trait theory research paper

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Applying an Allportian Trait Perspective to Sense of Purpose

Gabrielle n. pfund.

Department of Medical Social Sciences, Northwestern University, 625 N. Michigan Ave., 22nd Floor, Chicago, IL 60611 USA

Sense of purpose captures the extent to which one feels that they have personally meaningful goals and directions guiding them through life. While this construct has illustrated its ability to robustly predict desirable outcomes—ranging from happiness to mortality—the nature of this construct remains unclear. I begin by describing different definitions and measures from the purpose literature. From there, I review the debates suggesting that it should be classified as a component of identity development, a facet of well-being, or even a virtue. In the current paper, I argue that sense of purpose could be best served when qualified as a trait, building from the eight components of defining a trait from Allport’s ( 1931 ) paper: “What is a trait of personality?”. Using this classic piece as a framework, I integrate empirical and theoretical work on purpose and personality to dive into whether sense of purpose is a trait. I conclude by discussing the challenges and implications of bolstering sense of purpose if it is best classified as a trait.

Have a higher sense of purpose is predictively powerful, with more purposeful people reporting better physical, mental, social, and even financial outcomes (Pfund & Hill, 2018 ; Pfund, 2020 ). However, while the predictive power of sense of purpose is well-known, the nature of sense of purpose remains unclear. This may be, in part, due to the inconsistent definitions used for this construct, as well as the conflicting categorizations of where this construct belongs in the psychological sciences. To this end, the current work explores whether sense of purpose may be best defined as a trait of personality, using Gordon Allport’s (1937) foundational criteria of traits. However, before diving into the potential trait-like essence of sense of purpose, we must first define our terms.

Defining Sense of Purpose

The lack of clarity around the nature of this construct may be, in part, due to the various definitions and assessments associated with it. Table  1 includes a list of foundational purpose papers, their definitions of purpose and, when applicable and available, their measures. Clearly, the usage of terms to describe sense of purpose is convoluted: in certain cases, the same term is used to describe different conceptualizations of purpose. For example, purpose in life in Ryff’s ( 1989 ) and Scheier et al.’s ( 2006 ) representation of purpose represents a construct experienced on a spectrum, with higher scorers agreeing that items such as “I have a sense of direction and purpose in my life” and “To me, the things I do are worthwhile” describe them well. However, McKnight and Kashdan ( 2009 ) use this same term to describe “a central, self-organizing life aim that organizes and stimulates goals, manages behaviors, and provides a sense of meaning” (p. 242), rather than how purposeful one may feel. Damon, Menon, and Bronk ( 2003 ) build from this idea of an individual’s overarching goal for life with the additional criterion of that goal needing to help others to be identified as a purpose.

Terms, Conceptualizations (Definitions), and Operationalizations Assessment Techniques) for Purpose across Five Foundational Theoretical and Empirical Papers on Sense of Purpose and/or Purpose in Life

Note . * represents an item that must be reverse scored when computing a sense of purpose composite

In other instances, different measures are used assess the same construct. For example, Hill et al.’s ( 2016 ) conceptualization of purpose aligns with that of Ryff’s ( 1989 ) and Scheier et al.’s ( 2006 ), insofar that their measure was developed to assess one’s feelings of purposefulness rather than the presence or orientation of an individual’s specific broader life aim. As with efforts made by Scheier et al. ( 2006 ) in the development of the Life Engagement Test from the Purpose in Life subscale (Ryff, 1989 ), Hill and colleagues (2006) sought to develop a measure that took out the role of reflecting over extensive periods of time in determining one’s feelings of purposefulness. For example, in Ryff’s ( 1989 ) Purpose in Life subscale, items such as, “I sometimes feel as if I’ve done all there is to do in life,” requires a level of time sensitivity that Scheier et al. ( 2006 ) and Hill et al. ( 2016 ) worked to remove. Thus, while each of these measures can be connected to how purposeful an individual feels, and the extent to which they are following a direction through life, these more recent measures aim to capture feelings of purposefulness in the moment rather than across the entirety of one’s lifespan. In particular, these measures do so by focusing on the perceptions of one’s activities being perceived to be purposeful and connecting with their broader goals for life (e.g., “To me the things I do are worthwhile,” Scheier et al., 2006 ). Finally, across each of these measures and as a primary focus of the Life Engagement Test, sense of purpose is also represented by perceiving one’s activities as being personally meaningful (e.g., Ryff’s ( 1989 ) item: “My daily activities seem trivial and unimportant to me; Scheier et al.’s ( 2006 ) item: “I value my activities a lot”).

Moving beyond what purpose entails to what different purpose terms represent, with the Brief Purpose Measure (Hill et al., 2016 ) we begin to see a transition away from the term purpose in life being used to reflect how purposeful one feels; instead, the authors interchangeably used the more classical terms of purpose in life with sense of purpose and sense of purpose in life (Hill et al., 2016 ). This article helped ignite the transition to purpose in life capturing the articulated life aim that likely bolsters one’s sense of purpose, rather than one’s sense of purpose levels themselves. In line with these insinuated distinctions and the summation of past work, Pfund ( 2020 ) stated that purpose can be divided into two interrelated but distinct constructs: sense of purpose , the extent to which one feels that they have personally meaningful goals and directions guiding them through life, and purpose in life , an individual’s specific overarching goal and direction. The current work will use this same dual-faceted conceptualization of purpose; in particular, I will be focusing on how sense of purpose may be best understood as a trait, but mentioning purpose in life as is relevant to the categorization and comprehension of this construct. Finally, I will use the term purpose when referring to the broader literature across constructs or when referring to research that do not as cleanly distinguish sense of purpose and purpose in life.

Classifying Sense of Purpose

Though research on purpose has increased over the past several decades, the classification of this construct has drastically varied. Some consider sense of purpose as a component of well-being (Willroth, 2022), as in a necessary facet of what it means to be psychologically well. More specifically, sense of purpose has been deemed a domain of psychological well-being (Ryff, 1989 ; Ryff, 1995 ; Ryff, 2014 ), which emphasizes the more eudaimonic (i.e., feeling engaged with one’s life) rather than hedonic (i.e., feeling content with one’s life) form of the good life (Ryff & Boylan, 2016). Meanwhile, others assert that purpose in life is part of identity development (Bronk, Hill, et al., 2009 ; Hill & Burrow, 2012 ; Sumner et al., 2015 ), given that the process of figuring out one’s goals for life is necessarily informed by and also informs who they are. Finally, some take a moralistic perspective, claiming that living purposefully is a virtue or character strength (Han, 2015 ; Ryff & Kim, 2020 ). In fact, some researchers suggest that if one’s purpose in life is not “beyond the self,” then they cannot be classified as having a purpose at all (Damon et al., 2003 ). However, this definition is fairly contentious, as some state that it should not be in the hands of the researchers to determine whether individuals’ purposes are worthy (Burrow et al., 2021 ), and others have noted that the vast literature focusing on the benefits of purpose has used sense of purpose measures agnostic to an individual’s specific purpose orientation (Hill & Pfund, 2022). The classification of purpose in psychological sciences varies and is in part based on whether one is focusing on sense of purpose versus purpose in life.

More recently, work has begun to explore whether sense of purpose is more accurately identified as a trait (Pfund, 2020 ), or a construct that illustrates consistencies in thoughts, feelings, and behaviors between individuals across similar situations (Roberts, 2009 ). Establishing what sense of purpose can be categorized as is a necessary step in understanding how to help individuals develop and harness it. Additionally, determining an accurate classification of this construct provides guidance on both intervention ease and opportunities. Personality traits are often considered challenging to change, relative to other more malleable constructs like affect, which have been intervened upon using psychotherapeutic, experimental, and humanistic efforts (Boumparis, Karyotaki, et al., 2016 ; Heekerens & Eid, et al., 2021 ; Schutte & Malouff, 2019 ; Taylor, Lyubomirsky, & Stein, 2017 ). However, there is initial evidence that these dispositional characteristics can potentially be manipulated when utilizing intensive interventions (e.g., Hudson & Fraley, 2015 ; Hudson, Fraley, et al., 2021 ; Stieger, Flückiger, et al., 2021 ). Thus, to determine whether sense of purpose is a trait will be crucial in understanding if, when, and how we can change it, the challenges that will arise if we attempt to do so, and the methods that may be most effective in assuring that more individuals can benefit from the desirable outcomes sense of purpose predicts.

The Trait-like Nature of Sense of Purpose

Using Allport’s ( 1931 ) foundational paper “What is a trait of personality?”, I will discuss the eight components he set forth as necessary criteria to warrant the classification of this term, while also integrating more recent literature that evaluates these initial claims. Traits are commonly understood as consistency in thoughts, feelings, and behaviors across similar situations (Condon, Wood, et al., 2020 ; Roberts, 2009 ). Thus, to consider sense of purpose as a trait, one must think through whether and how sense of purpose may be composed of thoughts, feelings, and behaviors. On the cognitive front, sense of purpose has illustrated cognitive patterns (Lewis, 2020 ), insofar that purposeful individuals are typically more likely to recognize pathways necessary to overcoming obstacles and thinking of themselves as more capable to doing so (Bronk et al., 2009 ). Moreover, purposeful individuals are more likely to engage in coping strategies that rely on thinking of the bigger picture and finding the silver lining in challenging and anxiety-provoking situations (Author Citation), rather than ruminating or avoiding their problems (Lohani et al., 2022).

