Emotional dysregulation, alexithymia and neuroticism: a systematic review on the genetic basis of a subset of psychological traits

Neuroticism, alexithymia and emotion dysregulation are key traits and known risk factors for several psychiatric conditions. In this systematic review, the aim is to evaluate the genetic contribution to these psychological phenotypes. A systematic review of articles found in PubMed was conducted. Search terms included ‘genetic’, ‘GWAS’, ‘neuroticism’, ‘alexithymia’ and ‘emotion dysregulation’. Risk of bias was assessed utilizing the STREGA checklist. Two hundred two papers were selected from existing literature based on the inclusion and exclusion criteria. Among these, 27 were genome-wide studies and 175 were genetic association studies. Single gene association studies focused on selected groups of genes, mostly involved in neurotransmission, with conflicting results. GWAS studies on neuroticism, on the other hand, found several relevant and replicated intergenic and intronic loci affecting the expression and regulation of crucial and well-known genes (such as DRD2 and CRHR1). Mutations in genes coding for trascriptional factors were also found to be associated with neuroticism (DCC, XKR6, TCF4, RBFOX1), as well as a noncoding regulatory RNA (LINC00461). On the other hand, little GWAS data are available on alexythima and emotional dysregulation.


Introduction
It is now widely accepted that the psychological framework of each individual is shaped by a complex interplay of environmental, biological and genetic factors; the latter constitute an intriguing field of research, as a better understanding of the genomic variants concurring to a specific personality archetype or psychological trait may serve as an invaluable tool to comprehend and predict the intricacies of personal and collective mechanisms of information processing. Genetics has played a prominent role in psychiatric research since the very beginning of the era of etiological investigation of mental diseases (Burmeister et al., 2008), especially, regarding bipolar disorder and schizophrenia (Escamilla and Zavala, 2008;Henriksen et al., 2017). This intuition was later applied also to psychological and personality traits (Jang et al., 1996). Psychiatry genetics also benefitted greatly from recent advances in sequencing technologies; large-scale investigations such as genome-wide association studies (GWASs), in which thousands or even hundreds of thousands of genomes are correlated to a specific trait with a hypothesis-free approach, rather than considering a single gene-trait interconnection (Collins and Sullivan, 2013;Visscher et al., 2017), are now feasible.
Starting from this promising foothold, research into the field has expanded exponentially; among the various psychopathological domains, personality traits were one of the most obvious subjects to investigate, due to their unambiguous characterization (Rao and Broadbear, 2019), which retains its validity even in presence of potentially confounding sociocultural factors (Ayinde and Gureje, 2021) (at least, if compared to other psychological entities) (Zimmerman et al., 2015).
Most of the research on these topics employs the NEO Personality Inventory (named after its main original components, neuroticism, extraversion and openness to experiences) the same paper is already cited shortly after (McCrae), a well-recognized tool that explores five personality dimensions while providing an unparalleled perspective on multiple psychological domains (McCrae et al., 2005). Some of these traits share numerous features and, rather than being distinct entities, contribute to complex personality structures that in certain occasions also predispose to psychiatric morbidity. One such case is constituted by alexithymia, emotional dysregulation and neuroticism, which all fall into a category characterized by inadequate, exaggerated or inhibited emotional responses, with frequent mood shifts and increased incidence of psychiatric conditions (Bradley et al., 2011;Ormel et al., 2013;Hemming et al., 2019). These entities in turn serve as a manifestation of this spectrum of features, hence identifying their common genetic roots might serve as an adequate instrument to describe the higher rank system encompassing them. These traits have an estimated heritability factor between 30 and 60% (evaluated through twin concordance studies) (Picardi et al., 2011;Hawn et al., 2015;Boomsma et al., 2018).
Several association analyses have been carried out on these traits (see the Materials and methods section); however, an investigation on their causative factors while considering them as a complex, multifacetbut still individualentity might provide a whole new perspective on the subject. As shedding light on these constructs might prompt the development of new clinical and procedural approaches, and better understanding of their genetic basis might serve as a solid starting point, a review concerning these specific personality traits and their gene associations might as well prove valuable. To achieve this, the first step was to clearly define these traits separately, to subsequently merge the inferred information in a comprehensive report.

