GENETIC CORRELATES OF MALADAPTIVE ...

Running head: GENETIC CORRELATES OF MALADAPTIVE BELIEFS 1

Genetic correlates of maladaptive beliefs: COMT VAL158MET and irrational

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cognitions linked depending on distress

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Ioana Podinaa,*, Radu Poppb, Ioan Popb, Daniel Davida,c a

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Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania c

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Babeş-Bolyai University, Cluj-Napoca, Romania

Icahn School of Medicine at Mount Sinai, New York, USA

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Author Note

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a

Department of Clinical Psychology and Psychotherapy, Babeş-Bolyai University, No. 37,

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Republicii St., 400015, Cluj-Napoca, Romania

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b

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No. 6, Pasteur St., 400349, Cluj-Napoca, Romania

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c

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Madison Avenue St., 10029, New York, USA

Department of Medical Genetics, Iuliu Hatieganu University of Medicine and Pharmacy,

Department of Oncological Sciences, Icahn School of Medicine at Mount Sinai, No. 1425,

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*Corresponding author: Ioana R. Podina

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Department of Clinical Psychology and Psychotherapy, Babeş-Bolyai University

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No. 37, Republicii St., 400015, Cluj-Napoca, Romania

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Telephone/Fax number: +40 746367186/+40 0264434141

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E-mail: [email protected]

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Abstract

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Maladaptive/irrational beliefs are significant cognitive vulnerability mechanisms in

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psychopathology. They are more likely to be associated to a genetic vulnerability marker

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under conditions of emotional distress when irrational beliefs are more salient. Therefore, in

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the current study we investigated the COMT Val158Met gene variation in relation to irrational

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beliefs, assuming this relationship depended on the level of emotional distress.

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Two hundred and sixty seven genotyped volunteers were assessed for core/general

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maladaptive beliefs, as well as trait emotional distress. We focused on context-independent

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measures of irrational beliefs and emotional distress in the absence of a stressor. As expected,

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the relationship between COMT Val158Met and irrational beliefs depended on the level of

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emotional distress (f2 = .314). The COMT Val158Met-irrationality association was significant

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only when individuals fell in the average to above average range of emotional distress.

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Furthermore, within this range the Met allele seemed to relate to higher irrational beliefs.

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These results were significant for overall irrational beliefs and its subtypes, but not for

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rational beliefs, the functional counterpart of irrationality. In light of the study’s limitations,

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the results should be considered as preliminary. If replicable, these findings have potential

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implications for therapygenetics, changing the view that COMT Val158Met might be of

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greater relevance when treatment modality does not rely on cognitive variables.

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Keywords: Maladaptive beliefs, Irrationality, COMT Val158Met, Distress

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Introduction

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A key mechanism of change underlying response to Cognitive Behavioral Therapy (CBT)

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is related to changes in maladaptive beliefs embodied by irrational cognitions (Hofmann,

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Asmundson, & Beck, 2013). Irrational cognitions (i.e., appraisals) are illogical, inconsistent

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with reality, and hinder the person from achieving his/her goals (Ellis, 1994). According to

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Rational Emotive Behavioral Therapy (REBT; Ellis, 1994) there are four categories of

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irrational beliefs: (a) demandingness or rigid thinking (i.e., absolutistic requirements that a

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person/situation must be in a certain way; DEM), (b) global evaluation (e.g., global negative

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evaluations about oneself/self-downing; SD), (c) awfulizing (i.e., beliefs which perceive

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various activating events as catastrophic, namely the worst thing that could happen; AWF),

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and (d) low frustration tolerance/frustration intolerance (i.e., beliefs that one cannot tolerate

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an event/situation; LFT). During various activating events (e.g., the loss of a loved one)

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irrational beliefs generate dysfunctional consequences (e.g., depressed mood). While DEM

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(referred also as rigid thinking; RT) is conceptualized as a general primary irrational belief,

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SD, AWF, and LFT are conceptualized as secondary derivative beliefs, more proximal to

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distress. The latter beliefs are equally close and differentially related to distress. Indeed,

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several studies highlight the mechanistic role played by SD in depression, by AWF in

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anxiety, and by LFT in self-control problems (e.g., anger, addictions, binge eating, self-harm)

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(for a review, see Szentagotai & Jones, 2010).

