Rewarding Deficiency: A Fresh

Perspective on the Taq A1 Allele of

the D2DR Gene and Uniquely Human

Rewards

Greg Schundler Princeton University Department of Ecology and Evolutionary Biology,

Princeton, NJ 08544

*Research conducted with funding provided by the Anthony B. Evnin ’62 Senior Thesis

Fund in Ecology & Evolutionary Biology and the Charles E. Test ’37 Education Fund

In the 1990’s a flurry of associative behavioral genetic research connected an RFLP (“A1”)

several kilobases downstream from the gene encoding the dopamine-2 receptor (DRD2) to

several aberrant behaviors including alcoholism, cocaine addiction, nicotine addiction, obesity,

ADHD, and pathological gambling as well as reduced dopamine receptor density and relative

glucose metabolism in mesolimbic areas. In this study we examine these associations in an Ivy

League student population while also exploring the possibility that the allele may confer

beneficial behavioral phenotypes. Genotyping of DRD2 and subjective personality

questionnaires from 160 Caucasian Princeton University undergraduates failed to find an

association of the A1 allele with alcohol addiction, drug addiction, and other behaviors.

Interestingly, we find a strong negative trend between A1 prevalence and alcohol addiction.

Further, the study suggests alternative phenotypic manifestations of the A1 genotype by

showing positive trends of association with both academic quintile and

artistic/music/performance leadership. We hypothesize that lower dopamine receptor density

has the potential to confer beneficial behavioral phenotypes, suggesting future directions for

associative research and research regarding neural rewards systems.

INTRODUCTION

Untangling the genetic and environmental fibers of identity has been the aim of associative research,

especially in relation to alcoholism and drug abuse. With the support of association studies it is now

understood that 30-60% of variance in the risk of developing substance dependence may be genetic

(Tsuang, 1999; Hill, 1999). The gene coding for the dopamine type 2 receptor (DRD2) has been a

popular candidate for addiction paradigms given dopamine‟s central role in neural reward pathways

(Ikemoto, 1999). 80-90% of dopaminergic neurons in the brain are found in the midbrain areas of the

projections implicated in reward systems (Binder, 2001). RFLP has been successfully, but

controversially associated with addictive and other undesirable behaviors (Lawford, 2000; Smith,

1992; Noble, 1993; Noble, 1994b; Noble, 1994c; Chen, 1997; Comings, 1996a; Comings, 1996b).

Ernest P. Noble grouped these behaviors under the umbrella of “Reward Deficiency Syndrome”

(RDS), a neurological disease characterized by hypofunctional dopaminergic reward systems (Blum

& Cull, 1996). We hypothesized that 1) the undergraduate student body of Princeton University

represents a behaviorally unique sample group with genotypic differences to meta-analysis control

populations; and 2) A1 RFLP can be associated with a drive for overachievement.

METHODS

SUBJECTS/TEST PROCEDURE

All 194 participants were undergraduate students enrolled at Princeton University. Participants were

selected non-randomly as subjects were solicited in private eating clubs whose members were

acquainted with the experimenter. Participants were asked to sign a consent form before receiving an

explanation of how to perform a buccal swab using Epicentre Catch-All collection swabs. The

subjects were also asked to fill out a questionnaire. The questionnaire and collection swab from each

subject were sealed in an unmarked envelope. The questionnaire and collection swab from each

subject were sealed in a separate envelope, which was later assigned a random number.

GENOTYPING

Genomic DNA was isolated using Epicentre Buccalamp DNA Extraction Solution in conjunction

with the collection swabs according to manufacturer protocol [The buccal cell and extraction

solution was heated at 65°C for 30 minutes using a Thermolyne Type 17600 hot plate and then at

98°C for 15 minutes using a Thermolyne Type 16500 Dri-Bath heat block.]

5 microliters of the resulting solution was mixed with 20 microliters of distilled/deionized water and

25 microliters of PCR mixture. The PCR mixture, in turn, was composed of: 16.5 microliters of

distilled/deionized water, 5 microliters of Qiagen 10x magnesium chloride buffer, 1.25 microliters of

Qiagen dNTPS, a combination of two primers (According to Grandy et al. [1993] a “971” sequence

reading 5‟-CCG TCG ACG GCT GGC CAA GTT GTC TA-3‟ and a “5014” sequence reading 5‟-

CCG TCG ACC CTT CCT GAG TGT CAT CA -3‟), and 0.24 microliters of Qiagen Taq

polymerase.

PCR products were differentiated in 3% agarose gel in 10 microliter well injections composed of 8

microliters of PCR product, and 2 microliters of Cybergreen loading dye. PCR was deemed

successful is 310 bp bands appeared after differentiation.

Restriction digestion was performed on PCR products. 5 microliters of PCR product was mixed with

15 microliters of restriction enzyme mix. The restriction enzyme mix was composed of 12.7

microliters of distilled/deionized water, 2 microliters of New England Biolabs buffer (Variety #3),

0.2 microliters of New England Biolabs 100x BSA (Bovine Serum Albumin)and 0.1 microliters of

Taq endonuclease.

Restriction digest products were differentiated in 3% agarose gel in 10 microliter well injections

composed of 8 microliters of restriction digest product and 2 microliters of Cybergreen loading dye.

The major allele (A2) is cut by the restriction enzyme yielding 130 and 180 bp bands whereas the

minor allele (A1) remained uncut yielding a 310 bp band. If the individual was heterozygous, they

displayed 3 fragment bands of 130, 180, and 310 bp.