Neuroticism
Neuroticism as a concept was first theorized explicitly by Eysenck around 1950(Eysenck, 1952. People high in neuroticism (both meant as test scores and theoretical conception) are more prone to negative emotions than average; they also tend to be more impulsive, less able to delay gratification and more likely to suffer from life stressors. Neuroticism can, thus, be defined as the tendency to experience negative emotions such as fear, anxiety, irritability, feelings of guilt and anger (Widiger and Oltmanns, 2007). These difficulties often heavily influence the individual's ability to navigate effectively social contexts (Messina et al., 2010).
The concept of neuroticism is not only useful in the field of personality psychology but also when taking into account psychiatric clinical practice. In fact, it has been proven that people with high neuroticism scores have a higher chance of being diagnosed with a mental health disorder, especially regarding internalizing conditions such as anxiety and depressive disorders (Khan et al., 2005;Gale et al., 2016).
Currently, neuroticism is measured through a wide variety of scales. Perhaps the most well-known is the NEO Personality Inventory, a test made up of 240 items (McCrae et al., 2005). Other tests often used are Eysenck Personality Inventory (EPI) and Eysenck Personality Questionnaire (EPQ) (Knowles and Kreitman, 1965;Loo, 1979). Both these tests have a similar outlook on what 'neuroticism' means while differing in some aspects (Rocklin and Revelle, 1981).

Alexithymia
The alexithymia construct, first introduced by Nemiah and Sifneos in the early 70s (Sifneos, 1986), was the point of arrival of a decades-long research into the cognitive style of patients with psychosomatic diseases. The salient features initially identified encompassed the difficulty in recognizing and describing feelings in the context of an externally oriented cognitive style, as well as the perplexity in distinguishing between feelings and the bodily sensations of emotional arousal. This inability to correctly interpret bodily inputs can often lead to psychosomatic symptoms and reduced insight. Moreover, alexithymia has been linked to psychopathology in the spheres of borderline personality disorder, eating disorders and psychosis (Mannarini et al., 2016). Neuroscientific studies on alexithymia point toward the importance of amygdala functioning (Goerlich, 2018).
The main tools for the evaluation of alexithymia are the Toronto Alexithymia Scale (Taylor et al., 2003) and the Schalling-Sifneos Personality Scale (Sifneos, 1986). Alexithymia has been often associated with several psychopathological conditions, most prominently depression and eating disorders (Taylor, 1984). Upon the realization of its validity in the clinical and research fields, alexithymia was included as an item in various assessment and diagnostic questionnaires and furtherly investigated by dedicated inventories (Sifneos, 1973;Montagne et al., 2007;de Vroege et al., 2018).

Emotion dysregulation
Emotion dysregulation (ED) is a wide and multifacet psychological concept (Thompson, 2019). It encompasses several different psychological traits and is associated with Borderline personality disorder (Carpenter and Trull, 2013), autism spectrum disorders (Cai et al., 2018), attention deficit hyperactivity disorder (Shaw et al., 2014), post traumatic disorder (Powers et al., 2015) and bipolar disorder (Bayes et al., 2016). Moreover, it often leads to substance abuse (Garke et al., 2021), self-harm (Gratz and Roemer, 2008) or even suicidal behavior (Raudales et al., 2020). ED is thought to be connected to abuse and trauma, especially during childhood (Dvir et al., 2014). This personality feature is characterized by incapacity or difficulty in modulating emotions in order to fit them to the social context. People with ED often suffer from poorer attention, labile mood and overly intense emotions. This trait often produces abnormal behavior both in the externalizing and internalizing spectrum. ED has been shown to be linked to neuroticism (Paulus et al., 2016), and interestingly, there is even evidence of this connection from a neuroscientific standpoint (Yang et al., 2020;Silverman et al., 2019). According to Gratz and Roemer (2004), ED can be defined as a deficit in awareness and acceptance of emotions, with a lack of control of one's impulsive behavior. As such, ED often results in difficulties in employing appropriate strategies in social contexts.
Due to its broad definition, ED is investigated through a variety of scales, such as Difficulties in Emotion Regulation Scale (Gratz and Roemer, 2004), Emotion Dysregulation Scale (Powers et al., 2015), Emotional Expressivity Scale (Burgin et al., 2012), Connor-Davidson Resilience Scale (Connor and Davidson, 2003), Emotion Regulation Checklist (Shields and Cicchetti, 1997), Personality Assessment Inventory (Venta et al., 2018) and Sleep and Emotional Reactivity in Alcohol Use Disorder (NCT04979507, 2021). The multitude of instruments employed to assess ED might strengthen the belief that it might be a somewhat ill-defined concept in current literature (Cole, 2014); this translates into the tendency to study it from different perspectives and angles. However, such a tendency might as well negatively affect the reproducibility of the samples, which were attributed this trait by using different scales.

