The gene-immune-behavioral pathway: Gamma-interferon (IFN-γ) simultaneously coordinates susceptibility to infectious disease and harm avoidance behaviors

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Brain, Behavior, and Immunity xxx (2013) xxx–xxx

YBRBI 2206 No. of Pages 7, Model 5G

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Contents lists available at ScienceDirect

Brain, Behavior, and Immunity

journal homepage: www.elsevier .com/locate /ybrbi

The gene-immune-behavioral pathway: Gamma-interferon (IFN-c)simultaneously coordinates susceptibility to infectious disease and harmavoidance behaviors

0889-1591/$ - see front matter � 2013 Published by Elsevier Inc.http://dx.doi.org/10.1016/j.bbi.2013.09.012

⇑ Corresponding author. Address: Innovation Pole for Genomics, Genetics andBiology, Via Gambuli, Building D, 3rd floor, 06132 Perugia, Italy. Tel.: +393334307577.

E-mail address: [email protected] (V. Napolioni).

Please cite this article in press as: MacMurray, J., et al. The gene-immune-behavioral pathway: Gamma-interferon (IFN-c) simultaneously coordinaceptibility to infectious disease and harm avoidance behaviors. Brain Behav. Immun. (2013), http://dx.doi.org/10.1016/j.bbi.2013.09.012

James MacMurray a, David E. Comings b, Valerio Napolioni c,⇑a Carlsbad Science Foundation, Monrovia, CA, USAb City of Hope National Medical Center, Duarte, CA, USAc Innovation Pole for Genomics, Genetics and Biology, Perugia, Italy

34353637383940414243444546474849

a r t i c l e i n f o

Article history:Received 11 July 2013Received in revised form 19 September 2013Accepted 19 September 2013Available online xxxx

Keywords:BehaviorBrainGamma-interferonGeneticsImmunityInfectious diseaseHarm AvoidancePersonality traitPolymorphismSerotonin

a b s t r a c t

Cytokine gene variants are known to influence both infectious disease susceptibility and harm-avoidantbehaviors, suggesting that these risk variants may be pleiotropically linked to instinctual disease-avoid-ant traits. The gamma-interferon (IFNG) +874 T > A polymorphism (rs2430561) is an ideal candidate genevariant for immune-behavioral studies. It is a functional SNP, regulating IFNG mRNA expression; it isknown to modulate serotonergic activity and is therefore capable of modifying behavior; and it has pre-viously been associated with increased susceptibility to malaria, tuberculosis, leprosy and Chagas disease.We hypothesized that the infectious disease-high-risk IFNG +874 A-allele would be associated with fourpersonality traits previously reported as behavioral defenses against infection: Harm Avoidance (HA),Extraversion (E), Exploratory Excitability (Exp E), and Openness to Experience (O). We tested this hypoth-esis in a sample of 168 healthy university students from Southern California genotyped for IFNG +874T > A and evaluated by the Temperament and Character Inventory-Revised (TCI-R) and the NEO Five-Fac-tor Inventory (NEO-FFI). We found that the infectious disease-high-risk IFNG +874 A-allele was associatedwith increased HA (P = 0.001) and decreased E (P = 0.030) and Exp E (P = 0.030). These findings suggestthat the IFNG +874 A gene variant is linked both to infectious disease susceptibility and to proactivebehavioral defenses that reduce infection risk in healthy subjects.

� 2013 Published by Elsevier Inc.

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1. Introduction

Several recent studies have suggested that in regions with his-torically elevated infectious disease risk societies tend to adaptto the disease threat by developing customs and attitudes thatserve as defenses against infection (Schaller, 2011; Shrira et al.,2013). Subscribing to the ‘‘parasite stress’’ hypothesis, these inves-tigators argue that the immune system and social-inhibitorybehavioral repertoires are complementary, each with the goal ofreducing risk of infection. To that end, the so-called ‘‘behavioralimmune system’’ (Schaller, 2011) is described as an anti-pathogendefense. For example, the Harm Avoidance (HA) trait ‘fear ofstrangers’ has been posited as a means of avoiding outsiders whomay harbor novel pathogens against which the individual or grouphave reduced defenses (Fincher et al., 2008). As a result, under per-sistent disease threat, xenophobia is said to increase and people

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are thought to constrict social interactions to known in-groupmembers (Shrira et al., 2013). Indeed, studies examining thesemodels at the societal level have found that in regions that havesuffered from high levels of infectious diseases, people report low-er mean levels of extraversion and openness to novel experiences(Schaller and Murray, 2008).

Schaller’s recent review of the behavioral immune system (Sch-aller, 2011) posited two critical provisos regarding activation ofpathogen-defensive behaviors:

1. The acute phase immune response to pathogens is seen as reac-tive rather than proactive, increasing the value of behavioraldefenses, which can proactively reduce risk of infection.

2. The activation of immune defenses tends to be metabolicallycostly, using energy stores that might otherwise be expendedon behaviors that would increase fitness. Therefore, the arousalof this defensive motivational system should be restricted tothose individuals who are actually vulnerable to infection.

However, the notion that the immune system is reactive,whereas the behavioral system is proactive, represents a false

tes sus-

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dichotomy that overlooks the pleiotropic nature of genes thatbridge the immune system and the central nervous system(CNS). That is, some cytokine regulatory genes are known to mod-ify disease susceptibility and to also modify defensive behaviors.An example of this phenomenon is tumor necrosis factor-alpha(TNF-a). TNF-a is a proinflammatory cytokine that is activatedduring the acute phase immune response to extracellular patho-gens. The TNF-a gene contains a much studied functional G > A sin-gle nucleotide polymorphism (SNP) at position �308 (rs1800629).The TNF-a �308A-allele has been associated with increased sus-ceptibility to hepatitis infection (Pasha et al., 2013) and viral men-ingitis (Titmarsh et al., 2013). Additionally, studies in healthysubjects found that the TNF-a �308 A-allele was associated withincreased asthenia/fatigue (Jeanmonod et al., 2004). Other studieshave found that TNF-a levels were linked to anxiety (Arranzet al., 2007), Type D personality, characterized by negative emo-tions and inhibited self-expression (Mommersteeg et al., 2012),and increased stress response in a context of social-evaluativethreat (Dickerson et al., 2009). Collectively, these studies suggestthat some cytokine genes act pleiotropically via the CNS, in unin-fected individuals, to activate the behavioral inhibitory system inthose people who have greater susceptibility to infection. Thus,an immune-behavioral model hypothesizes that individuals whoare at greater risk of infection by virtue of specific gene variantsmay be ‘proactively’ aroused to engage in harm avoidant behavior.

