THE RELATION BETWEEN OXYTOCIN RECEPTOR GENE POLYMORPHISMS, ADULT ATTACHMENT AND INSTAGRAM SOCIABILITY: AN EXPLORATORY ANALYSIS - PSYARXIV
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The relation between Oxytocin Receptor Gene
polymorphisms, adult attachment and Instagram
sociability: an exploratory analysis
Alessandro Carolloa , Andrea Bonassia,b , Ilaria Cataldoa , Giulio Gabrielic ,
Moses Tandionod,e , Jia Nee Food,e , Bruno Leprib , Gianluca Espositoa,c,d,∗
a
Department of Psychology and Cognitive Science, University of Trento, Rovereto, Italy
b
Mobile and Social Computing Lab, Foundation Bruno Kessler, Trento, Italy
c
Psychology Program, School of Social Sciences, Nanyang Technological University,
Singapore, Singapore
d
Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore,
Singapore
e
Human Genetics, Genome Institute of Singapore, Singapore, Singapore
Abstract
So far literature considered the association between environmental factors
(i.e. involved in adult relationships) and genetic vulnerability on Oxytocin
Receptor Gene (OXTR) in the comprehension of social behavior. Although
an extensive knowledge on in-person social interactions has been obtained,
little is known about online social behavior. A gene-environment perspective
is adopted to examine how OXTR and adult attachment moderate Instagram
behavior.
The Experience in Close Relationships-Revised (ECR-R) questionnaire was
used to collect participants’ (N = 57, 16 males) attachment with their part-
ners. The genetic factors within the regions OXTR rs53576 (A/A homozy-
gotes vs. G-carriers) and rs2254298 (G/G homozygotes vs. A-carriers) were
assessed. Number of posts, followed people (”followings”) and followers were
obtained from Instagram, and the Social Desirability Index was calculated
as the ratio of followers to followings.
Interaction effects between OXTR groups and ECR-R scores on the number
∗
Corresponding author
Email address: gianluca.esposito@ntu.edu.sg | gianluca.esposito@unitn.it
(Gianluca Esposito)
Preprint submitted to PsyArXiv November 19, 2020of posts and SDI were hypothesised. Results showed an effect of rs53576
on the number of Instagram followings. Specifically, A/A homozygotes had
more followings than G-carriers independently of the quality of the relation-
ship with their partner.
These preliminary results are discussed in light of the debate of behavioral
genetics and offer insights into future investigations on social media behavior.
Keywords: Gene*Environment, Adult attachment, Close relationship,
Oxytocin Receptor Gene, rs53576, rs2254298, online behavior, social media,
social network, Instagram
Introduction
Human beings are social animals. People forge social bonds throughout
their life, from the first sight with the primary caregiver to the affection felt
for the partner. Socialization processes not only have a re-creative value, but
they are crucial for the development of social and life skills.
During human development, different environmental factors have been taken
into account in the study of social behavior and cognition [1, 2]. Among
them, parental bonding lays the foundations of an early child-centred en-
vironment [3, 4]. A lot of researchers have referred to Bowlby’s theory of
infant attachment in order to explain cognitive and behavioral development
[5], emotional arousal and regulation [6, 7], and typical or atypical develop-
ment [8, 9, 10]. Attachment theory has especially been used to understand
people’s social development from early infancy and childhood to adulthood
[11, 12].
The quality in the early relationships with parents during infancy and child-
hood could also affect the adult relationships with peers and the partner
[13, 14, 15, 16]. As a matter of fact, people from high-quality marriages
(i.e people that show better marital adjustment and higher marital satis-
faction) often manifest better physical and mental health than the ones in
low-quality marriages [17]. The protagonists of a positive marital experience
present higher psychological health in terms of lower levels of depression,
anxiety, balanced mood and a lower probability of receiving a diagnosis of
alcohol-related disorder [18, 19, 20, 21]. On the other hand, adults involved
in less optimal romantic relationships can show signs of mental illness [22].
In fact, negative social interactions with the partner can be associated with
increased likelihood of depression, anxiety, and suicidal ideation [23].
2However, social behavior is not only determined by the exposure to positive
and negative environments, such as the differential characteristics linked to
successful or dysfunctional relationships. Sociability finds its origins in the
biological roots of the individual starting from the genetic predispositions. In
the study of behavioral genetics on sociability, some authors have focused on
the genes that rule the levels of oxytocin regulated by the Oxytocin Recep-
tor Gene (OXTR). Generally, oxytocin is a neuropeptide that plays a crucial
role for social skills in both mammals and humans [24, 25, 26, 27, 28] and
is believed to be involved in the etiology of autism spectrum disorder (ASD)
[29, 30]. The link between oxytocin and social behavior emerges also from
the analysis of stimuli which can increase or decrease the levels of this neu-
ropeptide in the brain. For instance, oxytocin levels increase in response to
physical contact [31, 32] and also to the presence of conspecifics [33, 32]. Fur-
thermore, it has been suggested that higher levels of oxytocin could reinforce
social experiences by causing a reduction in the levels of anxiety [32, 34].
In particular, both OXTR/rs53576 and OXTR/rs2254298 single nucleotide
polymorphisms appear to be good candidates when looking for the genetic
modulation of social behavior and cognition [35]. For instance, previous
works reported that the G allele for the OXTR/rs53576 is associated with
more sensitive parental responsiveness [36], general sociability [37] and lower
rates of autistic traits [38] than A allele. Moreover, the cortisol response to
a stressful situation was reported to be reduced for G-carriers when accom-
panied by a source of social support [39] compared to A/A homozygotes.
Conversely, the study of Rodrigues et al. [40] reports that A-carriers dis-
played lower behavioral and dispositional empathy when compared to G/G
homozygote individuals on OXTR/rs53576. Furthermore, A/A homozygote
males tend to show lower positive affect [41]. Nonetheless, an absolute con-
sensus on the predisposition given by A or G allele does not yet exist [42].
Indeed, an increased sympathetic response (heart rate) to social distress
emerged for individuals with the A allele on the OXTR/rs2254298 having
an history of high paternal overprotection but not for G/G homozygotes
[43]. With regards to this genetic region, female G/G homozygotes showed
less attachment anxiety than A-carriers, while male G/G homozygotes ex-
hibited less autism-associated traits than A-carriers [44].
Considering the continuous and reciprocal interaction between the environ-
ment to which a person is exposed and one’s genetic profile, this paper aims
to adopt a Gene * Environment perspective in order to investigate complex
social behavior. The Diathesis Stress model [45, 46] suggests that the sus-
3ceptibility to psychological disorder of a genetically vulnerable individual is
the result of an exposure to an adverse event. Hence, the trigger given by
the environment and the person’s genetic vulnerability are both necessary
for the development of a disorder [47].
