Greg Schundler's undergraduate thesis from Princeton University

1. 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

2. 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%

3. 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.

4. 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.

5. 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

6. 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”

7. 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

8. 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

9. 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.

11. (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%

12. 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.

13. 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

14. 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)

15. 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.

16. References
Amadeo, Stephane et al. (1993) “D2 Dopamine Receptor Gene and Alcoholism.” Journal of Psychiatric Research, 27: 173-179.
American Psychiatric Association. 1994 Diagnostic and statistical manual of mental disorders. 4th Edition. (DSM-IV). Washington, DC: American Psychiatric Association.
Arinami, Tadao et al. (1993) “Association between Severity of Alcoholism and the A1 Allele of the Dopamine D2 Receptor Gene Taq1 A RFLP in Japanese.” Biological Psychiatry, 33: 108-114.
Badani, et al. (2000) “Modulation of morphine sensitization in the rat by contextual stimuli.” Psychopharmacology, 151: 273-282.
Barr, Cathy L & Kidd, Kenneth (1993) “Population Frequencies of the A1 Allele at the Dopamine D2 Receptor Locus.” Biological Psychiatry, 34: 204-209.
Berkowitz, A. (1996) “Our genes, ourselves?” Bioscience, 46: 42-51.
Berman, S.M. & Noble E.P. (1995) “Reduced visuospatial performance in children with the D2 dopamine receptor A1 allele.” Behavior Genetics, 1:45-48.
Berridge, K & Robinson, T. (1998) “What is the role of dopamine in reward: hedonic impact, reward learning, or incentive salience?” Brain Research Reviews, 28: 309-369.
Binder, Elisabeth et al. (2001) “Neurotensin and Dopamine Interactions.” Pharmacological Reviews, 53: 453- 486.
Blaszczynski, A. & Nower, Lia (2002) “A pathways model of problem and pathological gambling.” Addiction, 97: 487-499.
Blum, K. et al. (1990) “Allelic Association of human dopamine D2 receptor gene in alcoholism.” Journal of the American Medical Association, 263:
Blum, K. et al. (1991) “Association of the A1 Allele of the D2 Dopamine Receptor Gene With Severe Alcoholism.” Alcohol, 8: 409-416.
Blum, K., & Cull, J.. (1996) “Reward Deficiency Syndrome.” American Scientist, 84: 132-145.
Bolos, A.M. et al. (1990) “Population and pedigree studies reveal a lack of association between the dopamine D2 receptor gene and alcoholism.” Journal of the American Medical Association, 264: 3156-3160.
Bowirrat, Abdalla & Oscar-Berman, Marlene (2005) “Relationship Between Dopaminergic Neurotransmission, Alcoholism, and Reward Deficiency Syndrome.” American Journal of Medical Genetics Part B, 132: 29-37.
Brown, R.T. “Creativity: what are we to measure? In: Glover, J.A. et al. Handbook of Creativity, Cambridge University Press, Cambridge: 1989.
Chambers, R. Andrew et al. (2003) “Developmental Neurocircuitry of Motivation in Adolescence: A Critical Period of Addiction Vulnerability.” American Journal of Psychiatry, 160: 1041-1052.
Chen, W.J. et al (1997) “Dopamine D2 Receptor Gene and Alcoholism Among Four Aboriginal Groups and Han in Taiwan.” American Journal of Medical Genetics, 74: 129-136.