Objective
The aim of this systematic review is to investigate the influence of genetics on these three traits and to categorize known data on the subject, both from a GWAS and genetic association study perspective.

Materials and methods
This review adheres to the 2020 PRISMA guidelines (Page et al., 2021).

Eligibility criteria
Included articles were observational studies, either cross-sectional or longitudinal. The inclusion criteria were as follows: original article, written in English, reporting results of genetic analysis on humans in combination with measurements of neuroticism, ED or alexithymia using validated tools (questionnaires or tasks). The main outcome measures were all associations of either neuroticism, ED or alexithymia with specific alleles or intergenic variants. Exclusion criteria were: the study being a systematic review, a meta-analysis, an opinion article and methodological or technical contributions with no analysis over clinical data.

Information sources and search strategy
The authors used the electronic database PubMed in order to select studies. The following string was used for the systematic search: The last search was run on 14 July 2021.

Selection process
Three authors (O.B.B, G.P.M. and V.P.) independently assessed the abstracts of potentially eligible studies. Eligibility assessment was performed in an unblinded standardized manner. If there was doubt about whether the study was eligible for inclusion, the reviewers examined the full text of the articles. The published protocol required consensus in case the authors disagreed on the inclusion of a specific study. In case, the opinion was not unanimous, a majority vote would have been taken between all authors. The authors agreed on all the eligibility assessments of the studies, and no consensus vote needed to take place.

Data collection process and data items
Four authors (O.B.B., V.P., G.P.M. and B.B.) independently extracted the following categories of data from each included study: study design (GWAS or genetic association), population (number of subjects, ethnicity),  genes studied, polymorphisms and their effect on the traits.

Risk of bias
Risk of bias for individual studies was assessed using the STrengtheningthe REportingof Genetic Association Studies variant of the STROBE checklist for genetic association studies (Little et al., 2009). GWAS were not assessed through risk of bias, as no proper tool is available for such aim. An online tool was used to produce the summary graph S1 (McGuinness and Higgins, 2021).

Results
A total of 1340 studies were found after running the search line through PubMed. 301 studies were included for evaluation of the manuscript, 1039 were excluded on the basis of title and abstract and 99 were excluded after manuscript review and application of inclusion criet al., 2020;Tabak et al., 2020;Vaht et al., 2020;Zhao and Liu, 2020;Belonogova et al., 2021;Byrd et al., 2021;Heilbronner et al., 2021;Nestor et al., 2021) were finally selected: 27 GWAS and 175 observational genetic association studies. Of the latter, 142 were on neuroticism, 20 on alexitimia and 13 on emotional dysregulation (Fig. 1 Table 3) and brain-derived neurotrophic factor (BDNF, Table 4). The outcomes of the screening on alexithymia are shown in Table 5 and on ED in Table 6. Results concerning GWAS studies are shown in Table 7.
The most commonly used questionnaires for neuroticism were the NEO-PI and the Eysenck Personality Inventory (Questionnaire). Alexithymia in our sample was assessed more commonly through the TAS-20 and the BVAQ. Genetic association studies evaluating emotional dysregulation used a wider variety of different tools (see Table 6); further considerations on this trait should take into account this variability.
Since GWAS results are too vast to be meaningfully discussed, data with P ≤ 10 −8 was given special consideration. This threshold is considered the standard in newer GWAS, despite suggestions to lower it to 10 −7 (Chen et al., 2021).