Another proinflammatory cytokine, gamma-interferon (IFN-c),has had far-reaching impact on a suite of infectious diseases which,through recent evolutionary time, has accounted for the lion’sshare of infectious disease morbidity and mortality. As a result,IFN-c has been under enormous selection pressure throughoutthe roughly 100,000 years of evolution of anatomically modern hu-mans (Manry et al., 2011). The gene that encodes IFN-c is locatedon chromosome 2q14. A SNP located at position +874 in the firstintron of the human IFN-c gene (IFNG +874 T > A, rs2430561) influ-ences both the mRNA expression and secretion of IFN-c (Matoset al., 2007). The IFNG +874 A/A genotype has been associated withreduced IFN-c mRNA and reduced IFN-c levels (Matos et al., 2007),generative of a diminished acute phase immune response. As a re-sult of its inadequate response to pathogens both the IFNG +874 A/A genotype and IFNG +874 A-allele have been associated with in-creased susceptibility to malaria (Medina et al., 2011), tuberculosis(de Albuquerque et al., 2012), leprosy (Cardoso et al., 2010), brucel-losis (Rasouli and Kiany, 2007) and Chagas disease (Torres et al.,2010).

The nexus between immune function and human behavior ap-pears to largely derive from the ability of some cytokines to act,across the blood–brain barrier, to modulate central nervous systemactivity. Oxenkrug (2011) has shown that IFN-c up-regulatesindoleamine 2,3-dioxygenase (IDO), the rate-limiting enzyme fortryptophan, which decreases conversion of tryptophan into seroto-nin (5-HT). As a result, IFN-c may be linked to harm avoidantbehaviors by virtue of its ability to modulate 5-HT activity. Indeed,several studies (Hansenne and Ansseau, 1999; Koller et al., 2008;Wu et al., 2010) support the existence of a link between 5-HT activ-ity and the HA dimension of the biosocial model of Cloninger(Cloninger et al., 1993). In this context, it is also remarkable thatboth the IFNG +874 T > A SNP and its linked microsatellite IFNG+875CA (rs2234688) (Pravica et al., 2000) were found to be associ-ated with the kynurenine/tryptophan ratio in healthy subjects andin major depression (Raitala et al., 2005; Myint et al., 2013).

In summary, the IFNG +874 T > A SNP is an ideal candidate genevariant for immune-behavioral studies. It is the most commonlyreported mediator of infectious disease susceptibility; it is knownto be a functional SNP, regulating expression of the gene product;and it is known to modify CNS activity. It is therefore an idealbridging marker spanning both the immune response and behav-

Please cite this article in press as: MacMurray, J., et al. The gene-immune-behavceptibility to infectious disease and harm avoidance behaviors. Brain Behav. Im

ior. In this regard, a critical question in immune-behavioral studiesis whether the innate, genetically determined differences in infec-tious disease susceptibility elicit measurable differences in dis-ease-avoidant behaviors. More specifically, do genetic variants ofsusceptibility initiate, by alteration of CNS activity, enhancedbehavioral defenses against infection? To test this hypothesis wesought to examine four personality traits that have been previouslycited (Fincher et al., 2008; Schaller and Murray, 2008; Schaller,2011) as behavioral adaptations to disease threat: (1) increasedHarm Avoidance, (2) decreased Extroversion, (3) decreased explor-atory behavior, and (4) decreased Openness to Experience. Thesefour traits were assessed using the Temperament and CharacterInventory-Revised (TCI-R) (Cloninger, 1999) and the NEO Five-Fac-tor Inventory (NEO-FFI) (Costa and McCrae, 1992) in a sample of168 healthy university students from Southern California geno-typed for the IFNG +874 T > A SNP.

2. Materials and methods

2.1. Study sample

The study subjects were comprised of healthy non-HispanicCaucasian students (N = 168, 48.8% males), age range 23–49 (meanage 33.4) recruited from a Southern California University. All sub-jects were administered the Temperament and Character Inven-tory-Revised (TCI-R) (Cloninger, 1999) and 141 subjects wereevaluated by the NEO Five-Factor Inventory (NEO-FFI) (Costa andMcCrae, 1992).

From each subject, 7 ml of whole blood were collected in potas-sium ethylene diaminetetraacetic acid (EDTA)-containing vacu-tainer tubes and stored at �20 �C until use. All subjects providedwritten informed consent and the research protocol was approvedby the Research Ethics Committee at the City of Hope NationalMedical Center.

2.2. Psychometric tests

The assessment of personality and temperament measures wasperformed using the Temperament and Character Inventory-Re-vised (TCI-R) (Cloninger, 1999) and the NEO Five-Factor Inventory(NEO-FFI) (Costa and McCrae, 1992). TCI-R defines the tempera-ment and character dimensions forming an individual’s personal-ity. It includes four independent heritable dimensions oftemperament (Novelty Seeking, Harm Avoidance, Reward Depen-dence and Persistence) and three character dimensions (Self-Directedness, Cooperativeness and Self-Transcendence). NoveltySeeking is a tendency for active responses to new stimuli. HarmAvoidance is a tendency to respond intensively to signals of aver-sive stimuli, thereby inhibiting behavior. Reward Dependence is atendency to respond intensely to signals of reward, especially so-cial rewards, thereby maintaining and continuing particular behav-iors. Persistence is a tendency to persevere in behaviors that havebeen associated with reward or relief from punishment. The NEO-FFI measures the Big Five personality traits: Neuroticism, Extraver-sion, Openness to Experience, Agreeableness, and Conscientious-ness. Neuroticism expresses emotional stability, impulse controland anxiety. Extraversion defines the quantity and quality of inter-personal relationships, activity level and positive emotions. Open-ness to Experience reflects intellectual curiosity and the preferencefor novelty and variety. Agreeableness evaluates the tendency to behelpful, cooperative and sympathetic towards others. Conscien-tiousness denotes the level of organization, perseverance and themotivation in intentional activities.

