Functional effects of dopamine transporter gene genotypes on in vivo dopamine transporter functioning: a meta-analysis

ORIGINAL ARTICLE

Functional effects of dopamine transporter gene genotypes onin vivo dopamine transporter functioning: a meta-analysisSV Faraone1, TJ Spencer2, BK Madras3,4, Y Zhang-James1 and J Biederman2

Much psychiatric genetic research has focused on a 40-base pair variable number of tandem repeats (VNTR) polymorphism locatedin the 30-untranslated region (30UTR) of the dopamine active transporter (DAT) gene (SLC6A3). This variant produces two commonalleles with 9- and 10-repeats (9R and 10R). Studies associating this variant with in vivo DAT activity in humans have had mixedresults. We searched for studies using positron emission tomography (PET) or single-photon emission computed tomography(SPECT) to evaluate this association. Random effects meta-analyses assessed the association of the 30UTR variant with DAT activity.We also evaluated heterogeneity among studies and evidence for publication bias. We found twelve studies comprising 511subjects, 125 from PET studies and 386 from SPECT studies. The PET studies provided highly significant evidence that the 9R allelewas associated with increased DAT activity in human adults. The SPECT studies were highly heterogeneous. As a group, theysuggested no association between the 30UTR polymorphism and DAT activity. When the analysis was limited to the most commonlyused ligand, [123I]b-CIT, stratification by affection status dramatically reduced heterogeneity and revealed a significant associationof the 9R allele with increased DAT activity for healthy subjects. In humans, the 9R allele of the 30UTR polymorphism of SLC6A3regulates dopamine activity in the striatal brain regions independent of the presence of neuropsychiatric illness. Differences instudy methodology account for the heterogeneous results across individual studies.

Molecular Psychiatry (2014) 19, 880–889; doi:10.1038/mp.2013.126; published online 24 September 2013

Keywords: ADHD; dopamine transporter; genetics; meta-analysis; PET; SPECT

INTRODUCTIONThe dopamine active transporter (DAT) is a key regulator of thedopamine system and the gene that encodes it (SLC6A3) has beenthe focus of much research in biological psychiatry, having beenimplicated in several disorders including attention deficit hyper-activity disorder (ADHD),1–3 pediatric bipolar disorder,4 Tourettesyndrome5 and alcoholism,6 but not schizophrenia.7 Muchresearch in this area has focused on the DAT gene (SLC6A3),especially a 40-base pair variable number of tandem repeats(VNTR) polymorphism located in the 30-untranslated region(30UTR) of the gene, which has a regulatory role duringtranscription. This variant produces two common alleles with9- and 10-repeats (9R and 10R). In humans, meta-analyses suggestthe 10R allele of this polymorphism is associated with ADHD inyouth8 whereas the 9R allele is associated with ADHD in adults.9

Meta-analysis also associates the 9R allele with alcoholism,6 acommon comorbidity of ADHD in adults.

The DAT was initially implicated in ADHD by the stimulantdrugs, which are efficacious for the disorder and block the DAT,thereby increasing the concentration of dopamine in the synapticcleft. These effects are most pronounced in the nucleus accum-bens and dorsal striatum due to the high density of DATs in theseregions.10,11 Positron emission tomography (PET) studies inhumans show that both methylphenidate12 and amphetamine13

increase extracellular dopamine levels in the striatum. Single-photon emission tomography (SPECT) and PET studies also showthat methylphenidate treatment blocks the DAT.14 Consistent withthis, methylphenidate normalizes elevated DAT densities in a rat

model of ADHD.3 Based on a meta-analysis of nine in vivoSPECT and PET studies, Fusar-Poli et al.15 concluded that DATactivity was 14% higher in ADHD patients compared with controlsand that, among ADHD patients, DAT activity was higher amongpatients with a history of medication (although this latterconclusion has been questioned due to incorrect coding ofmedication status.16)

Functional in vitro studies have shown mixed results as towhether it is the 9R or 10R allele that increases DAT geneexpression.17–21 These results varied in the reporter gene designsand cell types used. A few studies measured in vivo striatal DAT geneexpression using postmortem brains, and the results were alsoinconsistent.22–25 MRI and magnetic resonance spectroscopy studieshave also produced heterogeneous results.26–28 A review ofneuropsychological studies found little evidence supporting theidea that the SLC6A3 30UTR is associated with deficits in cognition,29

with the possible exception of functions mediated by the striatum.27

PET and SPECT neuroimaging studies have examined theassociation of the 30UTR polymorphism with in vivo striatal DATbinding in humans. Such studies are particularly compellingbecause they directly measure the protein produced by the generather than measuring mRNA level, or downstream effects of brainactivation or cognition. DAT binding may be an intermediatephenotype that mediates the effects of DAT gene variants ondopamine-regulated brain functions and, ultimately, a wide arrayof behavior; including information processing, inhibition, emotion,movement, salience and reward. Advances in molecular imagingand the development of highly specific DAT binding ligands allow

1Departments of Psychiatry and of Neuroscience and Physiology, SUNY Upstate Medical University, Syracuse, NY, USA; 2Pediatric Psychopharmacology Unit, Psychiatry Service,Massachusetts General Hospital, Department of Psychiatry, Harvard Medical School, Boston, MA, USA; 3Division of Neuroscience, New England Primate Research Center,Southborough, MA, USA and 4Harvard Medical School, Boston, MA, USA. Correspondence: Dr SV Faraone, SUNY Upstate Medical University, 750 East Adams Street, Syracuse, NY13210, USA.E-mail: [email protected] 20 February 2013; revised 15 August 2013; accepted 16 August 2013; published online 24 September 2013

Molecular Psychiatry (2014) 19, 880–889& 2014 Macmillan Publishers Limited All rights reserved 1359-4184/14

www.nature.com/mp

http://dx.doi.org/10.1038/mp.2013.126

mailto:[email protected]

http://www.nature.com/mp

for the imaging of the DAT in humans facilitating the direct in vivoexamination of the product of the DAT gene and its polymorph-isms in humans. Thus, studies of the SLC6A3 30UTR polymorphismand DAT binding provide evidence as to whether this polymorph-ism regulates DAT functioning in living humans.