QUESTIONNAIRE

The questionnaire (see Appendix 1) asked the subject to identify sex, ethnicity, and academic

quintile. The rest of the questions were subjective and explored aspects of the subjects‟ personalities

on relative scales including work ethic, leadership, novelty and risk seeking behavior, addiction,

decision making, and chronology of goal orientation. The subjects self-reported on all questions.

Most questions allowed the subjects to answer with a five point severity scale. For questions

concerning addictions to certain substances/behaviors, the scale went from 0 to 4 with zero

representing no addiction. The rest of the questions were binary in nature (yes/no) assessing the

behavioral realms in which subjects considered themselves either leaders or risk-takers.

RESULTS

SUBJECT POPULATION

Of the 194 subjects 110 were male and 84 were female; 160 were White/Caucasian, 6 African

American/African/Black, 6 Latino/Hispanic, 7 Asian/Asian-American, and 15 of unknown or mixed

ethnicity. Because 1) allelic frequencies of the gene are highly correlated with race (Barr & Kidd,

1993), 2) Caucasians of different European nationality have not been shown to have significant

differences in A1 allelic frequency (Goldman, 1993), and 3) the Caucasian group was largest, all

subjects reporting non-Caucasian race were excluded from further analysis. The 160 Caucasian

participants were thus considered the experimental group; and the allelic associations within this

group were compared to the meta-analysis population of Noble‟s 2003 review (control).

153 subjects identified their academic quintile (the quintile system ranks students in 20% cohorts

according to GPA value): ~13% were in the first, ~21% in the second, ~31% were in the third, ~18%

in the fourth, and 16% in the fifth. Princeton academic quintiles seek to divide the class into five

equal groups based on academic performance.

GENOTYPE

The gene alleles were in expected Hardy-Weinberg equilibrium, yielding a 16.9% A1 allelic

frequency and an A1 allelic prevalence of 31.25%. 9 were homozygous for A1, 128 were

heterozygous, and 57 were homozygous for A2. Since both A1 genotypes are believed to confer like

phenotypes, A1 homozygotes and heterozygotes will be referred together as A1+ genotypes and A2

homozygotes will be referred to A1-.

SUBJECTIVE PERSONALITY METRICS

The distribution of responses to the questionnaire is summarized in Table 1. The percentages in

black denote the fraction of subjects who responded with the given answer choice; the red font

denotes what fraction of respondents to each answer choice was homozygous or heterozygous for the

A1 allele.

STATISTICAL ANALYSIS

Binomial logistical regressions were performed with genotype (A1+/A1-) as the dependent variable

and each personality metric held separately as a covariate. None of the personality metrics were

successful in predicting genotype with a log linear model. The significance values for these tests are

show in Table 2. Though the data collected was ordinal, regression analyses have been employed in

other studies to measure the interaction of allele with phenotype severity (Bau, 2000). All regression

analysis was performed with the aide of SPSS 12.0.

To achieve greater subsample sizes, categorical responses were divided into two groups, one with

answer choices of 1-3 severity and the other with 4-5 (or 6) severity. Fisher‟s Exact Test failed to

show a significant relationship between genotype and behavioral phenotype except for a negative

correlation with athletic risk taking.

The significance values for these tests is shown in Table 2; non-significant (p>0.05), but strong

trends (both tests p< 0.12) are shown in bright orange.

DISCUSSION

Though baseline prevalence of the A1 allele among Caucasians in this subset of the Princeton

population (31.3%) is consistent with the meta-analysis of twelve studies of 845 Caucasians (31.2%)

performed by Noble in 2003, we failed to associate the A1 allele of the DRD2 gene with any

addictive behaviors. Surprisingly, a strong negative trend was found between A1+ prevalence and

alcohol addiction. Further, the study suggests alternative phenotypic manifestations of the A1+

genotype by showing positive trends with both academic quintile and artistic/music/performance

leadership.

ALCOHOL ADDICTION

The failure to reproduce an association between the A1 genotype and alcoholism is not the first to

have occurred (Bolos, 1990; Gelernter, 1991; Schwab, 1991; Turner, 1992). The validity of this

study‟s negative associative trend must admittedly be scrutinized. First, the self-diagnosis of

“addiction” was wholly subjective. A large portion of the controversy of the conflicting A1 studies

regards the classification of phenotypes and the independence of the control population from abusers

(Noble, 2003; Arinami, 1993). Blum and Noble were able to show more significant results than other

associations by choosing more severe alcoholics; they even drew samples from tissue banks of those

deceased from alcohol related problems (Blum, 1991; Noble, 1990; Noble, 1991; Noble, 2003;

Gelernter, 1991; Gelernter, 1993). In stark contrast, those who admitted alcohol addiction in this

study were neither institutionalized nor rigorously diagnosed. Describing a phenotype by the answer

to a single question is not nearly as comprehensive as alcoholism self-assessments like the SADQ

and MAST (Noble, 2003), let alone professional medical and psychiatric evaluation.

Nonetheless, the sample used in this study did not necessarily lack alcoholic phenotypes, as 3.75%

of subjects rated their alcohol addiction at a level of 4 and 22.5% rated their addiction as a 3 or a 4.