ioral pathway: Gamma-interferon (IFN-c) simultaneously coordinates sus-mun. (2013), http://dx.doi.org/10.1016/j.bbi.2013.09.012


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2.3. IFNG +874 T > A (rs2430561) SNP genotyping

Genomic DNA was extracted from peripheral blood using astandardized salting-out method and DNA was stored at �20 �Cuntil gene analysis. IFNG +874 T > A (rs2430561) SNP genotypingwas performed by an Allele Specific-Polymerase Chain Reaction(AS-PCR) protocol as already reported (Pravica et al., 2000) withslight modifications. Briefly, PCR reactions were carried out in25 ll containing 1� Master Mix (Eppendorf, Hamburg, Germany),0.1 mM solution of each of the specific primers (antisense primer:50-TCAACAAAGCTGATACTCCA-30, sense +874T: 50-TTCTTACAACA-CAAAATCAAATCT-30, and sense +874A: 50-TTCTTACAACACAAAAT-CAAATCA-30) and 100 ng of genomic DNA, amplifying a segmentof 262 bp. The PCR was performed under the following conditions:initial denaturation at 95 �C for 5 min, 30 cycles of 40 s at 94 �C,40 s at 56 �C and a final extension of 5 min at 72 �C. The amplifiedproduct was analyzed by electrophoresis in 2% agarose gel contain-ing 5 ll of ethidium bromide (10 mg/ml) using trans-illuminationwith a source of ultra-violet light. The assignments of SNP geno-types were confirmed in 10% of randomly selected samples fromeach genotype by direct sequencing in an Applied Biosystems se-quencer (ABI PRISM™, Model 3100 Avant).

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2.4. Statistical analysis

Statistical analysis was performed using SPSS v.18.0. Genotypeand allele frequencies of the SNPs were calculated by direct count-ing. Hardy–Weinberg equilibrium (HWE) was tested by comparing,using the v2 test, observed and expected frequencies of the variousgenotypes. Data were expressed as mean ± standard deviation(SD). Correlations among personality traits, and between age andtotal personality trait scores were performed using Pearson’s r.The difference in personality trait scores between males and fe-males was tested using t-test. The effect of IFNG +874 T > A SNPon personality trait scores was tested by Multivariate Analysis ofVariance (MANOVA) using age and sex as covariates. Genotype ef-fect estimates were obtained from significant MANOVAs usingboth ‘‘parameter estimates’’ and ‘‘estimates of effect size’’ (g2) op-tions from the General Linear Model (GLM) analysis by setting IFNG+874 T/T genotype as the reference group. Outliers checking wasperformed by the inspection of data displayed as box-plots.

Statistical significance was set at P < 0.05. Nominal P values arepresented, given the preliminary nature of the study, and becausethe personality trait scores were non-independent variables and,under similar conditions, correction for multiple testing remainscontroversial (Pernerger, 1998; Rothman, 1990).

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3. Results

3.1. Distribution of personality trait scores (TCI-R and NEO-FFI) in thestudied sample

All main personality trait scores (7 from TCI-R and 5 from NEO-FFI) are reported in Supplementary Table 1. The distribution of per-sonality trait scores related to behavioral adaptations to diseasethreat (Harm Avoidance, Extraversion, Openness to Experienceand Exploratory Excitability) obtained from the sample studied isreported in Table 1. They are also reported separately accordingto gender. No significant correlation with age was reported.

The majority of the traits analyzed displayed significant differ-ences between the sexes (Supplementary Table S1), excepting forHarm Avoidance (t = �1.968, P = 0.051), Self-Transcendence(t = �0.822, P = 0.412), Extraversion (t = �0.159, P = 0.874), Open-ness to Experience (t = �1.592, P = 0.114) and Exploratory Excit-ability (t = �0.026, P = 0.979) (Table 1). The majority of


personality trait scores were found to correlate among themselvesexcepting for the four TCI-Temperament Dimensions (NoveltySeeking, Harm Avoidance, Reward Dependence, Persistence) (Sup-plementary Table S2).

3.2. Association of IFNG +874 T > A with personality trait scores

The genotyping of IFNG +874 T > A was accomplished and thegenotype distribution (Table 2) did not deviate from HWE(v2 = 0.193, d.f. 1, P = 0.660). Also, IFNG +874 T > A genotype distri-bution did not differ between the sexes (v2 = 0.622, d.f. 2,P = 0.733) (data not shown). Moreover, the allele frequencies (IFNG+874T = 56.0%) did not differ from those reported for the CEU (UtahResidents with Northern and Western European ancestry, IFNG+874T = 56.7%) population in dbSNP database (www.ncbi.nlm.nih.-gov/projects/SNP/) (Sherry et al., 2001) and the EUR (Europeans,IFNG +874T = 56.6%) super-population in the 1000 genomes data-base (www.1000genomes.org/) (McVean et al., 2012), respectively.

The effect of IFNG +874 T > A on personality traits was tested byMANOVA using age and sex as covariates, and the results obtainedon personality trait scores related to behavioral adaptations to dis-ease threat are reported in Table 2. We also reported in Supple-mentary Table 3 the results obtained on the personality traitscores not related to behavioral adaptation to disease threat, albeitthey did not showed any significant association with the IFNG +874T > A SNP.

Harm Avoidance (F = 7.086, P = 0.001) and Extraversion(F = 3.582, P = 0.030) scores were found to be significantly associ-ated with IFNG +874 T > A polymorphism (Table 2). In particular,carriers of the IFNG +874 A/A genotype displayed the highest scorefor Harm Avoidance (B = 8.015, t = 3.575, P = 0.0005, g2 = 0.073)(Fig. 1A) and the lowest score for Extraversion (B = �6.341,t = �2.608, P = 0.010, g2 = 0.048) (Fig. 1B), respectively (Supple-mentary Table S4). Notably, the association of Harm Avoidancewith IFNG +874 T > A SNP would survive a stringent Bonferroni’scorrection (P = 0.05/number of personality traits analyzed(N = 13) = 0.004). No association was detected for Openness toExperience (F = 0.230, P = 0.795), while we found a significant asso-ciation of IFNG +874 T > A with Exploratory Excitability (F = 3.577,P = 0.030), with carriers of the IFNG +874 A/A genotype displayingthe lowest score (B = �5.396, t = �2.661, P = 0.009, g2 = 0.042)(Fig. 1C). Interestingly, the lack of association between Opennessto Experience and IFNG +874 T > A genotypes was consistent withthe non-significant effect of IFNG +874 T > A on Novelty Seeking(F = 0.191, P = 0.827, Supplementary Table S3).