Results from such in vivo studies could advance our under-standing of the genetic control of dopamine as an importantneuromodulator of brain function. However, they have producedmixed results. Only two PET studies addressed this issue. Thesestudies of striatal structures reported that the 9R allele wasassociated with increased DAT activity.30,31 In contrast, ten SPECTstudies produced conflicting results. A meta-analysis of eight ofthese SPECT studies concluded that these studies did not providesupport for the putative association between the SLC6A3 30UTRpolymorphism and DAT activity in the brain.32

In vivo imaging of DAT is particularly relevant for ADHD giventhat DAT is the target of stimulant medications and, subsequently,a target protein for studies of pathophysiology. Clarifying thenature of the association of SLC6A3 variants would provide a keystep towards identifying part of ADHD’s pathophysiology.Depending upon the strength of the relationship, it could pointtoward a means of parsing ADHD’s heterogeneity, which couldhave implications for treatment development.

The goal of the present study was to clarify the PET and SPECTimaging studies by updating Costa et al.’s32 meta-analysis inseveral ways. Costa et al.’s32 meta-analysis is limited in severalways. It ignored two available SPECT studies,33,34 which couldpotentially add to our understanding of the magnitude of effectsand sources of heterogeneity and it was conducted prior topublication of the two recent PET studies described above.30,31

Costa et al. did not (or could not) assess the degree to whichheterogeneity of results could be accounted for by samplecharacteristics (affected vs. healthy), imaging method (PET vs.

SPECT) and, for SPECT studies, type of ligand. As we show in thismanuscript, attending to these key issues provides a betterunderstanding of the association between the SLC6A3 30UTRpolymorphism with in vivo striatal DAT binding in humans.

METHODSA PubMed literature search identified studies that met the followingcriteria: (1) use of SPECT or PET to assess DAT availability in the brains ofhuman subjects; (2) genotyping of the DAT gene (SLC6A3) 30UTR VNTR. (3)reporting of means and standard deviations of DAT availability stratified bygenotype; (4) reporting of the numbers of subjects in each genotypegroup. We used the following search algorithm in PubMed: ‘dopaminetransporter’ [TIAB] OR DAT1[TIAB] OR SLC6A3[TIAB] AND (imaging [TIAB] ORsingle-photon [TIAB] OR SPECT [TIAB] OR PET [TIAB] OR ‘positron emissiontomography’[TIAB]) AND (genetic [TIAB] OR genotype [TIAB] OR genotypes[TIAB] OR allele [TIAB] OR alleles [TIAB] OR polymorphism [TIAB]). If thereference sections of any of these articles suggested additional articles,these were also examined. The preferred reporting items for systematicreviews and meta-analyses (PRISMA) diagram in Figure 1 describes thenumber of articles identified and their disposition. If the required datawere not available in relevant articles, we contacted authors for that dataor extracted it from a prior summary of a subset of relevant studies.32

We computed separate meta-analyses for the PET and SPECT studies.Our meta-analysis used the random effects model of DerSimonian andLaird,35 which computes a pooled standardized mean difference weightedby sample size. We use the I2 index to assess the heterogeneity of effectsizes.36 Its value lies between 0 and 100 and estimates the percentage ofvariation among effect sizes that can be attributed to heterogeneity.A significant I2 suggests that the effect sizes analyzed are not estimatingthe same population effect size. We used meta-analytic regression toassess the degree to which effect sizes varied with methodologicalfeatures.37,38 The meta-analyses and meta-analytic regressions wereweighted by the reciprocal of the variance of the effect size. We usedEgger’s39 method to assess for publication biases.

Records identified throughdatabase searching

(n = 105)

Scr

een

ing

In

clu

ded

E

ligib

ility

Id

enti

fica

tio

n

Additional records identifiedthrough other sources

(n = 1)

Records after duplicates removed(n =106)

Records screened(n =106)

Records excluded(n =88)

Full-text articles assessedfor eligibility

(n =18)

Full-text articles excluded,with reasons

(n = 6)Wrong type of study, i.e.,

no human dopaminetransporter genotype or

imaging dataStudies included inqualitative synthesis

(n = 12)

Studies included inquantitative synthesis

(meta-analysis)(n = 12)

Figure 1. PRISMA flow diagram. Note: PRISMA¼ Preferred reporting items for systematic reviews and meta-analyses (http://www.prisma-statement.org/).

Meta-analysis of dopamine transporter gene associationsSV Faraone et al

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& 2014 Macmillan Publishers Limited Molecular Psychiatry (2014), 880 – 889

RESULTSTable 1 gives the characteristics of each study’s sample. Tenstudies used SPECT and these used four different ligands. Twostudies used PET, one using 11Altropane as the ligand and theother 11Cocaine. Six studies were of Caucasian samples, oneused a Korean sample and the others were of mixed ethnicity.As reported by Costa et al.,32 data from van Dyck et al.40

overlapped with data reported by Jacobsen et al.41 and Martinezet al.42 For our meta-analyses, we used the non-overlappingvan Dyck40 data that had been presented by Costa et al.32 Thetwelve studies comprised 511 subjects, 125 from PET studies and386 from SPECT studies.

Table 2 describes the data used in the meta-analysis. Somestudies provided data for both healthy and affected groups.Several disorders were studied: ADHD, schizophrenia, alcoholismand Parkinson’s disease. Most studies imaged the striatum orsubareas of the striatum. The only exception was Cheon et al.,34

who studied the left and right basal ganglia. If a study provideddata on a structure (for example, putamen) and subdivisions of thestructure (for example, left and right putamen), we only analyzedthe substructures. Each study compared a 10 repeat (10R)genotype group with a 9 repeat (9R) genotype group.As Table 2 shows, nearly all studies defined the 10R group asthose carrying two 10R alleles (10R/10R). The only exception wasLynch et al.,43 who also included 10R heterozygotes carrying a rareallele other than the 9R allele. The 9R genotype groups primarilycomprised 9R homozygotes and 9R/10R heterozygotes. However,some rare genotypes that did not include a 10R allele were alsoincluded in some samples.