Though Dr. Daniel Silverman, director of McCosh Health Services estimates that 7% of Princeton

students can be classified as “heavy drinkers”, he believes that only 2-3% will actually go on to be

clinically diagnosed after college, a percentage that does not differ greatly from the 3.83% of

Caucasians nationally (personal communication, May 1, 2007; Grant, 2006). Because college

promotes a culture of socializing and drinking, especially at the eating clubs solicited, it is difficult

to ascertain which of these level 3 and 4 alcohol abusers actually show signs of dependency. A1+

genotype has also been associated with late onset as opposed to early onset alcoholism (Armani,

1993 & Noble, 1994a). Comparison between the relative influences of genetic and environmental

factors suggests that genotypes react differently to similar environments; stressful environments

appear to moderate A1+ association with alcoholism (Berman & Noble, 1997; Munafo, 2006; Kreek,

2005a).

Genotypic candidates for RDS-based alcoholism are also not lacking on Princeton‟s campus as

Noble‟s 2003 meta-analysis of Caucasian A1 prevalence at 31.3% matches our results. If we assume

the alcohol addiction metric of this questionnaire is at best moderately accurate, our results suggest

that the alcohol-dependence phenotype has either a) not yet manifested in Princeton undergraduates

or b) been selected against through the admissions process and is replaced by other behavioral traits.

The proposed mechanism for RDS, the endophenotype of lowered brain glucose metabolism in the

putamen, nucleus accumbens (NAcc) and substantia nigra and reduced dopamine receptor binding

affinity, must still have the ability to impact non-alcoholic psychological and behavioral phenotypes

(Noble, 1997).

ACADEMIC ACHIEVEMENT, ARTS/MUSIC/PERFORMANCE LEADERSHIP

Associating the A1 allele with non-substance abusing behavior is not new. Associations of general

cognitive ability (Petrill, 1997), visuospatial performance (Berman, 1995), IQ (Tsai, 2002),

personality tests (Noble, 1998; Hill, 1999; Gebhardt, 2000) and creativity (Reuter, 2006) with the A1

allele have all been attempted with various outcomes. The Petrill study of 1997, though not

producing statistically significant results, found a trend between increasing IQ and increasing

prevalence of A1. Total creativity and more specifically verbal creativity, as measured by the test

battery “inventiveness” in the “Berlin Intelligence Structure Test”, were found to significantly

correlate with the A1 allele; A1 along with another genetic marker explained 9% of the variance in

scores (Reuter, 2006). This study showed strong, but non-significant trends between A1+ prevalence

and both academic quintile (.07 < p < 0.10) and arts/music performance leadership (.09 < p < 0.13).

Not only is there a relative trend across quintile groups for more A1+ genotypes in upper quintiles,

but A1 homozygotes show better academic performance (mean quintile: 2.25) than A1 heterozygotes

(mean quintile: 2.85), though differences in phenotype severity between homozygotes and

heterozygotes and a possible role of molecular heterosis is debated (Comings, 2000). A larger

sample size is needed to determine whether these trends are significant.

It is easier to study the brain activity involving chemical rewards since they are five to ten times fold

more rewarding than natural rewards (Knowlton, 2005), but how does the brain begin to crave the

salient features of complex, often abstract behaviors unique in humans? The severity of pathological

gambling is positively correlated with A1 (Bowirrat, 2005) and involves incredibly complicated

rules and computation of probabilities by frontal cortex. Gambling shows all the signs of addiction

with periods of intense craving, tolerance exhibited by ever-larger bets, and even withdrawal

symptoms when gambling stops (Holden, 2001). Mare Potenza‟s fMRI imaging research at Yale

shows that similar brain areas activate when film clips of gambling are shown to compulsive

gamblers as when images of cocaine use are shown to addicts. According to the neo-Pavlovian

“neuronal model”, cortex-engaging activity can eventually produce a habitual drive, that, when

resisted causes unpleasant arousal (Blaszczynski,, 2002). Thus, the evolutionarily more primitive

dopaminergic system is effective in conditioning uniquely human faculties; even language about

gambling can arouse reward related areas in fMRI of gamblers (Holden, 2001). Compulsive

shopping, kleptomania, and Internet addiction can also take on pathological features (Holden, 2001).

Quiz games and considering hypothetical reward discounting scenarios are sufficient to illicit

activity from reward pathways in fMRI Behaviors involved with doing well in school including

studying in preparation for tests, researching for papers, or earning points on problem sets might

draw upon the same faculties.

Conditioning young children to find school work rewarding is likely to influence their work ethic

later on. Before arriving at the University, Princeton students, whether by growing up in an

intellectually supportive family, having the luxury of quality teachers or belonging to a

socioeconomic group that values education, most likely experienced environments that enforced an

academic reward conditioning paradigm. The ability of preschool aged children to delay gratification

in simple experiments significantly predicted their academic performance and self-esteem as

adolescents (Shoda, 1990; Frederick, 2002). Thus, under the right environmental influences over-

pursuit of a reward (grades instead of alcohol) can transform a paradigm of predisposition to

alcoholism into one for achievement. One of the most consistent traits found in Princeton students as

well as students of the so-called “Ivy Plus” schools [Ivy League plus Stanford, Duke, and MIT] is

perfectionism (personal communication Silverman, May 1, 2007). Unlike the alcohol addiction

metric which is wholly subjective, the quintile (though self-reported in this case) is objectively

determined by the University. Whether it is winning money, setting a high score in a video game, or

receiving an A on an exam, it seems that dopaminergic pathways are incredibly successful in

keeping integrated in the rapidly changing evolutionary context of our species, helping us to assign

incentive salience to and maintain motivation to purse whatever is that we want.