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Unlike irrational beliefs, rational/adaptive beliefs are logical, functional, and empirically

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based. The core rational belief is preferential/flexible thinking (e.g., ―I prefer not to be

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laughed at, I am doing my best to avoid it, but I can tolerate it‖ instead of ―I must not be

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laughed at and I cannot tolerate that if it happens‖). While irrational beliefs are considered 3


cognitive vulnerability factors for a large spectrum of psychopathology, rational beliefs are

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considered protective factors against psychopathology (for a review, see Browne, Dowd, &

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Freeman, 2010; Caserta, Dowd, David, & Ellis, 2010).

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Core irrational beliefs (B) are relevant for psychopathology because they reflect

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general maladaptive thinking (e.g., ―I must do things perfectly and it is awful if I do not do

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them as such‖), which biases the processing of activating events (A) (e.g., public speaking);

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thus, specific irrational beliefs are generated (e.g., ― It will be awful if I do not give a perfect

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speech‖) that lead to negative consequences (C) (e.g., public speaking anxiety) (see the ABC

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model - Ellis, 1994). Both core/general and specific irrational beliefs have been linked to

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several psychopathological disorders, both in an associative and causal manner (for a review,

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see Browne, Dowd, & Freeman, 2010). General irrational beliefs are particularly seen as

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factors of cognitive vulnerability in psychopathology.

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Biological Underpinnings of Maladaptive Beliefs

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It has been speculated that irrational beliefs have a biological basis since the 1970’s

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(Ellis, 1976). For instance, there is preliminary evidence suggesting a connection between the

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neural mechanisms underlying specific irrational beliefs (Cristea et al., 2011) and self-

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mentalizing areas of the brain. However, while these studies are important to understand the

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biological basis (i.e., neural implementation) of irrational beliefs, there is no study that

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investigates the biological underpinnings of irrational thinking at a more basic, genetic level.

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Pinpointing a genetic candidate for maladaptive or irrational beliefs is all the more

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relevant as there is tentative evidence that irrational beliefs (a) are transmitted from parent to

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child (Johnson, 2010) and (b) the mechanism of transmission may be partially genetic. In line

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with this result, past research has indicated that maladaptive beliefs have a moderate heritable 4


component as it has been evidenced in a sample of 674 adolescent twins, where non-shared

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environmental factors explained 60% of variance in maladaptive beliefs (Chen & Li, 2014).

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We chose the Catechol-O-Methyltransferase (COMT) gene as a candidate gene for

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further research on irrational beliefs. COMT codes for a monoamine (e.g. dopamine,

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epinephrine, and norepinephrine) degrading enzyme. One of its variants, a G to A substitution

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(rs4680), which results in an amino acid change (Val158Met) at position/codon 158 in the

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COMT gene stirred special interest in psychopathology research. Individuals with 2 copies of

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the Met allele (Met/Met) have 25–75% reduction in COMT enzyme activity as compared to

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individuals with 2 copies of the Val allele (Val/Val) (Chen et al., 2004). This difference has

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an effect on the regulation of dopamine. It leads to an excess of synaptic dopamine in Met

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allele carriers (Tunbridge, Harrison, & Weinberger, 2006) with a negative impact on the

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emotional level disorders like anxiety and depression (Witte & Flöel, 2012). However, at the

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cognitive level dopamine effects on cognitive processes follow an inverted U-shaped dose-

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response curve, with both deficient and excessive amounts of dopamine activity resulting in

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poor cognitive tasks performance (Dumontheil et al., 2011).

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Though irrational beliefs are higher-order cognitive processes that might seem distal

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from genetic influences, related evidences seem to contradict this assumption on both

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empirical and theoretical grounds. Furthermore, we pinpoint COMT Val158MET as a

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candidate polymorphism for irrationality

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Empirical Arguments.

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First, a potential association between COMT Val158Met and irrational beliefs is in line

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with previous research that has associated this polymorphism to less cognitive flexibility

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(Bilder, Volavka, Lachman, & Grace, 2004), a hallmark of irrational beliefs (Dryden, 2003).

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Second, we examined COMT Val158Met in the current study given its large effect size

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association to prefrontal activation (Mier, Kirsch, & Meyer-Lindenberg, 2010), an area

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reputed to host maladaptive beliefs, predominantly the medial prefrontal cortex (Disner et al.,

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2011).

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Third, there are previous studies that have directly associated COMT Val158MET and

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specifically the Met allele to a restricted class of maladaptive beliefs (i.e., obsessive-

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compulsive beliefs; Alonso et al., 2013).