In the analyses that follow, differences between genotypegroups are expressed as the standardized mean difference (SMD)effect size. Positive SMDs favor the 9R allele as being associatedwith increased DAT activity; negative SMDs favor the 10R allele.Figure 2 shows the results for PET studies. In Figure 2, the dotgives the relative risk and the horizontal line gives the 95%confidence interval. The diamonds give the weighted SMD acrossstudies and the width of the diamond gives its 95% confidenceinterval. The first two diamonds give pooled results for theaffected and healthy subgroups. The last diamond gives results forall studies with the left and right ends of the diamond marking the95% confidence interval.

Across all PET study observations, the SMD of 0.31 wasstatistically significant (z¼ 3.61, Po0.0009). The SMD was sig-nificant for the healthy group (0.31, z¼ 3.1, P¼ 0.002). Althoughthe magnitude of the SMD was the same for the affected (allADHD) group, it was only marginally significant, perhaps due to thesmaller sample size (0.33, z¼ 1.9, P¼ 0.056). The low and non-significant I2 statistic of 0.0% indicates very low heterogeneity of

results across all observations. The test for publication bias was notsignificant (t¼ 1.3, P¼ 0.2). Consistent with the finding of noheterogeneity, meta-analysis regression found that the SMDs werenot associated with age (F(1, 9)¼ 1.9, P¼ 0.22), sex (F(1, 9)¼ 1.4,P¼ 0.26) brain region (F(6, 4)¼ 2.80, P¼ 0.17), affection status(F(1, 9)¼ 0.02, P¼ 0.88) or ligand (F(1, 9)¼ 1.38, P¼ 0.27). The non-significant PET findings should be viewed cautiously given thatthere were only two PET studies.

Figure 3 shows the results for SPECT studies. Across all studies,the SMD of 0.00 was not statistically significant (z¼ 0.00, P¼ 0.99).The SMD was positive and significant for the healthy group (0.46,z¼ 2.1, P¼ 0.035) and negative but not significant for the affectedgroup (–0.40, z¼ 1.7, P¼ 0.093). The test for publication bias wasnot significant (t¼ –2.0, P¼ 0.06). The I2 statistic was high andsignificant for the entire SPECT group (I2¼ 73.8%, Po0.0009).Although meta-analysis regression confirmed that one source ofheterogeneity was affection status (F(1, 13)¼ 5.04, P¼ 0.04),heterogeneity remained high when analyzing the affected(I2¼ 71.2%, P¼ 0.004) and healthy groups (I2¼ 63.1%, P¼ 0.006)separately. Neither sex (F(1, 13)¼ 0.1, P¼ 0.8) nor age weresignificant predictors of the SMDs (F(1, 13)¼ 0.5, P¼ 0.48).

We explored two additional sources of heterogeneity. AsFigure 3 shows, the findings of Cheon et al.34 are markedlydiscrepant from the other SPECT studies. Their study was the onlyone to study children (seven 10/10R homozygotes, two 9/10heterozygotes and two 10/11 heterozygotes). They were the onlyinvestigators to use [123I]IPT as a ligand and basal ganglia as theregion of interest. Consistent with this, meta-analysis regressionfound significant effects of brain region F(4, 10)¼ 3.86, P¼ 0.038)and ligand F(3, 11)¼ 5.9, P¼ 0.01). After excluding Cheon et al.34

from the analysis, the overall I2 statistic remained high andsignificant for the entire SPECT group (I2¼ 64.7%, Po0.001) butwas reduced to non-significance in the affected group (I2¼ 10.9%,P¼ 0.35). The SMDs for the overall group and the affected groupdid not achieve significance (P’s40.25).

As suggested by the meta-analysis regression, another potentialsource of heterogeneity among SPECT studies was the choice ofligand. However, only one ligand, [123I]b-CIT, was used withsufficient frequency to be analyzed separately. In this analysis, theoverall I2 statistic remained high and significant for the entireSPECT group (I2¼ 70.7%, Po0.001) but was low and non-significant when separately considering the affected (I2¼ 15.9%,P¼ 0.35) and healthy groups (I2¼ 0.0%, P¼ 0.47). The SMD wassignificant and positive for the healthy group (0.67, z¼ 5.2,Po0.009). In contrast, it was nearly significant and negative for theaffected group (–0.52, z¼ 1.9, P¼ 0.057).

DISCUSSIONThe prior meta-analysis of the association between the SLC6A330UTR polymorphism with in vivo striatal DAT binding in humansconcluded that there was no evidence to support the hypothe-sized association. In contrast, by analyzing a larger sample andincorporating relevant covariates, our meta-analyses have yieldedseveral firm conclusions based on 12 studies comprising 511subjects. Although limited by the existence of only two studies,the PET studies provided highly significant evidence indicatingthat the 9R allele is associated with increased DAT binding in thestriatal brain regions. Although the effect size was similar for thehealthy and affected samples, the latter effect size did not achievestatistical significance, probably due to the smaller number ofobservations. Notably, all observations from PET studies, albeitindividually not significant, were consistent with the 9R allelepredicting greater DAT binding in humans. This consistency ofresults was reflected in finding zero heterogeneity across theseobservations.