Then, we performed a post hoc analysis to check for possiblegender-specific associations. Thus, the effect of IFNG +874 T > Aon personality traits was tested by MANOVA using only age ascovariate and analyzing males and females separately (data notshown). Harm Avoidance was significantly associated with IFNG+874 T > A polymorphism in both sexes (Males, F = 3.642,P = 0.031; Females, F = 4.151, P = 0.019) (Fig. 1D), with carriers ofthe IFNG +874 A/A genotype displaying the highest scores (Males:B = 8.406, t = 2.337, P = 0.022, g2 = 0.065; Females: B = 8.078,t = 2.874, P = 0.005, g2 = 0.092) (Supplementary Table S4).

3.3. Association of IFNG +874 T > A with Harm Avoidance subscales

Given the significant association found between IFNG +874T > A and Harm Avoidance score, we performed a post hoc analysisto detect the effect of IFNG +874 T > A on Harm Avoidance sub-scales: Anticipatory worry (TCI-R-HA1), Fear of uncertainty (TCI-R-HA2), Shyness with strangers (TCI-R-HA3) and Fatigability andasthenia (TCI-R-HA4), respectively. Results are reported in Table 3.Fear of uncertainty (TCI-R-HA2) score correlated significantly withage (r = 0.192, P = 0.013), also showing a significant difference be-


http://www.ncbi.nlm.nih.gov/projects/SNP/

http://www.ncbi.nlm.nih.gov/projects/SNP/

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Table 1Demographics and personality trait scores related to behavioral adaptations to disease threat (Harm Avoidance, Extraversion, Openness to Experience and ExploratoryExcitability) of the sample studied. Data are expressed as mean ± standard deviation (SD). The correlation between Age and total personality trait scores was performed usingPearson’s r. The difference in personality trait scores between males and females was tested using t-test.

Age Total Males Females Correlation with Age Difference between sexes

Mean ± SD Range Mean ± SD Range Mean ± SD Range33.4 ± 7.7 23–49 33.8 ± 7.6 23–49 33.0 ± 7.8 23–49

Harm Avoidance (TCI-R-HA) 50.0 ± 10.6 31.5–78.5 48.5 ± 11.6 31.5–77.1 51.5 ± 9.4 35.9–78.5 r = 0.060, P = 0.441 t = �1,968, P = 0.051Extraversion (NEO-FFI-E) 53.2 ± 10.6 25.7–80.4 53.2 ± 11.3 25.7–80.4 53.1 ± 10.0 32.5–77.2 r = �0.134, P = 0.113 t = �0.159, P = 0.874Openness to Experience (NEO-FFI-O) 56.1 ± 10.6 31.0–79.1 54.9 ± 11.2 31.0–79.1 57.3 ± 9.9 33.0–79.0 r = �0.103, P = 0.222 t = �1.592, P = 0.114Exploratory Excitability (TCI-R-NS1) 53.3 ± 10.3 27.0–70.4 53.3 ± 9.1 27.0–70.4 53.3 ± 9.7 27.0–70.4 r = �0.114 P = 0.140 t = �0.026, P = 0.979

Table 2Association of IFNG +874 T > A (rs2430561) with personality trait scores related to behavioral adaptations to disease threat (Harm Avoidance, Extraversion, Openness toExperience and Exploratory Excitability). The effect of IFNG +874 T > A (rs2430561) on personality traits was tested by Multivariate Analysis of Variance (MANOVA) using age andsex as covariates. Data are expressed as mean ± standard deviation (SD). Significant values are reported in bold, highlighted in light grey. Box-plot of personality trait scoressignificantly associated with IFNG +874 T > A SNP are reported in Supplementary Fig. 1.

IFNG +874 T > A (rs2430561)

A/A (N = 34) A/T (N = 80) T/T (N = 54) F P-value

Harm Avoidance (TCI-R-HA) 54.4 ± 13.1 50.8 ± 10.0 46.1 ± 8.3 7.086 0.001Extraversion (NEO-FFI-E) 49.0 ± 10.2 53.7 ± 10.7 55.1 ± 10.0 3.582 0.030Openness to Experience (NEO-FFI-O) 57.5 ± 10.0 56.0 ± 11.5 55.4 ± 9.6 0.230 0.795Exploratory Excitability (TCI-R-NS1) 49.6 ± 10.0 53.7 ± 9.8 55.1 ± 7.7 3.577 0.030



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tween sexes (t = �3.471, P = 0.001). Also, Fatigability and asthe-nia (TCI-R-HA4) scores differed significantly between the sexes(t = �2.767, P = 0.006). IFNG +874 T > A was significantly associatedwith three out of four HA subscales, Anticipatory worry (TCI-R-HA1, F = 3.856, P = 0.030), Fear of uncertainty (TCI-R-HA2,F = 4.041, P = 0.019) and Fatigability and asthenia (TCI-R-HA4,F = 6.924, P = 0.001), respectively.

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4. Discussion

This study sought to determine whether the IFNG +874 T > ASNP, widely known to modify susceptibility to a variety of infec-tious diseases, was also associated in healthy subjects with diseaserisk-avoidant behavioral traits. We found that the IFNG +874A al-lele, linked to heightened disease susceptibility, was associatedwith increased Harm Avoidance and decreased Extroversion andExploratory Excitability. These findings suggest the possibility thatheightened genetic risk of disease at this genetic locus may bepleiotropically compensated by behavioral traits that minimizerisk of infection.