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Fourth, therapygenetics evidence indicates that COMT Val158Met modulates response

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to exposure based CBT for panic disorder (Lonsdorf et al., 2010), where changes in

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maladaptive cognitions are an important mechanism of change, making the association of

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COMT Val158Met with maladaptive beliefs all the more plausible.

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Fifth, though there is little research on the biological/genetic underpinnings of

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maladaptive beliefs, there is some evidence linking irrational beliefs to cathecolamines. As

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such, according to Kirk & Spillane (1984) irrational beliefs were identified as a strong

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predictor of both epinephrine and norepinephrine levels in stressful situations. Similarly, a

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study by Gruen et al. (2000) investigated whether maladaptive beliefs predicted changes in

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catecholamine biochemistry in baseline, immediately after stress exposure, and 40 minutes

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later. The results indicated that maladaptive beliefs significantly predicted stress-induced

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alterations in noradrenergic output.

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Though the association between irrational beliefs and cathecolamines has been

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documented so far only at the hormonal level, (a) research by Kirk and Spane (1984)

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alongside (b) the enlisted empirical arguments and (c) hereditary evidences for this pattern of

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thinking, pinpoint COMT Val158Met as a potential correlate of irrational beliefs.

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Theoretical Arguments. 6


In light of the current research in biology and genetics, Aron T. Beck has recently

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updated the cognitive model of depression, which is over 50 years old. He argues for a

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generic cognitive model of psychopathology, where genetic influences could be at the core of

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maladaptive beliefs (Beck & Haigh, 2014). Though the idea is not new, this generic cognitive

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model of psychopathology marks the integration of cognitive and genetic models of risk.

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However, the limited number of studies focused on the interplay between genetics and

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maladaptive beliefs makes the objectives of the current study all the more representative of

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this theoretical stand.

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Emotional Boosters of Maladaptive Beliefs

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Emotional distress is usually a booster for irrational beliefs, especially when it follows

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a stressor (Bridges & Harnish, 2010). Trait-like emotional distress (i.e., high levels of

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depression or anxiety) is a marker of vulnerability to psychopathology. Irrational beliefs tend

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to be higher when trait emotional distress is elevated (e.g., Deffenbacher et al., 1986; Chang,

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1997). Therefore, if a genetic vulnerability marker is present its association to irrational

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beliefs is much more likely in the context of increased emotional distress, where irrational

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beliefs are much more salient. Indeed, results show that COMT Val158Met influences on the

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brain and underlying cognitive responses are modulated by stress and mood (He et al., 2012;

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Buckert et al., 2012; Heinz & Smolka, 2006) such that they become apparent under

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manifestations of stress and emotional distress. All these arguments point to a potential

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association between COMT Val158Met and irrational beliefs depending on the level of

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emotional distress.

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Overview of the Present Study

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When considered together, genetic (i.e., COMT Val158Met) and cognitive (i.e.,

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irrational beliefs) psychopathology markers could extend our knowledge regarding the

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genetic underpinnings of core/general irrational beliefs. Given that this research is primarily

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focused on general irrationality (i.e., indexed by overall irrationality), the analyses were split

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into a-priori and post-hoc analyses. This a-priori/post-hoc distinction was made following

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Feise (2002) and several other authors (e.g., Bendera & Lange, 2001), who recommend the

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selection of a primary endpoint or global assessment measure in order to increase the

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statistical power of the research and reduce potential Type II errors given by multiple

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comparison corrections. This split is visible in the following:

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A-priori Assumptions Concerning the Primary Outcome

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First, in view of studies which regard the influence of genes on phenotype as

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modulated by the amount of stressor or subsequent emotional distress (Caspi et al., 2010); the

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current study intended to investigate if there is a link between COMT Val158Met

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polymorphism and irrationality and whether this link is dependent on the level of emotional

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distress. Thus, under a high level of emotional distress a stronger relationship between

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COMT Val158Met and irrational beliefs is expected.

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Second, in order to conduct a thorough research on the type of genetic model that is

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driving the relationship between COMT Val158Met and irrational beliefs we investigated

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dominant and recessive genetic models for the Met allele. In order to allow the detection of

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such effects, we mainly examined (a) Met/Met vs. Val carriers, which entailed a recessive

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genetic model for the Met allele; and (b) Met carriers vs. Val/Val, which entailed a dominant

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genetic model for the Met allele. The examination of these genetic models was restricted to

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the levels of emotional distress where a relationship between COMT Val158Met and irrational

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beliefs has been observed in the moderation analysis.