In contrast to the consistency of findings across PET studyobservations, the data from SPECT studies was highly and

Table 1. Description of studies providing data

Study Method Ligand Ethnicity

Cheon34 SPECT [123I]IPT KoreanContin87 SPECT [123I]FP-CIT CHeinz88 SPECT [123I] b-CIT CJacobsen41 SPECT [123I] b-CIT AA, CKrause75 SPECT 89MTC-TRODAT–1 CLafuente90 SPECT [123I]FP-CIT CLynch43 SPECT 89MTC-TRODAT–1 C, AA, otherMartinez42 SPECT [123I]b-CIT AA, CShumay30 PET [11Cocaine]PET AA, C, OtherSpencer31 PET [11Altropane] AA, CVan Dyck40 SPECT [123I]b-CIT CVan de Giessen91 SPECT [123I] b-CIT C

Abbreviatons: AA, African–American; C, Caucasian; PET, positron emissiontomography; SPECT, speeded photon emission tomography.


882

Molecular Psychiatry (2014), 880 – 889 & 2014 Macmillan Publishers Limited

Tabl

e2.

Datausedin

themeta-an

alysis

Stu

dy

Sam

ple

Mea

na

ge

an

dra

ng

eB

rain

reg

ion

9Rg

eno

typ

eg

rou

pN

wit

h9R

gen

oty

pes

Mea

no

f9R

gro

up

SDo

f9R

gro

up

10R

gen

oty

pe

gro

up

Nw

ith

10R

gen

oty

pes

Mea

no

f10

Rg

rou

pSD

of

10R

gro

up

Shumay

30

Healthy

34.5

AA:2

1.5–

45.5

CE:

20.5–4

9.5

Others:

22.2–4

0.3

Cau

date

9R/9R9R

/10R

9R/11R

470.79

0.14

10R/10R

440.76

0.15

Shumay

30

Healthy

34.5

AA:2

1.5–

45.5

CE:

20.5–4

9.5

Others:

22.2–4

0.3

Putamen

9R/9R9R

/10R

9R/11R

470.96

0.13

10R/10R

440.9

0.13

Shumay

30

Healthy

34.5

AA:2

1.5–

45.5

CE:

20.5–4

9.5

Others:

22.2–4

0.3

Ven

tral

striatum

9R/9R9R

/10R

9R/11R

470.81

.210

R/10R

440.8

0.19

Spen

cer31

Healthy

27.8

18–4

9Leftcaudate

9R/9R9R

/10R

9R/11R

183.58

90.84

710

R/10R

163.33

60.62

02

Spen

cer31

ADHD

32.8

18–5

2.3

Leftcaudate

9R/9R9R

/10R

9R/11R

153.40

10.74

2410

R/10R

192.98

80.34

48

Spen

cer31

Healthy

27.8

18–4

9Leftputamen

9R/9R9R

/10R

9R/11R

153.34

90.64

310

R/10R

193.17

40.37

01

Spen

cer31

ADHD

32.8

18–5

2.3

Leftputamen

9R/9R9R

/10R

9R/11R

183.38

60.62

4610

R/10R

163.31

20.49

96

Spen

cer31

Healthy

27.8

18–4

9Rightcaudate

9R/9R9R

/10R

9R/11R

153.26

60.55

5710

R/10R

193.05

10.34

3

Spen

cer31

ADHD

32.8

18–5

2.3

Rightcaudate

9R/9R9R

/10R

9R/11R

183.66

40.62

10R/10R

163.24

70.53

8

Spen

cer31

Healthy

27.8

18–4

9Rightputamen

9R/9R9R

/10R

9R/11R

153.29

60.52

6510

R/10R

193.19

0.33

5

Spen

cer31

ADHD

32.8

18–5

2.3

Rightputamen

9R/9R9R

/10R

9R/11R

183.35

50.70

8510

R/10R

163.24

50.60

71

SPEC

T[1

23I]b-

CIT

stu

die

sMartinez

42

Schizophrenia

39.2

±9

Striatum

9R/9R9R

/10R

77.9

2.1

10R/10R

147.8

1.5

Martinez

42

Healthy

40.0

±9

Striatum

9R/9R9R

/10R

9R/11R

68.2

1.5

10R/10R

158.2

1.3

Van

Dyck4

0Healthy

49.9

18–8

8Striatum

9R/9R9R

/10R

307.5

1.9

10R/10R

356.6

1.6

Van

deGiessen

91

Healthy/prior

Ecstasyuse

22.0

18–3

5Pu

tamen

9R/9R9R

/10R

3211

.42.72

10R/10R

459.6

2.06

Van

deGiessen

91

Healthy

22.0

18–3

5Cau

date

9R/9R9R

/10R

3213

.53.25

10R/10R

4511

.42.42

Heinz8

8Alcoholics/co

ntrols

36.7

±7

Cau

date

9R/10R

1023

9975

010

R/10R

1528

9572

4Heinz8

8Alcoholics/co

ntrols

36.7

±7

Putamen

9R/10R

1022

2663

510

R/10R

1528

4074

3Jaco

bsen41

Healthy

37±

9.3

Striatum

9R/9R9R

/10R

98.2

110

R/10R

187.1

1

SPEC

T[1

23I]

FP-C

ITst

ud

ies

Contin87

Parkinson’s

60.4

9–10

(N¼14

):63

±11

9–9(N¼6):

58±9

10–1

0(N¼16

)59±

7

Putamen

9R/9R9R

/10R

200.92

0.54

10R/10R

160.91

0.63

Lafuen

te90

Schizophrenia

24.0

9–9:

26±0

9–10

:22

.6±3

10–1

0:24

.8±6.5

Striatum

9R/9R9R

/10R

84.6

0.5

10R/10R

64.4

0.5

SPEC

T89

MTC

-TR

OD

AT–

1st

ud

ies

Krause

75

ADHD

37.6

19–5

4Striatum

9R/9R9R

/10R

121.31

.27

10R/10R

171.28

.34

Lynch

43

Healthy

46.5

18.3–8

3.3

Striatum

9R/10R

9R/*R

491.17

5.26

10R/10R

10R/*R

321.25

.22

Lynch

43

Parkinson’s

60.8

38.3–8

4.2

Striatum

9R/10R

9R/*R

58.65

.215

10R/10R

10R/*R

42.65

.2

SPEC

T[1

23I]

IPT

stu

die

sCheo

n34

ADHD

9.8

6–12

Leftbasal

gan

glia

10R/11R

9R/10R

42.22

1.39

10R/10R

76.91

2.5

Cheo

n34

ADHD

9.8

6–12

Rightbasal

gan

glia

10R/11R

9R/10R

42

1.2

10R/10R

77.1

1.95

Abbreviations:9R

,9repeatallele;10

R,10

repeatallele;*R,other

allele;xR

/yR¼xrepeatallele/y

repeatallele

gen

otype.