Harm Avoidance is a complex trait that is considered to be aninnate aspect of temperament, referred to as an early-appearingand enduring tendency toward behavioral inhibition (Cloninger,1987). Substantial data support the biological basis of Harm Avoid-ance and it has heritability estimates ranging from 50% to 65%(Ando et al., 2002; Stallings et al., 1996). In his pioneering work,Cloninger (1987) described the four core features of Harm Avoid-ance as asthenia/fatigue, fear of uncertainty, fear of strangers,and worry & pessimism. Harm Avoidance crosses clinical bound-aries. Even though the asthenia and social withdrawal characteris-tic of Harm Avoidance are core features of vegetative depression,Harm Avoidance is more closely related to anxiety disorders, andparticularly obsessive compulsive disorder (OCD), which has beendescribed as ‘‘an extreme on a continuum of evolved harm-avoid-ance’’ (Brüne, 2006) or as ‘‘a dysregulation of the neural circuitsthat are crucially involved in threat detection and harm avoidance’’(Feygin et al., 2006). Of course, major depression is often co-mor-bid with anxiety disorders, but co-morbidity studies have foundthat depressive patients with co-morbid anxiety accounted forthe bulk of Harm Avoidance among depressives (Enoch et al.,


2008). In turn, although several neurotransmitters contribute tothe establishment of Harm Avoidance, dysregulation of 5-HT activ-ity has been most clearly established (Hansenne and Ansseau,1999; Koller et al., 2008; Wu et al., 2010).

There are several noteworthy aspects of our results with regardto theoretical modeling either by proponents of the ‘‘behavioralimmune system’’ or those who study immune aspects of behavior.First, the most robust subscale association was with asthenia/fati-gue. This dimension of the Cloninger Harm Avoidance scale is themost commonly reported somatic complaint in clinical depression(Perugi et al., 2011). Asthenia is also a primary sign of the sicknessbehavior that accompanies infection and is considered an adaptiveresponse that enhances recovery by conserving energy to combatacute inflammation (Maes et al., 2012). It may be relevant thatstudies in the learned helplessness paradigm suggest that fatigueand neuronal sensitization are critical to producing the behavioralimpairments that follow prolonged exposure to inescapable stress(Minor and Hunter, 2002). Indeed, the behavioral associations withthe IFNG +874 A-allele suggest a fairly primitive bio-behavioralposture that may bear some resemblance to learned helplessness.In this regard, it is of interest that the experimental establishmentof learned helplessness is a classical means of inducing reductionsin serotonergic activity (Minor and Hunter, 2002).

In addition to harm avoidance, introversion and exploratoryexcitability, we had expected that the IFNG +874 A-allele mightalso be associated with reduced scores on the NEO ‘Openness toExperience’ scale, since it has been linked to disease-avoidance inbehavioral immune system studies (Schaller and Murray, 2008).However, the discrete behaviors assessed by the NEO Opennessto Experience scale are qualitatively different from those of theother target variables. Harm avoidance, introversion and reducedexploratory excitability are each components of a posture of prim-itive physical and psychological withdrawal. On the other hand,the NEO-FFI Openness to Experience scale is a composite of threesubscales that are suggestive of higher level cognitive functioning:‘Intellectual Interests,’ ‘Aesthetic Interests,’ and ‘Unconventional-ity.’ Indeed, this absence of association between IFNG +874T > ASNP and higher level cognitive functions may also explain the lackof association with the harm avoidance subscale ‘Fear of Strangers.’As a result of these discrepancies between expected and observed



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Fig. 1. Effect of IFNG +874 T > A (rs2430561) polymorphism on significantly associated personality trait scores. Graphs were obtained by GLM using sex and age as covariatesfor panels A, B and C. The graph reported in panel D was obtained by GLM using only age as covariate. Dots in the graphs represent the estimated marginal means for thevariable under-investigation according to rs2430561 genotypes. Vertical lines represent standard error bars around the point estimates. (A) Harm Avoidance score. (B)Extraversion score. (C) Exploratory Excitability score. (D) Harm Avoidance score according to gender.

Table 3Post-hoc analysis of Harm Avoidance (HA) subscales. Data are expressed as mean ± standard deviation (SD). (A) Harm Avoidance subscale scores. The correlation between Age andHA scores was performed using Pearson’s r. The difference in HA scores between males and females was tested using t-test. (B) Association of IFNG +874 T > A (rs2430561) HarmAvoidance subscale scores. The effect of IFNG +874 T > A (rs2430561) on HA subscale scores was tested by Multivariate Analysis of Variance (MANOVA) using age and sex ascovariates. Significant values are reported in bold, highlighted in light grey.

Total Males Females Correlation with Age Difference between sexes

Mean ± SD Range Mean ± SD Range Mean ± SD Range

AHarm Avoidance (HA) subscalesAnticipatory worry (HA1)

50.4 ± 12.4 36.7–136.7 50.4 ± 14.7 36.7–136.7 50.4 ± 9.9 36.7–82.5 r = 0.016, P = 0.839 t = 0.031, P = 0.395Fear of uncertainty (HA2)

48.6 ± 9.6 32.0–67.0 46.1 ± 10.2 32.0–67.0 51.1 ± 8.3 32.0–67.0 r = 0.192, P = 0.013 t = �3.471, P = 0.001Shyness with strangers (HA3)

49.7 ± 10.2 35.7–70.4 48.9 ± 10.8 35.7–70.4 50.5 ± 9.6 35.7–70.4 r = �0.026, P = 0.734 t = �1.023, P = 0.309Fatigability and asthenia (HA4) 50.4 ± 11.0 38.6–79.6 48.0 ± 10.1 38.6–79.6 52.6 ± 11.4 38.6–79.6 r = 0.045, P = 0.565 t = �2.767, P = 0.006

B IFNG +874 T>A (rs2430561)A/A (N = 34) A/T (N = 80) T/T (N = 54) F P-value

Harm Avoidance (HA) subscalesAnticipatory worry (HA1) 52.8 ± 12.9 51.8 ± 13.9 46.8 ± 8.5 3.586 0.030Fear of uncertainty (HA2) 51.9 ± 9.6 48.8 ± 10.0 46.4 ± 8.4 4.041 0.019Shyness with strangers (HA3) 51.3 ± 11.7 50.4 ± 9.9 47.6 ± 9.4 1.839 0.154Fatigability and asthenia (HA4) 55.2 ± 13.5 50.6 ± 10.3 47.0 ± 9.2 6.924 0.001



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outcomes, available evidence suggests that the gamma-interferongene variant is only associated with physical withdrawal and itssequelae, including fatigue, reduced involvement with others,


and reduced exploratory excitability. On the other hand, this genevariant appears to have little if any impact on cognitive interpreta-tions of the environmental surround. As a result, there is no special



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fear attached to strangers and no variation in asthetic or intellec-tual interests that comprise the individuals’ orientation towardopenness to experience.