Similarly, though novelty seeking behavior might lead to early experimentation with drugs and

alcohol, it can also lead to intellectual curiosity or creativity. Music, art, and performance, among

other human activities, help to bring about alternate states of consciousness. Can these states of mind

possibly acquire addictive characteristics in artists and performers? The idea of the artist or musician

who is consumed by his or her work or has compulsive tendencies is not new. The list of creative

geniuses who exhibited drug and alcohol abuse (Joyce, Hendrix, Van Gogh to name a few) suggests

that there may be a common link between creativity and addiction. It is no wonder that preference

for improvisational music has been associated with novelty seeking and that music has been found to

activate neural reward circuits (Holden, 2001). Whether the impulsion to create can be classified as

addiction must be further explored, but Howard Shaffer, head of Division on Addictions at Harvard

believes that any repeated experience can facilitate neuroadaptive processes that sensitize the

individual to certain behaviors (Holden, 2001). It would be interesting to explore whether visionaries

within and across disciplines share genes; a link between creativity and psychopathology has also

been implicated (Simonton, 2005).

OVERTHINKING AND GUT-FEELING

We show a non-significant positive correlation between the A1+ genotype and subjects acting on

their gut-feeling (as opposed to over-thinking) during decision making. Research performed at the

Princeton Center for Mind and Behavior suggests that two areas of the brain are in conflict when

making decisions between immediate and delayed, but greater gratification (Sanfey, 2006).

According to the model, our ventrally located, more evolutionary conserved brain regions such as

the ventral tegmental area and nucleus accumbens, urge us to act on instinct and can collectively

referred to as the “go” system. Complimentary to this is the so called “stop” system which controls

the ability to not only compute the consequences of decision outcomes, but also to actively inhibit

the urgings of the “go” system (McClure, 2004). Other nomenclature of the two systems of

intertemporal choice define the “delta” system as the dorsolateral prefrontal cortex (dlPFC) and the

right posterior parietal cortex (rPPC); and the “beta” system as the midbrain dopamine network, the

ventral striatum, and both the orbitofrontal and medial prefrontal cortex. Co-activation of traditional

limbic areas such as the amygdale and insular cortex are more likely to happen with the beta system,

suggesting a more emotional, gut reaction type choice behavior from these areas (Sanfrey,

2006).This dichotomy can also be defined as automatic processes (so called System 1) and

controlled processes (System 2), where automatic behaviors are often hard wired in subcortical

areas, quickly executed, specific in effect, and can occur subconsciously, while controlled behaviors

involve higher cognitive faculties, are slow to employ, and can integrate a wide range of inputs

(Sanfey, 2006). Evidence suggests that those suffering from alcoholism, drug addictions,

compulsions, and impulsions may have a common root in a reduced ability to recruit the “stop”

function in the face of salient stimuli. It seems that immediately available rewards engage the ventral

striatum and OFC, both rich in dopamine, whereas consideration of delayed rewards occurring in

fronto-parietal areas must be consciously initiated (McClure, 2004a).

Though the beta-delta model is consistent with many neuroimaging and lesion studies, it often

identifies the “go” system as a culprit of aberrant behaviors, as a perpetual devil on the shoulder

(Sanfrey, 2006; McClure, 2004). With proper conditioning is it possible to condition “angel on your

shoulder” responses in the “go” system? In other words, can we condition gut feelings of

compassion, responsibility, or duty? Similarly, can we condition ourselves to make gut-feeling

decisions that are designed to consistently benefit us in the long-term? The fact that both academic

performance and gut-feeling decision making are correlated to A1 entertains this possibility. Indeed,

brain structure and chemistry and neuronal firing patterns implicated in behaviors can change as they

are learned and have been hypothesized to become less dependent on dopamine release in the

accumbens system and more dependent on action of the caudate and putamen (Chambers, 2003;

Ikemoto, 1999). Though mesocortical projections form VTA are known to innervate deep cortical

layers (V and VI) of the PFC, piriform cortex, and entorhinal cortex and the substantia nigra is

thought to feed into superficial cortical layers (II and III) and the anterior cingulate (Binder, 2001),

reward pathways are not one way, but feed back on one another. Indeed, dopamine release in the

ventral striatum can be caused directly by excitatory signals from the cortex (Chambers, 2003).

Dopamine increases in the ventral striatum during the playing of a video game is another example of

the ventral stratum‟s ability to represent non-natural and even virtual, imagined rewards (Koepp,

1998). Either the mesolimbic system is capable of interpreting this kind of abstraction or more likely

receives inputs from the frontal cortex. Though they may at times “produce conflicting dispositions”

(Sanfey, 2006), the reward and decision system are highly integrated in helping the individual to

maximize his utility in the face of limited resources. Though we suspect that the Beta/System 1 area

would be most affected by RDS, how the A1 endophenotype is likely to affect the conditioning of

such rewards is the subject of future study.

POSSIBLE NEURAL MECHANISMS

Doe the behavioral phenotype of A1+ control only certain behaviors or does it govern the

association and reward processes of all experience? Individuals who have comorbid addictions (i.e.

polysubstance abuse) and type A personalities support the notion that rewarding events are mitigated

by the same mesolimbic system. Drugs have a variety of initial action sites, but evidence shows that

they eventually utilize a common brain mechanism (Ikemoto, 1999); is it also possible that

rewarding stimulus or experience, though acting initially on different sensory modalities acts on the

same reward pathways? Other evidence resists the notion that certain behaviors are preferred over

others (Wise, 1984). According to time discounting theory and economic psychology there is no

reason to believe that a single unitary time preference governs all intertemporal choices (Frederick,

2002). “Addiction transfer”, a phenomenon long recognized by psychologists whereby recovering

alcoholics relapse to a new addiction, supports this idea. In the past five years a similar phenomenon

has arisen in as many as 30% of gastric bypass surgery patients acquiring other compulsive

behaviors including alcoholism, gambling, and compulsive shopping (Spencer, 2006). This apparent

paradox has been reproduced in rat studies whereby rats administered dopamine antagonists reduce

their consumption of a conditioned food reward and replace it with increased consumption of a novel

unconditioned food reward (Salamone, 1991).