Sense of purpose has also been connected to affective tendencies in a few primary ways. First, people with a higher sense of purpose generally experience more positive emotions and fewer negative emotions, both in general and in daily life (Hill, Sin, et al., 2018 ; Pfund, Ratner, et al., 2021). Moreover, purposeful individuals experience less of an increase in negative affect following a stressor (Hill, Sin, et al., 2018 ) and less of an increase in positive affect following a positive event (Hill, Sin, et al., 2022 ), suggesting that sense of purpose may help promote less reactivity in daily emotions. This lower affective reactivity may also be due to how purposeful people rely on typically more effective and adaptive coping and emotion regulation strategies (Author Citation; Lohani et al., 2022).

Finally, when thinking through the behavioral aspects of sense of purpose, past work on purpose in life has noted that one’s purpose guides their daily behaviors (McKnight & Kashdan, 2009 ). However, unlike the cognitive and affective tendencies sense of purpose promotes, the behavioral components of purpose may be more idiosyncratic in nature. One quirk when thinking through sense of purpose as a trait may stem from the unique behavioral tendencies that likely differ across individuals depending on what it is that makes them feel purposeful. The Purpose as a Trait, Habit, and State (PATHS) Model has explored how one’s purpose in life likely shapes one’s daily activities (Hill, Pfund, & Allemand, 2023). Thus, how purpose promotes behavior likely differs depending on what one’s specific life aims may be. Put differently, a given individual’s purpose-produced behaviors could be consistent over time for them but different from another purposeful person’s behaviors.

While this more concise definition of a trait may suggest sense of purpose could be a trait, the utilization of Allport’s ( 1931 ) initial trait criteria in understanding the nature of sense of purpose provides a more conservative and in-depth interpretation of the construct. Thus, the following section will breakdown the nature of purpose through these eight separate trait principles. In order, these sections will consider if it (1) is a construct without implications, (2) a habit rather than a disposition, (3) solely a correlate of related variables instead of its own construct, (4) empirically established, (5) independent enough from other variables, (6) not a moral characteristic, (7) fairly consistent within an individual, while (8) still recognizing that within-person variability can exist. In each of these sections, I will discuss both how we know that it fits the relevant criterion as well as what may be missing to draw these conclusions. This paper will then conclude with a reflection on the implications of trait classification for sense of purpose.

Sense of Purpose is More than a Name

Allport ( 1931 ) suggests that a trait must have “more than a nominal existence” (p. 368), meaning that a disposition is not simply a framework that describes a person, but also is responsible for a collection of habits that align with the trait itself. Over the several decades in which the study of sense of purpose has prevailed, this construct’s definition has held a few core commonalities, as well as some differentiated nuances (Hill, Burrow, et al., 2010 ). However, across the most common conceptualizations of purpose (Damon et al., 2003 ; Kosine, Steger, & Duncan, 2008 ; McKnight & Kashdan, 2009 ; Ryff, 1989 ), one theme that constantly emerges regarding sense of purpose is being rooted in feeling that one has overarching aims for life (Hill, Burrow, et al., 2010 ). Those life aims provide a direction and a framework for individuals to set smaller and more specific goals in both the short- and long-term (McKnight & Kashdan, 2009 ).

Furthermore, purpose has been described as “a higher-order cognitive process which reciprocally stimulates goal selection and long-term goal maintenance,” (Lewis, 2020 ). In other words, people with a higher sense of purpose will be more likely to focus on long-term goal attainment by adjusting their actions and behaviors in the short-term to align with those future-oriented life aims that bolster their purposefulness. In both conceptualizations, purpose is described as a broader construct that, in turn, influences smaller happenings within an individual along the way. It is no surprise then that individuals with a higher sense of purpose find their daily activities more personally meaningful, and greater aligned with their directions for life (Scheier et al., 2006 ). Allport’s ( 1931 ) suggestion that our tendencies are more than simply generalized habits, and are rooted in broader, dispositional constructs distinguished as traits, is reflected in sense of purpose.

Sense of Purpose is More than a Habit

Allport ( 1931 ) also highlighted that “a trait is more generalized than a habit” (p. 368), and that “there may be said to be major, widely extensified traits, and minor, less generalized traits in a given personality,” (p. 369). Thus, sense of purpose does not need to supersede the existence of the Big Five personality traits to prove that it is a trait; rather, this construct must illustrate that it can explain a collection of habitual tendencies within or between individuals. The focus on narrower traits, like sense of purpose, responds to both new and old calls within personality science. Goldberg ( 1993 ) noted decades ago that “It should be clear that proponents of the five-factor model have never intend to reduce the rich tapestry of personality to a mere five traits… Rather, they seek to provide a scientifically compelling framework in which to organize the myriad individual differences that characterize humankind,” (p. 27). And, while the Big Five does provide a common system and language for personality scientists to use that plays an important role in the advancement and progress of trait theory, more recent work has reiterated the historical calls of Goldberg ( 1993 ) by illustrating the necessity of specificity when researching these traits.

In particular, there have been calls to go beyond the “Big Few” in personality psychology and focus on the lower-order individual differences in trait hierarchies ranging from domains and facets to the newly emerging nuances (Condon et al., 2020 ; Condon, 2018 ; Costa & McCrae, 1995 ; Mõttus et al., 2020 ; Paunonen & Ashton, 2001 ). Narrowing in on these lower-order individual differences better meets the goal of personality science to predict, explain, and describe (Mõttus, Wood, et al., 2020 ). In fact, research focusing on nuances, which can be understood as “the lowest level at which patterns of responses to items continue to have reliable specific variance,” (Condon et al., 2020 , p. 925), typically enabling better predictions than broader traits (Hall & Metz, 2020 ). Thus, if sense of purpose can show itself to be more than an individual habit, it will meet the calls of personality researchers across the decades to investigate more than the broadest level of individual differences.

Allemand and Flückiger ( 2017 ) highlight that a trait is a broader version of a collection of habits, and habits are often composed of momentary states. Building from this same framework, the PATHS model has since clarified how purpose can function as both a habit and a trait (Hill et al., 2023). Specifically, Hill and colleagues suggest that one’s overarching purpose in life likely guides one’s automated (i.e., habitual) thoughts, feelings, and behaviors as they in line with one’s purpose in life. Relatedly, purpose is theorized to guide both the creation of minor goals and the behaviors that coincide with them (McKnight & Kashdan, 2009 ). Furthermore, with a main component of sense of purpose being personally meaningful activity engagement (Scheier et al., 2006 ), purposeful people’s daily habits are likely either perceived to be more meaningful or that purposeful people are better at engaging in more meaningful activities than their lower sense of purpose counterparts. However, it may, in fact, be both: People with a higher sense of purpose report more positive daily events on average (Hill, Sin, et al., 2020); meanwhile, though sense of purpose is not associated with number of daily negative events, more purposeful people experience less of an increase in negative affect when experiencing a stressful event (Hill, Sin, et al., 2018 ). With habits being understood as an automatic tendency (Hull, 1930), Hill et al.’s (2023) more complex descriptions of purpose as a trait and habit may be more accurate. Sense of purpose may be connected to automatic, habitual tendencies in individuals’ interpretation of daily life, as well as the more active and intentional way that individual’s choose to live their daily lives.

Sense of Purpose is More than a Correlate

Another inherent component of traits in Allport’s ( 1931 ) definition is that they are, “dynamic, or at least determinative,” (p. 369). This condition is easily met with sense of purpose given that research abounds across a variety of domains highlighting that sense of purpose is not simply a correlate of outcomes, but a predictor and promoter of outcomes. As already mentioned, sense of purpose is a cognitive framework that directs short- and long-term goals, behaviors, and resource allocation (Lewis, 2020 ; McKnight & Kashdan, 2009 ). While the opening of this paper illustrated the far reach of sense of purpose regarding the critical outcomes it promotes, the current section takes a deep dive into one area of research to illustrate what may be interpreted as the determinative nature of sense of purpose.

When focusing specifically on the longitudinal associations between purpose and physical health, people who have a lower sense of purpose are more at risk for cardiovascular events (Cohen, Bavishi, & Raozanski, 2016 ; Kim, Sun, et al., 2013), stroke (Kim, Sun, Park, & Peterson, 2013b ), physical disability (Mota, Tsai, et al., 2016 ), and experiencing poorer subjective health overall (Windsor et al., 2015 ). These results align with potential mechanistic findings about purposeful people enacting better health behaviors like greater vegetable intake, more frequent flossing, and greater moderate and vigorous physical activity (Hill et al., 2019 ; Hooker & Masters, 2016 ; Pfund, DeLongis, et al., 2022); moreover, people with a higher sense of purpose report greater utilization of preventative health care services (Kim, Strecher, & Ryff, 2014 ). It is no surprise then that older adults who report a higher sense of purpose also report lower mortality rates (Boyle, Barnes et al., 2009 ; Hill & Turiano, 2014 ; Windsor et al., 2015 ). These findings hold when controlling for a variety of outcomes, ranging from depression (Boyle et al., 2009 ) to the Big Five personality traits (Hill et al., 2019 ), which will be discussed further in the next section. Pursuing one’s purpose in life necessitates a healthful future in which one has the time and the ability to pursue one’s life aims. The current discussion is one of many which exemplifies that sense of purpose is more than a simple correlate of crucial outcomes and shows the mechanistic pathways between sense of purpose and these desirable outcomes.