Discussion
In order to treat the topics in a hierarchical fashion, we decided to prioritize describing results concerning genes that were studied in 10 or more genetic association analyses in their own category. In the successive sections, we proceeded to explore the associations between neuroticism and less studied genes, and finally to discuss the possible genetic basis of the remaining traits.

Neuroticism: SLC6A4, COMT, MAO-A and BDNF
According to the results of our systematic review, no solid association seemed to emerge between neuroticism and these very thoroughly studied genes (Mandelli and Serretti, 2013). These genes have understandably been at the epicenter of scientific attention since the beginning of genetic research in psychiatry (Collier et al., 1996;Eley et al., 2003;Strauss et al., 2004) because of their hypothetical and plausible importance in brain function, especially regarding cortical networks (Matsumoto et al., 2003;Martinowich et al., 2007;Alia-Klein et al., 2008), since their respective proteins are involved in basic neurotransmission and, most importantly, in what was currently believed to be the main mechanism of action of many drugs such as antidepressants and antipsychotics (Sundaram and Mahajan, 1980;Meltzer, 1991).  Despite no meta-analysis being carried out, it is possible to state that these genes might not have a strong connection with neuroticism. Most of the papers found no association; however, in some studies, despite no direct association, these genes were found to significantly increase neuroticism scores when combined with several moderating factors such as sex, sociodemographic status and other secondary factors such as cluster C personality disorder diagnosis (Jacob et al., 2004), meditation practice (Jung et al., 2016) and alcohol abuse (Lovallo et al., 2014). In some cases (Brummett et al., 2003;Sen et al., 2004;Kotyuk et al., 2015;Chang et al., 2017), conflicting evidence emerged regarding which of the two alleles is involved in increasing risk of high neuroticism scores, further weakening the potential connection between these alleles and neuroticism.
As will be more thoroughly discussed further on, no GWAS found any link between these four genes and neuroticism, further weakening their potential in moderating neuroticism.

Neuroticism: other genes and genome-wide association study
Overall, more than 60 other genes were studied, most of them in single studies. Thirty-six of them were significantly associated with neuroticism (a full list of these genes can be found in Supplementary Materials, Supplemental Digital Content 1, http://links.lww.com/PG/ A295). Some of these genes are conventionally connected in some fashion to brain function, such as the 5-HTR1A and 2A genes and DRD2, DRD3, DRD4 (Missale et al., 1998;Pytliak et al., 2011); others are transcription factors, adherence proteins or do not have a known function in the nervous system.  (Mayer et al., 2006;Romanowska and Joshi, 2019) and act as enhancers (Ransohoff et al., 2018), thus allowing fine-tuning of more nuanced transcriptional equilibriums as compared to exons.
Most of the GWAS acquired some of their data from the same original dataset, the UK Biobank; as the Biobank was updated, though, the sample increased constantly in numbers. Moreover, several studies employed very interesting nonstandard statistical techniques, which show great promise. Multitrait analysis of GWAS (a variation of standard GWAS in which data on several traits is analyzed jointly in order to maximize detection rate), methylation analysis and pathway analysis are all helpful tools in order to increase both the precision and the scope of GWAS results.
Through pathway analysis, L1CAM, an intercellular signaling adhesion protein, was found to be implicated in increasing neuroticism . DCAF5 (Hill et al., 2020), coding for a protein involved in ubiquitin function regulation, is also linked to neuroticism scores. In the same study , another interesting association was PAX6, an embrional transcription factor. It is worth noting that, similarly to what was argued in  the above paragraph, exons with regulatory functions on other sequences or biochemical mechanisms tend to be the most common findings when searching for gene-environment associations in the field of psychiatry. As further proof to this statement, polymorphisms in RBFOX1, a splicing-regulating protein, have been replicated in multiple GWAS (Okbay et al., 2016;Luciano et al., 2018;Nagel et al., 2018Nagel et al., , 2020; variations in LINC00461, a noncoding RNA involved in miRNA and siRNA regulations, are as well a replicated finding (Okbay et al., 2016;Luciano et al., 2018;Nagel et al., 2018;Turley et al., 2018;Hill et al., 2020).
An association was found with two distinct chromosomal inversions (respectively on chromosomes 8 and 17) (Okbay et al., 2016). The exact mechanism by which these chromosomal alterations affect neuroticism is not clear. Widespread regulatory mechanisms disruption and alteration in 3D chromatin structure are a possible hypothesis; Okbay et al. (2016) suggest that the inversion might relocate some crucial regulatory regions.
A solid association can be assumed for CRHR1, since it was found in two GWAS and several genetic association studies. CRHR1 is the gene coding for the corticotropin-releasing factor receptor. As such, it is a key component of stress reaction and cortisol homeostasis. This receptor is also present in the brain and has been connected to satiety feelings (Tu et al., 2007) and depressive and anxiety symptoms (Rogers et al., 2013). No data could be found in literature regarding the exact effect of the variants linked to neuroticism; assuming that these variants decrease the efficacy of CRHR1 receptor, we can hypothesize that this mutation might lead to a reduction of hypothalamic control on cortisol production. A more direct mechanism can also be mentioned; several CRHR1 polymorphisms (not the one found in this review, though) were associated with cortisol reactivity in children (Sheikh et al., 2013).