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Post-hoc Analyses Concerning the Secondary Outcomes

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We have performed moderation and differential analyses for the core categories of

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irrational beliefs, that is (a) RT (rigid thinking or demandingness), (b) SD (i.e., self-downing),

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(c) AWF (i.e., catastrophizing/awfulizing), and (d) LFT (i.e., low frustration tolerance), as

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well as for rationality or flexible thinking.

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Given the focus on general genetic (i.e., COMT) and cognitive vulnerabilities to

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psychopathology, we focused on context-independent measures of cognitive vulnerability,

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that is in the absence of a stressor. Therefore, we screened for common emotional distress

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variables, such as trait anxiety and depression in a sample of undergraduate students, which

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were genotyped and which filled in measures of irrational beliefs.

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The scarce number of studies focused on the interplay between genetics and

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maladaptive beliefs, makes the objectives of the current study all the more relevant for this

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gap in the literature. Moreover, a link between COMT Val158Met and irrationality would

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challenge how this polymorphism is currently seen in therapygenetics research, as it is

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regarded as a polymorphism related to treatment change in exposure-based, but not cognitive-

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based treatment modalities (Lester & Elley, 2013; e.g., Lonsdorf et al., 2010).

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Method Participants

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We recruited 267 Caucasian volunteers (age: M = 23.112 years, SD = 5.040, 81.273 %

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women)1. Participants had the following genotype frequencies: 0.270, Met/Met (N = 71);

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0.440, Val/Met (N = 119); 0.290, Val/Val (N = 77), which were similar to the ones that were

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reported for Caucasians by Herderson et al. (2000). All the recruited participants were

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undergraduate students. They were screened for depression and anxiety levels. Although

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these scores were informative, they were not employed as selection criterion. This allowed us

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to investigate a broad segment of the irrationality continuum, given the strong relationship

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between emotional distress and irrationality.

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None of the volunteers mentioned had some form of cardiovascular or neurological

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condition or/and were under medication (e.g., anxiolytics, beta-adrenergic antagonists,

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psychotropics). The current research followed the recommendations specified by the

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Declaration of Helsinki (World Medical Association, 1964/2008). The study was approved

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by the University’s Research Council. Volunteers signed an informed consent and received

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credits for participation. In the following, we report all the measures of the current research,

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as well as all data exclusions and manipulations.

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Instruments

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Irrationality. The Attitudes and Beliefs Scale-Second Edition (ABS-II; DiGiuseppe,

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Leaf, Exner, & Robin, 1988; Macavei, 2002) is an instrument that measures core general

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rational and irrational cognitions. ABS-II is grouped into the following subscales: overall

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irrationality, overall rationality, RT (e.g., ―I must be liked by people I want to like me, and I

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The genotyped participants took part in other studies of the first author. They were initially recruited for the current battery of tests, and subsequently recruited for an additional exploratory study.

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do not accept their not liking me‖), SD (e.g., ―If important people dislike me, it is because I

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am an unlikable, bad person‖), AWF (e.g., ―It is awful to do poorly at important things, and I

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think it is a catastrophe if I do poorly‖), and LFT (e.g., ―It is unbearable to fail at important

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things, and I cannot stand failing at them‖). Higher values on the irrationality/rationality

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scales indicate higher irrationality or rationality levels. ABS-II has demonstrated good

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internal consistency, as well as good discriminatory properties between healthy and clinical

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groups (Macavei, 2002). In our sample, overall irrationality values (α = .945) and rationality

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values (α = .950) showed a good internal consistency. Internal consistency for irrationality

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subscales varied from α = .847 (LFT) to α = .862 (SD).

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Emotional Distress: Depression levels. Beck Depression Inventory –Second Edition

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(i.e., BDI-II; Beck, Steer, & Brown, 1996; David & Dobrean, 2012) is a 21-item instrument

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that assesses the severity of depression on a scale from 0 to 3, where higher values represent

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higher depressive levels. This measure has good reliability and validity in both healthy and

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depressed individuals (Beck et al., 1996). In the current study, the internal consistency was α

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= .886. With respect to the current sample, BDI-II values ranged from 0 to 28, with 128

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individuals reporting minimal (BDI-II cut off range: 0-13), 90 mild (BDI-II cut off range: 14–

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19), 49 moderate (BDI-II cut off range: 20-28), and 0 severe symptom levels (BDI-II cut off

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range: 29-63). A mean score of 14.146 (SD = 6.256) was observed in the recruited sample.