Studies of the evolutionary genetics of IFN-c found that the IFN-c pathway is essential and non-redundant in host defense due toits role in protective immunity against mycobacteria (Manryet al., 2011). Moreover, those investigators found that the IFNGgene is undergoing strong purifying selection entailing positiveselection for the IFNG +874 T-allele in European populations. In-deed, it is hard to imagine how the IFNG +874 A-allele has re-mained at such relatively high frequencies in disease-endemicregions unless it also conveys significant benefit. As an example,the IFNG +874 A-allele frequency in regions of Brazil endemic formalaria and Chagas Disease is 70% (Medina et al., 2011). Antagonis-tic pleiotropy, or ‘‘genetic tradeoff,’’ describes instances where onegene controls for more than one trait where at least one of thesetraits is beneficial to the organism’s fitness and at least one is det-rimental to the organism’s fitness (Elena and Sanjuán, 2003). Inturn, the most direct means of increasing fitness is throughenhancement of fertility, and cytokines are known to play animportant role in establishing a successful pregnancy. In this re-gard, two studies have shown that the IFNG +874 A-allele conferssignificantly greater protection against recurrent pregnancy loss,relative to IFNG +874 T-allele carriers (Prigoshin et al., 2004; Daheret al., 2003). Thus, in infectious disease endemic regions it appearslikely that the negative effects of the IFNG +874 A-allele on diseasesurvival are counterbalanced by its positive effects on reproductivesuccess. At the same time, the high frequency of the IFNG +874 A-allele may also be attributable to linkage disequilibrium (LD) withanother functional variant, which confers a significant advantage.Indeed, a strong LD has been reported between IFNG +874 T > Aand the downstream microsatellite IFNG +875CA (rs2234688)(Pravica et al., 2000). Interestingly, Manry et al. (2011) showed thatthe chromosomes harboring the IFNG +874A allele presented ahigher microsatellite diversity than those harboring the IFNG+874T allele (expected global heterozygosity 0.64 vs. 0.19, respec-tively). Moreover, the INFG +2109A > G (rs1861494) SNP located inintron 3 has been shown to be functional (Chevillard et al., 2003;Peresi et al., 2013) and it may eventually modify the effect exertedby the IFNG +874 T > A SNP.

The transformation of IFN-c gene variants into harm avoidantbehaviors may be due at least in part to the ability of this moleculeto down-regulate serotonergic activity (Oxenkrug, 2011). This,however, presents a conundrum in several ways. Harm avoidance,and especially asthenia, are common features of depression, whichis famously associated with reduced serotonergic activity (Leentj-ens et al., 2006). Nevertheless, as stated earlier, harm avoidanceis most commonly viewed as arising from threat-provoked anxiety(Brüne, 2006), and as being associated with increased, rather thandecreased, serotonergic activity (Hansenne and Ansseau, 1999).Moreover, although IFN-c provokes a significant reduction in sero-tonergic activity, the IFNG +874 A-allele produces less IFN-c pro-tein, and therefore a truncated 5HT reduction (Oxenkrug, 2011).Unfortunately, no one has yet performed a study of blood trypto-phan/serotonin levels in IFNG-genotyped subjects to clarify this is-sue. As a result, all we can really say at this point is that the IFNG+874 T > A gene variant may modify CNS activity and may there-fore influence behavior through such actions. Moreover, othercytokines are known to interact with multiple neurotransmittersystems, such as dopamine (van Heesch et al., 2013) and GABA(Weschenfelder et al., 2012) that may either act alone or in inter-action with serotonin to modulate these behaviors, whereas theimpact of IFN-c has to date only been studied with regard to thetryptophan-serotonin pathway (Oxenkrug, 2011; Raitala et al.,2005; Myint et al., 2013). Clearly, studies of more extensive cyto-kine-CNS interactions are warranted.


The findings reviewed here suggest the possibility that the IFNG+874 gene variant may have pleiotropic effects on both infectiousdisease susceptibility and risk-avoidant behaviors. However, theevidence suggests that these evoked behavioral repertoires areprimitive in nature, primarily inducing physical withdrawal, whilehaving little discernible effect on higher level cognitive functioningor complex interpersonal relations. As a result, traits such as Coop-eration or Reward Dependence appear to be largely unaffected.However, these findings only reflect genotype-dependent baselinedifferences between subjects and may have little bearing on howsubjects would respond if they were actually infected or experi-mentally challenged. For example, Mortensen et al. (2010) foundthat exposing research subjects to a disease prime facilitatedchanges in complex behaviors that would putatively serve to pro-mote avoidance of potentially infectious individuals. At this junc-ture we have no idea whether variations in cytokine genevariants would have an effect on the magnitude of such responsesto challenge, but such a possibility underscores the need for thesetypes of hybrid interdisciplinary studies.

Although we did not applied multiple testing corrections, be-cause of the non-independency among the psychometric variablesand the preliminary nature of the study, the present findings couldpotentially stem from false positive error inflation in multiplehypothesis testing and therefore they need to be replicated in fu-ture samples. It is also possible that using different ethnic groupswhose cultural differences may significantly affect the psychomet-ric scores would also alter their association with the IFNG +874T > A polymorphism.

In conclusion, we found that the IFNG +874A gene variant asso-ciated with heightened susceptibility to a variety of infectious dis-eases may also be linked to proactive behavioral defenses againstdisease infection. These data imply that this suite of harm avoidantbehaviors may be ‘‘hard-wired’’ as a pleiotropically-linked instinc-tual response in at-risk individuals. However, although the pairingof genetic susceptibility to disease with innate behavioral defensesis an appealing pleiotropic model, these associations and theirinterpretations need to be examined by other investigators in dif-ferent populations using larger sample sizes. Indeed, the presentstudy was undertaken with non-Hispanic Caucasian university stu-dents who primarily claimed European ancestry. However, it ispossible that the results are biased by hidden ethnic populationstratification. Therefore, replication studies in ethnically homoge-neous samples would be ideal.

5. Uncited reference

Dantzer (2001).

Appendix A. Supplementary data

Supplementary data associated with this article can be found, inthe online version, at http://dx.doi.org/10.1016/j.bbi.2013.09.012.