Conversely, the Differential Susceptibility model [48, 49, 50] stipulates that
the susceptibility to the environment depends on the so-called “plasticity
genes”. These genes lead to a broader individual plasticity, allowing the per-
son not only to suffer the effect of the exposure to negative environments,
but also to benefit from the exposure to good and healthy environments
[50, 51, 47]. This theoretical framework has highlighted the potential varia-
tion of individual patterns with regards to social sensitivity in the intersection
of genetic factors and environmental influences [43, 52].
Nowadays, socialization takes place either offline and online, suggesting the
need to evaluate both in-person social interaction and virtual social inter-
action. Examining social behavior means also taking into account people’s
virtual interactions, such as the ones happening on social networks (e.g. In-
stagram, Facebook, Twitter). As a matter of fact, it is not yet clear whether
the bases of social interactions are universally or specifically defined between
real and virtual social environments. For example, environmental influences
seem to play a role on regulating virtual social behavior [53, 54, 55].
The centrality that social networks are assuming in human life has exponen-
tially increased. Among all, Instagram allows people to connect with each
other by chatting, posting pictures and daily stories, following other users’
profiles. The pages available on Instagram range from private profiles to
business and advertisement ones, from humorous pages to accounts which
share the latest news. The centrality of social networks in daily life and their
implication for mental health [56, 57, 58, 59] stimulate new research which
try to unravel the dynamics of online social interactions.
The current exploratory study focuses on Instagram social behavior as a
result of potential interaction between genetic factors (i.e. Oxytocin Re-
ceptor Gene) and the environmental effects related to the quality of adult
relationships with their partners (i.e. adult attachment with an intimate
partner). Specifically, two OXTR Single-Nucleotide Polymorphisms (SNPs)
(OXTR rs53576 and OXTR rs2254298) as genetic factors and the Experience
in Close Relationship-Revised as the assessment for the recalled adult rela-
tionship were selected. Thee Instagram variables were collected: 1) number
of posts, 2) number of followed people (here called “followings”) and number
of followers. A further index, called “Social Desirability Index” (SDI) [60],
4was computed as the ratio of followers to followings.
In line with the Differential Susceptibility model, an interaction effect be-
tween the genetic factor and the close relationship scores was hypothesized
for each Instagram variable (see Appendix e). Specifically, regardless of the
gender, we formulated two directional hypotheses:
1) Instagram users with a genetic risk factor (OXTR rs224529 A carriers,
OXTR rs53576 G carriers) would show a higher number of posts and val-
ues of SDI when involved in a positive and comforting relationship with the
partner (Low Avoidance, Low Anxiety) compared to less vulnerable genetic
carriers (OXTR rs224529 G/G homozygotes, OXTR rs53576 A/A homozy-
gotes);
2) Instagram users with a genetic risk factor would show a lower number of
posts and SDI when involved in a negative and frustrating relationship with
the partner (High Avoidance, High Anxiety) compared to less vulnerable
genetic carriers.
Methods
Participants
61 non-parent adults were recruited among the students of Nanyang Tech-
nological University of Singapore. The participants of the study were all
Singaporean. Exclusion criteria were: (i) present or lifetime history of psy-
chiatric, neurological or genetic disorders, (ii) not having an Instagram ac-
count, and (iii) being older than 30 years. We could not take into account
four participants’ data because of failures in either the online compilation of
questionnaires or in Instagram data extraction. In the end, our final sample
consisted of 57 Singaporean adults (16 males) between 18 and 25 years-old
(M = 20.89, SD = 1.59) (see Appendix e).
Data Collection
Behavioral, genetic and Instagram data were collected (see Appendix e).
With regard to the behavioral assessment, every participant completed on-
line the self-report questionnaire Experience in Close Relationship-Revised
(ECR-R). With regards to genetic data, a sterile cotton swab for genotyping
was used to collect a buccal mucosa sample of DNA from each participant.
Instagram data were collected by asking the participants to provide the link
of their own Instagram profile. An ad-hoc Python script downloaded the
5variables of interest. When the automatic script failed, Instagram data were
collected manually.
Close Relationships
In order to assess participants’ relationship with the partner, the Ex-
perience in Close Relationship-Revised questionnaire was used. Developed
by Fraley et al. [61], the ECR-R (average Cronbach’s α = 0.90) is a 36-
item self-report questionnaire on a 7-point response scale, that examines the
close relationship across two dimensions: Anxiety (Cronbach’s α = 0.93) and
Avoidance (Cronbach’s α = 0.86). While the former is usually linked to wor-
ries about the relationship, and fear of rejection from the partner, the latter
mostly refers to fear of intimacy and closeness to the partner. We calculated
the scores for the two constructs of anxiety and avoidance by following the
scoring indications reported by Picardi et al. [62].
Genetic assessment
The genetic assessment of the present study employed the method re-
ported by Bonassi et al. [63]. DNA extraction and genotyping were per-
formed by ACGT, Inc. (Wheeling, IL). DNA was extracted by using Ora-
gene DNA purification reagent and its concentrations were evaluated through
spectroscopy (NanoDrop Technologies, USA). Each DNA sample was in-
creased by polymerase chain reaction (PCR) for the OXTR gene rs53576
region target with the primers 5-GCC CAC CAT GCT CTC CAC ATC-3
and 5-GCT GGA CTC AGG AGG AAT AGG GAC-3. A PCR reaction of
20 ll, comprising 1.5 ll of genomic DNA from the test sample, PCR buffer, 1
mM each of the forward and reverse primers, 10 mM deoxyribonucleotides,
KapaTaq polymerase, and 50 mM MgCl2 was executed. PCR operation com-
prised 15 minute denaturation at 95 ◦ C, and 35 cycles at 94 ◦ C (30 s), 60
◦
C(60 s), 72 ◦ C (60 s) and a final 10 minute step at 72 ◦ C. PCR reactions
were genotyped with an ABI 3730xl Genetic Analyzer (Applied Biosystems
Inc.) and normalized with GeneScan 600 LIZ (Applied Biosystems, Inc.) size
standards on each sample. Genotypic data were inspected using GeneMap-
per ID (Applied Biosystems, Inc.).
Within this DNA region, participants with at least one G allele (G/G ho-
mozygotes or A/G) were categorized into a single G-carriers group. The
distribution in our sample was 39% for A/A homozygous and 61% for G-
carriers. The frequencies of the genotype were: A/A = 22 (38.60%), A/G
= 30 (52.63%), G/G = 5 (8.77%), and the distribution was consistent with
6the Hardy-Weinberg Equilibrium (X 2 (1) = 1.376, ns). Participants’ gender
(X 2 (1) = 0.17, ns) did not significantly differ between the two groups A/A
vs G.