Sense of Purpose is Empirically and Statistically Established

In the next section of his paper, Allport purports that “the existence of a trait may be established empirically or statistically,” (p. 370). There are a few ways in which sense of purpose meets this call through both its statistical distinction and its test-retest reliability. Regarding the first point, in the development of Purpose in Life subscale (Ryff, 1989 ; Ryff & Keyes, 1995 ), the authors assured that it was statistically unique from related constructs. The authors used factor analyses across several samples to find that sense of purpose was distinct from five other factors that comprised psychological well-being (Ryff, 1989 ; Ryff & Keyes, 1995 ). In fact, these findings hold up when considering the convergent validity using partner reports (Schmutte & Ryff, 1997 ). Given the nature of what that particular scale was assessing, future sense of purpose measures tried to address the possibility that this measure was confounded with well-being.

The Life Engagement Test was developed with eight separate samples to focus on feeling that one has valued goals and meaningful activities “because they provide a purpose for living,” (Scheier et al., 2006 , p. 291). This scale addressed concerns about the former by intentionally disentangling the developed items from associated constructs like life satisfaction and contentment, illustrating convergent validity with related variables such as self-esteem, optimism, self-mastery while maintaining correlations of a size to illustrate the constructs were distinct. Research has also considered both the test-retest reliability the Life Engagement Test assessed the test-retest reliability in four of its eight original samples by following up the participants four months after the original assessment (Scheier et al., 2006 ). The associations of these follow-ups ranged from 0.61 to 0.76, suggesting an appropriate amount of stability of this measure. The development of these commonly used measures support both the statistical and empirical establishment of sense of purpose.

Sense of Purpose is Fairly Independent

Allport also aptly notes that, “traits are only relatively independent of each other,” (p. 370). Some have suggested that sense of purpose, given its goal-directed nature, may fall under the categories of conscientiousness (Goldberg, 1999 ), which is often defined as, “a spectrum of constructs that describe individual differences in the propensity to be self-controlled, responsible to others, hardworking, orderly, and rule-abiding,” (Roberts, Lejuez, et al., 2014 ). Others have purported that high sense of purpose is simply the combination of high conscientiousness and high extraversion (Hofstee et al., 1992 ), the latter of which can be understood as high sociability, activity-orientation, and assertiveness (John, Nauman, & Soto, 2008). However, through large meta-analytic projects as well as projects illustrating its incremental validity alongside correlated constructs, sense of purpose has revealed itself to be a “relatively independent” construct.

First, meta-analytic work has unveiled that sense of purpose is associated with various personality facets and traits, though not to an extent that it appears to be capturing the same dispositional tendency within a person. One study looked at the associations between Ryff’s ( 1989 ) Purpose in Life subscale with traits from both the Big Five and HEXACO (Anglim, Horwood, et al., 2020 ). In both personality trait frameworks, the strongest associations with purpose were displayed with conscientiousness. For the 15 studies that used the NEO Big Five measure ( n  = 5,699), the association between sense of purpose and conscientiousness was strongest, at 0.50 (95% CI [0.44, 0.55]), followed by the negative association between sense of purpose and neuroticism ( r  = − .45, 95% CI [-0.51, − 0.38]). People who are high on conscientiousness are generally orderly, industrious, and self-disciplined (John et al., 2008 ). While sense of purpose may not necessarily be connected to being orderly, the industriousness facet of conscientiousness likely taps into what Ryff ( 1989 ) sought to capture in sense of purpose items such as, “I enjoy making plans for the future and working to make them a reality.” Moreover, with a major aspect of neuroticism being negative affectivity (Wilt & Revelle, 2015), and work connecting sense of purpose to fewer negative emotions and lower stress reactivity (Hill, Sin, et al., 2018 ; Pfund, Ratner, et al., 2021), the negative association between sense of purpose and neuroticism can likely be explained through these contrary affective dispositions.

Meanwhile, for the five studies that used HEXACO ( n  = 2,033), the association between purpose and conscientiousness was strongest, at 0.47 (95% CI [0.43, 0.50]), followed by that with extraversion (0.41, 95% CI [0.33, 0.49]). In both cases, sense of purpose showed a notable positive correlation with conscientiousness, but not of a magnitude that causes concern for these constructs overlapping; the positive associations with conscientiousness and extraversion and the negative association with neuroticism align with correlations found in less represented samples as well (Meléndez et al., 2019 ). Other research has explored the association between sense of purpose with specific personality trait domains. In these studies, sense of purpose was most strongly associated with the industriousness aspect of conscientiousness—though not the organization aspect—and the enthusiasm aspect—but not the assertive one—of extraversion (Sun et al., 2018 ). According to Allport ( 1931 ), the associations between traits is to be expected, and, as such, the existence of associations does not undermine sense of purpose as a unique construct. In fact, these associations help us better understand the nuanced sense of purpose-Big Five connection, insofar that purposeful people seem to be hardworking, enthusiastic, and generally more emotionally stable, but not necessarily more assertive or organized.

One limitation with this approach is that it only considers the zero-order correlation between sense of purpose and these traits and, as such, does not account for whether there is unique variance in sense of purpose when accounting for all these traits. A method to address this limitation is to consider whether sense of purpose predicts outcomes above and beyond correlated variables, the support for which is vast. When accounting for the Big Five specifically, sense of purpose predicts better subjective health outcomes and health behaviors (Hill, Edmonds, et al., 2016 ; Hill, Edmonds, & Hampson, 2019 ) greater initial and prospective income and net worth (Hill, Turiano, et al., 2016 ), as well as romantic relationship satisfaction and commitment (Pfund, Brazeau, et al., 2020 ). However, the Big Five personality traits are not the only constructs with which researchers fear for purpose’s overlap.

The covariance of Ryff’s ( 1989 ) sense of purpose measure and different aspects of well-being has been one of particular concern (Scheier et al., 2006 ). In this regard, some work has considered whether Ryff’s ( 1989 ) sense of purpose measure predicts outcomes when controlling for depression. One study found that sense of purpose predicted risk for mortality when accounting for depression, as well as neuroticism and disability (Boyle et al., 2009 ). These findings are in part explained by research on sense of purpose and health behaviors. For example, more recent work has found that sense of purpose predicted walking speed in older adults while accounting for depressive symptoms, baseline health factors, and health behaviors (Kim, Kawachi, et al., 2017 ), and it predicted physical inactivity, sleep problems, and unhealthy BMI risks when accounting for health status and depression (Kim, Shiba, et al., 2020 ). Finally, when considering affective well-being and the Big Five personality traits, research has found that sense of purpose predicts romantic relationship quality across the lifespan (Pfund, Brazeau, et al., 2020 ) as well as better relationship satisfaction with parents and university friends in college students (Pfund et al., 2022 ). Thus, it is not simply that purposeful people feel more positively. In line with suggestions that purposeful people are better at organizing their day-to-day activities (McKnight & Kashdan), having a higher sense of purpose may connect to prioritizing the people in their lives, helping support better relationship outcomes. Across depressive symptoms, personality traits, and affective well-being, sense of purpose displays incremental validity, supporting the “relative independence” for which Allport ( 1931 ) calls in establishing a trait.

Sense of Purpose is Not a Virtue

The trait criterion with which I anticipate the greatest conflict regarding sense of purpose is that “a trait of personality, psychologically considered, is not the same as moral quality,” (Allport, 1931 , p. 371). However, the extent to which one would classify sense of purpose as a moral characteristic is dependent upon the definition of purpose that they follow. Damon and colleagues ( 2003 ) defined purpose as, “a stable and generalized intention to accomplish something that is at once meaningful to the self and of consequence to the world beyond the self,” (p. 121). This focus on prosociality has been emphasized as one of the core commonalities of different purpose definitions (Hill, Burrow, et al., 2010 ), which likely explains why some have begun describing purpose as a virtual or character strength (Han, 2015 ; Ryff & Kim, 2020 ). However, some have clarified the original prosocially-oriented language of Damon and colleagues’ ( 2003 ) definition of purpose by stating that the “action in the broader world can be prosocial, antisocial, or neutral in nature,” (Bronk, 2011 , p. 33). While this statement takes the morality out of the previous definition, it is also important to note that the other commonly used measures of sense of purpose make no mention of purpose helping others or needing to affect others at all (Hill et al., 2016 ; Ryff, 1989 ; Scheier et al., 2006 ).

In fact, this purpose definition as one without a morally valenced view actually aligns with Allport and Odbert’s ( 1936 ) original categorization of purposefulness into the extensive group of personal traits which were “generalized and personalized determining tendencies—consistent and stable model of an individual’s adjustment to [their] environment” (p. 26) and were also described as “more neutral and less censorial” relative to some of the other word groupings. Thus, when considering more recent interpretation of Damon et al.’s ( 2003 ) definition of purpose, the sense of purpose measures being used in the current paper (Hill et al., 2016 ; Ryff, 1989 ; Scheier et al., 2006 ), or even Allport and Odbert’s ( 1936 ) expansive list of characteristic groupings, sense of purpose is likely not a moral quality. The measures of interest in the current paper do not assess the quality or nature of the activities and direction of individuals, and, as such, are not attached to a societal norm of morality or virtue.

