Open Access

DRD2 and DRD4 genes related to cognitive deficits in HIV-infected adults who abuse alcohol

  • Karina Villalba1Email author,
  • Jessy G. Devieux1,
  • Rhonda Rosenberg1 and
  • Jean Lud Cadet2
Behavioral and Brain Functions201511:25

https://doi.org/10.1186/s12993-015-0072-x

Received: 15 May 2015

Accepted: 17 August 2015

Published: 27 August 2015

Abstract

Background

HIV-infected individuals continue to experience neurocognitive deterioration despite virologically successful treatments. The causes of neurocognitive impairment are still unclear. However, several factors have been suggested including the role of genetics. There is evidence suggesting that neurocognitive impairment is heritable and individual differences in cognition are strongly driven by genetic variations. The contribution of genetic variants affecting the metabolism and activity of dopamine may influence these individual differences.

Methods

The present study explored the relationship between two candidate genes (DRD4 and DRD2) and neurocognitive performance in HIV-infected adults. A total of 267 HIV-infected adults were genotyped for polymorphisms, DRD4 48 bp-variable number tandem repeat (VNTR), DRD2 rs6277 and ANKK1 rs1800497. The Short Category (SCT), Color Trail (CTT) and Rey-Osterrieth Complex Figure Tests (ROCT) were used to measure executive function and memory.

Results

Results showed significant associations with the SNP rs6277 and impaired executive function (odds ratio = 3.3, 95 % CI 1.2–2.6; p = 0.004) and cognitive flexibility (odds ratio = 1.6, 95 % CI 2.0–5.7; p = 0.001). The results were further stratified by race and sex and significant results were seen in males (odds ratio = 3.5, 95 % CI 1.5–5.5; p = 0.008) and in African Americans (odds ratio = 3.1, 95 % CI 2.3–3.5; p = 0.01). Also, DRD4 VNTR 7-allele was significantly associated with executive dysfunction.

Conclusion

The study shows that genetically determined differences in the SNP rs6277 DRD2 gene and DRD4 48 bp VNTR may be risk factors for deficits in executive function and cognitive flexibility.

Keywords

Dopamine DRD2 gene DRD4 gene HIV Alcohol abuse

Background

Human immunodeficiency virus (HIV) is a global epidemic that affects approximately 36 million people worldwide [1]. In addition to its deleterious effects on the cell-mediated immune system, HIV can also damage cells in the central nervous system and lead to HIV-associated neurocognitive disorders (HAND) [2]. The manifestations of HAND have significantly changed in response to the introduction of antiretroviral therapy (ART). For example, the incidence of HIV-associated dementia has declined. However, the prevalence of asymptomatic and mild neurocognitive impairment have increased with increased longevity [3]. HAND encompasses a wide range of cognitive impairment that includes deficient memory and attention, decreased executive function, and behavioral changes, such as apathy or lethargy [4].

Cognitive control processes regulating thought and action are multifaceted functions influenced by heritable genetic factors and environmental influences [5]. Individuals increasingly select and modify their experiences partly based on their genetic predispositions [68]. Friedman et al. indicated that individual differences in executive function including inhibiting dominant responses, updating working memory representations, and shifting between task sets, are almost 99 % heritable [7]. Cognitive neuroscience and pharmacology associate dopamine and serotonin as neuromodulators of cognition [5]. Furthermore, studies found associations between dopamine polymorphisms with sustained attention, memory, and executive function phenotypes in both clinical and non-clinical populations [912].

Dopamine neurons are located in the ventral midbrain and are involved in several cognitive functions that influence performance, motor control, reward, and cognition [1315]. Dopamine modulates executive function by co-jointly adjusting neurochemical transmission in the prefrontal cortex (PFC) [5, 16]. The PFC plays a central role in the top-down control of many higher-order executive tasks. It is involved in learning, memory, categorization, inhibition control, and cognitive flexibility [17, 18]. Activation of D1, D2, D3, and D4 receptors modulate the excitability of receptor cells and PFC neural network activity. [19] The SNP rs1800497 (also known as TaqIA) of the D2 receptor gene DRD2 is one of the most extensively investigated genes related to neuropsychiatric disorders [19, 20]. This DRD2-associated polymorphism is located within the coding region of a neighboring gene, ANKK1 and is associated with a reduced number of dopamine binding sites in the brain [21]. The SNP rs1800497 is located more than ten kilobase-pairs downstream from the coding region of the DRD2 gene in chromosome 11q23 and is, therefore, unlikely to alter DRD2 directly [22]. Proximity of the two genes may reflect functional relationship and may be associated with dopaminergic phenotypes by being in linkage disequilibrium [6, 23]. Polymorphism DRD2 SNP rs6277 has been reported to affect D2 receptor density in the striatum [24]. Several studies have shown that SNP rs6277 is associated with prefrontal cortex-mediated behaviors including attentional control, planning and verbal reasoning [20]. A study on cognitive flexibility showed that SNP rs6277 was a strong predictor of learning from negative reward prediction errors by avoiding those responses linked to negative outcomes [6, 25].

The dopamine D4 receptor is widely expressed in the central nervous system, particularly in the frontal cortex, hippocampus, amygdala and hypothalamus [15, 26]. The dopamine D4 receptor DRD4 gene is located on chromosome 11p15.5 and has a highly variable number of tandem repeats in the coding sequence [27]. The polymorphism is a 48 bp VNTR sequence in exon 3, encoding the third intracellular loop of D4 receptor [28]. The most common polymorphic variants of the receptor are D4.7, and D4.4 [29, 30]. Individuals with D4.7 repeat show both reduced binding affinities and receptor densities for dopamine neurotransmission [31]. The D4.7 repeat is correlated with impulsivity and lower levels of response inhibition [32]. Several studies have analyzed the association between the D4.7-repeat allele in DRD4 gene and attention-deficit hyperactivity disorder (ADHD) [10, 26].

Memory deficits and executive dysfunction are highly prevalent among HIV-infected adults [33]. These conditions can affect their quality of life, antiretroviral adherence, and HIV risk behaviors [34]. The causes of asymptomatic neurocognitive impairment are still unclear. However, several factors have been suggested including the role of genetics [33]. Cognitive functions are influenced by dopamine. Thus, genetic differences in the dopamine system genes may exacerbate the development of neurocognitive impairment in an individual [5, 35]. The present study explored potential associations with DRD2 rs6277, ANNK1 rs1800497 and DRD4 48 bp VNTR polymorphism and cognitive functions in HIV-infected adults.

Methods

Participants

This study utilized a cross-sectional design, using baseline data gathered between 2009 and 2012 as part of a longitudinal randomized controlled trial for reducing risk behaviors among HIV-infected alcohol abusers. The main study recruited a total of 379 individuals. However, the current study used 267 biologically-unrelated individuals, because 112 participants declined to provide blood samples for genetic testing. Recruitment was made in a multicultural, low income, urban areas of Miami-Dade County, Florida. Participants were between 18 and 60 years of age, HIV-positive and willing to present documentation to confirm serostatus, consumed alcohol within the last 3 months, with a history of alcohol abuse or dependence within the past 2 years, and, at the time of recruitment, were not showing overt signs of major psychiatric disorders. Additionally, availability to provide a blood specimen was required. All participants provided signed informed consent as approved by the Institutional Review Board (IRB) at Florida International University.