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Though these clinical cut-offs were informative regarding possible depression diagnoses,

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they were not an inclusion criterion.

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Emotional Distress: Trait anxiety. The Trait Anxiety Inventory (STAI-X2;

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Spielberger, Gorsuch, Lushene, Vagg, & Jacobs, 1983; Pitariu, Miclea, & Munteanu, 1987) is

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a 20-item scale that assesses one’s tendency to experience anxiety symptoms. Its scores are 11


rated on a 4-point Likert scale varying from 1 (i.e., almost never) to 4 (i.e., almost always),

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higher scores indicating higher trait anxiety levels. Previous studies have shown that STAI-

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X2 has adequate psychometric properties (Cocia, Uscatescu, & Rusu, 2012). In our sample,

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STAI-X2 displayed a good internal consistency (α = .927). The STAI- X2 is not a clinical

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instrument as it does not assess impairment and thus no clinical cutoff levels exist. However,

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to our knowledge in the current sample the mean of STAI-X2 (M = 42.966; SD = 11.164,

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min score = 21 and max score = 69) fell within the moderate range of trait anxiety levels,

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where scores could vary from 20 to 80 points. As in the case of BDI-II, STAI-X2 scores were

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informative for the purposes of this study; however, they were not an inclusion criterion.

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Genotyping

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Following peripheral blood extraction, genomic DNA was extracted from leukocytes

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using a 300µl blood sample DNA isolation protocol (Wizzard Genomic DNA Purification

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Kit, Promega, Milan, Italy). Genotyping was performed via polymerase chain reaction-

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restriction fragment length polymorphism (PCR-RFLP) based on Albaugh et al.’s (2010)

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protocol. A 109-base-pair (bp) fragment was amplified via PCR in a gradient thermocycler

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(Mastercycler Gradient, Eppendorf, Hamburg, Germany) using COMTforward, 5’-CTCA

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TCACCATCGAGATCAA-3’ and COMTreverse 5’-CCAGGTCTGACAACGGGTCA-3’

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primers. Val and Met alleles were determined using RFLP with restriction endonuclease

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NlaIII. Resulting fragments were visualized on a UV trans-illuminator. The fragment lengths

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for each genotype were: Val-Val (86 and 23 bp), Val/Met (86, 68, 23, and 18 bp), and

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Met/Met (68, 23 and 18 bp).

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Procedure

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The current study had a cross-sectional design. Its procedure entailed (a) receiving an

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informed consent, (b) genotyping and (c) filling in BDI-II and STAI-X2 measures of general

3

emotional distress alongside the ABS-II scale, which assessed general/core rational and

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irrational beliefs. We focused on context-independent measures of cognitive vulnerability and

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emotional distress, meaning that no stressor was induced to trigger maladaptive beliefs.

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Data Analysis

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Key variables, such as irrationality, rationality, depression, and anxiety were

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examined for outliers as a function of genotype. Values more than 3 SDs from the mean were

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excluded from the analysis. Following this rationale, six outliers were observed across the

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key variables and removed from the analyses. However, since the presence of outliers did not

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significantly change any of the results, the original outputs are reported in the results section.

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Moreover, depression and anxiety symptoms overlapped substantially, with a

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correlation of r (265) = .786 p < .001. In light of this overlap, the influence of anxiety on

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subsequent analyses cannot be properly isolated from the influence of depression on the

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recruited sample (see, Miller & Chapman, 2001). Given that we focused on general cognitive

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vulnerabilities to psychopathology, an alternative approach would be to partial out emotional

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distress, that is a composite score created by standardizing BDI-II and STAI-X2 scores into z

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scores and summing them up (for a similar approach, see Watkins et al., 2006; Rutledge et

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al., 2009). As we wanted to avoid negative values for emotional distress and facilitate its

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interpretation, we converted composite z scores into T scores. The composite score for

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emotional distress was a robust substitute for depression and anxiety, having a .918

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correlation to each of the emotion related measures.