Why some behaviors are pursued with compulsion and others are pursued to a normal intensity

within the same individual is difficult to ascertain. Perhaps the salience of a certain “crutch

behavior” becomes more relevant than others; obese people are 25% less likely to abuse substances

(Spencer, 2006). If reward and motivation systems are interpreted as a prioritizing agent that

organize behavior to maximize survival in the face of changing internal and external states, then

choices must eventually be made between behaviors based on decision utility (Frederick, 2002;

Chambers, 2003). The effectiveness of drugs in monopolizing this behavioral economy is evident in

the classic example of self-stimulating or administering rats‟ neglect of food and water intake to the

point of death (Olds, 1977). The dopaminergic system integrates information in a general approach-

seeking system and must balance two roles: 1) to form habits to efficiently exploit familiar

environments/stimuli [habit response system] and 2) to quickly adapt to a new environment/stimulus

by maintaining investigative behavior [flexible response system] (Ikemoto, 1999).

The association of A1 with reduced striatum receptor binding and density has been the most robust

causal link to the manifestations of RDS (Pohjalainen, 1998; Thompson, 1997; Jonsson, 1999) The

actual behavioral manifestation of reduced dopamine-2 receptor density, however, is difficult to

ascertain (Thompson, 1997). Though dopamine has been thought of both as a hedonic marker (e.g.

serotonin) and as a facilitator of association (in classical conditioning), the incentive salience theory

as advanced by Berridge and Robinson does best to unify the varied, and sometimes conflicting,

experimental results of dopamine research (Berridge & Robinson, 1998). Whereas serotonin might

mark whether we “like” something, dopamine creates the motivational state that makes us “want”

something. Dopamine allows associative conditioning so that eventually it is those things related to

the actual object of want that motivate us and direct our behavior. Conditioned stimuli begin to take

on the affective properties that the unconditioned stimulus once possessed (Cox, 2005). Although a

lab rat shows bursts of dopaminergic activity when first consuming sugar water, after a time it

begins to show dopaminergic bursts when it sees the cage that it associates with sugar reward

(Berridge & Robinson, 1998). These associations are often powerful and can slip by our conscious

recognition and are believed to underlie the strong tendency for alcoholics and other drugs addicts to

relapse when tipped off by places, people, or things associated with drug use. Orbitofrontal cortex,

for example, is able to associate certain playing cards with rewards, though the subject may not be

aware of any pattern in a card game (Cox, 2005). Reward systems seem to not only interact with the

hippocampus to cement these associations, but also seem to constantly monitor the accuracy of these

associations by comparing expected and actual reward. Under this error prediction model,

dopaminergic activity is maximal when the timing or magnitude of a reward is unpredicted. On the

level of electrophysiological recordings, dopamine neurons of the nucleus accumbens shell seem to

fire tonically when the animal is at rest, then burst phasically upon the presentation of a novel

stimulus or reward-associated stimulus (Ikemoto, 1999). Dopamine neurons don‟t seem to react in

the consummatory phase unless the stimulus is new or the consumed reward is significantly less or

more than the expected reward associated with the initial stimulus (dopamine burst).

I hypothesize that within McClure‟s suggested incentive salience computation framework (McClure,

2003, it is possible that the hypofunctional dopamine system is more likely to generate negative error

signals leaving the individual constantly in pursuit of new behavior or engagement of the old

behavior at greater intensity. In other words, the valuation signal of rewards of the anticipatory phase

is consistently higher than the value of hedonic signal of the consummatory phase. This view seems

most congruent with experimental research showing that reduced dopaminergic neuron function via

GABAergic inhibition or competitive binding of cis-flupentixol, though affecting approach behavior,

does not actually affect a rat‟s desire or motivation to attain rewards (Berridge & Robinson, 1998).

Thus, hypofunctional dopamine function characteristic of RDS does not cause malfunction of the

conditioning/reward learning process or the affective experience of pleasure, but is more likely to

contribute to a greater likelihood of negative prediction error. Lesions in the ventral tegmental area

(perhaps analogous to reduced receptor density) have shown to decrease the ability for rats to

tolerate frustrating situations (Simon, 1979). An anxiety or impatience driving the pursuit of new

reward behaviors could be the permanent state of the RDS individual, especially since novel places,

females, or foods are shown to be especially rewarding (Badani, 2000; Fiorini, 1997). This anxiety

state does well to explain A1 correlation with ADHD and the observation that rats bred for addictive

traits were shown to spend more time running on their cage wheels than controls (Holden, 2001).

Those with A1+ alleles have been found to exhibit significantly higher “Reward Dependence 2

(Persistence)” scores on personality tests (Noble, 1998). Reward paradigms developed for rats are

difficult to apply to complex human behaviors. Whether lesion studies or chemical antagonism of

D2 receptors in normal animals accurately reflects the endophenotype of the A1+ allele is a mystery.

If receiving good grades or creativity is considered a rewarding experience on par with intaking

sucrose solution, what aspects of the academic or creative process can be regarded as anticipatory,

approach, and consummatory phases? Indeed, “triangulation between brain mechanism, animal

behaviors, and human subjective responses” must be accomplished to tease out the answers to these

questions (Ikemoto, 1999).