Participants were evaluated for alcohol use by the Timeline Followback (TLFB) and the Alcohol Use Disorders Identification Test (AUDIT) test. All participants were assessed using the same battery of neurocognitive tests and in the same order. Nonverbal memory was measured with the Rey-Osterrieth Complex Figure Tests (RCFT). Cognitive flexibility was measured with the Color Trails (CTT B), and executive function was measured by the Short Category Test (SCT).

Genotyping

DNA was extracted from whole blood by manual extraction using the QIAamp DNA Mini Kit (Valencia, CA). SNPs rs6277 and rs1800497 were genotyped using the TaqMan® SNP Genotyping Assays (Foster City, CA, USA). Allelic discrimination analysis was performed on the Bio-Rad CFX96™ real-time PCR machine (Hercules, CA, USA).

For VNTR D4, Bio-Rad CFX Manager software (version 3.0) was used for data acquisition and genotype assignment. The primer sequences used for the D4 amplification were obtained from a previous study [36]. The sequence was as follows: 5′ CTGCTGCTCTACTGGGC 3′ sense and 5′ GTGCACCACGAAGGAAGG 3′ antisense The 25 μl reaction mixture contained: 1× PCR amplification buffer (Qiagen, Valencia, CA, USA), 300 μM dNTPs, 0.5 μM of each primer, 0.5 U Taq DNA polymerase (Qiagen) and 50 ng of genomic DNA. The temperature cycle consisted of an initial denaturation at 94 °C for 5 min, followed by 30 cycles of annealing for 40 s at 54 °C, extension for 40 s at 72 °C, denaturing for 40 s at 94 °C, and then the final extension for 6 min at 72 °C. The amplification products were separated on a 3 % agarose gel electrophoresis according to the number of repeats. The size of the amplified fragments was from 500 to 750 bp (2–7 copies of the 48pb repeat). These genetic markers were chosen based on prior evidence of the SNPs conferring risk to neurocognitive deficits or a theoretical association with executive function.

Neurocognitive measures

The neurocognitive test battery included standardized measures of multiple domains of cognitive function selected for their sensitivity to HIV-associated neurocognitive impairment. The neurocognitive tests were assessed in the following domains:
  1. 1.

    Visual Memory-Rey ROCT evaluated visuospatial construction and nonverbal memory [37]. It consists of a complex geometric figure that is copied and then redrawn from memory [38]. Copy and accuracy of correctly copied or recalled elements were measured based on a score from 0 to 36. The figure was divided into 18 components. Each piece was evaluated with respect to its drawing accuracy with higher scores indicating better accuracy.

     
  2. 2.

    Cognitive flexibility CTT-B evaluated cognitive flexibility. Participants were presented with numbered colored circles that required starting with a pink colored number one circle and alternating between pink and yellow colored circles as fast as possible [39]. The test measured time in seconds to complete, with higher scores indicating poor performance. High test–retest reliability scores ranging from 0.85 to 1.00 [39].

     
  3. 3.

    Executive function SCT evaluated executive function. It consisted of five booklets with 20 cards per subtest and required the individual to formulate an organizing concept for each subtest. The number of errors on each booklet was added and the total number of errors determined impairment with lower scores representing better executive function [40]. Test–retest coefficients range from 0.60 to 0.96 depending upon the severity of impairment in the sample.

     

Neurocognitive tests were completed at baseline. Trained personnel administered the tests in the same order and according to standardized procedures.

Alcohol use

The TLFB method assessed alcohol use and other drugs of abuse. This method obtains estimates of substance use by using a calendar format and providing retrospective estimates of the participant’s substance use over the last 3 months [41]. The AUDIT is a screening tool that is sensitive to early detection of high-risk drinking behaviors [42].

Analysis

Since 112 (29 %) individuals did not to participate in this study, thus, data were evaluated for potential selection bias. Statistical analyses were performed using Stata v.11 (StataCorp, College Station, TX, USA). Logistic and linear regression methods were used to calculate crude and multifactorial (self- reported ethnicity/race, alcohol use severity, viral load, CD4 count, cannabis and cocaine use) adjusted odds ratios (OR), including a 95 % confidence intervals (CIs) and test for interaction. All statistical tests were two-tailed, and the threshold for statistical significance was set at P < 0.05. Ethnic and gender -specific associations were calculated through stratified analyses. Genotyping counts were tested for Hardy–Weinberg equilibrium using an exact test. For the DRD4 polymorphism, the Pearson’s X2 and Student’s t test were used to compare group differences. For DRD4 48 bp VNTR, alleles were grouped in short (S; <7 repeat) and long (L; ≥7 repeat) as described in previous studies [43, 44]. For statistical analysis, participants were placed in one of two genotype groups 7-allele present (homozygous for the short allele) or 7-allele absent (heterozygous or homozygous for the long allele).

To standardize cognitive measures for this study, standardized T-scores were developed by using multiple linear regression methods analyzing the influence of age, sex, education, and ethnicity on each cognitive test score. Each of the cognitive domains was included as dependent variables. The continuous predictor was age, and the categorical predictors were sex, education and race/ethnicity. For each regression, all the predictors were included in the model, retaining only the variables that significantly contributed to the prediction of cognitive test score. The β weights of each of these predictors in the final model, as well as the standard error of each regression model, were used to calculate predicted scores on each test. These predictive scores were subtracted from each individual actual composite score to calculate residual scores. Residual scores were then converted to T-scores (mean 50; SD = 10). T-scores were used to determine cognitive impairment according to the Frascati criteria [45], as shown in Table 1. For the cognitive domains, scores were developed as follows: executive function (SCT), visual memory (RCFT) and cognitive flexibility (CTT-B).
Table 1

Categories of HIV-associated neurocognitive disorder according to Frascati criteria

 