Even if it were the case that the current work’s definition of sense of purpose could be classified as a moral quality, Allport ( 1931 ) also acknowledged that there are likely instances where a construct “with social significance, may likewise represent true traits of personality,” (p. 371). Allport and Odbert ( 1936 ) later expand on this reasoning, explaining that the consistency of thoughts, feelings, and behaviors of characteristics tied to social judgements may be followed due to people’s desire to meet social norms rather than the characteristics themselves being trait-like. Furthermore, Allport (1927) expressed concerns that these moral evaluations likely differ across locations and time. With some definitions of purpose requiring the extra criterion of being beyond-the-self or worthwhile as defined by the researchers (Bronk & Damon, 2021), these critiques align with those put forth by other purpose researchers that to evaluate worthiness requires a level of subjectivity that also ignores the broader and various social contexts in which individuals exist (Burrow et al., 2021 ). Thus, calls have been reiterated to remove the researcher’s judgement and evaluation of an individual’s own purpose from the process of declaring them as purposeful, and simply assess whether they have a higher or lower sense of purpose (Hill & Pfund, 2022).

Finally, though not the expressed concern of Allport ( 1931 ), some might feel skeptical of the inclusion of sense of purpose as a trait given its connection to well-being. Earlier on in its creation as a construct, it was viewed as one of six indicators of psychological well-being (Ryff, 1989 ; Ryff & Keyes, 1995 ). There are two primary arguments to address here. First, purpose and well-being, though associated, can exist without each other (Baumeister & Vohs, 2013 ); thus, someone can be high on trait sense of purpose while still having lower well-being or vice versa. Second, ample research has illuminated that different aspects of well-being can be trait-like. For example, positive and negative affect, key components of subjective well-being, are often considered to be fairly dispositional in nature (Tellegan, 1985 ; Watson & Clark, 1999 ), and some indicators of psychological well-being show relative lifespan stability (Mann, Deyoung, & Krueger, 2021 ). In the multifaceted realm of well-being research, both subjective well-being and self-esteem are constructs often described as traits (Luhmann et al., 2014 ; Ormel, VonKorff, Jeronimus, & Riese, 2017 ; Orth & Robins, 2014 ). Additionally, broad frameworks of personality psychology, such as the Neo-Socioanalytic Framework of Personality, include the Big Five personality traits plus positive and negative affect as traits (Roberts & Nickel, 2017 ). Thus, even if sense of purpose is potentially a component of well-being, that should not, in turn, disqualify it as a trait. Between this point and the separation from the value-laden terminology by which Allport ( 1931 ) was concerned, sense of purpose continues to fit the necessary criteria to be identified as a trait.

Sense of Purpose is Consistent Enough

Allport’s penultimate criterion for what he considers qualities of a personality trait focuses more on what a trait does not have to have rather than what it does: “acts, and even habits, that are inconsistent with a trait are not proof of the non-existence of the trait,” (p. 371). In other words, an individual’s thoughts, feelings, or behaviors deviating from their level on a given trait does not invalidate the existence of that trait. Some have highlighted this condition when acknowledging that traits will reveal themselves differently depending on the situation in which one resides (Funder, 2008 ), and we need to focus on consistency in thoughts, feelings, and behaviors across similar situations rather than across all situations (Roberts, 2009 ). Others have suggested that traits are a distribution of states (Fleeson, 2001 ); thus, there will be deviations from trait-level (or mean state-level in the distribution) regardless of the situation in which an individual is (Fleeson, 2004 ). At this point, traits have been described as a sum of habits, which are a sum of states (Allemand & Flückiger, 2017 ); even as states deviate from traits, traits are still strong predictors of an individual’s most frequent states (Fleeson & Gallagher, 2009 ). Thus, Allport ( 1931 ) was correct in asserting that a deviating act is “not proof of the non-existence of the trait,” (p. 371). In fact, over three decades later, Allport (1966) notes, “the variability of behavior cannot be overlooked… I have learned that my earlier views seemed to neglect the variability induced by ecological, social, and situational factors. This oversight needs to be repaired through an adequate theory that will relate the inside and outside systems more acutely,” (p. 9).

The way this criterion fits into the purpose literature can be tricky given the lack of work on short-term sense of purpose variability. One of the only studies that has considered daily sense of purpose levels found that certain engaging in certain activities (like assisting one’s family members) and avoiding others (like less leisure time) were associated with greater daily sense of purpose in adolescents (Kiang, 2012 ); however, this study did not evaluate the extent to which daily sense of purpose was associated with trait-level sense of purpose, or how the proportion of variability decomposed at the within- versus between-person level. Some work looking at daily sense of purpose during the COVID-19 pandemic found that half of the variance in daily purposefulness was between-person and the other half within-person (Hill, Klaiber, et al., 2021 ). Another study using a measurement burst design found that the variability in daily sense of purpose is greater between-people than within-people (Pfund, Hofer, et al., 2021 ). Furthermore, this work found that these amount of daily within-person variability was consistent across bursts, meaning that people were consistent across a year in how much variability they experienced from one day to the next. Thus, there is evidence for both within- and between-person variability in short-term sense of purpose levels, though initial evidence points to consistency in variability within individuals over time. Though this particular question is one the requires more research, the research that exists aligns with Allport’s ( 1931 ) general point that “there are in every personality instances of acts that are unrelated to existent traits” (p. 371). In the case of sense of purpose, these instances are not so great as to prove the trait does not exist.

Sense of Purpose is Within and Between

The final trait criterion which Allport ( 1931 ) puts forth is that “a trait may be viewed either in the light of the personality which contains it, or in the light of its distribution in the population at large,” (p. 372). Thus, the presence of a trait can vary within an individual, or a trait can be viewed as something between individuals, even though it may not exist within every individual. These distinctions, which Allport ( 1931 ) describes as a “unique aspect” versus a “universal aspect” (p. 372), align with the idiographic and nomothetic perspectives put forth in personality psychology (Barenbaum & Winter, 2008 ). Idiographic approaches to personality focus on the structure of traits within an individual (Beck & Jackson, 2020 ), while more commonly used nomothetic approaches focus on between-person differences (Barenbaum & Winter, 2008 ). Currently, most quantitative research on sense of purpose has taken a more nomothetic perspective by considering at what points in the lifespan we can anticipate seeing differences on purpose (Bronk et al., 2009 ; Hedberg et al., 2011 ; Hill & Weston, 2019 ), as well as the outcomes associated with between-person differences on this trait (Boyle, Barnes, et al., 2009 ; Hill, Sin, et al., 2018 ; Kim, Sun, et al., 2013a ).

Little purpose research has taken a quantitatively idiographic approach when understanding this construct. Work investigating the implications for daily purposeful pursuits on affective well-being, meaning in life, and self-esteem found that some of these associations differed based on whether someone has social anxiety disorder (Kashdan & McKnight, 2013 ), highlighting the potential for structural differences with this construct. Furthermore, qualitative work has highlighted that some older adults do not view purpose as having a role at that stage in their life (Lewis et al., 2020 ). Though focused on people’s opinions of purpose rather than the presence of sense of purpose in their lives, this findings may connect to Allport’s ( 1931 ) suggestion that there are likely few traits which will be present in all individuals. Individuals identify as having different purposes in life (i.e., purpose orientations; Hill, Burrow, et al., 2010 ), though their reported sense of purpose levels may be the same. Given that the activities to pursue one’s purpose in life necessarily differ based on those overarching goals (McKnight & Kashdan, 2009 ), another consideration is the presence of sense of purpose within individuals may not differ so much as their enaction of purposeful thoughts, feelings, and behaviors (see Hill et al., 2023). Relatedly, there has been recent recognition that the content which promotes one’s sense of purpose may function differently between people when considering the broader context in which individuals reside (Burrow et al., 2021 ). While the opportunities for research on this question regarding the more nuanced, idiographic nature of sense of purpose remains, there is vast evidence to support the nomothetic existence of this trait.

Allport ( 1931 ) created a clear roadmap for evaluating whether a construct can be considered a trait. Sense of purpose meets the demands for most of the criteria set forth, such as illustrating that it “has more than a nominal existence” (Allport, 1931 , p. 368), and that it has been “established empirically or statistically,” (Allport, 1931 , p. 369). Some of the conclusions about whether sense of purpose is a trait may differ depending on how one conceptualizes it, like when considering whether it “is not the same as a moral quality,” (p. 371); hence, the sense of purpose definition I put forth in the current paper is one removed from some with more value-laden tendencies. Finally, some of Allport’s ( 1931 ) suggested criteria require further research, like his final point about traits having both “its unique and universal aspects,” (p. 372). However, this point acknowledges the nuances surrounding traits rather than providing a more stringent hurdle over which trait researchers must jump. Though some of the criteria may require further research, sense of purpose generally aligns with many of the criteria established in Allport’s ( 1931 ) trait theory, thus providing another individual difference to add to the collection for personality researchers to consider beyond the Big Five.

A broader goal in this classification is to better understand the changeability of sense of purpose. Sense of purpose predicts positive outcomes throughout the lifespan (Pfund, 2020 ), while also being less salient in younger and older adults (Mann et al., 2021; Pinquart, 2002 ). Thus, the classification of this powerful construct also provides a guiding framework for both the obstacles and promises ahead as we uncover how to make sense of purpose more accessible to all. While personality researchers have explored interventions efforts to change traits (e.g., Hudson, Fraley, et al., 2021 ; Stieger, Flückiger, et al., 2021 ), the long-term efficacy and predictive implications of these interventions are still under investigation.