The same procedure was used to assess the OXTR gene rs2254298 region.
However, the forward and reverse primers were instead 5-TGA AAG CAG
AGG TTG TGT GGA CAG G-3 and 5-AAC GCC CAC CCC AGT TTC
TTC-3 respectively. For this DNA region, participants with at least one
A allele (A/A homozygotes or G/A) were classified into a single A-carriers
group. The distribution in our sample was 56% for G/G homozygotes and
44% for A-carriers. The frequencies of the genotype were: A/A = 4 (7.02%),
G/A = 21 (36.84%), G/G = 32 (56.14%), and the distribution was consistent
with the Hardy-Weinberg Equilibrium (X 2 (1) = 0.047, ns). Participants’
gender (X 2 (1) = 0.81, ns) did not significantly differ between the two groups
G/G vs A.
Instagram Variables
Four variables were considered as highly representative of online social
behavior on Instagram. Number of posts indicates the number of content
published by a participant’s profile. On Instagram, the number of posts is
equal to the number of published pictures, since this social network allows
accounts to publish pictures and video with an auxiliary text. The number of
followed people (”followings”) is an index that reflects the number of profiles
that a participant follows. Conversely, the number of followers is defined by
the number of profiles that follows the considered participant. Additionally,
the Social Desirability Index (SDI) was computed as the ratio of followers
to followings. This parameter, previously adopted as a measure of general
sociability [60, 64], estimates the balance between the number of followers
and followings for each account.
Statistical Analysis
Statistical analysis and data visualization were performed using R (R-
core base version 4.0.0.). Instagram data were standardized in z-scores and
inspected for normality. Values that were at least 2 SD above and below
the mean of the distribution were considered as outliers. Overall, 8 outliers
were detected: 3 for following numbers, 2 each for post numbers and SDI,
1 for follower numbers. The extreme values of each Instagram variable were
replaced by the mean in observations excluding outliers.
Normality of ECR-R and Instagram scores was tested by computing values
7of skewness and kurtosis (see Table 1) and their distribution were visualized
via boxplots, density plots and quantile-quantile plots.
Variable Skewness Kurtosis
Anxiety -0.38 -0.02
Avoidance -0.15 -0.39
Followings 0.35 -0.62
Followers 0.78 0.16
SDI -0.70 2.75
Posts number 1.56 1.81
Log-transformed posts number 1.14 0.43
Table 1: Summary of skewness and kurtosis values for ECR-R and Instagram variables.
Log-transformed number of posts shows increased values compared to the original sampling
of the same variable.
Only the variable number of posts did not follow the Gaussian distri-
bution. Thus, the related sampling was adjusted and log-transformed (see
Table 1). Subsequently, the assumption of the homogeneity of variance was
verified.
Four preliminary Student’s t-tests (corrected α = 0.0125) were computed to
exclude any potential influence of gender on the Instagram variables.
The hypothesis-driven approach adopted for Instagram number of posts and
SDI was extended in form of exploratory analysis to number of followings
and followers (see Appendix e). Therefore, a Bonferroni’s correction was
separately applied for the two hypothesis-driven (corrected α = 0.025) and
two exploratory tests (corrected α = 0.025). Bonferroni’s method was chosen
rather than alternative approaches to apply a more conservative correction.
OXTR rs2254298 and OXTR rs53576 were considered as single predictors in
distinct analyses.
For each Instagram variable, one mixed ANCOVA was performed with the
Instagram value as dependent variable, OXTR gene genotype rs53576 (A/A
and G-carriers) as a between-subject factor and the ECR-R dimensions (Anx-
iety and Avoidance) as continuous covariates. For each Instagram variable,
one mixed ANCOVA was also performed with the OXTR gene genotype
rs2254298 (G/G and A-carriers) as a between-subject factor and the other
parameters unchanged. A previous sensitivity power analysis in GPower
(power = 0.80) estimated a large effect size equal to 0.42 for an analysis of
covariance (ANCOVA: fixed effects, main effects and interactions; see Ap-
8pendix e).
Only one main effect on OXTR rs53576 was considered and represented by
scatterplot and barplot. A post-hoc two-tailed Student’s t-test was com-
puted to detect a hypothetical significant difference between the two genetic
groups on Instagram number of posts. Partial eta squared and Cohen’s d
were applied to assess the magnitude of the significant effects.
Results
Four preliminary two-tailed Student’s t-tests were performed to exclude
any significant effect of participant gender on the Instagram variables. As
expected, no significant differences in the standardized Instagram number of
posts, followings, followers, and on the standardized SDI were found between
male and female participants.
A main effect of OXTR rs53576 genotype was found on Instagram number
of followings (F (1,56) = 5.71, p < 0.021, pη 2 = 0.10). Post-hoc two-tailed
Student’s t test confirmed that the number of Instagram followings was sig-
nificantly different between the A/A vs G allele groups (t = 2.30, df = 55,
p < 0.025, d < 0.63) (see Figure 1). In contrast with our exploratory hy-
pothesis, a) no gene*environment interaction effects were found for OXTR
rs53576; b) with regards to OXTR rs2254298, no main effect or interaction
effect were detected. Means and standard deviations of the measured vari-
ables for OXTR rs53576 and rs2254298 are reported in Table 2 and Table 3,
respectively.
Discussion
Two gene*environment interactions on the number of posts and SDI
(OXTR rs53576 SNP * close relationship in adulthood; OXTR rs2254298
SNP * close relationship in adulthood) were initially hypothesised.
Contrary to the predictions, a main effect of gene OXTR rs53576 on Insta-
gram number of followings was found. Post-hoc tests revealed differential In-
stagram behavior between the two genetic groups within the OXTR rs53576.
Independent of the recalled adult relationship, A/A homozygotes showed a
high number of followings compared to G-carriers.
9Figure 1: (A) Effect of OXTR rs53576 on the standardized Instagram number of fol-
lowings. Green circles = A/A homozygotes; blue circles = G-carriers. Lines constitute
the linear models for A/A homozygotes (green) and G-carriers (blue). (B) Comparison
between the number of Instagram followings in A/A homozygotes (green) and G-carriers
(blue) (* p < 0.025).
Some potential considerations could be advanced in relation to the theoreti-
cal framework of interest. In reference to the Diathesis Stress model [45, 46],
a given allele is designated as protective or risk factor to social stressors
[65, 66]. Oxytocin effects on social conduct have been explored starting from
the intersection between stress, anxiety, fear and and prosociality [34, 67].