Neurocognitive statusa

Functional statusb

Asymptomatic neurocognitive impairment

1 SD below the mean in 2 cognitive domains

No impairment in activities of daily living

Mild neurocognitive impairment or disorder

1 SD below the mean in 2 cognitive domains

Impairment in activities of daily living

HIV-associated dementia

2 SD below the mean in 2 cognitive domains

Notable impairment in activities of daily living

SD standard deviation

aNeurocognitive testing should include an assessment of at least five domains, including attention–information processing, language, abstraction-executive, complex perceptual motor skills, memory (including learning and recall), simple motor skills, or sensory, perceptual skills

bNo agreed measures exist for HIV-associated neurocognitive disorder criteria

Results

Of the 379 participants recruited for the main study, 70 % (N = 267) provided blood samples. The participants were 94 (34 %) females and 173 (65 %) males. The average age in the sample was: (males: mean 45.1 SD = 7.1; females: mean 45.3 SD = 65.9). The majority of participants self-identified as African-American 203 (76 %), followed by Hispanic 43 (16 %) and Caucasian 21 (8 %). A total of 190 (69 %) had completed high school. At baseline, participants provided recent (within one month from intake) lab tests of CD4 count and viral load. Lab reports showed viral load as undetectable for 128 (48 %) of the sample and an average CD4 count of 440 cells/mm3 (SD = 287). The overwhelming majority of participants, 219 (81 %) reported current use of antiretroviral medications, including Combivir, Emtriva, Epivir, Epzicom, Retrovir, Trizivir, Truvada, Videxec, Viread, Zerit, Ziagen, Crixivan, Invirase, Kaletra, Lexia, Novir, Prezista, Reyataz, Viracept, Intelence, Rescriptor, Sustiva and Viramune. Selection bias was not observed when participants’ characteristics in the main study were compared to those in the present study. Results suggest that participants were similar in age, education, sex, ethnicity and HIV clinical characteristics as shown in Table 2.
Table 2

Demographic and clinical characteristics of main study and current study participants

 

Main study

Current study

P values

n = 112

N = 267

Age, mean (SD)

44.1 (7.7)

45.1 (7.1)

0.66

Sex, no (%)

  

0.72

 Male

67 (60)

173 (65)

 

 Female

45 (40)

94 (34)

 

Education no (%)

  

0.24

 8th grade or less

13 (12)

19 (7)

 

 High school diploma

73 (65)

190 (69)

 

 Some college

26 (23)

57 (24)

 

Race/ethnicity no (%)

  

0.26

 Caucasian

17 (15)

21 (8)

 

 African–American

80 (72)

203 (76)

 

 Hispanic

15 (13)

43 (16)

 

Alcohol use, mean (SD)

 Number of standard drinks (past 90 days)

100 (50.1)

190 (100.1)

0.10

 Lifetime

22 (10.5)

23.8 (10.9)

0.24

 AUDIT score

14 (7.5)

16 (8.0)

0.09

Other drugs, mean (SD)

 Number of times cocaine use (past 90 days)

23.5 (16.8)

33.5 (19.8)

0.25

 Number of times marijuana use (past 90 days)

19.3 (12.5)

25.6 (20.9)

0.63

HIV characteristics, mean (SD)

 CD4 count

412.9 (318.4)

441.4 (286.9)

0.73

Viral load no (%)

  

0.16

 Undetectable

45 (40)

128 (48)

 

 50–10,000

39 (35)

80 (30)

 

 10,001–30,000

8 (7)

29 (11)

 

 30,000 or more

20 (18)

29 (11)

 

 Taking ART

76 (68)

216 (81)

0.84

Cognitive measures, mean (SD)

 Executive skills T-scores

50.1 (9.0)

45.2 (10.9)

0.93

 Memory skills (learning) T-scores

45.9 (10.1)

48.2 (9.1)

0.18

 Memory skills (recall) T-scores

48.1 (9.8)

40.0 (10.5)

0.11

 Cognitive flexibility T-scores

40.4 (10.4)

45.7 (10.8)

0.09

 Visual memory T-scores

47.9 (11.9)

43.1 (13.8)

0.09

Alcohol and other drugs of abuse

The TLFB determined alcohol and other drugs use. Questions included a total number of standard drinks consumed in the last 90 days, the total number of heavy drinking days (<5 standard drinks) in the last 90 days, and lifetime alcohol use. A standard drink is defined as 12 oz of beer, 5 oz of wine, 1.5 oz of liquor all of which contain approximately 13.6 g of absolute alcohol [46]. Results showed a mean AUDIT score of 16, which is categorized as a harmful drinking level. In addition, a total of 101 (38 %) of the participants scored >20 which is indicative of possible alcohol dependence. Lifetime alcohol use averaged 23.8 years for this sample. Additional detailed information on other substance use was also assessed. The main drugs used, besides alcohol, were cocaine and marijuana, with an average use in the last 90 days of 33 and 25 times, respectively.

The Frascati criteria were used to measure asymptomatic neurocognitive impairment, (1 standard deviation below the mean in at least 2 cognitive domains). Results for the neurocognitive measures were below average (T-score: mean 50; SD = 10). The cognitive domains with the lowest average scores were cognitive flexibility (mean 45.7; SD = 10.8) and executive function, (mean 45.2; SD = 10.9).

DRD2 polymorphism and cognitive flexibility

Results of the analyses are presented in Tables 3 and 4. All SNPs were in Hardy–Weinberg equilibrium. The SNP rs6277 of DRD2 gene showed an overall association with impaired cognitive flexibility (odds ratio = 1.6, 95 % CI 1.2–2.6; p = 0.004) and with executive function (odds ratio = 3.3, 95 % CI 2.0–5.7; p = 0.001). The association between SNP rs1800497 and cognitive flexibility was non-significant. Results were stratified by sex and race for cognitive flexibility and executive function. Testing showed an increased risk for executive function impairment in African Americans (odds ratio = 3.1, 95 % CI 2.3–3.5; p = 0.001), and an even greater risk for males (odds ratio = 3.5, 95 % CI 1.5–5.5; p = 0.008). There was, a significant gender interaction for cognitive flexibility (pinteraction = 0.013 for sex), but not for executive function (pinteraction = 0.35 for sex). At total of 40 (16 %) of participants carried SNPs rs6277 and rs1800497. Interaction with alcohol was not significant (p = 0.32) and no significant gene–gene interactions for DRD4 and DRD2 were found (results not shown).
Table 3

DRD2 and ANKK1 associations with cognitive domains

Chr.

Position

Gene

Variant

Minor Allele

A/A

A/B

B/B

MAF

Domain

OR allele a (95 % CI)

P value

11

11:113412737

DRD2

rs6277

T

80

118

60

0.23

Cognitive flexibility

1.6 (1.2–2.6)

0.004

11

11:113412737

DRD2

rs6277

T

80

118

60

0.23

Executive function

3.3 (2.0–5.7)

0.001

11

11:113400106

ANKK1

rs1800497

T

102

117

40

0.16

Cognitive flexibility

1.1 (0.7–1.8)

0.71

ORs adjusted for self-reported ethnicity/race, alcohol use severity, viral load, CD4 count, cannabis and cocaine use

MAF minor allele frequency

aOR per allele (ORallele) for the additive model

Table 4

DRD2 associations with cognitive flexibility and executive function in gender, race/ethnicity groups and alcohol use (ORs and 95 % CIs)

 

Females

Males

Hispanics

African American

Alcohol use

DRD2 rs6277 (executive function)

1.3

3.5 (1.5–5.5)

p = 0.008

2.6

3.1 (2.3–3.5)

p = 0.01

2.6

pinteraction = 0.35

pinteraction = 0.05

 

DRD2 rs6277 (cognitive flexibility)