Given the uphill battle with intervening on traits, some may wonder why I am seeking to classify sense of purpose as a trait. When responding to others’ disbelief of the existence of traits, Allport (1966) deems the common examples his critics call upon to disprove and mark their concerns of the reality of traits “the fallacy of misplaced concreteness,” (p. 1). In particular, he describes their concerns over statements of behaviors like “John behaves aggressively,” to spiral to the bigger claim of, “John has a trait of aggression,” (p. 1). In this case, Allport (1966) is explaining to his adversaries that claiming something to be a trait does not cause it to become one. The current predicament with whether sense of purpose is a trait could be considered the reverse of this scenario: the fallacy of misplaced ephemerality.

Thus, to those confused with the current work’s exploration, I will point out that the nature of sense of purpose will not transform based on how we categorize it. Instead, our categorization will shift our perceptions surrounding challenges in intervening upon it. By taking a more conservative and, perhaps, daunting approach in classifying sense of purpose as a trait, researchers invested in interventions will regard the changeability of this construct with more humility and caution, which, in turn, will lead to more rigorous, thoughtful, and empirically-driven intervention efforts. To support individuals in bolstering their sense of purpose is the goal, and correctly categorizing it will guide our paths in more effectively accomplishing that goal.

Gabrielle N. Pfund was supported by a National Institute on Aging Grant 5T32AG00030-45 at Washington University in St. Louis in the initial drafting of this manuscript.

Declarations

There are no disclosures or conflicts of interest to report. This work is a theoretical paper and, thus, does not involve human or animal participants, or an informed consent process.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Comparison and Contrast of Trait-Based, Situational and Transformational Leadership Theories

24 Pages Posted: 19 Aug 2012

Ciprian Patrulescu

Duquesne University

Date Written: April 10, 2009

This research paper compares and contrasts several leadership theories, the evolution of the theories, and future research implications. The leadership theories discussed in the paper include the trait-based, situational, and transformational leadership theories. Trait-based theories represented the earliest attempts to quantitatively study leaders and leadership effectiveness by focusing on the innate skills and traits of the person. Situational theories expanded to explore the influence of different situations on leadership. Lastly, transformational leadership theories further expanded on the role of the leader in motivating followers through inspiration.

Keywords: leadership models, leadership paradigms

Suggested Citation: Suggested Citation

Ciprian Patrulescu (Contact Author)

Duquesne university ( email ).

600 Forbes Avenue Pittsburgh, PA 15282 United States

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• Published: 12 August 2024

A genome-wide investigation into the underlying genetic architecture of personality traits and overlap with psychopathology

• Priya Gupta 1 , 2 ,
• Marco Galimberti   ORCID: orcid.org/0000-0001-6052-156X 1 , 2 ,
• Yue Liu 3 ,
• Sarah Beck   ORCID: orcid.org/0000-0003-4176-2936 1 , 2 ,
• Aliza Wingo   ORCID: orcid.org/0000-0002-6360-6726 4 , 5 ,
• Thomas Wingo   ORCID: orcid.org/0000-0002-7679-6282 3 ,
• Keyrun Adhikari   ORCID: orcid.org/0000-0001-9129-1699 1 , 2 ,
• Henry R. Kranzler   ORCID: orcid.org/0000-0002-1018-0450 6 , 7 ,
• VA Million Veteran Program ,
• Murray B. Stein   ORCID: orcid.org/0000-0001-9564-2871 8 , 9 ,
• Joel Gelernter   ORCID: orcid.org/0000-0002-4067-1859 1 , 2 &
• Daniel F. Levey   ORCID: orcid.org/0000-0001-8431-9569 1 , 2  

Nature Human Behaviour ( 2024 ) Cite this article

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• Genetic variation
• Human behaviour

Personality is influenced by both genetic and environmental factors and is associated with other psychiatric traits such as anxiety and depression. The ‘big five’ personality traits, which include neuroticism, extraversion, agreeableness, conscientiousness and openness, are a widely accepted and influential framework for understanding and describing human personality. Of the big five personality traits, neuroticism has most often been the focus of genetic studies and is linked to various mental illnesses, including depression, anxiety and schizophrenia. Our knowledge of the genetic architecture of the other four personality traits is more limited. Here, utilizing the Million Veteran Program cohort, we conducted a genome-wide association study in individuals of European and African ancestry. Adding other published data, we performed genome-wide association study meta-analysis for each of the five personality traits with sample sizes ranging from 237,390 to 682,688. We identified 208, 14, 3, 2 and 7 independent genome-wide significant loci associated with neuroticism, extraversion, agreeableness, conscientiousness and openness, respectively. These findings represent 62 novel loci for neuroticism, as well as the first genome-wide significant loci discovered for agreeableness. Gene-based association testing revealed 254 genes showing significant association with at least one of the five personality traits. Transcriptome-wide and proteome-wide analysis identified altered expression of genes and proteins such as CRHR1, SLC12A5, MAPT and STX4 . Pathway enrichment and drug perturbation analyses identified complex biology underlying human personality traits. We also studied the inter-relationship of personality traits with 1,437 other traits in a phenome-wide genetic correlation analysis, identifying new associations. Mendelian randomization showed positive bidirectional effects between neuroticism and depression and anxiety, while a negative bidirectional effect was observed for agreeableness and these psychiatric traits. This study improves our comprehensive understanding of the genetic architecture underlying personality traits and their relationship to other complex human traits.

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Identification of pleiotropy at the gene level between psychiatric disorders and related traits

Gene-based association analysis identifies 190 genes affecting neuroticism

Multivariate genetic analysis of personality and cognitive traits reveals abundant pleiotropy

Personality dimensions influence behaviour, thoughts, feelings and reactions to different situations. A valuable construct within the field of psychological research has converged on five different dimensions to characterize human personality: neuroticism, extraversion, agreeableness, conscientiousness and openness 1 , 2 . Personality dimensions could be playing an important role in the susceptibility and resilience to diagnosis of psychiatric disorders and their relationship with other health-related traits and responses to treatment.

The last decade has seen an increasing interest in understanding the dimensions of human personality through the lens of genetics. Depression is one mental disorder that has been studied with respect to its relationship to personality traits, with a large portion of genetic risk for depression being captured by neuroticism 3 . The same study found a modest negative association of genetic depression risk with conscientiousness, with small contributions from openness, agreeableness and extraversion. Neuroticism is one of the most studied dimensions of the ‘big five’ personality traits and numerous studies have found positive correlations with depression, anxiety and other mental illnesses 3 , 4 , 5 . Schizophrenia has also been associated with personality traits, especially neuroticism, which has been shown to increase risk for diagnosis 6 . A study using data from the Psychiatric Genomics Consortium (PGC) and personal genomics company 23andMe found two genomic loci to be common between neuroticism and schizophrenia. This study also reported six loci shared between schizophrenia and openness 7 .

The past 15 years have seen an explosion in the use of the genome-wide association study (GWAS). In 2010, Marleen de moor et al. from the Genetics of Personality Consortium (GPC) published a GWAS of the ‘big five’ personality traits conducted with 17,375 adults from 15 different samples of European ancestry (EUR) 8 . This study found two genome-wide significant (GWS) variants near the RASA1 gene on 5q14.3 for openness and one near KATNAL2 on 18q21.1 for conscientiousness but no significant associations for other personality traits. GPC then conducted studies on extraversion and neuroticism in their second phase and meta-analyses were performed. A GWAS of neuroticism that was conducted on approximately 73,000 subjects identified rs35855737 in the MAG1 gene as a GWS variant 9 . Although the sample size was increased substantially to 63,030 subjects in phase II, no GWS variants were detected for extraversion in that study 10 . In 2016, Lo et al. identified six loci associated with different personality traits, including loci for extraversion 11 . A paper that investigated neuroticism along with subjective well-being and depressive symptoms leveraging the UK Biobank (UKB) and other published data 12 was published this same year. A more detailed picture of neuroticism genetics was presented by Nagel et al. 2018 13 , where the authors collected neuroticism genotype data of 372,903 individuals from the UKB and performed a meta-analysis by combining the summary statistics from this UKB sample, 23andMe and GPC phase 1 samples, increasing the total sample size to 449,484. They identified a total of 136 loci and 599 genes showing GWS associations to neuroticism. In 2021, Becker et al. conducted a polygenic index study and created a resource with GWAS meta-analysis summary statistics combining different data cohorts for a large number of traits, including neuroticism, thus increasing the total sample size of neuroticism meta-analysis to 484,560 and increasing the number of novel GWS loci (although this was not the focus of this work) 14 . They also identified six genomic loci for extraversion.

In this work, we conducted GWAS of each of the ‘big five’ personality traits in a sample of ~224,000 individuals with genotype data available from the Million Veteran Program (MVP). Using linkage disequilibrium score regression (LDSC), we estimated the single-nucleotide polymorphism (SNP)-based heritability of each of the five personality traits. We then combined the MVP data with other sources of personality GWAS summary statistics from GPC and UKB and performed meta-analyses for each of the five personality traits, including as many as ~680,000 participants for the largest meta-analysis of neuroticism so far. To gain insights into the biology of these traits, we performed transcriptome-wide association studies (TWAS) and proteome-wide association studies (PWAS) followed by pathway and drug perturbation analyses and variant fine-mapping. We also studied the overlap of these personality traits with anxiety and other complex traits through phenome-wide genetic correlations and conditional analyses. We performed drug perturbation analyses with genes associated with neuroticism and found convergence on drugs for major depressive disorder (MDD). Finally, we conducted Mendelian randomization (MR) experiments to investigate the causal relationship of neuroticism and agreeableness, the two most genetically divergent traits, with depression and anxiety.