For instance, an oxytocin-dependent reduction of anxiety was discovered to
facilitate prosocial behaviors [26, 68]. In the intersection of hereditary fac-
tors and environmental triggers which shape human development, an alter-
native explanation is provided by the Social Salience Hypothesis of oxytocin
[69, 70, 71]. This well-supported hypothesis attributes a distinctive pattern
10OXTR rs53576
Number of Posts
ECR-R dimension Low/AA Low/G High/AA High/G
Avoidance 0.70 (0.07) 0.61 (0.06) 0.49 (0.04) 0.50 (0.06)
Anxiety 0.65 (0.07) 0.54 (0.06) 0.54 (0.06) 0.58 (0.07)
Number of Followings
ECR-R Dimension Low/AA Low/G High/AA High/G
Avoidance 0.54 (0.31) -0.25 (0.21) -0.22 (0.22) -0.44 (0.14)
Anxiety 0.42 (0.26) -0.18 (0.20) -0.08 (0.34) -0.40 (0.14)
Number of Followers
ECR-R Dimension Low/AA Low/G High/AA High/G
Avoidance 0.13 (0.15) -0.06 (0.16) -0.07 (0.16) -0.34 (0.07)
Anxiety 0.18 (0.14) -0.13 (0.12) -0.14 (0.16) -0.26 (0.12)
Social Desirability Index
ECR-R Dimension Low/AA Low/G High/AA High/G
Avoidance -0.13 (0.04) -0.10 (0.10) -0.20 (0.04) -0.19 (0.08)
Anxiety -0.19 (0.05) -0.05 (0.10) -0.13 (0.04) -0.26 (0.07)
Table 2: Mean values in OXTR rs53576 A/A homozygotes and G-carriers divided by ECR-
R dimensions (high or low) on Instagram variables. Standard error means are reported
between parentheses.
to each OXTR allele depending on the carrier’s ability in enduring difficulties
generated by a hostile environment [68]. From most of the previous research,
the protective factor A allele seems to confer higher resilience to adverse
conditions but also lower levels of sociability than carriers with the more
vulnerable allele G [72, 73]. Conversely, in a few cases A/A individuals were
found to display increased social attitudes and higher social sensitivity than
G-carriers [74, 75]. Overall, the role played by the OXTR rs53576 A and G
alleles on social behavior is debated [68, 73, 30].
In connection to our finding, distinct biological roots could have differently
shaped social media behavior among genetic groups (i.e. OXTR rs53576
A/A versus G-carriers), even the level of sociability on social networks as
Instagram. A high number of followings could represent the desire of the In-
stagram user to be involved in social interactions [76, 60]. Specifically, A/A
homozygotes could exhibit a genetically-founded inclination to online socia-
bility witnessed by a higher frequency of online behaviors (i.e. Instagram
number of followings) than G-carriers. As a protective factor, A allele could
11OXTR rs2254298
Number of Posts
ECR-R dimension Low/GG Low/A High/GG High/A
Avoidance 0.65 (0.06) 0.64 (0.07) 0.52 (0.05) 0.48 (0.06)
Anxiety 0.61 (0.06) 0.56 (0.06) 0.59 (0.06) 0.54 (0.07)
Number of Followings
ECR-R Dimension Low/GG Low/A High/GG High/A
Avoidance -0.07 (0.23) 0.34 (0.33) -0.41 (0.18) -0.31 (0.16)
Anxiety -0.04 (0.21) 0.23 (0.27) -0.38 (0.21) -0.18 (0.23)
Number of Followers
ECR-R Dimension Low/GG Low/A High/GG High/A
Avoidance -0.03 (0.16) 0.10 (0.17) -0.22 (0.13) -0.28 (0.07)
Anxiety -0.02 (0.13) 0.04 (0.14) -0.20 (0.16) -0.24 (0.10)
Social Desirability Index
ECR-R Dimension Low/GG Low/A High/GG High/A
Avoidance -0.14 (0.06) -0.07 (0.12) -0.22 (0.10) -0.16 (0.04)
Anxiety -0.15 (0.08) -0.06 (0.10) -0.21 (0.08) -0.21 (0.06)
Table 3: Mean values in OXTR rs2254298 G/G homozygotes and A-carriers divided by
ECR-R dimensions (high or low) on Instagram variables. Standard error means are re-
ported between parentheses.
predispose the user to be less anxious in prosocial activities and to actively
seek contact with other Instagram users. In contrast, G allele could predis-
pose the user to be less interested or less motivated to search for others on
Instagram. Given the contradictory results from the literature on the alleles
of OXTR rs53576, it is complex to ascertain which allele is the bearer of a
detrimental effect.
In light of the Differential Susceptibility model [51], the genetic properties
related to a given allele could not determine the impact on social behavior
only in terms of the ”positive” (protective factor) versus ”negative” (risk
factor) predisposition [77]. Rather, every individual could exhibit a range of
social behaviors as a function of the interaction between genetic sensitivity
to experiences and environmental influences [50, 78].
In a prior study, Bonassi et al. [60] reported that the interaction between
OXTR/rs2254298 and the early parental bonding could modulate the Insta-
gram number of posts and SDI. Nevertheless, authors did not detect any
gene*environment effect for OXTR/rs53576 on Instagram social behavior.
12In contrast to the previous study [60], the current results suggest that no
association between the quality of adult close relationships and the genetic
predispositions for OXTR/rs53576 or OXTR/rs2254298 could better explain
Instagram social behavior under a gene*environment perspective. However,
it is important to specify the different nature of environment considered for
the experimental design of the two studies. The early attachment with the
caregivers (i.e. parental bonding in terms of care and overprotection) repre-
sents the most significant relationship from the first years of life [79]. Quality
of early experiences, combined with allelic expression, affects the main de-
velopmental stages [80]. Adult attachment (i.e. close relationship with the
partner) belongs rather to mature life experiences which could only par-
tially influence social behavior [81]. The larger influence of early experiences
compared to adult experiences is more evident if we consider the sensitive
and critical periods in which the neural circuits develop [82], an index of
brain plasticity at its maximal potential to be shaped by the environment
[83, 84, 85].
These observations find agreement with a previously published meta-analysis
which identified only a significant association between OXTR/rs53576 and
general sociability, but no association between the same genetic region and
close relationship main dimensions [37]. An interesting view is also offered by
a study which found a partial modulation of social anxiety by OXTR, with
the A allele more affected by a less secure adult attachment [86]. Another
paper did not detect any associations between OXTR/rs53576 and insecure
adult attachment [87]. In the context of close relationships, individuals with
the GG genotype also reported higher marital satisfaction than A carriers
[88].