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Moderation Analyses

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Six hierarchical linear regression analyses were performed to investigate the potential

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moderating role played by emotional distress on the outcome (i.e., rationality, irrationality

4

and its subtypes). As such, the predictors (i.e., genotype and emotional distress) were entered

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in the first step and the moderator was entered in the second step. The genotype was entered

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with an additive genetic model. Significant interaction effects were probed by means of the

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Process script (Hayes, 2012). Thus, simple slope analyses were employed to examine the

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relationship between the predictor and the outcome at 1 SD above and 1 SD bellow the mean

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value of the moderator.

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The effect size for moderation in a regression analysis is represented by Cohen's f2.

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According to Cohen’s (1992) conventions, a small effect size corresponds to an f2 value of .02

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- .14, a medium effect size corresponds to an f2 value of .15-.34, and a large effect size

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corresponds to an f2 value of .35 or above. In the case of the current study, significant

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moderation results had a medium effect size (see Table 3 and Table 4).

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G*Power analysis (3rd version, Faul, Erdfelder, Lang, & Buchner, 2007) revealed that

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in the case of multiple regression analyses with three predictors (interaction included), set at

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an alpha of .05, and a power ≥ 0.80, approximately 395 participants are required to detect a

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small effect size (f2 = .02) of the moderation effect. As such, the current sample is powered to

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detect only moderate-to-large effects of the moderation results.

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Differential Analyses

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Subsequent to a significant moderation, a series of planned contrasts were performed

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to explore in detail what type of genetic model was driving the association between genotype 14


and outcome. In order to allow the detection of such effects, we mainly (a) Met/Met vs. Val

2

carriers, which entailed a recessive genetic model for the Met allele; (b) Met carriers vs.

3

Val/Val, which entailed a dominant genetic model for the Met allele. All the analyses were

4

performed in SPSS.

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A-priori/Post-hoc analyses

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Moderation and differential analyses were split into a-priori and post-hoc analyses. No

7

multiple test adjustments were performed for post-hoc analyses, which are in congruence

8

with the recommendations of Bendera & Lange (2001) and several other authors (e.g.,

9

Rothman, 1990) in the case of exploratory or supplementary investigations.

10

Strictly correcting for all the post-hoc analyses would require an adjusted p value less

11

than 0.008 for the interaction results, for instance. None of the post-hoc analyses reported

12

here would survive this correction. However, we find this to be a very conservative measure

13

which increases the chances for a Type II error. Nonetheless, post-hoc results are the

14

outcomes of exploratory and not confirmatory analyses and should be interpreted with

15

caution.

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Results

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Descriptive Analyses

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Table 1 displays the inter-correlations among the dimensional variables. Mean scores

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for depression and anxiety (i.e., emotional distress), overall rationality, overall irrationality,

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as well as irrationality subscales are presented in Table 2, split by genotype.

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Table 1. Descriptive data and correlations between dimensional key variables.

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Running head: GENETIC CORRELATES OF MALADAPTIVE BELIEFS

1. Emotional Distress

2. Overall Rationality

1

2

3

1

-.228*

.456*

1

3. Overall Irrationality

4. Rigid Thinking

4

5

6

7

.225*

.557*

.451*

.412*

-.457*

-.490*

-.480*

.779*

.768*

.884*

.768*

1

.413*

.643*

.721*

1

.663*

.584*

1

.787*

-.531* -.379*

1

5. Self-Downing

6. Awfulizing

1

7. Low-Frustration Tolerance 1 2 3 4

Note: *p < .05, Bonferonni Holm corrected for multiple comparisons The remaining of the preliminary analyses are split into demographics and emotional distress categories, as in the following paragraphs. Demographics. The genotype distribution followed the Hardy-Weinberg equilibrium

5

(HWE),

6

differences among genotype distributions with respect to gender,

7

age, t (265) = .055, p = .956, d = .006. Just as in the case of genotype, overall irrationality [r

8

(265) = - .079, p = .199] and rationality scores [r (265) = .081, p = .186] were not related to

9

age. Similarly, there were no gender differences in terms of overall irrationality [t (265) = -

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3.123, p = .077. With respect to demographics, there were no significant .664, p = .415, and

.308, p = .758] and overall rationality [t (265) = 1.116, p = .245, d = .137].

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Emotional Distress. There were no significant differences among genotype

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distributions in terms of depression [t (265) = -.295, p = .768, d = -.036] and anxiety [t (265) 16


= .972, p = .332, d = .119]. In accordance with previous literature, while overall irrationality

2

was positively associated to depression [r (265) = .436, p < .001] and anxiety [r (265) = .399,

3

p