On the molecular level it is known that dopamine inhibits cAMP activity in the post synapse through

G-protein coupled interactions (Cravchick, 1996). This inhibition often materializes in autoinhibition

that decrease dopamine release and firing rate to maintain the tonic firing rate observed in resting

individuals (Binder, 2001). Since dopamine autoreceptors have a five to ten fold higher affinity for

dopamine than D2 receptors, low dopamine levels would bias inhibitory activity and diminished

dopaminergic activity. The reduced D2 receptor density characteristic of A1+ individuals thus might

have a non-linear cumulative inhibitory effect, especially since dopaminergic synapses employ non-

specific volume transmission. (Binder, 2001) Thus the likelihood of a binding event occurring is

reduced for two reasons and most likely keeps dopamine transmission below a threshold where

negative feedback dominates. The molecular consequences of reduced dopamine receptor density

and affinity is the subject of future study.

LIMITATIONS AND CONCLUSIONS

More rigorous and consistent phenotyping will be required to explore the trends presented in these

studies. This study fails to make the distinction between alcohol abuse and alcohol dependence

where one is manifested in impairments of normal living and other total disability, respectively

(American Psychiatric Association, 1994). While diagnosing substance dependency itself requires

time and trained professionals, determining the presence and severity of behavioral addictions that

have complex phenotypic expressions is a problem that is only beginning. A future study done in

this model would benefit by administering a more detailed personality test such as the

Tridimensional Personality Questionnaire (TPQ); the Temperament and Character Inventory (TCI),

which measures reward dependence, persistence, and novelty seeking; the NEO Personality

Inventory-Revised, or the Baratt Impulsiveness Scale (Noble, 1998). All of these tests are designed

to include many questions that angle in on a few traits (Kreek, 2005). The RDS-restlessness model

suggested above, whereby an individual would be in a constant state of dissatisfaction with current

behaviors and searching for new behaviors to engage seems to be more consistent with the behavior

of a creative individual who seeks novel processes and produces novel products, than a good student

who successfully engages the rules of an established academic system to earn a known product, good

grades. In the future it may make sense to divide academic achievement by field of study as some

departments or professors may reward memorization of known facts and others original and

innovative thinking. This categorization sense especially in light of the fact that A1 has been

associated with verbal, but not mathematical creativity (Reuter, 2006). There exists a tradeoff

between quantity of subjects included in a study (which can make up for poor phenotyping) and

quality of phenotyping (Munafo, 2006). Given the circumstances for this study the former was a

more realistic approach.

The subject group was drawn opportunistically from those socially acquainted with the

experimenter. The selection criteria of the experimenter for friendships and the eating clubs where

subjects were solicited could have a complicating effect, especially considering the widely supported

stereotypes between the average Princeton student and the members of so called Bicker clubs. In

addition, half of the subjects reported being Greek, whereas only about a quarter of students are

thought to participate in Greek life on Princeton‟s campus.

Behavioral neuroscientists need to be in accord when referring to behavioral phenotypes to maintain

the distinction between lay and scientific nomenclature. This study in its brevity fails at this, by, for

example, grouping “novelty seeking” and “risk seeking” as the same behavior. Risk-taking should

concern decision making under uncertainty, while novelty seeking ought to describe “reactivity to

novel stimuli” (Kreek, 2005). Similarly, “addiction” defines “behavioral engagement despite adverse

consequences” and not “impaired self control” (Potenza, 2007). The call for clearly defined, non-

overlapping behavioral phenotypes is widespread (Chambers, 2003; Kreek, 2005; Potenza, 2007;

Brown, 1989).

This study does well to show the limitations of association experiments. A questionnaire that

attempts to seek p = 0.05 significance levels needs only to ask subjects of twenty personality

attributes before a correlation happens by chance. The negative association of A1 with athletic risks

is the perfect example of a successful association that has little to do with any previous behavioral

paradigm concerning dopamine function. The notion that a single gene can magically predict

complicated behavioral phenotypes is enticing, but most likely will not be found given the polygenic

nature of personality traits and large influence of environmental factors.

The Taq A region is so far downstream that it once was considered regulatory; now it is thought to

be part of a different gene entirely, an ANKK1 kinase gene that codes for serine/threonine (Neville,

2004). Lower receptor density and reduced glucose metabolism have been associated with the allele,

but how the allele produces these endophentoypes remains unknown (Jonsson, 1999 & Noble, 1997).

Whether the gene product of this region regulates transcription, translation, mRNA modification,

protein folding or some other aspect of protein production remains to be discovered. DRD2 variants

not coded by this interval have been found to affect as something as subtle as the degradation rate of

mRNA (Kreek, 2005b). It will be essential to find a causal link between the gene, the gene product,

the endophenotype, and the associated behavior(s), if the legacy of A1 association studies is to be

lasting.