Alexithymia
Data on alexithymia are sparse and less comprehensive compared with neuroticism. Thus, it is not possible to extract conclusive results on the subject. On the other hand, the assessment of alexithymia is extremely homogeneous, as nearly all included studies employed the TAS-20 questionnaire. Some relevant results can nevertheless be extracted.
Interestingly, SLC6A4 association with alexithymia emerged in two studies (Kano et al., 2012;Terock et al., 2018). Moreover, the risk factor was the short variant, which is commonly thought as having an influence on psychopathology, especially in the depression and anxiety spheres (Juhasz et al., 2015). Thus, more data on this peculiar gene-trait association are warranted and needed. Though, it must be underlined that three other studies found no association between SLC6A4 and alexithymia.
Other intriguing associations are those with some chromosomal abnormalities such as Turner and Klinefelter syndromes. Increased psychiatric burden is not a novelty in these syndromes.
For example, there is evidence of higher levels of alexithymia in Turner syndrome if compared with Noonan syndrome and healthy controls (Roelofs et al., 2015).
Other studies concerning Klinefelter syndrome pointed out higher levels of psychological distress, such as depression, paranoid ideation, phobias, psychoticism and obsessive thoughts, and a central role of alexithymia in the development of these aspects (Skakkebaek et al., 2018;Giagulli et al., 2019;van Rijn and Swaab, 2020;Fabrazzo et al., 2021). It is well known in current literature that Klinefelter syndrome might also predispose to the development of maladaptive psychological constructs, such as obsessive-compulsive symptoms associated with lower total, verbal and performance IQ scores, although there is no clear evidence on etiopathological process and if these symptoms are due to genetic makeup or environment and social stigma (Fisher et al., 2015).
These syndromes are known to be associated with a variable degree of social functioning impairment (van Rijn et al., 2018); it is, therefore, worth considering that the areas of performance more commonly affected might predispose to a specific personality trait (with the genetic footprint as a unifying mark).
Noonan syndrome is a cluster of monogenic conditions involving several genes in the RAS-MAPK pathway (PTPN11, SOS1, SHOC2, MAP2K1/2 and KRAS in the included paper).
Also, patients with Noonan syndrome showed higher levels of introversion, alexithymia, anxiety and depression, which may predispose to internalizing problems (Roelofs et al., 2015;Roelofs et al., 2020).
There is also evidence that global and social functioning is negatively correlated with family quality of life and a negative environment (neglect) (Davico et al., 2022), pointing out that environmental factors, in this case, play a relevant role. A single GWAS was found studying alexithymia (Mezzavilla et al., 2015). Interestingly, this study found several exonic associations: TMEM88B (a transmembrane protein, most likely involved in intercellular signaling), ABCB4 [a transporter protein that is linked to some forms of cancer and multidrug resistances (Nayagam et al., 2020, p. 1)], TP53AIP1 (whose protein is part of the p53 pathway) and ARHGAP32 from the Rho G protein pathway. All of these proteins have very indirect known effects on synapses; two of them, namely, ABCB4 and TP53AIP1, might be involved in neurogenesis of the Novo neurons or in organizing cortical architecture.