In the EUR GWAS in the MVP cohort, we identified in total 34 unique independent genomic loci significantly associated ( P value <5 × 10 −8 ) with at least one of the five personality traits (Table 1 ). The highest numbers of loci were found for extraversion and neuroticism (11 for each) while conscientiousness showed only two loci. In the MVP we identified 4,036 GWS variants ( P  < 5 × 10 −8 ) for neuroticism across 7 independent genomic loci harbouring genes including MAD1L1 , MAP3K14 , CRHR1 , CRHR1-IT1 and VK2 ( P  < 5 × 10 −8 ). Of these seven loci, two ( rs2717043 and rs4757136 ) were also reported to be GWS in Nagel et al. 13 . We identified 11 GWS loci for extraversion, the largest number of GWS loci to be identified for this trait. Associations for extraversion were found near several genes, including CRHR1 , MAPT and METTL15 (total 90 genes). For the two conscientiousness loci, the first locus maps to a region near the genes FOXP2 , PPP1R3A and MDFIC and the second locus maps to the ZNF704 gene, all of which are protein coding genes. For openness, 7 loci were identified spanning over 39 genes, including BRMS1 , RIN1 and B3GNT1 . For agreeableness, 3 loci were identified spanning 19 genes, including SOX7 , PINX1 and FOXP2 . The Manhattan plots for all five traits are shown in Supplementary Fig. 1 .

Two GWS variants were found for agreeableness in the African ancestry (AFR) sample. Variants rs2393573 (effect size, −0.106; standard error of the mean (s.e.m.), 0.018; 95% confidence interval (CI) −0.071, 0.141; P  = 7.502 × 10 −9 ) and rs112726823 (effect, −0.720; s.e.m., 0.130; 95% CI 0.465, 0.975; P 3.268 × 10 −8 ) mapped near CCDC6 and ARHGAP24 . We did not find any GWS variants for any of the other four personality traits in the AFR sample; the multiple subthreshold findings from this analysis may reach the GWS threshold in a larger sample. A list of lead independent SNPs found in the AFR sample for each trait is provided in Supplementary Tables 1 – 5 .

Meta-analysis in EUR populations

The meta-analysis for neuroticism showed associations with 208 independent GWS loci. The increased power due to the inclusion of MVP data resulted in the identification of 79 additional GWS loci, which were not significant in the previous study 13 . Only five loci identified previously ( rs1763839 , rs2295094 , rs11184985 , rs579017 and rs76923064 ) were no longer significant in our meta-analysis. A total of 17 loci of these 79 have also been discovered in the polygenic index study (Supplementary Table 6 ). Thus, we found 62 novel loci associated with neuroticism in our meta-analysis. SNPs and loci were mapped to genes based on chromosomal position, expression quantitative trait loci (eQTL) and chromatic interaction 15 . A total of 231 genes were found significant in the MAGMA (Multi-marker Analysis of GenoMic Annotation) gene-based test 16 . NSF , KANSL1 , FMNL1 , PLEKHM1 and CRHR1 ( P  < 2.850 × 10 −40 ) were among the top significant hits. The largest number of significant loci are located on chromosome 11, followed by chromosome 1. The GWS associations also include two loci with variants rs7818437 (effect, −0.021; s.e.m., 0.002; 95% CI −0.017, 0.025; P  = 7.599 × 10 −17 ) and rs76761706 (effect, −0.035; s.e.m., 0.002; 95% CI −0.031, 0.039; P  = 2.850 × 10 −40 ) located in inversion regions on chromosome 8 and 17, respectively. Variants in these two inversion regions were also previously reported to be significantly associated with neuroticism in the study by Okbay et al. 12 .

For extraversion, after meta-analysing the MVP and GPC data, the number of significant loci increased to 14. The lead signals were located on chromosomes 1–6,11,12, 17 and 19. The most significant locus harbours genes in/near WSCD2 ( P  < 3.449 × 10 −11 ) located on chromosome 12.

Chromosome 11 contains significant variant associations from three traits, namely neuroticism, extraversion and agreeableness, with neuroticism and extraversion both having findings near the ‘basic helix-loop-helix ARNT like 1’ ( ARNTL1 , also known as BMAL1 ) gene, with opposing and significant direction of effect at common variants. Complete information of all identified significant loci for each of the five traits with full statistics is provided in Supplementary Tables 6 – 10 . The cohorts used in meta-analysis are depicted in Fig. 1a . Manhattan plots for meta-analyses of each of the five traits are depicted in Fig. 2 .

a , Data collection of the five personality traits. b , Genetic correlation matrix among the five personality traits (meta-data). The heritability value of the respective trait is written in parenthesis. c , A karyogram showing the regions with significant local genetic correlation ( r G  > 0.3) between different personality traits.

The GWS variants in light-green colour. Reported P values are two-sided and not corrected for multiple testing. GWS threshold ( P  = 5 × 10 −8 ) is used to define significant variants and depicted by red line.

Trans-ancestry analysis

We performed trans-ancestry meta-analysis of the five personality traits combining EUR and AFR GWAS for each of the five traits using inverse variance weighing in METAL 17 . For neuroticism, the trans-ancestry analysis identified a total of 216 GWS loci, of which 16 are novel, that is, they were not GWS in the EUR meta-analysis (Supplementary Tables 11 – 15 ). Of the 208 GWS loci for neuroticism in the EUR meta-analysis, 200 remained GWS in trans-ancestry analysis, while the remaining 8 showed a marginally higher P value and thus do not pass the threshold for being GWS in trans-ancestry. For agreeableness and conscientiousness, in addition to the loci that were shown to be GWS in their respective EUR meta-analysis, two more novel loci ( rs140242735 located on chromosome 8 and rs10864876 located on chromosome 2 for agreeableness and conscientiousness, respectively) were identified as GWS in the trans-ancestry analysis. In case of openness, two loci out of the three that identified as GWS in EUR remained GWS in the trans-ancestry analysis. For extraversion, in total 13 were identified as GWS in the trans-ancestry analysis, of which 10 were also GWS in the EUR meta-analysis and 3 were newly identified.

We performed TWAS for each of the ‘big five’ personality traits in EUR (meta-analysis) using FUSION 18 and the GWAS summary statistics. We performed a multi-tissue TWAS in 13 different brain subtissues and blood using their respective expression profiles from Genotype Tissue-Expression project (GTEx v8) 19 . From a total 10,386 genes tested, we identified a total 175, 24, 5, 1 and 11 genes showing significant gene–trait associations across the 13 subtissues in neuroticism, extraversion, agreeableness, conscientiousness and openness, respectively, after Bonferroni correction for 135,018 tests (10,386 genes across 13 tissues) (Fig. 3a ). Figure 3a shows the distribution of associations found across the 13 tissues for each trait. The highest number of gene–trait associations were found in brain caudate basal ganglia, cerebellum, cerebral hemisphere and frontal cortex regions for neuroticism and extraversion, while fewer TWAS gene–trait associations were identified for the other three personality traits, presumably owing to the comparatively lower power of their respective GWAS datasets.

a , A bar chart showing the number of significant TWAS genes per transcripts found of four personality traits with significant findings in respective subtissues. Scatter plots of neuroticism ( b ), agreeableness ( c ), extraversion ( d ) and openness ( e ) with TWAS z -scores of each gene transcript plotted on the y axis and its respective chromosomal location plotted on the x axis. The significant hits are shown in red circles with mapped gene names as labels. The blue horizontal line indicates the significance threshold of the z -score corresponding to the Bonferroni-corrected, two-sided P value. Conscientiousness data is reported in Supplementary Table 22 .

CRHR1, KANSLI1-AS1 and MAP-IT1 are among the top TWAS gene associations ( P  < 1.32 × 10 −23 ) for neuroticism (Fig. 3b ). The strong association of CRHR1 (encoding corticotropic-releasing hormone receptor), which in some prior work has been shown to be associated with treatment response to depression 20 , may suggest some common underlying elements regulating both neuroticism and depression. Extraversion also shows strong gene–trait associations with CRHR1, KANSL1-AS1 and MAPT-IT1 but with an opposite direction of effect to neuroticism. This may indicate some common genetic components whose differential behaviour regulates neuroticism and extraversion. There are nine such genes showing opposite direction of effect in neuroticism and extraversion (Supplementary Table 3 ).

LOC10271024064 and LRFN4 showed the strongest associations with openness and LINCR-0001 and FAM167A showed the strong associations with agreeableness, while only one gene, AP1G1 , showed association with conscientiousness in the 13 tissues considered. The complete list of all GWS TWAS gene hits for the five personality traits is provided in Supplementary Table 22 .

We investigated the association of personality traits with protein expression using PWAS. Based on the availability of protein profiles and the observed TWAS signal, dorsolateral prefrontal cortex brain protein profiles were chosen for the PWAS analysis. The PWAS identified 47 proteins to be significantly associated with neuroticism. Next, we checked the colocalization signal for these PWAS lead genes. Out of 47 PWAS lead genes, 35 genes showed a colocalization signal (H4 probability >0.5).