‘Meanof9R

group’refersto

themeanDATactivity

forthe9R

groupan

dthe‘s.d.’co

lumngives

thestan

darddev

iations.


883


significantly heterogeneous. Meta-analysis regression suggestedthat heterogeneity among SPECT studies could be due to the brainregion, affection status or choice of ligand. Due to the distributionof these features across samples, it was not possible to assess theirjoint effects. However, when the analysis was limited to [123I]b-CIT,the most commonly used ligand, which was used in 5 of the 10SPECT studies, stratifying by affection status dramatically reducedthe heterogeneity of results. This sub-analysis was consistent withthe PET studies in finding a significant association of the 9R allelewith increased DAT activity for the healthy group.

Unlike the PET studies, which showed a trend association of the9R allele and DAT activity for the affected group, for SPECT studies,the effect for the affected group favored the 10R allele and wasnearly significant. This difference is likely due to the difference inthe composition of the affected sub-samples. For the PET studies,these were all ADHD patients. For SPECT studies, a variety ofdisorders were studied: ADHD, alcoholism, schizophrenia andParkinson’s disease.

Our results suggest that heterogeneity of findings across studiescan be explained by methodological differences. DAT bindingvaries considerably in different brain regions, and within thestriatum, density gradients are detected from superior to inferior,medial to lateral and anterior to posterior regions, particularly inthe caudate nucleus.44 Several studies report differences betweenDAT binding in caudate and putamen.31 In a study of ADHD,Jucaite et al.45 reported decreased DAT binding in the midbrainbut increased DAT binding in the caudate. Due to poor spatialdiscrimination in SPECT, most of the SPECT studies report acombined striatal value that may obscure apparent effects, ifdiscrete regional expression of SLC6A3 is modified by the VNTR(Table 2, Figure 3).

One must also consider variability among DAT radioligandswhen interpreting the heterogeneity of findings across studies.Although the core structures of these potent DAT radioligands arederived from CFT (or WIN 35,428), only Altropane contains a4-fluoro substituent on the phenyl ring, rendering it relativelyselective, 28-fold, for the DAT over the serotonin transporter anddisplaying favorable kinetic properties.46,47 Among all the studiesincluded in our meta-analysis, only Spencer et al.31 used altropane.The more commonly used ligands, CIT, IPT, FP-CIT and TRODAT,contain 4-chloro or 4-iodo substituents, which markedly reduceDAT:serotonin transporter specificity (TRODAT 3:1, CIT: 1:1, FP-CIT3:1, IPT: 5:1) and require varying lengths of time for theradioligand to wash out from serotonin transporter sites andother non-specific sites.48–50 In addition, differences in lipophilicproperties may affect the ability of the ligands to detectintracellular vs. membrane-bound DAT.

Combining disorders may also obscure findings, as differentpathophysiologies may be associated with greater or lesser DATbinding. These could be due to either other genetic andenvironmental risks or medications that alter membrane SLC6A3expression. Other variables that are hard to control for include ageand smoking status, which are known to affect DAT dramati-cally.40,51 DAT densities also decline as a function of age,30 and itremains unclear whether the expression levels of 9R and 10R DATalleles are equally affected by age. In our analyses, age was notpredictive of SMDs, which suggests that age effects do notmoderate the effects of SLC6A3 alleles. This finding is, however,tempered by the fact that we only had access to mean ages fromeach study, which ranged from 9.8 to 60.8. Shumay et al.’s30 PETstudy found that the association of 30UTR genotypes and DATbinding was significant across all regions (caudate, putamen,ventral striatum) for younger subjects but not in older subjectsdespite the use of comparable sample sizes for both groups. Theirstudy also found that the age-related decline in DAT levels wasgreatest for carriers of the 9R/9R 30UTR genotype.

The strength of the PET-derived data together with consistencywith SPECT imaging data that used CIT as a probe, insinuates the

9R allele in the 30UTR as a regulator of SLC6A3 expression. Theassociation of the 9R allele with higher DAT binding site densitycould result from a number of possible pathways: interactionof the 30UTR with regulatory proteins or microRNAs, shunting ofmRNA to distinct compartments in the neuron, regulation ofmRNA stability, turnover, increases in translational efficiency,52,53

or even remote interaction with regulatory elements ofother genes that may affect SLC6A3 expression, stability andtrafficking.54 A parsimonious interpretation of the functionalconsequences of elevated DAT is more efficient in clearingextracellular dopamine, yielding lower extracellular levels andreduced dopamine signaling. It may be feasible to test thishypothesis by monitoring extracellular dopamine indirectly, usingdisplacement of D2 receptor occupancy with [11C]raclopride as asurrogate for direct measures of dopamine, in 9R and 10R carriers.

The role of the DAT in pathophysiology and therapeuticresponse has catalyzed research into the association of DATdensity with pathology, and the interrogation of whether SLC6A3alleles are relevant to SLC6A3 expression, regulation andmembrane transporter density. Based on a meta-analysis of 9SPECT and PET studies, Fusar-Poli et al.15 concluded that DATdensity was 14% higher in ADHD patients compared with controls.Mill et al.25 measured dopamine transporter mRNA levels in thecerebellum, temporal lobe and lymphocytes and reported thatdopamine transporter mRNA expression increased with thenumber of 10R alleles. Brookes et al.22 also found that the 10Rallele increased levels of SLC6A3 mRNA in human postmortemmidbrain tissue. However, Zhou et al.24 and Pinsonneault et al.23

failed to find a differential effect of 9R and 10R alleles on SLC6A3expression in postmortem brains.