17. Comings D.E. et al. (1996a) “A study of the dopamine D2 receptor gene in pathological gambling.” Pharmacogenetics 6:223–234.
Comings, D.E. et al. (1996b) “Polygenetic inheritance of Tourette syndrome, stuttering, attention deficit hyperactivity disorder, conduct and oppositional defiant disorder: The additive and subtractive effect of the three dopaminergic genes: DRD2, DBH, and DAT1” American Journal of Medical Genetics, 67: 264-268.
Comings, David E. & MacMurray, James P. (2000) “Molecular Heterosis: A Review.” Molecular Genetics and Metabolism, 71: 19-31.
Cox, S.M.L. et al. (2005) “Learning to Like: A Role for Human Orbitofrontal Cortex in Conditioned Reward.” The Journal of Neuroscience, 25: 2733-2740.
Cravchick, Anibal & Goldman, David (2000) “Neurochemical Individuality.” Archives of General Psychiatry, 57: 1105-1114.
Fiorino, D et al. (1997) “Dynamic changes in nucleus accumbens dopamine efflux during the Coolidge effect in male rats” Journal of Neuroscience, 17:.4849–4855.
Frederick, Shane et al. (2002) “Time Discounting and Time Preference: A Critical Review.” Journal of Economic Literature, 40: 351-401.
Gebhardt, C et al. (2000) “Non-association of dopamine D4 and D2 receptor genes with personality in healthy individuals. Psychiatry Genetics, 10: 131-137.
Gelernter J, et al. (1991) “No association between an allele of the D2 dopamine receptor gene (DRD2) and alcoholism.” Journal of the American Medical Association, 266:1801–1807.
Gelernter J. et al. (1993) “The A1 allele at the D2 dopamine receptor gene and alcoholism: a reappraisal.” Journal of the American Medical Association , 269:1673–1677.
Grandy, D.K. (1993) “PCR detection of the Taq A RFLP at the DRD2 Locus.” Human Molecular Genetics, 2: 2197.
Grant, B et al. (2006) “The 12 Month Prevalence and Trends in DSM-IV Alcohol Abuse and Dependence: United States, 1991-1992 and 2001-2002.” National Survey on Alcohol and Related Conditions, Volume 29.
Hill, Shirley Y. et al. (1999) “Personality Traits and Dopamine Receptors (D2 and D4): Linkage Studies in Families of Alcoholics.” American Journal of Medical Genetics, 88: 634-641.
Holden, C (2001) “Behavioral Addictions: Do They Exist?” Science, 294: 980-982.
Ikemoto, Satoshi & Panksepp, Jaak (1999) “The role of nucleus accumbens dopamine in motivated behavior: a unifying interpretation with special reference to reward-seeking.” Brain Research Reviews, 31: 6- 41.
Jonsson, EG et al. (1999) “Polymorphisms in the dopamine D2 receptor gene and their relationships to striatal dopamine receptor density of healthy volunteers.” Molecular Psychiatry, 4: 290-296.
Knowlton, Leslie (2005) “Why Are Drugs Abused by Humans?” Psychiatric Times, 12: Issue 2.
Koepp, MJ et al. (1998) “Evidence for striatal dopamine release during a video game.” Nature, 393: 266-268.
Kreek, MJ et al. (2005a) “Genetic influences on impulsivity, risk taking, stress responsivity and vulnerability to drug abuse and addiction.” Nature Neuroscience, 8: 1450-1457.
Kreek, MJ et al. (2005b) “Pharmacogenetics and Human Molecular Genetics of Opiate and Cocaine Addictions and Their Treatments.” Pharmacological Reviews, 57: 1-26.

18. Lawford B.R. et al. (2000) “The D2 dopamine receptor A1 allele and opioid dependence: association with heroin use and response to methadone treatment. Am J Med Genet 96:592–598.
McClure, S.M., Laibson, D.I, Loewenstein, G. et al. (2004) “Separate Neural Systems Value Immediate and Delayed Monetary Rewards.” Science, 306: 503-507.
McClure, S.M. et al. (2003) “A computational substrate for incentive salience.” Trends in Neuroscience, 26: 423-428.
Missale, Cristina et al. (1998) “Dopamine Receptors: From Structure to Function.” Physiological Reviews, 78: 189-212.
Munafo, M.R. (2006) “Candidate gene studies in the 21st century: meta-analysis, mediation, moderation” Genes, Brain and Behavior, 5: 3-8.
Neville et al. (2004) “Identification and characterization of ANKK1: a novel kinase gene closely linked to DRD2 on chromosome band 11q23.” Human Mutation, 23: 540-545.
Noble E. et al. (1993) “Allelic association of the D2 dopamine receptor gene with cocaine dependence. Drug Alcohol Dependence, 33:271–285.
Noble, E & Noble, R (1994a) “D2 Dopamine Receptor Gene and Obesity” International Journal of Eating Disorders, 15: 205-217.
Noble E et al. (1994b). “D2 dopamine receptor gene and cigarette smoking: a reward gene?” Med Hypotheses 42:257–260.
Noble E et al. (1994c). “Allelic association of the human D2 dopamine receptor gene with obesity.” International Journal of Eating Disorders15:205–217.
Noble, E et al. (1997) “D2 Dopamine Receptor Polymorphism and Brain Regional Glucose Metabolism.” American Journal of Medical Genetics, 74: 162-166.
Noble, E. P., Ozkaragoz, T., Ritchie, T.L. (1998) “D2 and D4 Dopamine Receptor Polymorphisms and Personality.” American Journal of Medical Genetics, 81: 257-267.
Olds, J. (1977) Drives and Reinforcements: Behavioral Studies of Hypothalamic Function, Raven Press
Petrill, S.A., Plomin R., McClearn G.E., et al. (1997) “No Association Between General Cognitive Ability and the A1 Allele of the D2 Dopamine Receptor Gene.” Behavior Genetics, 27: 29-31.
Petrill, Stephen et al. (1997) “No Association Between General Cognitive Ability and the A1 Allele of the D2 Dopamine Receptor Gene.” Behavior Genetics, 27: 29-30.
Pohjalainen, T. et al. (1998) “The A1 allele of the human D2 dopamine receptor gene predicts low Dd2 receptor availability in healthy volunteers” Molecular Psychiatry 3, 256-260
Potenza, Marc N. (2007) “To Do or Not To Do? The complexities of Addiction, Motivation, Self-Control, and Impulsivity.” American Journal of Psychiatry, 164: 4-6.
Reuter, M. et al. (2006) “Identification of first candidate genes for creativity: A pilot study.” Brain Research, 1069: 190-197.
Salamone, JD (1991) “Haloperidol and nucleus accumbens dopamine depletion suppress lever pressing for food but increase free food consumption in a novel food choice procedure.” Psychopharmacology, 104: 515–521.