Five, two, two and four proteins were discovered for extraversion, agreeableness, conscientiousness and openness, respectively (Fig. 4 ). A complete list of all PWAS lead genes is provided in Supplementary Table 23 .

A Manhattan plot is displayed showing the significant protein associations observed for neuroticism. The red line in the plot depicts the Bonferroni-corrected, two-sided P value threshold at 5% FDR. The boxes on the right show the significant proteins found for the respective four personality traits.

We first used LDSC to calculate SNP-based heritability of each of the five personality traits within the MVP EUR cohort. The intercepts of the LDSC indicated no evidence for population stratification, with observed values of 1.01, 1.02, 0.99, 1.02 and 1.00 for neuroticism, extraversion, agreeableness, conscientiousness and openness, respectively. The SNP heritability ranges from 4% to 7% (Supplementary Fig. 2 ), with extraversion showing the highest heritability point estimate of all traits (neuroticism h 2  = 0.0655; s.e.m., 0.004; 95% CI 0.058, 0.073; agreeableness h 2  = 0.042; s.e.m., 0.003; 95% CI 0.036, 0.048; extraversion h 2  = 0.071; s.e.m., 0.003; 95% CI 0.065, 0.077; openness h 2  = 0.048; s.e.m., 0.003; 95% CI 0.042, 0.054; and conscientiousness h 2  = 0.047; s.e.m., 0.003; 95% CI 0.041, 0.053).

For the MVP AFR cohort, cov-LDSC was utilized to estimate personality heritabilities ( Methods ) 21 . Relative to the MVP EUR cohort, neuroticism and extraversion showed lower heritability (4.47% and 3.30%, respectively) in the AFR cohort, while for agreeableness, the heritability was similar (4.24%) (Supplementary Table 1 ). The values were not significant for conscientiousness and openness in AFR.

Before combining the MVP cohort-derived summary statistics with other data sources, we calculated the genetic correlation between the MVP personality summary statistics and other respective sources (Supplementary Table 2 ). A correlation coefficient value of 0.80 (s.e.m., 0.02) observed for the neuroticism summary statistics from the MVP cohort and Nagel et al. study 13 suggests that there is limited heterogeneity between the two datasets and supports their use in a meta-analysis. As shown in Supplementary Table 2 , the genetic correlations were high for all other four traits across data sources as well.

LDSC was used to estimate SNP-based heritability in the EUR participants for each personality trait in the meta-analysis. The SNP heritability values in the meta-analyses were similar to what was observed in the MVP-only cohort for the different traits in the EUR, with a decrease in heritability of extraversion from 7.1% to 5.1% (Fig. 1b ).

Genetic correlation estimates were also obtained between the meta-analysis summary statistics for the five personality traits. We found a significant degree of varying genetic overlap among the five personality traits. The genetic correlations are presented in Fig. 1b . The highest correlation is observed between neuroticism and agreeableness with a r G  = −0.51 (s.e.m., 0.030; P  = 3.813 × 10 −64 ).

Next, we estimated the genetic correlations of 1,437 traits listed in the Complex Traits Genetics Virtual Lab 22 summary statistics record to find other traits related to the five personality traits (Supplementary Tables 16 – 20 ). A total of 325 traits showed significant genetic correlation following multiple testing correction to one or more personality traits. We found MDD and anxiety showed varying degrees of significant correlations to different personality traits as shown in Fig. 5 . The highest genetic correlation is between neuroticism and anxiety ( r G  = 0.80). Neuroticism and agreeableness both show high genetic correlations to these traits, but in opposite directions with MDD (neuroticism r G  = 0.68; s.em. 0.02; P  < 5.00 × 10 −100 and agreeableness r G  = −0.35; s.e.m. 0.04; P  = 1.53 × 10 −22 ), manic behaviour (neuroticism r G  = 0.44; s.e.m. 0.08; 95% CI 0.641, 0.719; P  = 1.11 × 10 −8 and agreeableness r G  = −0.35; s.e.m. 0.11; 95% CI −0.134, 0.566; P  = 1.556 × 10 −3 ), anxiety (neuroticism r G  = 0.80; s.e.m. 0.06; 95% CI 0.682, 0.918; P  = 1.54×10 −46 and agreeableness r G  = −0.32; s.e.m. 0.08; 95% CI −0.163, 0.477; P  = 7.28 × 10 −5 ) and irritability (neuroticism r G  = 0.70; s.e.m. 0.02; 95% CI 0.661; 0.739, P  < 5.00 × 10 −100 and agreeableness r G  = −0.62; s.e.m. 0.04; 95% CI −0.542, 0.698; P  = 9.76 × 10 −61 ).

The y axis is the genetic correlation. Error bars (in black) indicate the 95% CIs of the estimated genetic correlation. Anxiety indicates substances taken for anxiety; medication is prescribed for at least 2 weeks. Heavy DIY activities describes the types of physical activity in last 4 weeks; for example, weeding, lawn mowing, carpentry and digging. Manic behaviour describes manic/hyper behaviour for 2 days. Detailed results for all traits, including the sample size of each of the traits, is presented in the Supplementary Tables 16 – 20 .

Local genetic correlations

Global genetic correlations use the average squared signal over the entire genome, which may sometimes mask opposing local correlations in different genomic regions. To counter that, we also calculated the local genetic correlations among the five personality trait pairs using Local Analysis of [co]Variant Association (LAVA) 23 . All personality pairs showed varying degree of correlation in different genomic regions except for the neuroticism–openness pair, which showed negligible global ( r G  = −0.01) and no local genetic correlation between the two. The highest number of correlated genomic chunks were found for neuroticism–extraversion and neuroticism–openness pairs (Fig. 1c and Supplementary Table 21 ).

Variant fine-mapping

To identify well-supported possible causal variants from the large list of SNPs showing associations with the personality traits, we performed genome-wide variant fine-mapping using PolyFun 24 . In total, 166 unique variants were fine-mapped across the five personality traits. The number of variants fine-mapped for neuroticism, extraversion, agreeableness, conscientiousness and openness were 155, 8, 4, 7 and 3, respectively. The complete list of variants fine-mapped for each of the personality traits is provided in the Supplementary Tables 24 – 28 .

Relationship between personality and psychiatric disorders

We performed additional analyses to help understand the significant differential genetic correlation observed between neuroticism and agreeableness with different psychiatric disorders such as MDD and anxiety.

Conditional analysis

Because the genetic correlation between anxiety and neuroticism was so high, we performed multi-trait-based conditional and joint analysis of neuroticism summary statistics conditioned on anxiety and MDD summary statistics individually. The anxiety and MDD summary statistic used is based on data from UKB, MVP and PGC with individuals of EUR ancestry (see Methods for details). We performed a similar analysis with agreeableness, which had a negative correlation with both MDD and anxiety, as a negative control.

After conditioning on MDD, the SNP heritability of the conditioned neuroticism summary statistic reduced significantly from 7.8% to 3% (Table 2 ). Out of the original 208 GWS leads, only 42 remained significant after conditioning, indicating there is substantial genetic overlap between neuroticism and MDD, which gets removed after conditioning. In case of conditioning on anxiety, again there is a decrease in neuroticism heritability, but to a lesser extent (Table 2 ). On conditioning agreeableness on MDD and anxiety, no significant reduction in heritability was observed. However, loss of one genomic locus, rs7240986 (18:53195249:A:G), was observed after conditioning on either anxiety or MDD for agreeableness.

Drug perturbation analysis

We performed a drug perturbation analysis to find drug candidates for neuroticism-enriched genes using gene2drug software 25 . Gene2drug utilizes the Connectivity Map transcriptomics data of ~13,000 cell lines exposed to different drugs, and based on these gene expression profiles and then pathway expression profiles (PEPs), it first matches the query gene to its pathway and then to its potential candidate drug. This analysis predicted 298 unique drugs to correspond to the 231 significantly associated neuroticism genes. The top-scoring drug was found to be desipramine, which is a tricylic antidepressant. Some of the other drugs predicted are flupenthixol (anti-psychotic), tetryzoline (α-adrenergic agonist), doxorubicin (anthracycline/chemotherapy) and digitoxigenin (cardenolide). Based on these results, we repeated the drug perturbation analysis with depression-enriched genes. While there were only 51 genes common between neuroticism and depression gene sets, there was a convergence on drugs in the perturbation analysis. Out of 286 and 298 drugs predicted for depression and neuroticism, respectively, 167 drugs were common to both. The complete list of drugs is presented in Supplementary Tables 29 and 30 .

After establishing genetic overlap of neuroticism with MDD and anxiety, we carried out an MR analysis to explore the possibility of a causal relationship between genetic risk for neuroticism and MDD or anxiety. The results of the MR analysis using different methods are presented in Table 3 . The results of MR indicate a bidirectional causal effect, with the exposure of MDD on neuroticism outcome showing an inverse variance weighting (IVW) effect value of 0.429 at a significant P value (2.072 × 10 −85 ). The exposure of neuroticism on MDD shows a higher causal effect value of 0.834 with a significant P value (6.413 × 10 −103 ). We performed sensitivity analysis of MR using MRlap, which corrects for different sources of bias, including sample overlap, because there are overlapping participants between the exposure and outcome datasets 26 . With MRlap, we observe similar results with positive significant corrected β values in MRlap performed between MDD and neuroticism in both directions (Supplementary Table 4 ).