In vitro studies of the functional effects of the 30UTR haveproduced conflicting results. A study of HEK–293 cells reportedthat cells containing the 10R allele had a SLC6A3 expression thatwas 50% greater than cells with the 9R allele.18 Similarly, using aluciferase reporter system in COS–7 cells Fuke et al.17 foundgreater SLC6A3 mRNA expression for constructs containing the10R allele compared with other alleles. In contrast to thesefindings suggesting that the 10R allele is associated with greatertranscription, using the human neuroblastoma cell line, Inoue-Murayama, et al.21 reported that the 9R allele led to more SLC6A3mRNA expression than the 10R allele. Increased DAT geneexpression associated with the 9R allele was also reported byMiller and Madras55 using HEK–293 cells transfected with 30UTRvariants using two different promoters. Greenwood and Kelsoe20

found that the 9R allele led to non-significantly increasedtranscriptional regulation in dopaminergic substantia nigra(SN4741) cell lines. Mill et al.19 also reported non-significantlygreater mRNA expression for 9R compared with 10R constructsevaluated in SH-SY5Y and HEK–293 cell lines. In agreement withthese findings, if the 9-repeat allele was subcloned upstream ofthe viral promoter coupled to a green fluorescent protein reporter,the construct enhanced transcription in an immortalized dopami-nergic cell line derived from mouse substantia nigra.56

These differences among studies could be due to usingdifferent experimental constructs to introduce the mutation intocell lines, the amount of flanking sequence included in theconstruct, choice of reporter gene, along with variable presence ofother SLC6A3 transcription regulators across different celllines.19,57 Clarifying this issue will require detailed analyses ofpolymorphisms of length or of single nucleotides in the DAT genethat conceivably contribute to the dynamic processes regulatingDAT density in the brain. The grouping together of multiple allelesby the number/length of repeat sequences of the 30UTR couldmask the relevance of other sequence variations, which contributeto DAT gene regulation, SLC6A3 expression levels and phenotype,either in conjunction with, or independently of the 30UTR.

The inconsistencies from in vitro and postmortem brainexpression studies support in vivo imaging as a direct method


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to monitor the relevance of 9R and 10R genotypes on SLC6A3regulation and function. In a functional magnetic resonanceimaging study, ADHD patients homozygous for the 10R alleleshowed significant hypoactivation in the left dorsal anteriorcingulate cortex compared with 9R carriers.26 In another functionalmagnetic resonance imaging study, carriers of a haplotypeincluding the 10R allele showed differentially modulated neuralactivation to reward-predicting cues in the caudate nucleus.58 In asample of adults, Hoogman et al.59 found that the 9–6 haplotypeof SLC6A3 was associated with ADHD and that ADHD adultsshowed striatal hypoactivation during reward anticipation. There

was not, however, an association between SLC6A3 genotypes andstriatal hypoactivation. In a magnetic resonance spectroscopystudy, Sherk et al28 reported that the 10R allele was associatedwith higher ratios of NAA/Cho and NAA/Cr in the left putamen.They concluded that the 30UTR VNTR polymorphism modulatesdopaminergic activity, and neuronal function in putamen.

Our meta-analyses suggest that, in humans, the 9R allele of the30UTR polymorphism leads to increased DAT activity in the striatalbrain regions. These results imply that some of the DAT (andtherefore dopamine) regulation could be due to the presence (orabsence) of the 9R allele. The relationships among the 30UTR

Values greater than zero suggest the9R allele is associated with increased

DAT binding

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.

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Figure 2. Meta-analysis of the dopamine transporter gene 30UTR VNTR polymorphism with in vivo dopamine transporter activity assessed byPET. Note: For each comparison, the dot gives the relative risk and the horizontal line gives the 95% confidence interval; the diamonds givethe weighted SMD across studies and the width of the diamond gives its 95% confidence interval. The first two diamonds give pooled resultsfor the affected and healthy subgroups. The last diamond gives results for all studies with the left and right ends of the diamond marking the95% confidence interval. I-squared is a measure of heterogeneity among studies.

Values greater than zero suggest the9R allele is associated with increased

DAT binding

.

.

Overall (I-squared = 73.8%, p = 0.000)

Subtotal (I-squared = 63.1%, p = 0.006)Martinez 2001

Van de Giessen 2009

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-5 -4 -3 -2 -1 0 1 2Standardized Mean Difference

Figure 3. Meta-analysis of the dopamine transporter Gene 30UTR VNTR polymorphism with in vivo dopamine transporter activity assessed bySPECT. Note: For each comparison, the dot gives the relative risk and the horizontal line gives the 95% confidence interval; The diamonds givethe weighted standardized mean difference across studies and the width of the diamond gives its 95% confidence interval. The first twodiamonds give pooled results for the affected and healthy subgroups. The last diamond gives results for all studies with the left and right endsof the diamond marking the 95% confidence interval. I-squared is a measure of heterogeneity among studies.


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polymorphism, DAT binding and pathophysiology remain com-plex. This complexity is well illustrated by the ADHD literature forwhich meta-analyses show: (a) significantly increased DAT densityin ADHD, which was greatest for those having had prior stimulanttreatment;15 (b) an increased prevalence of the 10R allele in ADHDyouth;8 and (c) an increased prevalence of the 9R allele in ADHDadults.60 Because the positive studies in our meta-analysis all usedadult samples, our results are consistent with the adult associationstudies. We cannot, however, explain why the 10R allele has beenassociated with ADHD in youth, which is either a false-positivefinding or reflects the complex regulation of SLC6A3, which wediscuss below. Given current sample sizes, reconciling apparentgenetic differences between childhood and persistent ADHD hasnot been feasible. It is, however, notable that the only in vivo DATimaging study to find a significant association of the 10R allelewith DAT binding was also the only study of children. This patternis consistent with the pattern of genetic association seen in adultand child studies.