The obsession with eradicating the socially costly (estimated at $500 billion annually in America)

issue of addiction is reflected in both academic and pharmaceutical research (Uhl, 2004; Berkowitz,

1996; Wahlsten, 1999; Thanos, 2001). COGA, or the Collaborative Study on the Genetics of

Alcoholism, by title, makes no secret in establishing its research goals and the drugs Zyban

(cigarette addiction) and Rimonabant (obesity) are just two examples of commercial produced

medications under inspection as a treatment for multiple addictive disorders including gambling,

obesity, nicotine dependence, and alcoholism (Spencer, 2006). Though we owe much of our

foundational knowledge of reward systems to drug abuse research, (Missale, 1998) the ever

expanding neuroscientific community must be cautious in allowing such blinders to limit

perspectives on personality. Exploring the neural mechanisms behind achievement and positive

personality attributes should be as worthy of research as those of ailments and abuse. In addition,

when personalities become pathologies, the damage is two fold. Failing to recognize the possible

attributes of a given class of individuals can stigmatize them while also encouraging self fulfilling

prophecy. Self-fulfilling prophecy has permeated “Reward Deficiency Syndrome” itself; the

attempts to associate A1 with negatively perceived behavior are far more numerous than any

attempts seeking to explore its benefits. The distribution of the A1+ genotype does not seem to

exhibit any negative evolutionary selection, as its prevalence ranges from 10 to 70% globally (Barr

& Kidd, 1993; Armani, 1993). Reuter notes in his 2006 Brain Research paper that “it is beyond

question that most notably, divergent thinking and originality, which are at the core of creativity, are

the prerequisites for new inventions, innovative creation, and technical progress.” Indeed,

extraordinarily creative, driven, or intelligent individuals can have “abnormal” or “extraordinary”

brains, depending on perspective.

Dopamine itself is not completely understood and shows evidence of complex interactions with

other receptor subtypes and neurotransmitters. Neurotensin for example antagonizes dopamine

activity through allosteric interactions, altering the ion currents (Binder, 2001). Our preoccupation

with dopamine and its receptor genes should not prevent us from seeking to understand the other

characters in the cast.

The above study adds to the controversy surrounding A1 associative studies, not only because it fails

to reproduce A1 association with aberrant behaviors, but also because it associates the gene with

non-pathological and potentially beneficial personality traits such as academic performance and

creative inspiration. How and where associations occur and what genes make associations stronger

in some individuals over others remains on the frontier of neuroscience. The advent of new genetic

screening techniques (e.g. SNP micoarrays) and the streamlining of old ones (primer based PCR,

commercial buccal collection kits) is likely to both zero on relevant chromosomal regions while also

making larger sample populations more accessible for association studies. The results from such

studies must continually be regarded in the context of mechanistic processes.

ACKNOWLEDGEMENTS

This thesis study was made possible with funding from the Anthony B. Evnin ‟62 Senior Thesis

Fund in Ecology & Evolutionary Biology and the Charles E. Test ‟37 Education Fund. The authors

would like to thank everyone who provided insight and assistance including Sam McClure, Jin Lee,

Virginia Kwan, Leonid Kruglyiak, Daniel Silverman, Jeanne Altmann, and Bart Hoebel. Thank you

to my family, friends, and teachers for your steadfast support.

(Percentages

approximate) 1 2 3 4 5

Grade Priority

3%

0%

11%

29%

41%

35%

38%

32%

6%

30% Hours

Homework 7%

9%

43%

32%

25%

43%

16%

24%

9%

29%

Follower to

Leader 0%

1%

0%

21%

42%

53%

29%

25%

30%

Risk/Novelty

to Averse

Seeking

2%

67%

15%

29%

33%

34%

41%

29%

9%

29%

Addictive

Personality 12%

37%

21%

36%

35%

34%

24%

24%

8%

17%

Overthinking

Gutfeeling 17%

22%

36%

28%

31%

33%

15%

42%

2%

67%

Goals

Outlook 15%

38%

31%

33%

27%

31%

24%

31%

3%

20% Success

Outlook 6%

44%

10%

47%

25%

28%

50%

31`%

10%

27% 0 1 2 3 4

Addiction

Alcohol 30%

38%

25%

33%

23%

31%

19%

20%

4%

17% Addiction

Drugs 70%

32%

11%

44%

9%

21%

8%

8%

2%

33% Addiction Sex 27%

44%

19%

37%

29%

23%

18%

17%

7%

36% Addiction

Food 16%

42%

21%

35%

20%

22%

31%

30%

11%

29% Addiction

Grades 22%

34%

33%

29%

31%

36%

10%

31%

4%

29%

Addiction

Success 6%

22%

13%

30%

23%

46%

34%

25%

24%

28% Addition

Winning 8%

33%

13%

48%

26%

33%

33%

27%

21%

24%

LEADERSHIP

REALM Academic 37%

35% Athletic 51%

27% Social 77%

31% Religious 4%

29% Student Government 11%

47% Personal/Family 61%

31% Community Service 19%

23% Arts/Performance/Music 13%

48% Other 13%

20%

RISK REALM

Academic 37%

27% Athletic 49%

23% Social 74%

33% Arts/Music/Performance 17%

44% Substance 40%

30% Other 24%

26%

Table 1 Distribution of answers to the personality questionnaire. Severity scale type

questions on the left and binary response question on the right: percentage of subjects

responding to answer choice in black, percentage of subjects within the category

exhibiting the A1+ genotype in red. Trends warranting further investigation are

highlighted in orange.

Table 2: Significance values from linear regression and Fisher Exact Test for various

answer responses. Linear regression shows trends along a severity continuum; Fisher

Exact Test analysis performed between arbitrarily separated 1-3 and 4-5 answer

categories in severity scale type questions. The sign denotes how the genotypic effect

would work on phenotype.