Although there is unanimity in assuming the variation of rs53576 as genetic
targets responsible for social functioning and prosocial activity [36, 41, 89,
90], the culture-dependent variation of allelic distributions should not be ig-
nored [91]. Several studies also tested the socio-emotional behaviour and
distress explained by variations in OXTR/rs53576 [40, 92, 42, 93] or by its
potential interaction with attachment [87] on an Eastern sample. Only few
studies investigated social behaviour [94] and online sociability on a Singa-
porean sample [60, 64].
Overall, OXTR rs53576 could shape the level of Instagram sociability inde-
pendently from the potential influence of the adult close relationship with
one’s partner.
Although no gene*environment interactions were found, this preliminary
13finding could inspire future research in exploring online social interactions
under a gene-environment perspective.
Limitations and Future Directions
The present study has some methodological limitations. Firstly, the sam-
ple size was small (N = 57) with a strong prevalence of female participants.
Secondly, no interaction effect was detected between genes and close rela-
tionship scores. This issue could be traced back to the limited sample size,
a condition that is likely to determine that power values were not sufficient
to obtained the hypothesised effect. Thirdly, no results were found for the
region OXTR rs2254298, which was previously found to be highly involved
in social behavior [95].
Overall, the present results should be interpreted with high caution in the
panorama of genetic association studies [96, 97, 98]. Data interpretation is
even more challenging in presence of a genetic influence independent of the
environmental exposure [99, 100] on an underexplored social variable such
as Instagram number of followings. Although there is no strong evidence of
Gene-Environment interactions on online social behaviors, the existing liter-
ature on general or in-person sociability or emotional support reveals that
the allelic distribution varies across ethnicities and multiple factors could de-
termine the role that A or G alleles play in the individuals’ life [42]. The
same differential social response towards other Instagram users between A/A
homozygotes and G-carriers could also depend on baseline differences, such
as personality traits, ethnicity, vulnerability to psychiatric disorders [71]. All
together, these factors could similarly have implicitly modulated the online
sociability of the Singaporean participants. A stimulating question would be
if OXTR rs53576 A and G allele maintain the same influence on physical
(i.e. in-person social interaction) and virtual environments (i.e. online social
interaction on Instagram).
Future studies should measure such factors and control their impact on In-
stagram behaviour to exclude potential biases.
Moreover, future directions should increase the sample size and explore close
relationships through alternative questionnaires as self-reported measures or
observational techniques (i.e. using the Adult Attachment Interview [101]).
Recent studies have also adopted neuroimaging techniques to measure brain-
to-brain synchrony of couples [102, 103]. Finally, different genetic regions as
potential factors of predisposition should be considered as well.
14These preliminary and exploratory results are shared with the scientific com-
munity to refine future research in the field.
Pre-registration
The public registration of this study can be found on the Open Science
Framework at the following address: https://osf.io/t78fu
Data Repository
The data of this study can be found in the NTU’s Data repository (DR-
NTU Data) at the following address: https://doi.org/10.21979/N9/GUSTLQ
Ethics
The research was authorized by the Ethical Committee of Nanyang Tech-
nological University (IRB-2015-08-020-01). Informed consent was obtained
from all participants. The study followed the Declaration of Helsinki. The
genetic assessment was conducted on anonymized samples at the Nanyang
Technological University (Singapore). Instagram data and questionnaires’
data were anonymized at the beginning of the data collection.
Authors Contribution
Conceptualization and Experimental Design: IC, GE, BL. Data Collec-
tion: IC. Data Curation: AB, GG. Genetic Analysis: MT, JNF. Data Anal-
ysis, Data Interpretation, Writing: AC, AB. Revision: AC, AB, BL, IC, GG,
GE. Supervision: GE, BL.
Funding Information
This research was supported by grants from the NAP SUG to GE (M4081597,
2015-2021).
Acknowledgements
All participants in this study are gratefully acknowledged. We would
also like to acknowledge Lim Mengyu (Nanyang Technological University,
Singapore) for editing the manuscript.
15Conflict of Interest
The authors declare that the research was conducted in the absence of any
commercial or financial relationships that could be construed as a potential
conflict of interest.
References
[1] S. R. Kaler, B. Freeman, Analysis of environmental deprivation: Cog-
nitive and social development in romanian orphans, Journal of Child
Psychology and Psychiatry 35 (1994) 769–781.
[2] M. J. Farah, L. Betancourt, D. M. Shera, J. H. Savage, J. M. Giannetta,
N. L. Brodsky, E. K. Malmud, H. Hurt, Environmental stimulation,
parental nurturance and cognitive development in humans, Develop-
mental science 11 (2008) 793–801.
[3] J. Bowlby, Attachment and loss: Volume ii: Separation, anxiety and
anger, in: Attachment and Loss: Volume II: Separation, Anxiety
and Anger, London: The Hogarth Press and the Institute of Psycho-
Analysis, 1973, pp. 1–429.
[4] J. Bowlby, Attachment and loss: Vol. 3: Loss, Hogarth Press and the
Institute of Psycho-Analysis, 1980.
[5] Y.-h. Ding, X. Xu, Z.-y. Wang, H.-r. Li, W.-p. Wang, The relation of in-
fant attachment to attachment and cognitive and behavioural outcomes
in early childhood, Early human development 90 (2014) 459–464.
[6] E. Rognoni, D. Galati, T. Costa, M. Crini, Relationship between adult
attachment patterns, emotional experience and eeg frontal asymmetry,
Personality and Individual Differences 44 (2008) 909–920.
[7] A. Dalsant, A. Truzzi, P. Setoh, G. Esposito, Maternal bonding in
childhood moderates autonomic responses to distress stimuli in adult
males, Behavioural brain research 292 (2015) 428–431.
[8] O. Nakash-Eisikovits, L. Dutra, D. Westen, Relationship between at-
tachment patterns and personality pathology in adolescents, Journal
of the American Academy of Child & Adolescent Psychiatry 41 (2002)
1111–1123.
16[9] M. Dozier, K. C. Stovall-McClough, K. E. Albus, Attachment and psy-
chopathology in adulthood., The Guilford Press, 2008.
[10] V. Lingiardi, F. Gazzillo, La personalità ei suoi disturbi: Valutazione
clinica e diagnosi al servizio del trattamento, Raffaello Cortina, 2014.
[11] M. D. S. Ainsworth, S. M. Bell, D. F. Stayton, Infant-mother attach-
ment and social development: Socialization as a product of reciprocal
responsiveness to signals., Cambridge University Press, 1974.
[12] G. Bohlin, B. Hagekull, A.-M. Rydell, Attachment and social func-
tioning: A longitudinal study from infancy to middle childhood, Social
Development 9 (2000) 24–39.
[13] D. W. Griffin, K. Bartholomew, Models of the self and other: Funda-
mental dimensions underlying measures of adult attachment., Journal
of personality and social psychology 67 (1994) 430.