Emotion dysregulation
ED assessment in the included studies was far from homogenous (see Table 6 and Introduction section). Evidence drawn from this review must, therefore, be carefully evaluated taking into consideration this fact.
SLC6A4 was the most represented among genes investigated regarding ED. Some theoretical models were proposed in 2007 (Canli and Lesch, 2007) trying to link neuroscience evidence with the genetic data available at the time. The main idea was that SLC6A4 functional variants might induce the amygdala nuclei to be more or less reactive to external stimuli, thus inducing a stronger or weaker emotional response according to the polymorphism. Since our review did not focus on endophenotypes but rather on a more direct association with the trait of interest, we can not undermine or prove such hypothesis. Seven gene-association studies were found regarding SLC6A4 and its relation with ED (mostly in children and adolescents, Table 6); four supported the null hypothesis (Jorm et al., 1998;Kochanska et al., 2009;Weiss et al., 2014;Noroña et al., 2018), whereas two reported an association with the short allele and one reported an association with a polymorphism (rs4680) (Murakami et al., 2009;Amstadter et al., 2012;Viddal et al., 2017). Moreover, GWAS studies on ED did not report association with SLC6A4 (more details in later paragraphs).
An association with the oxytocin receptor was found among a large sample of female children (Byrd et al., 2021). In fact, it is not unreasonable to assume that such a gene might have psychological and psychiatric implications. Interestingly, oxytocin blood and brain level seem to be connected to both prosocial behavior and anger reactions (Aragón et al., 2015;Byrd et al., 2021) and, most importantly in this context, to empathy (Barchi-Ferreira and Osório, 2021).
Our search provided two GWAS eligible for discussion on ED (see Table 7). Both have relatively small samples. One of them (Powers et al., 2016) found a single association with an intronic regulatory region of ILR2A, the gene coding for the interleukin 2A receptor. There is also other evidence linking this receptor to psychiatric conditions in general (Nässberger and Träskman-Bendz, 1993;Rapaport and Stein, 1994) and even directly to alexithymia (Gil et al., 2007;De Berardis et al., 2014). A plausible explanation as to why an interleukin gene could potentially have an impact on personality can be hypothesized through microglia activity in synaptic pruning and modulation in general. The importance of these often neglected cells in brain circuitry homeostasis has already been proven in several neurologic conditions such as Alzheimer's and multiple sclerosis (Augusto-Oliveira et al., 2019;Li et al., 2022) and even in psychotic disorders (Germann et al., 2021). It is, therefore, not unreasonable to assume that interleukin 2A might influence microglia actions on synaptic remodeling and thus affect personality.

Limitations
Several factors must be accounted for when proposing an interpretation of our results. The diverse nature of assessment tools increases potential biases; a more standard approach in order to measure under-investigated traits such as ED is warranted. An example of such an attitude can be seen in alexithymia research, with the TAS-20 widely being recognized as the main questionnaire.
Another limitation is that most of the studies reviewed did not explain how the study size was arrived at: the reason for this lack of conformity probably lies in the fact that the aforementioned studies did not include a power analysis in their design. Furthermore, characteristics of study participants, such as demographics and social information, were not provided in a majority of the selected studies.