None of the studies analyzed addressed other mechanisms thatmight influence DAT binding. Additional mechanisms wereimplicated in a study by our group, which reported that, whilethe 30UTR polymorphism increased DAT binding regardless ofADHD status, ADHD made an additional, independent contributionto DAT binding.31 This suggests that there are additional ADHD-associated genetic or non-genetic mechanisms that influence DATbinding. For example, DAT is constitutively recycled through theendosome.61 Although majority of DAT is sequestered intracellularlyin the recycling endosome, only membrane-associated DAT isfunctionally available for the reuptake of dopamine. An endosomesodium–hydrogen exchanger protein, encoded by SLC9A9, hasbeen implicated in ADHD62–66 and disruptive genetic variants ofSLC9A9 have been found in ADHD and autism cases.67,68 A meta-analysis of ADHD GWAS data sets implicated the CHMP7 gene(charged multivesicular body protein 7), another protein involved inendosomal sorting/recyling pathway.69 These findings suggestthat endosomal pathway genes may be compromised in ADHDand that DAT membrane density could be altered due to mutationsin these genes.70,71

The variability of our DAT binding findings in affected vs.healthy subjects, in child vs. adult samples and in various differentbrain regions could be influenced by both genetic and non-genetic factors, as well as their interactions, about which we donot currently have a full understanding. Non-genetic mechanismsthat alter the striatal DAT density include caffeine, cigarettesmoking and alcohol consumption.51,72–74 The effect of stimulantmedications on DAT density is of considerable interest givenFusar-Poli’s15 report that increased DAT density in ADHD could beaccounted for by stimulant treatment. Their conclusion, however,has been questioned due to incorrect coding of medicationstatus.16 Our study cannot shed much light on this issue becauseonly three of the studies examined ADHD patients and these allused treatment-naı̈ve samples.31,34,75

There are several potential genetic mechanisms underlying DATvariability. Some studies suggest that the effects of the SLC6A330UTR VNTR are limited to specific haplotypes formed with anintron 8 VNTR.2,9,29,76–78 In their study of DAT density using[11Cocaine]PET, Shumay et al.,30 showed that the intron 8 VNTRwas associated with DAT levels in caudate and putamen. Incontrast, Guindalini et al.79 using SPECT with TRODAT–1 did notdetect this association. Haplotype analyses by Shumay et al.,30

suggested that higher DAT levels were associated with the 5Rintron 8 allele. Thus, it may be the 10R–5R haplotype formed bythe 30UTR/intron8 haplotype is worthy of further study as thevariant increases DAT density.

SLC6A3 has not been highly conserved during evolution,especially as regards the 30UTR VNTR region.80 This would beexpected to cause variability in gene expression and functionality.Such effects could contribute to the inconsistencies among

previous studies. Moreover, DAT is expressed in a region-specificmanner in the brain81 and demonstrates an age-dependentprofile,82 with multiple alternative transcription initiation andsplice isoforms existing. The SLC6A3 gene region is enriched fortranscription factor and miRNA-binding sites and DNAmethylation sites. Many of these regulatory sites are co-localizedwith known variants such as sequence repeats, single-nucleotidepolymorphisms (SNPs) and copy number variations.80

For example, the number of the 30UTR VNTR repeats can changethe length of the transcribed mRNA, which may alter the secondarystructure and degradation rate of the mRNA. This may also alter theefficiency of the miRNA-binding sites due to changes of sequenceand of secondary structure. This cascade of signaling couldcontinue with changes in transcription and degradation rates.Furthermore, miRNA expression itself is often tissue specific anddevelopmentally regulated.83 Some miRNAs simultaneouslyinteract with both the 30 and 50UTR regions.84 Shumay et al80

showed the presence of such sites in the 30UTR region of SLC6A3,indicating that variations in the 50 regulatory region of the genemay influence the function of the 30UTR VNTR via long-rangeinteractions. Consistent with this, Brookes et al.1 reported repli-cated associations between ADHD and SNPs in the 50 regulatoryregion of SLC6A3. Drgon et al.85 reported that haplotypes of twoSNPs in the 50 regulatory region were associated with in vivoDAT activity measured by [11Cocaine]PET and also with striatalDAT activity in postmortem brain samples.

Signals from the ENCODE chromatin interaction analysis withpaired-end tag sequencing (ChIA-PET) also indicate variouslong-range physical interactions of the 30UTR VNTR. By changingthe length and sequence of the mRNA 30UTR, the VNTR may alterthese long-range interactions in synergy with other 50-haplotypesthat together regulate SLC6A3 transcription and splicing. Theselong-range regulatory interactions, mediated through complextranscription factor interactions, are often sensitive to cell-typespecificity and developmental stage.86 These interactions offeranother explanation for some of the discrepancies seen betweenadults and children in genetic association studies and among thein vitro studies.

Our conclusions are limited by methodological issues. Becauseonly two PET studies were available, the power for these studies,which comprised 125 subjects, was lower than the power for theSPECT studies, which comprised 386 subjects. Although this lowpower does not vitiate the statistically significant results, the lackof significance for the affected group should be viewed cautiously.A range of disorders in which dopamine dysregulation has beenimplicated (Parkinson’s schizophrenia, ADHD, alcoholism) wererepresented in our analyses. The variability in data from thesecohorts may conceivably reflect DAT regulatory processes, whichcompensate for dysfunctional dopamine signaling. Cheon et al.’sstudy,34 the most prominent outlier, was the only study conductedin children and used IPT as a SPECT probe. IPT shows an unusuallyhigh sensitivity to age-dependent DAT decline.49 Information onthe age of each 9R or 10R subject would improve interpretation ofthese data. Like all meta-analyses, our analyses of covariates werelimited by the information provided in the papers we reviewed.Notably, we did not have sufficient information about any singledisorder to draw firm conclusions about their potentialmoderating effects on SLC6A3 DAT associations. As is apparentfrom the Tables, our analyses were also limited by the variability ofbrain regions across studies. None of the studies in the meta-analysis adjusted their analyses for genomic background usingancestrally informative SNPs. We could not correct the meta-analysis for ethnicity (a coarse measure of genomic background),as there were too many ‘mixed’ ethnic samples. If the SLC6A3polymorphism we studied is associated with genomic backgroundand if genomic background is associated with DAT availability,these results could be spurious. Caution about ethnic effects issuggested by the work of Shumay et al.,30 which found