[14] J. A. Feeney, Attachment, caregiving, and marital satisfaction, Per-
sonal Relationships 3 (1996) 401–416.
[15] C. E. Hamilton, Continuity and discontinuity of attachment from in-
fancy through adolescence, Child development 71 (2000) 690–694.
[16] I. Cataldo, A. Bonassi, B. Lepri, J. N. Foo, P. Setoh, G. Esposito,
Recalled parental bonding interacts with oxytocin receptor gene poly-
morphism in modulating anxiety and avoidance in adult relationships,
bioRxiv (2020).
[17] J. Holt-Lunstad, W. Birmingham, B. Q. Jones, Is there something
unique about marriage? the relative impact of marital status, relation-
ship quality, and network social support on ambulatory blood pressure
and mental health, Annals of behavioral medicine 35 (2008) 239–244.
[18] R. H. Coombs, Marital status and personal well-being: A literature
review, Family relations (1991) 97–102.
[19] S. R. Cotten, Marital status and mental health revisited: Examining
the importance of risk factors and resources, Family Relations (1999)
225–233.
17[20] R. W. Simon, Revisiting the relationships among gender, marital sta-
tus, and mental health, American journal of sociology 107 (2002) 1065–
1096.
[21] S. R. Braithwaite, R. Delevi, F. D. Fincham, Romantic relationships
and the physical and mental health of college students, Personal rela-
tionships 17 (2010) 1–12.
[22] S. Braithwaite, J. Holt-Lunstad, Romantic relationships and mental
health, Current Opinion in Psychology 13 (2017) 120–125.
[23] Z. I. Santini, A. Koyanagi, S. Tyrovolas, J. M. Haro, The association
of relationship quality and social networks with depression, anxiety,
and suicidal ideation among older married adults: Findings from a
cross-sectional analysis of the irish longitudinal study on ageing (tilda),
Journal of affective disorders 179 (2015) 134–141.
[24] I. F. Bielsky, L. J. Young, Oxytocin, vasopressin, and social recognition
in mammals, Peptides 25 (2004) 1565–1574.
[25] G. Domes, M. Heinrichs, A. Michel, C. Berger, S. C. Herpertz, Oxy-
tocin improves “mind-reading” in humans, Biological psychiatry 61
(2007) 731–733.
[26] M. Heinrichs, G. Domes, Neuropeptides and social behaviour: effects
of oxytocin and vasopressin in humans, Progress in brain research 170
(2008) 337–350.
[27] K. MacDonald, T. M. MacDonald, The peptide that binds: a system-
atic review of oxytocin and its prosocial effects in humans, Harvard
review of psychiatry 18 (2010) 1–21.
[28] I. Cataldo, A. Azhari, B. Lepri, G. Esposito, Oxytocin receptors (oxtr)
and early parental care: an interaction that modulates psychiatric dis-
orders, Research in developmental disabilities 82 (2018) 27–38.
[29] A.-K. Wermter, I. Kamp-Becker, P. Hesse, G. Schulte-Körne,
K. Strauch, H. Remschmidt, Evidence for the involvement of genetic
variation in the oxytocin receptor gene (oxtr) in the etiology of autis-
tic disorders on high-functioning level, American Journal of Medical
Genetics Part B: Neuropsychiatric Genetics 153 (2010) 629–639.
18[30] I. Cataldo, A. Azhari, G. Esposito, A review of oxytocin and arginine-
vasopressin receptors and their modulation of autism spectrum disor-
der, Frontiers in molecular neuroscience 11 (2018) 27.
[31] K. Uvnäs-Moberg, Physiological and endocrine effects of social con-
tact., New York Academy of Sciences, 1997.
[32] P. T. Ellison, P. B. Gray, Endocrinology of social relationships., Har-
vard University Press, 2009.
[33] C. E. Detillion, T. K. Craft, E. R. Glasper, B. J. Prendergast, A. C. De-
Vries, Social facilitation of wound healing, Psychoneuroendocrinology
29 (2004) 1004–1011.
[34] M. Heinrichs, T. Baumgartner, C. Kirschbaum, U. Ehlert, Social sup-
port and oxytocin interact to suppress cortisol and subjective responses
to psychosocial stress, Biological psychiatry 54 (2003) 1389–1398.
[35] R. Kumsta, M. Heinrichs, Oxytocin, stress and social behavior: neu-
rogenetics of the human oxytocin system, Current opinion in neurobi-
ology 23 (2013) 11–16.
[36] M. J. Bakermans-Kranenburg, M. H. van IJzendoorn, Oxytocin re-
ceptor (oxtr) and serotonin transporter (5-htt) genes associated with
observed parenting, Social cognitive and affective neuroscience 3 (2008)
128–134.
[37] J. Li, Y. Zhao, R. Li, L. S. Broster, C. Zhou, S. Yang, Association of
oxytocin receptor gene (oxtr) rs53576 polymorphism with sociality: a
meta-analysis, PloS one 10 (2015) e0131820.
[38] S. Wu, M. Jia, Y. Ruan, J. Liu, Y. Guo, M. Shuang, X. Gong, Y. Zhang,
X. Yang, D. Zhang, Positive association of the oxytocin receptor gene
(oxtr) with autism in the chinese han population, Biological psychiatry
58 (2005) 74–77.
[39] F. S. Chen, R. Kumsta, B. Von Dawans, M. Monakhov, R. P. Ebstein,
M. Heinrichs, Common oxytocin receptor gene (oxtr) polymorphism
and social support interact to reduce stress in humans, Proceedings of
the National Academy of Sciences 108 (2011) 19937–19942.
19[40] S. M. Rodrigues, L. R. Saslow, N. Garcia, O. P. John, D. Keltner,
Oxytocin receptor genetic variation relates to empathy and stress re-
activity in humans, Proceedings of the National Academy of Sciences
106 (2009) 21437–21441.
[41] M. J. Lucht, S. Barnow, C. Sonnenfeld, A. Rosenberger, H. J. Grabe,
W. Schroeder, H. Völzke, H. J. Freyberger, F. H. Herrmann, H. Kroe-
mer, et al., Associations between the oxytocin receptor gene (oxtr)
and affect, loneliness and intelligence in normal subjects, Progress in
Neuro-Psychopharmacology and Biological Psychiatry 33 (2009) 860–
866.
[42] H. S. Kim, D. K. Sherman, J. Y. Sasaki, J. Xu, T. Q. Chu, C. Ryu,
E. M. Suh, K. Graham, S. E. Taylor, Culture, distress, and oxytocin
receptor polymorphism (oxtr) interact to influence emotional support
seeking, Proceedings of the National Academy of Sciences 107 (2010)
15717–15721.