0.9

1.8 (1.2–2.9)

p = 0.01

1.9

1.5

1.6 (1.4–2.4)

p = 0.03

pinteraction = 0.013

pinteraction = 0.72

pinteraction = 0.32

DRD4 48 bp VNTR polymorphism and executive function

The allele frequencies for DRD4 48 bp VNTR were similar to those observed in African populations in other studies [47, 48]. In this study, the most frequently detected alleles of the 48 bp VNTR of the D4 receptor were for DRD4-allele 4 (353/484, 72.9 %), and DRD4-allele 7 (66/484, 13.7 %). To a lesser degree DRD4-allele 2 (38/484, 7.8 %), DRD4-allele 3 (7/484, 1.5 %), DRD4-allele 5 (11/484, 2.3 %), and DRD4-allele 6 (9/484, 1.8 %) were also present. The nine and ten repeat alleles were not detected in this study population. The genotype distribution of the 242 participants is shown in Table 5. One hundred and eighty-six participants were grouped into the 7-absent allele group (<7 repeats), and 56 were grouped into the 7-present allele group (≥7 repeats). When comparing allele groups, the 7-allele present and 7-allele absent groups did not differ in sex, race/ethnicity, alcohol use or CD4 count. The 7-absent allele group mean score was associated with a higher rate of error in the Short Category Test measuring executive function than the 7-present group (mean 0.17, 95 % CI 1.17–1.29; p = 0.008). In addition, a multiple linear regression with executive function as the dependent variable and age, sex, alcohol use, genotype group and race/ethnicity as the independent variables showed that DRD4 7-absent allele and age had a significant effect on executive function. Whereas, sex, alcohol use and race/ethnicity did not show a significant effect (data not shown).
Table 5

D4 Receptor 48 bp repeat genotype group classification

D4 receptor 48 bp repeat genotype

N

%

Genotype group

2/2

8

3.3

1

2/3

2

0.8

1

2/4

20

8.4

1

3/4

2

0.8

1

3/6

3

1.0

1

4/4

140

57.8

1

4/5

5

2.0

1

4/6

6

2.6

1

4/7

40

16.7

2

5/7

6

2.4

2

7/7

10

4.2

2

Group 1 7-absent group: <7-fold repeat of the 48 bp repeat of D4 receptor, Group 2 7-present group: ≥ 7-fold repeat of the 48 bp repeat of D4 receptor

Discussion

This study provides evidence that suggests genetically determined differences in DRD2 gene polymorphism (rs6277) and DRD4 gene (48 VNTR) are associated with impaired executive function and cognitive flexibility. However, no associations were found with SNP rs1800497. It is well-recognized that genes are likely to affect more than one cognitive function, and variations in cognitive functions are likely to be influenced by more than one gene [49]. Similarly, this study showed that the DRD2, SNP rs6277 is associated with impairment in two cognitive domains: executive function and cognitive flexibility. Conversely, executive function is influenced by DRD2 and DRD4 genetic polymorphisms. Although recent publications stress the need to consider gene–gene interactions, our results showed no such interactions [49].

We showed that SNP rs6277 C/C-carriers were less efficient in task switching as it took them more time to complete the Color Trails Test than T/T and C/T carriers. Similarly, the total number of errors in the Short Category Test was higher for C/C-carriers representing poorer executive function. Our results are in line with others that reported an association between C-homozygotes and poorer executive functioning and memory [50, 51], and lower cognitive ability in C/C-carriers measured in five different cognitive domains [52]. However, other studies showed that T-homozygotes are associated with dysfunctional impulsivity [53] and that carriers of at least one T-allele showed a significantly poorer performance in the identification of T1 in the Attentional Blink phenomenon [54]. These differences may be related to SNP rs6277 in the DRD2 gene that changes the receptor’s affinity and regulates the DRD2 availability, but its effect differs depending on the brain region under investigation [55, 56].

The clinical implications for the role for the DRD2 SNP rs6277 has been associated to learning [57], reward sensitivity [58], substance abuse [5961], nicotine modulation of working memory [62], pharmacological interventions [63, 64] as well as in schizophrenia [24, 65, 66]. Altogether, this evidence suggest a reasonably and significant role for the SNP rs6277 in psychiatric disorders. Thus, DRD2 SNP rs6277 may also play a role in executive function and cognitive flexibility in patients with HAND.

The DRD4 48 bp VNTR polymorphism has been previously linked to Attention-deficit/hyperactivity disorder (ADHD) phenotypes [10, 6770]. In particular, the specific allele (7-repeat) of the 48pb VNTR polymorphism in the coding region of DRD4 may be a risk factor in the development of ADHD [10]. ADHD is known to alter prefrontal cognitive functions that are often related to dopaminergic dysfunction [71]. Thus, following previous studies on ADHD, this study sought to assess whether cognitive functions (cognitive flexibility and executive function) were associated with the DRD4 48 bp VNTR polymorphism in HIV-infected adults. Results showed that the 7-absent allele group was significantly associated with executive dysfunction. The effect of the DRD4 VNTR on executive function reported herein is comparable with a familial study that reported a significant association between the 7-absent allele group and lower scores in working memory and executive function [67]. Similarly, several studies on DRD4 VNTR showed that DRD4 7-absent allele group was associated with worse cognitive functioning than the DRD4 7-present allele group [69, 72]. However, the results of this study are in conflict with the findings of other similar studies. One study found poorer inhibitory performance in the 7-present allele group versus the 7-absent allele group [68]. Another, found that 7-present allele group performed better that the 7-absent allele group on verbal memory, but for visuo-constructive ability and set shifting the 7-absent allele group performed better than the 7-present allele group [73]. This poses important questions with respect to the relationship between genetic risk and neurocognitive performance. There are several potential explanations for these conflicting results. Including higher and lower than average levels of synaptic dopamine may lead to neurocognitive impairment [74]. This is a particularly interesting since the 7-present allele is associated with reduced receptor functioning [73]. The combinations of certain risk genotypes rather than one single risk genotype may lead to the presence of cognitive dysfunction [75]. These relationships have not been fully tested and require further research, especially since cognitive endophenotypes are important for HIV-associated neurocognitive impairments.

It is important to note limitations that restricted conclusions in certain areas. First, due to the exploratory nature of the study, multiple statistical comparisons were made. Because of the low power of the study to detect smaller effect sizes, some important associations may not have emerged as statistically significant. These results should be viewed with caution and should be replicated before a definitive conclusion can be drawn. Second, due to the vast number of HIV antiretroviral drugs used by study participants, we did not adjust for HIV medication type. Since certain HIV antiretroviral drugs may also affect cognition, this may potentially confound the results. Third, two main approaches are used to approximate individual ancestry in association studies, self-reported race and ancestry informative markers. We did not use ancestry informative markers due to DNA requirements. Instead, we used self-reported ancestry that may capture common environmental influences as well as ancestral background. However, self-identified racial categories may not always consistently predict ancestral population clusters. Finally, since this was a cross-sectional study stemming from a behavioral intervention trial of HIV-infected subjects, we did not have a healthy control group. Although we adjusted for alcohol and drug use, the results may not adequately explain whether impairments in cognitive flexibility and executive function were correlated with the presence of SNP rs6277 and VNTR 7-absent allele or mediated by HIV and alcohol/drug use. Nonetheless, these results may serve as an initial point for future research in cognitive phenotypes for HAND in adults. Molecular genetics, as applied in the present study, offers further analytic insight beyond behavioral assessment and neuroimaging, and may present a reasonable instrument for the differentiation of executive control processes.