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significantly different SLC6A3 genotype distributions betweenCaucasians and African–Americans. In their study, the associationbetween SLC6A3 genotypes and DAT density was significant forCaucasians but not African–Americans.

Moreover, heterogeneity of genomic background or of envir-onmental exposures relevant to DAT binding might haveaccounted for the heterogeneity of findings if they had beenmeasured. We also could not adjust our analyses for smoking,which was not consistently reported. Taken together, theseproblems limited our ability to find significant covariate effects,but they would not have created spurious results.

Our results along with the limitations of our work highlightdirections for future functional studies of genetic variants usingin vivo studies of DAT density. PET imaging has clearly been mosteffective in producing replicable results. Thus, attempting to‘replicate’ these findings with other imaging methods would notbe appropriate. Future studies should genotype ancestrallyinformative SNPs to assure that differences in genomic back-ground do not affect results. Careful documentation of medica-tions, smoking history and alcohol use is also essential. And, giventhe complex regulation of SLC6A3, genotyping tag SNPs in allregulatory regions would be a real advance.

Despite these limitations, our meta-analyses suggest that inhuman adults, the 9R allele of the 30UTR polymorphism of the DATgene regulates dopamine activity in the striatal brain regionsindependent of the presence of ADHD or other disorders. Futurein vivo neuroimaging studies of the DAT should attend to themethodological features we highlighted as contributing to theheterogeneity of findings across studies.

CONFLICT OF INTERESTIn the past year, Dr Faraone received consulting income and/or research supportfrom Akili Interactive Labs, VAYA Pharma and SynapDx and research support from theNational Institutes of Health (NIH). His institution is seeking a patent for the use ofsodium–hydrogen exchange inhibitors in the treatment of ADHD. In previous years,he received consulting fees or was on Advisory Boards or participated in continuingmedical education programs sponsored by: Shire, Alcobra, Otsuka, McNeil, Janssen,Novartis, Pfizer and Eli Lilly. Dr Faraone receives royalties from books published byGuilford Press: Straight Talk about Your Child’s Mental Health and Oxford UniversityPress: Schizophrenia: The Facts. In the last two years, Dr Thomas Spencer has been anAdvisor or on an Advisory Board of the following sources: Alcobra, Ironshore, theDepartment of Defense and the National Institute of Mental Health. In the last twoyears, Dr Thomas Spencer has received research support from of the followingsources: Shire Laboratories Inc, Cephalon, Eli Lilly & Company, Janssen, McNeilPharmaceutical, Novartis Pharmaceuticals and the Department of Defense. Inprevious years, Dr Thomas Spencer has received research support from, has beena speaker for or on a speaker bureau or has been an Advisor or on an Advisory Boardof the following sources: Shire Laboratories, Inc, Eli Lilly & Company, Glaxo-SmithKline, Janssen Pharmaceutical, McNeil Pharmaceutical, Novartis Pharmaceuticals,Cephalon, Pfizer and the National Institute of Mental Health. Dr Spencer receivesresearch support from Royalties and Licensing fees on copyrighted ADHD scalesthrough MGH Corporate Sponsored Research and Licensing. Dr Spencer has a USPatent Application pending (Provisional Number 61/233686), through MGH corporatelicensing, on a method to prevent stimulant abuse. Bertha K Madras, PhD, has thefollowing financial interests: She is patent holder of 19 patents, including 11C- or131I-altropane, other DAT imaging agents and DAT inhibitors, the majority of whichare licensed to Alseres. Alseres licensed Altropane from Harvard University; NavideaBiopharmaceuticals, a radiopharmaceutical developer, is evaluating an option tolicense Altropane from Alseres. In the past year, she has received consulting fees fromPrexa Pharmaceuticals, NIDA, research support from NIDA, has been an advisor toNIDA Council, CDC, and a non-reimbursed advisor to the Hilton Foundation andConvecta. In 2012, she received speaker fees from the following sources: McGillUniversity, Dartmouth University, BOLD Coalition, Student Assistant Services androyalties as editor or author of four books, from Cold Spring Harbor Press,Neuroscience-Net, American Psychological Association. Joseph Biederman, MD iscurrently receiving research support from the following sources: Elminda, Janssen,McNeil, and Shire. In 2010, Dr Joseph Biederman did not receive any outside income.In 2009, Dr Joseph Biederman received a speaker’s fee from the following sources:Fundacion Areces, Medice Pharmaceuticals and the Spanish Child PsychiatryAssociation. In previous years, Dr Joseph Biederman received research support,consultation fees or speaker’s fees for/from the following additional sources: Abbott,

Alza, AstraZeneca, Bristol Myers Squibb, Celltech, Cephalon, Eli Lilly and Co., Esai,Forest, Glaxo, Gliatech, Janssen, McNeil, Merck, NARSAD, NIDA, New River, NICHD,NIMH, Novartis, Noven, Neurosearch, Organon, Otsuka, Pfizer, Pharmacia, ThePrechter Foundation, Shire, The Stanley Foundation, UCB Pharma, Inc. and Wyeth.Yanli Zhang-James, MD, PhD, does not have any conflict of interest.

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Functional effects of dopamine transporter gene genotypes on in vivo dopamine transporter functioning: a meta-analysis