[43] G. Esposito, A. Truzzi, P. Setoh, D. L. Putnick, K. Shinohara, M. H.
Bornstein, Genetic predispositions and parental bonding interact to
shape adults’ physiological responses to social distress, Behavioural
brain research 325 (2017) 156–162.
[44] F. S. Chen, S. C. Johnson, An oxytocin receptor gene variant predicts
attachment anxiety in females and autism-spectrum traits in males,
Social Psychological and Personality Science 3 (2012) 93–99.
[45] S. M. Monroe, A. D. Simons, Diathesis-stress theories in the context
of life stress research: implications for the depressive disorders., Psy-
chological bulletin 110 (1991) 406.
[46] M. Zuckerman, J. H. Riskind, Vulnerability to psychopathology: A
biosocial model, 2000.
[47] G. Esposito, A. Azhari, J. L. Borelli, Gene× environment interac-
tion in developmental disorders: where do we stand and what’s next?,
Frontiers in psychology 9 (2018) 2036.
[48] J. Belsky, Variation in susceptibility to environmental influence: An
evolutionary argument, Psychological inquiry 8 (1997) 182–186.
20[49] J. Belsky, Differential susceptibility to rearing influence, Origins of the
social mind: Evolutionary psychology and child development (2005)
139–163.
[50] J. Belsky, M. J. Bakermans-Kranenburg, M. H. Van IJzendoorn, For
better and for worse: Differential susceptibility to environmental influ-
ences, Current directions in psychological science 16 (2007) 300–304.
[51] J. Belsky, M. Pluess, Beyond diathesis stress: differential susceptibility
to environmental influences., Psychological bulletin 135 (2009) 885.
[52] A. Truzzi, M. H. Bornstein, V. P. Senese, K. Shinohara, P. Setoh, G. Es-
posito, Serotonin transporter gene polymorphisms and early parent-
infant interactions are related to adult male heart rate response to
female crying, Frontiers in physiology 8 (2017) 111.
[53] E. Yaakobi, J. Goldenberg, Social relationships and information dis-
semination in virtual social network systems: An attachment theory
perspective, Computers in Human Behavior 38 (2014) 127–135.
[54] D. Blackwell, C. Leaman, R. Tramposch, C. Osborne, M. Liss, Ex-
traversion, neuroticism, attachment style and fear of missing out as
predictors of social media use and addiction, Personality and Individ-
ual Differences 116 (2017) 69–72.
[55] S. Flynn, C. Noone, K. M. Sarma, An exploration of the link between
adult attachment and problematic facebook use, BMC psychology 6
(2018) 34.
[56] S. Cohen, Social relationships and health., American psychologist 59
(2004) 676.
[57] D. Umberson, J. Karas Montez, Social relationships and health: A
flashpoint for health policy, Journal of health and social behavior 51
(2010) S54–S66.
[58] D. Karaiskos, E. Tzavellas, G. Balta, T. Paparrigopoulos, P02-232-
social network addiction: a new clinical disorder?, European Psychiatry
25 (2010) 1–1.
21[59] C. Schou Andreassen, S. Pallesen, Social network site addiction-an
overview, Current pharmaceutical design 20 (2014) 4053–4061.
[60] A. Bonassi, I. Cataldo, G. Gabrieli, J. N. Foo, B. Lepri, G. Esposito,
Oxytocin receptor gene polymorphisms and early parental bonding in-
teract in shaping instagram social behavior, International Journal of
Environmental Research and Public Health 17 (2020) 7232.
[61] R. C. Fraley, N. G. Waller, K. A. Brennan, An item response the-
ory analysis of self-report measures of adult attachment., Journal of
personality and social psychology 78 (2000) 350.
[62] A. Picardi, P. Vermigli, A. Toni, R. D’Amico, D. Bitetti, P. Pasquini,
Further evidence of the validity of the italian version of the question-
naire “experiences in close relationships”(ecr), a self-report instrument
to assess adult attachment, Italian Journal of Psychopathology 8 (2002)
282–294.
[63] A. Bonassi, T. Ghilardi, A. Truzzi, I. Cataldo, A. Azhari, P. Setoh,
K. Shinohara, G. Esposito, Dataset on genetic and physiological adults’
responses to social distress, Data in brief 13 (2017) 742–748.
[64] A. Bonassi, I. Cataldo, G. Gabrieli, M. Tandiono, J. N. Foo, B. Lepri,
G. Esposito, The interaction between serotonin transporter allelic vari-
ation and maternal care modulates sociability on instagram, PsyArXiv
(2020).
[65] R. E. Ingram, D. D. Luxton, Vulnerability-stress models, Development
of psychopathology: A vulnerability-stress perspective 46 (2005).
[66] K. van Heeringen, Stress-diathesis model of suicidal behavior, The
neurobiological basis of suicide 51 (2012) 113.
[67] P. S. Churchland, P. Winkielman, Modulating social behavior with
oxytocin: how does it work? what does it mean?, Hormones and
behavior 61 (2012) 392–399.
[68] R. A. Bethlehem, S. Baron-Cohen, J. van Honk, B. Auyeung, P. A.
Bos, The oxytocin paradox, Frontiers in behavioral neuroscience 8
(2014) 48.
22[69] A. H. Kemp, A. J. Guastella, The role of oxytocin in human affect: a
novel hypothesis, Current Directions in Psychological Science 20 (2011)
222–231.
[70] B. A. Tabak, Oxytocin and social salience: a call for gene-environment
interaction research, Frontiers in neuroscience 7 (2013) 199.
[71] S. G. Shamay-Tsoory, A. Abu-Akel, The social salience hypothesis of
oxytocin, Biological psychiatry 79 (2016) 194–202.
[72] F. Krueger, R. Parasuraman, V. Iyengar, M. Thornburg, J. Weel,
M. Lin, E. Clarke, K. McCabe, R. Lipsky, Oxytocin receptor genetic
variation promotes human trust behavior, Frontiers in human neuro-
science 6 (2012) 4.
[73] D. Andreou, E. Comasco, C. Åslund, K. W. Nilsson, S. Hodgins, Mal-
treatment, the oxytocin receptor gene, and conduct problems among
male and female teenagers, Frontiers in human neuroscience 12 (2018)
112.
[74] H. R. Laursen, H. R. Siebner, T. Haren, K. Madsen, R. Grønlund,
O. Hulme, S. Henningsson, Variation in the oxytocin receptor gene is
associated with behavioral and neural correlates of empathic accuracy,
Frontiers in behavioral neuroscience 8 (2014) 423.
[75] E. L. Smearman, D. A. Winiarski, P. A. Brennan, J. Najman, K. C.