References

Ando, J., Ono, Y., Yoshimura, K., Onoda, N., Shinohara, M., Kanba, S., et al., 2002. Thegenetic structure of Cloninger’s seven-factor model of temperament andcharacter in a Japanese sample. J. Pers. 70, 583–609.

Arranz, L., Guayerbas, N., De la Fuente, M., 2007. Impairment of several immunefunctions in anxious women. J. Psychosom. Res. 62, 1–8.

Brüne, M., 2006. The evolutionary psychology of obsessive–compulsive disorder:the role of cognitive metarepresentation. Perspect. Biol. Med. 49, 317–329.

Cardoso, C.C., Pereira, A.C., Brito-de-Souza, V.N., Dias-Baptista, I.M., Maniero, V.C.,Venturini, J., et al., 2010. IFNG +874 T > A single nucleotide polymorphism isassociated with leprosy among Brazilians. Hum. Genet. 128, 481–490.

Chevillard, C., Moukoko, C.E., Elwali, N.E., Bream, J.H., Kouriba, B., Argiro, L., et al.,2003. IFN-gamma polymorphisms (IFN-gamma +2109 and IFN-gamma +3810)are associated with severe hepatic fibrosis in human hepatic schistosomiasis(Schistosoma mansoni). J. Immunol. 171, 5596–5601.




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680



28 September 2013

Cloninger, C.R., 1987. A systematic method for clinical description and classificationof personality variants. Arch. Gen. Psychiatry 44, 573–588.

Cloninger, C.R., Svrakic, D.M., Przybeck, T.R., 1993. A psychobiological model oftemperament and character. Arch. Gen. Psychiatry 50, 975–990.

Cloninger, C.R., 1999. The Temperament and Character Inventory–Revised, Centerfor Psychobiology of Personality. Washington University, St. Louis, MO.

Costa, P.T., McCrae, R.R., 1992. Revised NEO Personality Inventory (NEO-PI-R) andNEO Five-Factor Inventory (NEO-FFI) Professional Manual. PsychologicalAssessment Resources, Odessa, FL.

Daher, S., Shulzhenko, N., Morgun, A., Mattar, R., Rampim, G.F., Camano, L., et al.,2003. Associations between cytokine gene polymorphisms and recurrentpregnancy loss. J. Reprod. Immunol. 58, 69–77.

Dantzer, R., 2001. Cytokine-induced sickness behavior: mechanisms andimplications. Ann. N. Y. Acad. Sci. 933, 222–234.

de Albuquerque, A.C., Rocha, L.Q., de Morais Batista, A.H., Teixeira, A.B., Dos Santos,D.B., Nogueira, N.A., 2012. Association of polymorphism +874 A/T of interferon-c and susceptibility to the development of tuberculosis: meta-analysis. Eur. J.Clin. Microbiol. Infect. Dis. 31, 2887–2895.

Dickerson, S.S., Gable, S.L., Irwin, M.R., Aziz, N., Kemeny, M.E., 2009. Social-evaluative threat and proinflammatory cytokine regulation: an experimentallaboratory investigation. Psychol. Sci. 20, 1237–1244.

Elena, S.F., Sanjuán, R., 2003. Climb every mountain? Science 5653, 2074–2075.Enoch, M.A., White, K.V., Waheed, J., Goldman, D., 2008. Neurophysiological and

genetic distinctions between pure and comorbid anxiety disorders. DepressAnxiety 25, 383–392.

Feygin, D.L., Swain, J.E., Leckman, J.F., 2006. The normalcy of neurosis: evolutionaryorigins of obsessive–compulsive disorder and related behaviors. Prog.Neuropsychopharmacol. Biol. Psychiatry 30, 854–864.

Fincher, C.L., Thornhill, R., Murray, D.R., Schaller, M., 2008. Pathogen prevalencepredicts human cross-cultural variability in individualism/collectivism. Proc.Biol. Sci. 275, 1279–1285.

Hansenne, M., Ansseau, M., 1999. Harm avoidance and serotonin. Biol. Psychol. 51,77–81.

Jeanmonod, P., von Känel, R., Maly, F.E., Fischer, J.E., 2004. Elevated Plasma C-reactive protein in chronically distressed subjects who carry the A allele of theTNF-alpha �308 G/A polymorphism. Psychosom. Med. 66, 501–506.

Koller, G., Zill, P., Skoruppa, T., Bondy, B., Preuss, U.W., Soyka, M., 2008. Low level ofharm avoidance is associated with serotonin transporter functional haplotypein alcohol-dependent individuals. Psychiatr. Genet. 18, 59–63.

Leentjens, A.F., Scholtissen, B., Vreeling, F.W., Verhey, F.R., 2006. The serotonergichypothesis for depression in Parkinson’s disease: an experimental approach.Neuropsychopharmacology 31, 1009–1015.

Maes, M., Berk, M., Goehler, L., Song, C., Anderson, G., Gałecki, P., et al., 2012.Depression and sickness behavior are Janus-faced responses to sharedinflammatory pathways. BMC Med. 10, 66.

Manry, J., Laval, G., Patin, E., Fornarino, S., Tichit, M., Bouchier, C., Barreiro, L.B.,Quintana-Murci, L., 2011. Evolutionary genetics evidence of an essential,nonredundant role of the IFN-c pathway in protective immunity. Hum.Mutat. 32, 633–642.

Matos, G.I., Covas Cde, J., Bittar Rde, C., Gomes-Silva, A., Marques, F., Maniero, V.C.,et al., 2007. IFNG +874T/A polymorphism is not associated with Americantegumentary leishmaniasis susceptibility but can influence Leishmania inducedIFN-gamma production. BMC Infect. Dis. 7, 33.

McVean, G.A., Abecasis, D.M., Auton, R.M., Brooks, G.A.R., Depristo, D.R., Durbin, A.,et al., 2012. An integrated map of genetic variation from 1,092 human genomes.Nature 491, 56–65.

Medina, T.S., Costa, S.P., Oliveira, M.D., Ventura, A.M., Souza, J.M., Gomes, T.F., et al.,2011. Increased interleukin-10 and interferon-c levels in Plasmodium vivaxmalaria suggest a reciprocal regulation which is not altered by IL-10 genepromoter polymorphism. Malar. J. 10, 264.