This study may pave the way for future research integrating the examination of genetic factors in behavioral prevention interventions with HIV-infected populations. Studies that incorporate genetic factors in combination with neurocognitive testing would benefit from also including the effects of genetic factors on cognitive functioning in healthy individuals since gene-by-disorder interactions might be expected. Furthermore, it would be beneficial to investigate haplotypes rather than genotypes in studies on cognitive performance in HAND. Since most of the polymorphisms have a small relative effect on cognition, to detect an effect, a larger sample is optimal. In addition to the genes analyzed in this study, other genes related to cognitive function should be included.

In summary, the present study provides evidence that genetically determined differences in genes DRD2 SNP and DRD4 48 bp VNTR may contribute to deficits in executive function and cognitive flexibility for patients with HAND. Additionally, rs6277 showed an association with impairment in two cognitive domains (executive function and cognitive flexibility) while executive function seemed to be influenced by DRD2 and DRD4 genetic polymorphisms. Finally, DRD4 48 bp VNTR (7-allele absent group) was associated with executive dysfunction, which is in line with the recent suggestion that either higher or lower levels of synaptic dopamine may lead to neurocognitive impairment.

Declarations

Authors’ contributions

KV and JGD designed the study protocol. KV and RR drafted the manuscript. KV performed the statistical analysis. All authors contributed to the interpretation of the data. JGD and JLC provided critical revision of the draft for important intellectual content. All authors read and approved the final manuscript.

Acknowledgements

We are very grateful to Mehmet T. Dorak MD, Ph.D. from Liverpool Hope University, for his expert advice in genetics and the use of his genetics laboratory for our experiments during his time at Florida International University. He received no financial compensation for his contribution.

Compliance with ethical guidelines

Competing interests This work was supported by National Institute on Alcohol Abuse and Alcoholism (Grant R01AA017405). Karina Villalba was supported by National Institute of General Medical Sciences of the National Institutes of Health (Grant R25 GM061347). No potential conflicts of interest by any of the authors.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Department of Health Promotion and Disease Prevention, Robert Stempel College of Public Health and Social Work, Florida International University
(2)
NIDA Intramural Program, Molecular Neuropsychiatry Research Branch