19. Sanfey, A.G. et al. (2006) “Neuroeconomics: Cross-currents in Research on Decision-making.” Trends in Cognitive Neuroscience, 10: 108-116.
Schwab, S. et al. (1991) “Allelic association of human dopamine D2 receptor DNA polymorphism ruled out in 45 alcoholics.” American Journal of Human Genetics, 49:203.
Shoda, Yuichi & Mischel, Walter (1990) “Predicting Adolescent Cognitive and Self-Regulatory Competencies From Preschool Delay of Gratification: Identifying Diagnostic Conditions.” Developmental Psychology, 26: 978-986.
Simon, H. et al. (1979) “Definitive disruption of spatial delayed alterations in rats after lesions in the ventral mesoencephalic tegmentum. Neuroscience Letters, 15: 319-324.
Simonton, D.K. (2005) “Are Genius and Madness Related? Contemporary Answers to an Ancient Question..” Psychiatric Times, Volume 22, Issue 7.
Smith S.S. et al. (1992) “Genetic vulnerability to drug abuse: the D2 dopamine receptor Taq I B1 restriction fragment length polymorphism appears more frequently in polysubstance abusers.” Archive of General Psychiatry 49:723–727.
Smith, John Maynard. Evolutionary Genetics. New York: Oxford University Press, 1989.
Spencer, Jane. (2006) “The New Science of Addiction.” The Wall Street Journal, 18 July, D1.
Thanos, P.K. et al. (2001) “Overexpression of Dopamine D2 Receptors Reduces Alcohol Self- Administration.” Journal of Neurochemistry, 78: 1094-1103.
Thompson J. et al. (1997).” D2 dopamine receptor gene (DRD2) TaqIA polymorphism: reduced dopamine D2 receptor binding in the human striatum associated with the A1 allele.” Pharmacogenetics 7:479– 484.
Tsuang, M.T. et al. (1999) “Genetic and environmental influences on transitions in drug use.” Behavioral Genetics, 29: 473-479.
Tsai, Shih-Jen et al. (2002) “Dopamine D2 Receptor and N-Methyl-D-Aspartate Receptor 2B Subunit Genetic Variants and Intelligence.” Neuropsychobiology, 45: 128-130.
Turner, et al. (1992) “Lack of association between an RFLP near the D2 dopamine receptor gene and severe alcoholism.” Biological Psychiatry 31: 285-290.
Uhl, G.R. (2004) “The burden of complex genetics in brain disorders.” Archive of General Psychiatry, 61: 223-229.
Wahlsten, D. (1999) “Single-gene Influences on Brain and Behavior.” Annual Review of Psychology, 50: 599- 624.
Wise, R.A. & Bozarth, M.A. (1984) “Brain Reward Circuitry: Four Circuit Elements „Wired‟ in Apparent Series.” Brain Research Bulletin, 12: 203-208.

20. 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

21. 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? ____________________________________