Johnson, Social stress and the oxytocin receptor gene interact to pre-
dict antisocial behavior in an at-risk cohort, Development and psy-
chopathology 27 (2015) 309.
[76] M. Sherlock, D. L. Wagstaff, Exploring the relationship between fre-
quency of instagram use, exposure to idealized images, and psycho-
logical well-being in women., Psychology of Popular Media Culture 8
(2019) 482.
[77] G. I. Roisman, D. A. Newman, R. C. Fraley, J. D. Haltigan, A. M. Groh,
K. C. Haydon, et al., Distinguishing differential susceptibility from
diathesis–stress: Recommendations for evaluating interaction effects,
Development and psychopathology 24 (2012) 389.
23[78] B. J. Ellis, W. T. Boyce, Biological sensitivity to context, Current
directions in psychological science 17 (2008) 183–187.
[79] M. Malekpour, Effects of attachment on early and later development,
The British Journal of Development Disabilities 53 (2007) 81–95.
[80] P. Zimmermann, C. Mohr, G. Spangler, Genetic and attachment in-
fluences on adolescents’ regulation of autonomy and aggressiveness,
Journal of Child Psychology and Psychiatry 50 (2009) 1339–1347.
[81] A. Lomanowska, M. Boivin, C. Hertzman, A. S. Fleming, Parenting
begets parenting: A neurobiological perspective on early adversity and
the transmission of parenting styles across generations, Neuroscience
342 (2017) 120–139.
[82] J. W. Couperus, C. A. Nelson, Early Brain Development and Plastic-
ity., Blackwell publishing, 2006.
[83] D. Rice, S. Barone Jr, Critical periods of vulnerability for the de-
veloping nervous system: evidence from humans and animal models.,
Environmental health perspectives 108 (2000) 511–533.
[84] E. Herlenius, H. Lagercrantz, Development of neurotransmitter sys-
tems during critical periods, Experimental neurology 190 (2004) 8–21.
[85] I. Cataldo, M. J.-Y. Neoh, W. F. Chew, J. N. Foo, B. Lepri, G. Espos-
ito, Oxytocin receptor gene and parental bonding modulate prefrontal
responses to cries: a nirs study, Scientific Reports 10 (2020) 1–11.
[86] S. Notzon, K. Domschke, K. Holitschke, C. Ziegler, V. Arolt, P. Pauli,
A. Reif, J. Deckert, P. Zwanzger, Attachment style and oxytocin re-
ceptor gene variation interact in influencing social anxiety, The World
Journal of Biological Psychiatry 17 (2016) 76–83.
[87] O. Gillath, P. R. Shaver, J.-M. Baek, D. S. Chun, Genetic correlates
of adult attachment style, Personality and Social Psychology Bulletin
34 (2008) 1396–1405.
[88] J. K. Monin, S. O. Goktas, T. Kershaw, A. DeWan, Associations
between spouses’ oxytocin receptor gene polymorphism, attachment
security, and marital satisfaction, PloS one 14 (2019) e0213083.
24[89] H. Tost, B. Kolachana, S. Hakimi, H. Lemaitre, B. A. Verchinski, V. S.
Mattay, D. R. Weinberger, A. Meyer-Lindenberg, A common allele in
the oxytocin receptor gene (oxtr) impacts prosocial temperament and
human hypothalamic-limbic structure and function, Proceedings of the
National Academy of Sciences 107 (2010) 13936–13941.
[90] A. Kogan, L. R. Saslow, E. A. Impett, C. Oveis, D. Keltner, S. R.
Saturn, Thin-slicing study of the oxytocin receptor (oxtr) gene and
the evaluation and expression of the prosocial disposition, Proceedings
of the National Academy of Sciences 108 (2011) 19189–19192.
[91] J. Sasaki, H. Kim, J. Xu, Religion and well-being: An analysis of an
oxytocin receptor polymorphism (oxtr) and culture, Journal of Cross-
Cultural Psychology 42 (2011) 1394–1405.
[92] Y. Kawamura, X. Liu, T. Akiyama, T. Shimada, T. Otowa, Y. Sakai,
C. Kakiuchi, T. Umekage, T. Sasaki, H. S. Akiskal, The association be-
tween oxytocin receptor gene (oxtr) polymorphisms and affective tem-
peraments, as measured by temps-a, Journal of affective disorders 127
(2010) 31–37.
[93] O. Weisman, K. A. Pelphrey, J. F. Leckman, R. Feldman, Y. Lu,
A. Chong, Y. Chen, M. Monakhov, S. H. Chew, R. P. Ebstein, The as-
sociation between 2d: 4d ratio and cognitive empathy is contingent on
a common polymorphism in the oxytocin receptor gene (oxtr rs53576),
Psychoneuroendocrinology 58 (2015) 23–32.
[94] M. J. Y. Neoh, S. Peipei, A. Bizzego, M. Tandiono, J. N. Foo, G. Es-
posito, Gene-environment interactions in other-race face recognition:
Early caregiving experience and oxytocin receptor genotype interact to
increase speed of other-race categorization, PsyArXiv (2020).
[95] R. P. Ebstein, A. Knafo, D. Mankuta, S. H. Chew, P. San Lai, The
contributions of oxytocin and vasopressin pathway genes to human
behavior, Hormones and behavior 61 (2012) 359–379.
[96] M. R. Munafò, Reliability and replicability of genetic association stud-
ies., Addiction (2009).
25[97] C.-y. Liu, A. Maity, X. Lin, R. O. Wright, D. C. Christiani, Design
and analysis issues in gene and environment studies, Environmental
Health 11 (2012) 1–15.
[98] K. J. Mitchell, Neurogenomics-towards a more rigorous science., The
European Journal of Neuroscience 47 (2018) 109–114.
[99] J.-H. Lee, H.-T. Kim, D.-S. Hyun, Possible association between sero-
tonin transporter promoter region polymorphism and impulsivity in
koreans, Psychiatry research 118 (2003) 19–24.
[100] D. B. Bugental, J. E. Grusec, Socialization processes, Handbook of
child psychology 3 (2007).
[101] C. George, N. Kaplan, M. Main, et al., Adult attachment interview,
The Authors, 1996.
[102] A. Azhari, W. Leck, G. Gabrieli, A. Bizzego, P. Rigo, P. Setoh, M. H.
Bornstein, G. Esposito, Parenting stress undermines mother-child
brain-to-brain synchrony: A hyperscanning study, Scientific reports
9 (2019) 1–9.
[103] A. Azhari, M. Lim, A. Bizzego, G. Gabrieli, M. H. Bornstein, G. Es-
posito, Physical presence of spouse enhances brain-to-brain synchrony
in co-parenting couples, Scientific reports 10 (2020) 1–11.
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