References

  1. CDC—Estimates of New HIV Infections in the United States _ Statistics and Surveillance—Statistics Center—HIV_AIDS. 2011Google Scholar
  2. Clifford DB, Ances BM (2013) HIV-associated neurocognitive disorder. Lancet Infect Dis 13(11):976–986. doi:https://doi.org/10.1016/s1473-3099(13)70269-x PubMed CentralView ArticlePubMedGoogle Scholar
  3. Bottiggi KA, Chang JJ, Schmitt FA, Avison MJ, Mootoor Y, Nath (2007) The HIV Dementia Scale: predictive power in mild dementia and HAART. J Neurol Sci 260(1–2):11–15. doi:https://doi.org/10.1016/j.jns.2006.03.023 View ArticlePubMedGoogle Scholar
  4. Gray F, Chretien F, Lorin de la Grandmaison G, Force G, Keohane C (2001) Neuropathology and neurodegeneration in human immunodeficiency virus infection. Pathogenesis of HIV-induced lesions of the brain, correlations with HIV-associated disorders and modifications according to treatments. Clin Neuropathol 20(4):146–155PubMedGoogle Scholar
  5. Barnes J, Nandam LS, O’Connell RG, Bellgrove MA (2011) The molecular genetics of executive function: role of monoamine system genes. Biol Psychiatry 69(12):127–143. doi:https://doi.org/10.1016/j.biopsych.2010.12.040 View ArticleGoogle Scholar
  6. Frank MJ, Fossella JA (2011) Neurogenetics and pharmacology of learning, motivation, and cognition. Neuropsychopharmacol Off Publ Am Coll Neuropsychopharmacol 36(1):133–152. doi:https://doi.org/10.1038/npp.2010.96 View ArticleGoogle Scholar
  7. Friedman NP, Young SE, Defries JC, Corley RP, Hewitt JK (2008) Individual differences in executive functions are almost entirely genetic in origin. J Exp Psychol Gen 137(2):201–225. doi:https://doi.org/10.1037/0096-3445.137.2.201 PubMed CentralView ArticlePubMedGoogle Scholar
  8. Haworth CM, Luciano M, Martin NG, de Geus EJ, van Beijsterveldt CE, Bartels M, Posthuma D, Boomsma DI, Davis OS, Kovas Y, Corley RP, Defries JC, Hewitt JK, Olson RK, McGue M, Thompson LA, Hart SA, Petrill SA, Lubinski D, Plomin R (2010) The heritability of general cognitive ability increases linearly from childhood to young adulthood. Mol Psychiatry 15(11):1112–1120. doi:https://doi.org/10.1038/mp.2009.55 PubMed CentralView ArticlePubMedGoogle Scholar
  9. Bosia MA, Pirovano A, Ermoli E, Marino E, Bramanti P, Smeraldi E, Cavallaro R (2010) HTTLPR functional polymorphism in schizophrenia: executive functions vs. sustained attention dissociation. Prog Neuropsychopharmacol Biol Psychiatry 34(1):81–85. doi:https://doi.org/10.1016/j.pnpbp.2009.10.001 View ArticlePubMedGoogle Scholar
  10. Kramer UM, Schule R, Cunillera T, Schols L, Marco-Pallares J, Cucurell D, Camara E, Rodriguez-Fornells A, Munte TF (2009) ADHD candidate gene (DRD4 exon III) affects inhibitory control in a healthy sample. BMC Neurosci 10:150–161. doi:https://doi.org/10.1186/1471-2202-10-150 PubMed CentralView ArticlePubMedGoogle Scholar
  11. Reuter M, Kuepper Y, Hennig J (2007) Association between a polymorphism in the promoter region of the TPH2 gene and the personality trait of harm avoidance. Int J Neuropsychopharmacol 10(3):401–404. doi:https://doi.org/10.1017/S1461145706007073 View ArticlePubMedGoogle Scholar
  12. Sarosi AG, Balogh G, Domotor E, Szekely A, Hejjas K, Sasvari-Szekely M, Faludi G (2008) Association of the STin2 polymorphism of the serotonin transporter gene with a neurocognitive endophenotype in major depressive disorder. Prog Neuropsychopharmacol Biol Psychiatry 32(7):1667–1672. doi:https://doi.org/10.1016/j.pnpbp.2008.06.014 View ArticlePubMedGoogle Scholar
  13. Oak James OJ, Van Tol Hubert HM (2000) The dopamine D receptor: one decade of research. Eur J Pharmacol 405:25View ArticleGoogle Scholar
  14. Chinta SJ, Andersen JK (2005) Dopaminergic neurons. Int J Biochem Cell Biol 37(5):942–946. doi:https://doi.org/10.1016/j.biocel.2004.09.009 View ArticlePubMedGoogle Scholar
  15. Cadet JL, McCoy MT, Beauvais G, Cai NS (2010) Dopamine D1 receptors, regulation of gene expression in the brain, and neurodegeneration. CNS Neurol Disord: Drug Targets 9(5):526–538View ArticleGoogle Scholar
  16. Floresco SB (2013) Prefrontal dopamine and behavioral flexibility: shifting from an “inverted-U” toward a family of functions. Front Neurosci 7:62. doi:https://doi.org/10.3389/fnins.2013.00062 PubMed CentralView ArticlePubMedGoogle Scholar
  17. Robbins TW, Arnsten AF (2009) The neuropsychopharmacology of fronto-executive function: monoaminergic modulation. Annu Rev Neurosci 32:267–287. doi:https://doi.org/10.1146/annurev.neuro.051508.135535 PubMed CentralView ArticlePubMedGoogle Scholar
  18. Kehagia AA, Murray GK, Robbins TW (2010) Learning and cognitive flexibility: frontostriatal function and monoaminergic modulation. Curr Opin Neurobiol 20(2):199–204. doi:https://doi.org/10.1016/j.conb.2010.01.007 View ArticlePubMedGoogle Scholar
  19. Hung CWBCE, Van TH (2000) Polymorphisms in dopamine receptors: what do they tell us? Eur J Pharmacol 410:183View ArticleGoogle Scholar
  20. Mitaki SI, Maniwa K, Yamasaki M, Nagai A, Nabika T, Yamaguchi S (2013) Impact of five SNPs in dopamine-related genes on executive function. Acta Neurol Scand 127(1):70–76. doi:https://doi.org/10.1111/j.1600-0404.2012.01673.x View ArticlePubMedGoogle Scholar
  21. Smith L, Watson M, Gates S, Ball D, Foxcroft D (2008) Meta-analysis of the association of the Taq1A polymorphism with the risk of alcohol dependency: a HuGE gene-disease association review. Am J Epidemiol 167(2):125–138. doi:https://doi.org/10.1093/aje/kwm281 View ArticlePubMedGoogle Scholar
  22. Neville MJ, Johnstone EC, Walton RT (2004) Identification and characterization of ANKK1: a novel kinase gene closely linked to DRD2 on chromosome band 11q23.1. Hum Mutat 23(6):540–545. doi:https://doi.org/10.1002/humu.20039 View ArticlePubMedGoogle Scholar
  23. He M, Yan H, Duan ZX, Qu W, Gong HY, Fan ZL, Kang JY, Li BC, Wang JM (2013) Genetic distribution and association analysis of DRD2 gene polymorphisms with major depressive disorder in the Chinese Han population. Int J Clin Exp Pathol 15(6):1142–1149Google Scholar
  24. Stelzel C, Basten U, Montag C, Reuter M, Fiebach CJ (2010) Frontostriatal involvement in task switching depends on genetic differences in d2 receptor density. J Neurosci 30(42):14205–14212. doi:https://doi.org/10.1523/JNEUROSCI.1062-10.2010 View ArticlePubMedGoogle Scholar
  25. Berman SMNE (1995) Reduced visuospatial performance in children with the D2 dopamine receptor A1 allele. Behav Genet 25(1):45–58View ArticlePubMedGoogle Scholar
  26. Bellgrove M, Lowe N, Kirley A, Robertson IH, Gill M (2005) DRD4 gene variants and sustained attention in attention deficit hyperactivity disorder (ADHD): effects of associated alleles at the VNTR and -521 SNP. Am J Med Genet Part B Neuropsychiatric Genet Off Publ Int Soc of Psychiatric Genet 136B(1):81–86. doi:https://doi.org/10.1002/ajmg.b.30193 View ArticleGoogle Scholar