SIGN BINOMIAL

LINEAR

REGRESSION

FISHER’S

EXACT

TEST

SIGN BINOMIAL

LINEAR

REGRESSION

FISHER’S

EXACT

TEST

QUINITLE + 0.10 0.07 ARTS/MUSIC/

PERFORMANCE

RISK TAKER

+ 0.11 0.84

PRIORITY OF

GRADES + 0.55 1 SUBSTANCE- RELATED

RISK TAKER - 0.73 0.86

HOURS OF

HOMEWORK + 0.59 0.43 ATHLETIC RISK TAKER - 0.03 0.04

FOLLOWER ->

LEADER - 0.40 0.21 OTHER RISK TAKER - 0.45 0.55

ACADEMIC

LEADER + 0.33 0.48 TOTAL RISK TAKING

SCORE - 0.41 0.73

ATHLETIC

LEADER - 0.40 0.41 ADDICTIVE

PERSONALITY - 0.12 0.14

SOCIAL

LEADER - 0.78 0.84 ALCOHOL

ADDICTION - 0.08 0.10

RELIGIOUS

LEADER - 0.87 1 DRUG

ADDICTION - 0.14 0.15

STUDENT GOVT

LEADER + 0.15 0.17 SEX

ADDICTION - 0.03 0.24

PERSONAL/

FAMILY

LEADER

- 0.77 0.86 FOOD

ADDICTION - 0.26 0.73

COMM. SERVICE

LEADER - 0.29 0.38 GRADES

ADDICTION - 0.87 1

ARTS/MUSIC/

PERFORMANCE

LEADER

+ 0.09 0.13 SUCCESS ADDICTION

- 0.58 0.17

OTHER

LEADER - 0.25 0.31 WINNING

ADDICTION - 0.12 0.13

TOTAL

LEADERSHIP

SCORE

+ 0.91 0.22 OVERTHINKING-

GUTFEELING + 0.06 0.12

RISK/NOVELTY

AVERSE-

>SEEKING

- 0.45 0.61 GOALS OUTLOOK

- 0.40 0.70

ACADEMIC RISK

TAKER - 0.39 0.48 SUCCESS OUTLOOK - 0.211 0.60

SOCIAL RISK

TAKER

+ 0.37 0.58

FIGURES

Figure 3: Location of dorsolateral prefrontal cortex,

anterior insula, and anterior cingulate cortex

(Sanfrey et al, 2006)

Figure 4: Hyperbolic Time Discounting

represented by Risk Averse curve as behavior of

normal individuals, Risk Seeking curve or Risk

Neutral curve potentially representing those with

A1+ allele (Glimcher, 2004)

Figure 2: Locations of posterior cingulate cortex (PCC), medial prefrontal cortex (mPFC), ventral striatum (V. Str.), right

parietal (R. Par.), dorsolateral prefrontal cortex (dlPFC), and interior orbitofrontal cortex (IOFC); schematic summary of

beta-gamma dichotomy interaction between later, greater rewards and earlier, lesser rewards (Sanfrey et al, 2006)

Figure 1: Mesolimbic and mesocoritcal projections

superimposed on brain image (Rajadhyaksha, 2005)

Figure 2: A model for the distinct role of dopaminergic projects outside of “liking” and conditioning

processes. A1+ individuals likely have no difficulty forming associations or feeling pleasure, but may

be predisposed to perpetually high expectations and thus negative reward prediction error.

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Appendix 1

Thank your for taking part in this behavioral genetics study. To help me enrich the

data that I am collecting, please answer the following questions. The answers to

these questions, as your test results, will remain completely confidential (they

cannot be traced to your name by the experiments let alone by anyone else). Seal

your questionnaire in the envelope with your buccal swab when you are finished. If

you do not feel comfortable answering certain questions, leave them blank.

Which of the following best describes your ethnicity? Check all that apply.

____African, African American or Black

____Native American or Alaskan Native

____ Asian or Asian American

____Latino or other Hispanic

____ Native Hawaiian or Pacific Islander

____White or Caucasian

____Other: Please specify_______________________

What is your academic quintile? Circle one.

1st 2nd 3rd 4th 5th

How important are grades to you? Circle one

1 2 3 4 5

Lowest priority Highest priority

How many hours per day do you work on academics outside of class (i.e. problem sets, readings,

papers)? Circle one.

1 or less 2 or 3 3 or 4 4 or 5 5 or more

Do you consider yourself a leader or a follower?

1 2 3 4 5

Follower Leader

In what areas do you serve as a leader? Circle all that apply.

Academic Social Student/College Government

Athletic Religious Personal/Family

Arts/Music/Performance Community Service Other

Do you consider yourself to be risk/novelty averse or risk/novelty seeking?

1 2 3 4 5

Averse Seeking

If you take risks, in what area of your life do you take them?

Academic Social Arts/Music/Performance

Substance-related Athletic Other

Do you consider yourself to have an addictive personality?

1 2 3 4. 5

Not Addictive Very Addictive

What are you addicted to and to what extent?

0 = not at all 1 = somewhat 4 = extremely

Alcohol 0 1 2 3 4

Drugs 0 1 2 3 4

Sex 0 1 2 3 4

Food 0 1 2 3 4

Grades 0 1 2 3 4

Success 0 1 2 3 4

Winning 0 1 2 3 4

When you make decisions, do you tend to “over-think” them or do you “go on your gut feeling”?

1 2 3 4 5

Over think Gut Feeling

When you think of accomplishing goals, on what time course do you think? Circle one.

Days Weeks Months Years Decades

When you think of success, on what time course do you think? Circle one.

Days Weeks Months Years Decades

Sex M F Do you participate in Greek life? Circle one. Yes No

Which eating club are you in? ____________________________________