Conclusion
DNA is mostly made of such apparently 'junk' regions; as research in genomics and proteomics advances, though, more and more evidences are mounting up to prove the crucial role that intronic and intergenic regions actually have in transcriptional regulation, splicing, chromatin density regulation, methylation and finer modulation mechanisms.
In fact, it could even be hypothesized that the 'major' receptor genes are too widespread among the brain, and thus, a mutation in such genes might prove to be too constraining to effectively influence personality and other more subtle psychological traits. On the other hand, a neuroscientific model taking into account millions of base pairs scattered across all chromosomes and constantly influencing both exon and each other's activity might constitute a better framework and foundation to better understand the way the human brain works. This point of view also has a stronger phylogenetic basis as compared with a model that only encompasses few loci; in fact, having a larger portion of DNA devoted to fine regulation and modulation of brain function allows evolutionary mechanisms more room to act and apply selective pressure (be it positive or negative) that would in turn influence the architecture of a complex organ such as a brain.
This theoretical approach is supported by GWAS results in general, and by GWAS results in neuroticism especially. As detailed in the Discussion section, even when accounting only exonic mutations, most of these affect regulatory and transcriptional proteins (i.e. LINC00461, RBFOX1 and L1CAM) rather than, for example, ion channels or receptors. Evaluating the biochemical impact of such proteins is a complex endeavor that GWAS studies alone cannot fully address; nevertheless, such studies can inform the direction of protein-functionality studies.
Key neurodevelopmental proteins are also potentially involved in the neuroticism pathogenesis, as pointed out by polymorphisms in genes such as DCC, XKR6 and TCF4. Scientific literature, in fact, supports the hypothesis that neuroticism's phenotype might develop quite early in life (Zupančič and Kavčič, 2013;Ask et al., 2021). Brain structure differences in high neuroticism children, namely in several whole-brain parameters as well as the prefrontal, occipital and lateral temporal cortex (Restrepo-Lozano et al., 2022) have also been reported, despite polygenic risk score associations being inconclusive.
Even though the majority of findings regarded nonconding regions or regulatory exons, GWAS on neuroticism identified significantly and replicated polymorphisms in two key brain receptors: CRHR1 and DRD2. These findings provide an interesting direction for future research, as these receptors' function is relatively well-known in the brain (Rogers et al., 2013;Berke, 2018).
Little GWAS data are currently available on alexithymia and emotional dysregulation; more research is needed in this area, as the data currently available already yielded some interesting results (TMEM88B, ABCB4 and TP53AIP1 for alexithymia, and ILR2A for emotional dysregulation). Since the studies had small samples for GWAS standards (Hong and Park, 2012;Haram et al., 2014;Powers et al., 2016;Heilbronner et al., 2021) it is likely that more associations could be detected in the future.
Neuroticism, alexithymia and emotional dysregulation constitute key concepts both in psychology research and clinical practice among psychiatrists. These traits influence treatment outcome and prognosis from a variety of directions: therapeutic alliance, compliance with medication and social functioning. Being this the case, understanding their etiopathology and genesis is of crucial importance. Clearly, as is often the case in psychiatry and brain research, genetic factors cannot fully explain the variability of the phenomena by themselves; the environment must play a key role as well. Nevertheless, a full understanding of the genetic side of the coin is necessary and might have huge clinical implications, both directly and indirectly. Genetic panels predicting psychopathological risk might be developed, and pharmaceutical research can and will benefit greatly from a deeper knowledge of these genetic mechanisms. Such mechanisms can be unveiled through additional research, especially in the form of GWAS. contributed to the interpretation of the studies and to the synthesis of results. The first draft was written by G.P.M, V.P., B.B. and O.B.B. under the supervision of G.C., V.B., S.S., B.N. and V.R.; the final manuscript was approved by all the authors.
Protocol and registration: methods of the analysis and inclusion criteria were specified in advance and documented in a protocol. The protocol, published in advance, can be retrieved as Prospero ID CRD42021267732.
Availability of data, code and other materials: the database of the studies, with the extracted data items, can be shared upon reasonable request to the corresponding author.

Conflicts of interest
There are no conflicts of interest.