  27. Bellgrove M, Ziarih G, Robertson M, Ian H (2006) The cognitive genetics of attention deficit hyperactivity disorder (ADHD): sustained attention as a candidate phenotype. Cortex 42(6):838–845. doi:https://doi.org/10.1016/s0010-9452(08)70426-x View ArticlePubMedGoogle Scholar
  28. Ding YC, Chi HC, Grady DL, Morishima A, Kidd JR, Kidd KK (2002) Evidence of positive selection acting at the human dopamine receptor D4 gene locus. Proc Natl Acad Sci USA 99(1):309–314. doi:https://doi.org/10.1073/pnas.012464099 PubMed CentralView ArticlePubMedGoogle Scholar
  29. Van Tol HH, Caren MW, Guan HC, Ohara K, Bunzow JR, Civelli O, Kennedy J, Seeman P, Niznik HB, Jovanovic V (1992) Multiple dopamine D4 receptor variants in the human population. Nature 358(6382):149–152View ArticlePubMedGoogle Scholar
  30. Lichter JB, Kennedy JL, Van Tol HH, Kidd KK, Livak KJ (1993) A hypervariable segment in the human dopamine receptor D4 (DRD4) gene. Hum Mol Genet 2(6):767–773View ArticlePubMedGoogle Scholar
  31. Schoots O, Van Tol HH (2003) The human dopamine D4 receptor repeat sequences modulate expression. Pharmacogenomics J 3(6):343–348. doi:https://doi.org/10.1038/sj.tpj.6500208 View ArticlePubMedGoogle Scholar
  32. Eisenberg D, Modi M, Beauchemin J, Dang D, Lisman SA, Lum JK, Wilson DS (2007) Examining impulsivity as an endophenotype using a behavioral approach: a DRD2 TaqI A and DRD4 48-bp VNTR association study. Behav Brain Funct BBF 3:2. doi:https://doi.org/10.1186/1744-9081-3-2 View ArticlePubMedGoogle Scholar
  33. Foley JM, Wright MJ, Gooding AL, Ettenhofer M, Kim M, Choi M, Castellon SA, Sadek J, Heaton RK, van Gorp WG, Marcotte TD, Hinkin CH (2011) Operationalization of the updated diagnostic algorithm for classifying HIV-related cognitive impairment and dementia. Int Psychogeriatr 23(5):835–843. doi:https://doi.org/10.1017/S1041610210002085 PubMed CentralView ArticlePubMedGoogle Scholar
  34. Simioni S, Annoni JM, Rimbault A, Bourquin I, Schiffer V, Calmy A, Chave JP, Giacobini E, Hirschel B, Du Pasquier RA (2010) Cognitive dysfunction in HIV patients despite long-standing suppression of viremia. AIDS 24(9):1243–1250PubMedGoogle Scholar
  35. Enge SF, Lesch KP, Reif A, Strobel A (2011) Serotonergic modulation in executive functioning: linking genetic variations to working memory performance. Neuropsychologia 49(13):3776–3785. doi:https://doi.org/10.1016/j.neuropsychologia.2011.09.038 View ArticlePubMedGoogle Scholar
  36. Li T, Deng H, Cai G, Liu J, Liu X, Wang R, Xiang X, Zhao J, Murray RM, Sham PC, Collier DA (1997) Association analysis of the dopamine D4 gene exon III VNTR and heroin abuse in Chinese subjects. Mol Psychiatry 2(5):413–416View ArticlePubMedGoogle Scholar
  37. Trauss E, Sherman E, Spreen O (2006) A compendium of neuropsychological tests: administration, norms, and commentary, 3rd edn. Oxford University Press, New YorkGoogle Scholar
  38. Deckersbach T, Henin A, Mataix-Cols D, Otto W, Wilhelm S, Rauch L, Scott BL, Jenike M (2000) Reliability and validity of a scoring system for measuring organizational approach in the complex figure test. J Clin Exp Neuropsychol 22(5):641–648View ArticleGoogle Scholar
  39. Delia F (1994) Louis SP, Uchiyama Lyons Craig, and White Travis. Color Trails Test. Psychological Assessment Resources IncGoogle Scholar
  40. Wetzel L, Boll T (1987) Short category test, booklet format. Western Psychological Services, Los AngelesGoogle Scholar
  41. Sobell LC, Sobell MB (1995) Alcohol timeline followback users’ manual. Addiction Research Foundation, Toronto CanadaGoogle Scholar
  42. Maisto S, Conigliaro J, McNeil M, Kraemer K, Kelley M (2000) An empirical investigation of the factor structure of the AUDIT. Psych Assess 12(3):346–353View ArticleGoogle Scholar
  43. Popp J, Leucht S, Heres S, Steimer W (2009) DRD4 48 bp VNTR but not 5-HT 2C Cys23Ser receptor polymorphism is related to antipsychotic-induced weight gain. Pharmacogenomics J 9(1):71–77. doi:https://doi.org/10.1038/tpj.2008.5 View ArticlePubMedGoogle Scholar
  44. Zalsman G, Frisch A, Lev-Ran S, Martin A, Michaelovsky E, Bensason D, Gothelf D, Nahshoni E, Tyano S, Weizman A (2003) DRD4 exon III polymorphism and response to risperidone in Israeli adolescents with schizophrenia: a pilot pharmacogenetic study. Eur Neuropsychopharmacol 13(3):183–185. doi:https://doi.org/10.1016/s0924-977x(03)00006-3 View ArticlePubMedGoogle Scholar
  45. Antinori AA, Becker JT, Brew BJ, Byrd DA, Cherner M, Clifford DB, Cinque P, Epstein LG, Goodkin K, Gisslen M, Grant I, Heaton RK, Joseph J, Marder K, Marra CM, McArthur JC, Nunn MP, Pulliam L, Robertson R, Sacktor N, Valcour V, Wojna VE (2007) Updated research nosology for HIV-associated neurocognitive disorders. Neurology 69(18):1789–1799. doi:https://doi.org/10.1212/01.WNL.0000287431.88658.8b PubMed CentralView ArticlePubMedGoogle Scholar
  46. Fama RR, Nichols BN, Pfefferbaum A, Sullivan EV (2009) Working and episodic memory in HIV infection, alcoholism, and their comorbidity: baseline and 1-year follow-up examinations. Alcohol Clin Exp Res 33(10):1815–1824. doi:https://doi.org/10.1111/j.1530-0277.2009.01020.x PubMed CentralView ArticlePubMedGoogle Scholar
  47. Matthews LJ, Butler PM (2011) Novelty-seeking DRD4 polymorphisms are associated with human migration distance out-of-Africa after controlling for neutral population gene structure. Am J Phys Anthropol 145(3):382–389. doi:https://doi.org/10.1002/ajpa.21507 View ArticlePubMedGoogle Scholar
  48. Roman T, Almeida S, Hutz M (1999) Lack of association of the dopamine D4 receptor gene polymorphism with alcoholism in a Brazilian population. Addict Biol 4:203–207View ArticlePubMedGoogle Scholar
  49. Lindenberger U, Nagel IE, Chicherio C, Li SC, Heekeren HR, Backman L (2008) Age-related decline in brain resources modulates genetic effects on cognitive functioning. Front Neurosci 2(2):234–244. doi:https://doi.org/10.3389/neuro.01.039.2008 PubMed CentralView ArticlePubMedGoogle Scholar
  50. Rodriguez-Jimenez R, Jimenez-Arriero MA, Ponce G, Bagney A, Aragues M, Palomo T (2007) Performance in the Wisconsin Card Sorting Test and the C957T polymorphism of the DRD2 gene in healthy volunteers. Neuropsychobiology 54(3):166–170View ArticleGoogle Scholar
  51. Xu H, Kellendonk CB, Simpson EH, Keilp JG, Bruder GE, Polan HJ (2007) DRD2 C957T polymorphism interacts with the COMT Val158Met polymorphism in human working memory ability. Schizophr Res 90(1–3):104–107. doi:https://doi.org/10.1016/j.schres.2006.10.001 View ArticlePubMedGoogle Scholar
  52. Bolton JL, Marioni RE, Deary IJ, Harris SE, Stewart MC, Murray GD (2010) Association between polymorphisms of the dopamine receptor D2 and catechol-O-methyl transferase genes and cognitive function. Behav Genet 40(5):630–638. doi:https://doi.org/10.1007/s10519-010-9372-y View ArticlePubMedGoogle Scholar