Minor, T.R., Hunter, A.M., 2002. Stressor controllability and learned helplessnessresearch in the United States: sensitization and fatigue processes. Integr.Physiol. Behav. Sci. 37, 44–58.

Mommersteeg, P.M., Pelle, A.J., Ramakers, C., Szabó, B.M., Denollet, J., Kupper, N.,2012. Type D personality and course of health status over 18 months inoutpatients with heart failure: multiple mediating inflammatory biomarkers.Brain Behav. Immun. 26, 301–310.

Mortensen, C.R., Becker, D.V., Ackerman, J.M., Neuberg, S.L., Kenrick, D.T., 2010.Infection breeds reticence: the effects of disease salience on self-perceptions ofpersonality and behavioral avoidance tendencies. Psychol. Sci. 21, 440–447.


Myint, A.M., Bondy, B., Baghai, T.C., Eser, D., Nothdurfter, C., Schüle, C., et al., 2013.Tryptophan metabolism and immunogenetics in major depression: a role forinterferon-c gene. Brain Behav. Immun. 31, 128–133.

Oxenkrug, G.F., 2011. Interferon-gamma-inducible kynurenines/pteridinesinflammation cascade: implications for aging and aging-associated psychiatricand medical disorders. J. Neural. Transm. 118, 75–85.

Pasha, H.F., Radwan, M.I., Hagrass, H.A., Tantawy, E.A., Emara, M.H., 2013. Cytokinesgenes polymorphisms in chronic hepatitis C: impact on susceptibility toinfection and response to therapy. Cytokine 61, 478–484.

Peresi, E., Oliveira, L.R., da Silva, W.L., da Costa, E.A., Araujo Jr., J.P., Ayres, J.A., et al.,2013. Cytokine polymorphisms, their influence and levels in Brazilian patientswith pulmonary tuberculosis during antituberculosis treatment. TuberculosisRes. Treat. 2013, 285094.

Perugi, G., Canonico, P.L., Carbonato, P., Mencacci, C., Muscettola, G., Pani, L., et al.,2011. Unexplained somatic symptoms during major depression: prevalence andclinical impact in a national sample of Italian psychiatric outpatients.Psychopathology 44, 116–124.

Pernerger, T.V., 1998. What’s wrong with Bonferroni adjustments? BMJ 316, 1236–1238.

Pravica, V., Perrey, C., Stevens, A., Lee, J.H., Hutchinson, I.V., 2000. A singlenucleotide polymorphism in the first intron of the human IFN-gamma gene:absolute correlation with a polymorphic CA microsatellite marker of high IFN-gamma production. Hum. Immunol. 61, 863–866.

Prigoshin, N., Tambutti, M., Larriba, J., Gogorza, S., Testa, R., 2004. Cytokine genepolymorphisms in recurrent pregnancy loss of unknown cause. Am. J. Reprod.Immunol. 52, 36–41.

Raitala, A., Pertovaara, M., Karjalainen, J., Oja, S.S., Hurme, M., 2005. Association ofinterferon-gamma +874(T/A) single nucleotide polymorphism with the rate oftryptophan catabolism in healthy individuals. Scand. J. Immunol. 61, 387–390.

Rasouli, M., Kiany, S., 2007. Association of interferon-gamma and interleukin-4 genepolymorphisms with susceptibility to brucellosis in Iranian patients. Cytokine38, 49–53.

Rothman, K.J., 1990. No adjustments are needed for multiple comparisons.Epidemiology 1, 43–46.

Schaller, M., Murray, D.R., 2008. Pathogens, personality, and culture: diseaseprevalence predicts worldwide variability in sociosexuality, extraversion, andopenness to experience. J. Pers. Soc. Psychol. 95, 212–221.

Schaller, M., 2011. The behavioural immune system and the psychology of humansociality. Philos. Trans. R. Soc. Lond. B Biol. Sci. 366, 3418–3426.

Sherry, S.T., Ward, M.H., Kholodov, M., Baker, J., Phan, L., Smigielski, E.M., et al.,2001. DbSNP: the NCBI database of genetic variation. Nucleic Acids Res. 29,308–311.

Shrira, I., Wisman, A., Webster, G., 2013. Guns, germs, and stealing: exploring thelink between infectious disease and crime. Evol. Psychol. 11, 270–287.

Stallings, M.C., Hewitt, J.K., Cloninger, C.R., Heath, A.C., Eaves, L.J., 1996. Genetic andenvironmental structure of the Tridimensional Personality Questionnaire: threeor four temperament dimensions? J. Pers. Soc. Psychol. 70, 127–140.

Titmarsh, C.J., Moscovis, S.M., Hall, S., Tzanakaki, G., Kesanopoulos, K., Xirogianni, A.,Scott, R.J., Blackwell, C.C., 2013. Comparison of cytokine gene polymorphismsamong Greek patients with invasive meningococcal disease or viral meningitis.J. Med. Microbiol. 62, 694–700.

Torres, O.A., Calzada, J.E., Beraún, Y., Morillo, C.A., González, A., González, C.I., et al.,2010. Role of the IFNG +874T/A polymorphism in Chagas disease in a Colombianpopulation. Infect. Genet. Evol. 10, 682–685.

van Heesch, F., Prins, J., Korte-Bouws, G.A., Westphal, K.G., Lemstra, S., Olivier, B.,et al., 2013. Systemic tumor necrosis factor-alpha decreases brain stimulationreward and increases metabolites of serotonin and dopamine in the nucleusaccumbens of mice. Behav. Brain Res. 253C, 191–195.

Weschenfelder, J., Sander, C., Kluge, M., Kirkby, K.C., Himmerich, H., 2012. Theinfluence of cytokines on wakefulness regulation: clinical relevance,mechanisms and methodological problems. Psychiatr. Danub. 24, 112–126.

Wu, I.T., Lee, I.H., Yeh, T.L., Chen, K.C., Chen, P.S., Yao, W.J., et al., 2010. Theassociation between the harm avoidance subscale of the TridimensionalPersonality Questionnaire and serotonin transporter availability in thebrainstem of male volunteers. Psychiatry Res. 181, 241–244.



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The gene-immune-behavioral pathway: Gamma-interferon (IFN-γ) simultaneously coordinates susceptibility to infectious disease and harm avoidance behaviors