 0.108
0.032 − 0.013 0.033 − 0.094 0.059 − 0.067
0.375 − 0.243 0.380 − 1.616 0.783 − 0.972
0.708 0.808 0.704 0.107 0.434 0.331
0.194 − 0.038 0.217 0.032 − 0.021 − 0.086
2.430 − 0.746 2.724 0.601 − 0.304 − 1.343
0.015 0.456 0.007 0.548 0.761 0.180
0.090 0.035 0.127 − 0.062 0.038 − 0.130
0.994 0.602 1.400 − 1.008 0.476 − 1.806
0.321 0.547 0.162 0.314 0.634 0.071
We carried out an analysis of covariance with age as a covariate and genotype as the predictor. Results from male and female participants were obtained separately (β, regression coefficient; t, Wald t-test statistic). WURS-25, 25-item Wender Utah Rating Scale; VNTR, variable number of tandem repeats.
P-values obtained from the haplotype-based (sliding window approach) quantitative trait association analysis between the five single nucleotide polymorphism markers in the DAT1 gene and the 25-item Wender Utah Rating Scale total score with each component factor Table 4
Male P-values rs27072–rs11133767 rs27072–rs11133767–rs429699 rs27072–rs11133767–rs429699–rs2704 rs27072–rs11133767–rs429699–rs27048–rs2937639 rs11133767–rs429699 rs11133767–rs429699–rs2704 rs11133767–rs429699–rs27048–rs2937639 rs429699–rs2704 rs429699–rs27048–rs2937639 rs27048–rs2937639
Female
WURS-25
Impulsivity
Inattention
Instability
WURS
Impulsivity
Inattention
Instability
0.693 0.853 0.705 0.105 0.819 0.845 0.032 0.717 0.020 0.005
0.876 0.703 0.659 0.540 0.473 0.722 0.417 0.590 0.340 0.238
0.398 0.752 0.654 0.256 0.981 0.549 0.174 0.341 0.083 0.024
0.630 0.898 0.798 0.004 0.832 0.896 0.0002 0.863 0.00001 0.00002
0.221 0.443 0.502 0.550 0.137 0.271 0.198 0.592 0.402 0.290
0.927 0.405 0.623 0.664 0.422 0.589 0.437 0.265 0.381 0.535
0.024 0.078 0.152 0.378 0.026 0.070 0.290 0.852 0.721 0.322
0.270 0.616 0.516 0.416 0.086 0.217 0.165 0.598 0.394 0.280
The reported values were global haplotypic P-values. Haplotype frequencies were estimated with the EM-algorithm using PLINK software. Data from male and female participants were analyzed separately. WURS-25, 25-item Wender Utah Rating Scale.
(P = 0.008). For this reason, subsequent analyses were carried out on men and women separately. Upon analysis of male and female data separately, we found no evidence of a significant association, with the exception of nominally significant signals in the haplotype analyses of male participants (haplotype rs27048–rs2937639, P = 0.005, FDR = 0.09; rs429699–rs27048–rs2937639, P = 0.02, FDR = 0.17; Tables 3 and 4). Quantitative trait association analysis of 25-item Wender Utah Rating Scale factor scores
Next, an association analysis was carried out using each factor score as the response variable (Tables 3 and 4). According to this analysis, rs2937639 showed a potential association with the MOOD factor in male participants (P = 0.008). Furthermore, all haplotypes containing rs2937639 showed a significant association with the same factor in male participants (P = 0.00001–0.004). Although the allele-wise association between rs2937639 and the
MOOD factor did not fulfill the FDR criteria (FDR = 0.08), three haplotype-wise associations (rs11133767–rs429699–rs27048–rs2937639, rs429699–rs27048–rs2937639, and rs27048–rs2937639) fulfilled the FDR criteria (FDR = 0.0007–0.004). Meanwhile, no evidence of an association with any of the investigated markers (either allele-wise or haplotype-wise) was found in female participants.
Discussion In the study described here, a potential genetic association between several markers within the DAT1 gene and ADHD-related features was investigated in 1289 nonclinical adult participants. The WURS-25 was used to measure ADHD-related features in each individual. Qualitative trait genetic association was then assessed using the WURS-25 score as the qualitative trait, and each marker and the constructed haplotypes as predictor variables.