  53. Colzato LS, van den Wildenburg WP, Van der Does AJ, Hommel B (2010) Genetic markers of striatal dopamine predict individual differences in dysfunctional, but not functional impulsivity. Neuroscience 170(3):782–788. doi:https://doi.org/10.1016/j.neuroscience.2010.07.050 View ArticlePubMedGoogle Scholar
  54. Felten AM, Kranczioch C, Markett S, Walter NT, Reuter M (2013) The DRD2 C957T polymorphism and the attentional blink—a genetic association study. Eur Neuropsychopharmacol J Eur Coll Neuropsychopharmacol 23(8):941–947. doi:https://doi.org/10.1016/j.euroneuro.2012.09.010 View ArticleGoogle Scholar
  55. Hirvonen MM, Lumme V, Hirvonen J, Pesonen U, Nagren K, Vahlberg T, Scheinin H, Hietala J (2009) C957T polymorphism of the human dopamine D2 receptor gene predicts extrastriatal dopamine receptor availability in vivo. Prog Neuropsychopharmacol Biol Psychiatry 33(4):630–636. doi:https://doi.org/10.1016/j.pnpbp.2009.02.021 View ArticlePubMedGoogle Scholar
  56. Hirvonen MM, Laakso A, Nagren K, Rinne JO, Pohjalainen T, Hietala J (2009) C957T polymorphism of dopamine D2 receptor gene affects striatal DRD2 in vivo availability by changing the receptor affinity. Synapse 63(10):907–912. doi:https://doi.org/10.1002/syn.20672 View ArticlePubMedGoogle Scholar
  57. Frank MJ, Haughey HM, Curran T, Hutchison KE (2007) Genetic triple dissociation reveals multiple roles for dopamine in reinforcement learning. Proc Natl Acad Sci USA 104(41):16311–16316PubMed CentralView ArticlePubMedGoogle Scholar
  58. Davis C, Kaplan AS, Carter J, Reid C, Curtis C, Patte K, Hwang R, Kennedy JL (2008) Reward sensitivity and the D2 dopamine receptor gene: a case-control study of binge eating disorder. Prog Neuropsychopharmacol Biol Psychiatry 32(3):620–628View ArticlePubMedGoogle Scholar
  59. Gelernter J, Weiss R, Brady K, Panhuysen C, Yang BZ, Kranzler HR, Farrer L (2006) Haplotype spanning TTC12 and ANKK1, flanked by the DRD2 and NCAM1 loci, is strongly associated to nicotine dependence in two distinct American populations. Hum Mol Genet 15(24):3498–3507View ArticlePubMedGoogle Scholar
  60. Hill SY, Zezza N, Thalamuthu A, Weeks DE, Matthews AG, Mukhopadhyay I (2008) Dopaminergic mutations: within-family association and linkage in multiplex alcohol dependence families. Am J Med Genet B Neuropsychiatr Genet 147B(4):517–526PubMed CentralView ArticlePubMedGoogle Scholar
  61. Ponce G, Jiménez-Arriero MA, Rodríguez-Jiménez R, Aragüés M, Martín-Suñé N, Huertas E, Palomo T (2008) DRD2 and ANKK1 genotype in alcohol-dependent patients with psychopathic traits: association and interaction study. Br J Psychiatry J Mental Sci 193(2):121–125View ArticleGoogle Scholar
  62. Jacobsen LK, Mencl WE, Gelernter J (2006) C957T polymorphism of the dopamine D2 receptor gene modulates the effect of nicotine on working memory performance and cortical processing efficiency. Psychopharmacology 188(4):530–540View ArticlePubMedGoogle Scholar
  63. Lerman C, Wileyto EP, Epstein LH, Rukstalis M, Patterson F, Kaufmann V, Restine S, Hawk L, Niaura R, Berrettini W (2006) Role of functional genetic variation in the dopamine D2 receptor (DRD2) in response to bupropion and nicotine replacement therapy for tobacco dependence: results of two randomized clinical trials. Neuropsychopharmacol Off Publ Am Coll Neuropsychopharmacol 31(1):231–242Google Scholar
  64. Zai CC, De Luca V, Müller DJ, King N, Zai GC, Remington G, Meltzer HY, Lieberman JA, Potkin SG, Kennedy JL (2007) Association study of tardive dyskinesia and twelve DRD2 polymorphisms in schizophrenia patients. Int J Neuropsychopharmacol 10(5):639–651View ArticlePubMedGoogle Scholar
  65. Monakhov M, Abramova L, Kaleda V, Karpov V (2008) Association study of three polymorphisms in the dopamine D2 receptor gene and schizophrenia in the Russian population. Schizophr Res 100:302View ArticlePubMedGoogle Scholar
  66. Ponce G, Perez-Gonzalez R, Aragues M, Palomo T, Rodriguez-Jimenez R, Jimenez-Arriero MA (2009) The ANKK1 kinase gene and psychiatric disorders. Neurotox Res 16(1):50–59. doi:https://doi.org/10.1007/s12640-009-9046-9 View ArticlePubMedGoogle Scholar
  67. Loo SK, Rich EC, Ishii J, McGough J, McCracken J, Nelson S (2008) Cognitive functioning in affected sibling pairs with ADHD: familial clustering and dopamine genes. J Child Psychol Psychiatry 49(9):950–957. doi:https://doi.org/10.1111/j.1469-7610.2008.01928.x View ArticlePubMedGoogle Scholar
  68. Langley K, van den Bree M, Thomas H, Owen M, O’Donovan M, Thapar A (2004) Association of the dopamine D4 receptor gene 7-repeat allele with neuropsychological test performance of children with ADHD. Am J Psychiatry 161(1):133–138View ArticlePubMedGoogle Scholar
  69. Manor I, Corbex M, Eisenberg J, Gritsenkso I, Bachner-Melman R, Tyano S (2004) Association of the dopamine D5 receptor with attention deficit hyperactivity disorder (ADHD) and scores on a continuous performance test (TOVA). Am J Med Genet Part B Neuropsychiatric Genet Off Publ Int Soc Psychiatric Genet 127B(1):73–77. doi:https://doi.org/10.1002/ajmg.b.30020 View ArticleGoogle Scholar
  70. Barkley RA (1997) Behavioral inhibition, sustained attention, and executive functions: constructing a unifying theory of ADHD. Psychol Bull 121(1):65–94View ArticlePubMedGoogle Scholar
  71. Barr CL (2001) Genetics of childhood disorders: XXII. ADHD, part 6: the dopamine D4 receptor gene. J Am Acad Child Adolesc Psychiatry 40(1):118–121View ArticlePubMedGoogle Scholar
  72. Swanson J, Oosterlaan J, Murias M, Schuck S, Flodman P, Spence MA, Wasdell M, Ding Y, Smith M, Mann M, Carlson C, Kennedy JL, Leung P, Zhang YP, Chen C, Whalen CK, Babb KA, Moyzis R, Posner MI (2000) Attention deficit/hyperactivity disorder children with a 7-repeat allele of the dopamine receptor D4 gene have extreme behavior but normal performance on critical neuropsychological tests of attention. Proc Natl Acad Sci USA 97(9):4754–4759. doi:https://doi.org/10.1073/pnas.080070897 PubMed CentralView ArticlePubMedGoogle Scholar
  73. Boonstra AM, Kooij JJ, Buitelaar JK, Oosterlaan J, Sergeant JA, Heister JG (2008) An exploratory study of the relationship between four candidate genes and neurocognitive performance in adult ADHD. Am J Med Genet Part B Neuropsychiatric Genet Off Publ Int Soc Psychiatric Genet 147(3):397–402. doi:https://doi.org/10.1002/ajmg.b.30595 View ArticleGoogle Scholar
  74. Fossella J, Fan J, Wu Y, Swanson JM, Pfaff DW, Posner MI (2002) Assessing the molecular genetics of attention networks. BMC Neurosci 4(3):3–14Google Scholar
  75. Mill J, Williams BS, Craig I, Taylor A, Polo-Tomas M, Berridge CW, Poulton R, Moffitt TE (2006) Prediction of heterogeneity in intelligence and adult prognosis by genetic polymorphisms in the dopamine system among children with attention-deficit/hyperactivity disorder: evidence from 2 birth cohorts. Arch Gen Psychiatry 63(4):462–469View ArticlePubMedGoogle Scholar

Copyright

© Villalba et al. 2015