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124 Psychiatric Genetics 2015, Vol 25 No 3
In the initial analysis, no meaningful association was found between any of the investigated markers and WURS-25 total scores. However, qualitative differences between men and women in the epidemiologic and symptomatic characteristics suggested that we might proceed with further analyses on men and women separately (Biederman et al., 2008; Cho et al., 2010). After this separation, a weak association was found between rs2937639 and WURS-25 total scores in male participants (P = 0.04). This large P-value does not seem meaningful in and of itself; however, when the haplotypes containing rs2937639 and nearby markers were examined, the haplotype rs2937639–rs429699 pair yielded more convincing evidence of an association between the DAT1 gene and WURS-25 score (P = 0.005). These discoveries warranted further analysis. Subsequent analysis using the multifactor structure of WURS-25 showed that the mood instability factor (MOOD) was significantly associated with rs2937639 and some haplotypes, but that inattention (INATT) and impulsivity (IMP) were not. Although the allelic association with rs2937639 did not pass the FDR test (nominal P = 0.008, FDR = 0.08), haplotype analysis showed a convincing association with this factor (FDRs = 0.0007–0.004). This suggests that the weak signal between rs2937639 and the WURS-25 total score may actually result from the effect of the mood instability factor. However, an alternative explanation is that the WURS-25 total score and each specific factor score reflect different genetic effects. In a linkage study with bipolar families, a stronger signal with the inattention factor was found in a different region (10p14, LOD = 3.35), although a linkage signal with the WURS-25 total score was found in the 12q24 region (LOD =2.05; Joo et al., 2010). Some studies also support the latter explanation. In one study with Korean mood disorder patients, impulsivity and inattention factors were associated significantly with bipolar disorder, but mood instability was associated with a major depressive disorder (Joo et al., 2012). Another study that investigated the association between DAT1 and ADHD-related features in bipolar patients found that only the mood instability factor had a noteworthy association with markers in DAT1 (Greenwood et al., 2013). Together, these results suggest that ADHDrelated features have a multidimensional structure, and that individual dimensions may be differentially influenced by distinct genetic factors. Recently, a study of Taiwanese ADHD patients found a significant association between the rs27048–rs429699 haplotype and the inattentive subtype of ADHD (Shang et al., 2011). This haplotype is situated much closer to the VNTR than rs2937639. Considering the accumulated literature on the association between the VNTR polymorphism and attention in clinical populations, it may be that the attentional dimension is associated with the 3′-end
haplotypic block and that mood instability is associated with the 5′-end block (Rommelse et al., 2008). It was estimated that an adult with ADHD has an at least 2–8-fold higher risk of another psychiatric disorder in his/ her lifespan (Kessler et al., 2006). This risk is not much different in subthreshold ADHD individuals (Cho et al., 2009). It was hypothesized that more fundamental abnormalities than mere hyperactivity may underlie various psychiatric conditions (Brown, 2008). Until now, the inability to regulate attention or a broader deficit in executive function was most commonly highlighted (Brown et al., 2010). On the basis of the assumption that a deep connection exists between ADHD and mood disorders, many studies have attempted to identify attentional deficits in mood disorders as a common denominator. According to one meta-analysis, sustained attention deficit persisted even during remission in bipolar patients (Bora et al., 2008). However, mood instability and emotional hyper-reactivity are also problems in ADHD. Moreover, ADHD is frequently misdiagnosed as bipolar disorder because of severe mood dysregulation. Therefore, it is possible that the link between ADHD and mood disorders involves not only cognitive impairment but also mood disruption. We cannot consider the two dimensions of cognitive impairment and mood dysregulation separately because both are influenced by deficits in executive function. However, relatively little attention has been paid to the mood dysregulation aspect to date. The fundamental problem in ADHD patients is likely not a single overarching abnormality, but rather a group of interconnected but separate abnormalities. In addition, this diversity of problems may explain the high frequency of a variety of psychiatric and/or behavioral comorbidities. In summary, we attempted to identify the genetic influence of the DAT1 gene on ADHD-related features in healthy adults. Although we found no convincing evidence for this connection, we found that one of the DAT1 polymorphisms (rs2937639) was associated with the WURS-25 mood instability factor. As only a handful of markers were used, and these did not cover all the possible genetic variations in a gene as large as DAT1, it was unclear whether the DAT1 gene haplotype block was associated or a specific marker was haphazardly associated with the mood instability factor. However, the haplotype-wise analysis results supported the former possibility. Our finding supports the concept that the genetic association between DAT1 and the mood instability component typically found in ADHD patients may be the basis of the extensive comorbidities among ADHD, mood disorders, and other conditions. Further studies involving other clinical conditions that are frequently comorbid with ADHD will be necessary for a more comprehensive understanding of the genetic role of DAT1 in both the clinical and general populations. In addition, this study supports the idea that mood
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Association between DAT1 and ADHD-related traits Jeong et al. 125
instability is a meaningful subphenotype of ADHD, which differs in its genetic background from other clinical features, such as inattention and impulsivity. Studies using mood instability as a subphenotype of ADHD may help dissect ADHD in a new perspective, and will contribute toward a more comprehensive understanding of DAT1 in ADHD-related features in a broader population that includes both diagnosed ADHD patients and the general population.
Acknowledgements This work was supported by a National Research Foundation of Korea Grant funded by the Korean government (KRF-2008-531-E00045, 2011-0003164). Conflicts of interest
There are no conflicts of interest.
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