We also investigated the casual relationship of neuroticism with anxiety. On performing MR with anxiety exposure on neuroticism, we found a β value of 0.179 ( P  = 1.248 × 10 −15 ) and a corrected β value with MRlap of 0.531 ( P  = 7.781 × 10 −14 ) showing evidence of causality. On reversing the direction, the causality effect was stronger as seen by higher β value of 0.70 ( P  = 5.767 × 10 −61 ) with MR and corrected β value of 0.548 ( P  = 1.129 × 10 −40 ) with MRlap. This suggests that there is stronger evidence of causal effect of neuroticism on anxiety as compared with the reverse based on the genetic susceptibility. GWAS of anxiety and anxiety disorders are still relatively underpowered compared with neuroticism, limiting the number of available genetic instruments available for testing as exposures.

We investigated the causal effect of agreeableness on MDD and anxiety and vice versa. In the case of MR of MDD exposure on agreeableness outcome, a β value of −0.284 ( P  = 5.775 × 10 −13 ) was observed indicating negative causal effect of MDD on agreeableness (Table 3 and Supplementary Table 4 ). The causal effect is bidirectional with similar values observed in the opposite direction as well. The results are consistent with genetic correlation findings where negative correlation was observed between agreeableness and MDD. MR analysis of agreeableness and anxiety also indicated bidirectional causal effect. However, here both the traits have limited instruments available.

Out-sample polygenic risk score prediction

We conducted polygenic prediction analysis to validate our findings using the Yale–Penn cohort 27 , which had NEO Personality Inventory (NEO PI-R) scores and genotype information available for 4,532 EUR individuals, and used those data to predict PRS for each of the big five personality traits ( Methods ). We found modest but significant r 2 values in line with previous reports for all personality traits 14 : neuroticism of 2%, extraversion of 2%, openness of 2%, agreeableness of 3% and conscientiousness of 1%.

We conducted a GWAS meta-analysis study of each of the ‘big five’ personality traits in a sample size of up to 682,688 participants. We combined original GWAS results from the MVP (available for all five traits) with summary statistics from the UKB (neuroticism only) and GPC (all traits except neuroticism) cohorts to perform a well-powered meta-analysis for EUR GWAS in each trait. We identified 468 independent significant SNPs associations mapping to 208 independent genomic loci, of which one-third are novel. We identified 231 significant gene associations with neuroticism in the gene-based analysis. The current study was also successful in identifying 23 significant genomic locus associations for the four other personality traits studied, for which prior knowledge in the literature was very limited. In AFR, we found lower heritabilities for neuroticism and extraversion and no significant results for conscientiousness and openness. We identified two GWS variants for agreeableness in AFR. This is probably a reflection of low power and underlines the critical need to increase recruitment in underrepresented groups. Our work provides new data to inform the underlying genetic architecture of personality traits.

Neuroticism, the trait with the largest available sample size in this study, is characterized by emotional instability, increased anxiousness and low resilience to stressful events. As such, it has been the focus of previous efforts in GWAS. As seen previously, neuroticism overlaps substantially with psychopathology, where it is usually viewed as a precursor or risk factor for depressive and anxiety symptoms. Extraversion had the second largest sample size and had the highest SNP-based heritability in the MVP. In our data, scoring high on extraversion was genetically correlated with risk-taking behaviours and had the second strongest negative genetic correlation with neuroticism. Agreeableness assays show how someone relates with other people, that is, how trusting one is or how likely to find fault in others. This trait was the most negatively correlated with neuroticism and irritability as well as MDD, anxiety and manic symptoms. Conscientiousness items relate to discipline and thoroughness, with specific questions being ‘are you lazy’ and ‘does a thorough job’. This trait was most closely associated with ‘types of physical activity in last 4 weeks: ‘heavy do-it-yourself (DIY)’. Finally, openness 10-item Big Five Inventory (BFI-10) items assay imagination and artistic interest. Openness was positively associated with extraversion and risk taking in our data. Educational attainment was positively correlated with openness and negatively associated with neuroticism, while the other three personality traits showed essentially no such overlap (Fig. 5 ). Since these are self-reported items, they naturally reflect one’s own assessment of one’s personality traits, which might filter actual traits and behaviour through a lens of how one wishes to appear or be perceived.

Using these GWAS summary statistics, with excellent power for neuroticism and moderate power for the other traits, we investigated the heritability of the different personality traits and studied genetic correlations among them using LDSC. SNP-based heritability for all five personality traits in EUR were statistically significant. Out of all the personality pairs studied, the strongest relationship was a negative genetic correlation observed between neuroticism and agreeableness ( r G  = −0.51, Fig. 1b ). Examining the genetic correlations of the five personality traits with 1,437 external traits including depression (neuroticism r G  = 0.68 and agreeableness r G  = −0.35), manic behaviour (neuroticism r G  = 0.44 and agreeableness r G  = −0.35), anxiety (neuroticism r G  = 080 and agreeableness r G  = −0.33) and irritability (neuroticism r G  = 0.70 and agreeableness r G  = −0.62) further reflected a pattern of opposing relationships between these traits (Fig. 5 and Supplementary Tables 16 – 20 ). We also calculated local genetic correlations between personality pairs using LAVA, which helped in identifying the genomic regions playing roles in differential overlap in the genetic architecture of personality. This analysis identified several regions where the effect direction differed from the whole genome genetic correlation.

The MVP, our discovery dataset, is one of the world’s largest biobanks and is a valuable resource for genetic studies. Some previously published personality trait studies had significant contribution from UKB data. It is important to quantify the heterogeneity in these independent cohorts and the different definitions of personality phenotype within each. We investigated the genetic correlation between traits defined on the basis of different inventories (BFI-10, EPQ-RS and NEO-FFI) of personality ascertainment with different cohorts, namely MVP, UKB (part of Nagel et al. study) and GPC, respectively. For neuroticism, Nagel et al. and MVP studies showed a high r G value of 0.80 making these two independent cohorts suitable for meta-analysis (Supplementary Table 1 ). Similarly, for extraversion, NEO-FFI and two-item inventories showed high r G of 0.89 in the extraversion data of GPC and MVP studies. While for agreeableness, openness and conscientiousness, the r G s between MVP and GPC cohort were lower (0.63–0.72); this may be due to the small size of the GPC dataset for these traits and the correspondingly large standard errors around the point estimate. The point estimate is not necessarily biased in any particular direction, we only mean there is uncertainty. This limitation will be addressed by future GPC studies with larger sample sizes. No novel loci were identified in the meta-analysis with GPC for these traits.

TWAS revealed common genes with changes in gene expression but with opposite direction of effect for some personality traits. A study by Ward et al. in 2020 reported five of these genes (Supplementary Table 3 ) as eQTLs showing significant associations with mood instability 28 . This is further supported by the local genetic correlation studies (Supplementary Sheet 5 ) where we found genomic region 45883902-47516224 on chromosome 17, which harbours genes KANSL1-AS1 , MAPT and MAPT-IT1 , showing negative local genetic correlation between neuroticism and extraversion with a ρ value of −0.57 and r 2 value of 0.32.

rs1876829 , which maps to CRHR-Intronic Transcript 1, emerged as the lead SNP ( P  = 7.872 × 10 −39 ) for neuroticism in the GWAS analysis. We also found multiple eQTL SNPs in this genomic region ( rs8072451 , rs17689471 , rs173365 and rs11012 ) for the CRHR1 gene to be significantly associated ( P value ranging from 1 × 10 −5 to 1 × 10 −37 ). The TWAS analysis showed significant association of this gene with neuroticism in nervous system tissues including caudate basal ganglia, frontal cortex, hippocampus and spinal cord cervical region. CRHR1 encodes the receptor of corticotropin-releasing hormone family, which are major regulators of the hypothalamic–pituitary–adrenal pathway 29 . Genetic variation in the corticotropin-releasing hormone system has been linked to several psychiatric illnesses 30 . Another study reported hypermethylation at corticotropin-releasing hormone-associated CpG site, cg19035496, in individuals with high general psychiatric risk score for disorders such as depression, anxiety, post-traumatic stress disorder and obsessive compulsive disorder 31 . Further, a study by Gelernter et al. found that CRHR1 significantly associated with re-experiencing post-traaumatic stress disorder symptoms 32 and also maximum habitual alcohol intake 33 . This gene is also involved in hippocampal neurogenesis 30 , while reduced hippocampal activation is associated with elevated neuroticism 34 . This makes CRHR1 a good lead candidate to be followed in future studies to understand the molecular processes impacted by genetic variation underlying a range of psychiatric traits including neuroticism.

While gene expression associations give a wide array of information on the involvement of different genes regulating the different biological processes underlying the biology of traits, searching protein expression associations confers several advantages, as proteins are the final implementers in the functioning of all cells for many biological processes. Through PWAS studies, we found 47 proteins showing significant association with neuroticism in the dorsolateral prefrontal cortex. The PWAS analysis also identified leucine-rich repeat and fibronectin type III domain-containing 5 (LRFN5) protein association with neuroticism, and this protein is also involved in synapse formation. This protein has shown higher levels in patients with MDD and has been suggested as a potential MDD biomarker 35 .

Examples of genes for which we found converging evidence in neuroticism for transcript and protein-level associations with neuroticism include low-density lipoprotein receptor-related protein 4 (LRP4), syntaxin 4 (STX4) and metabolism of cobalamin associated B (MMAB) (Supplementary Table 31 ). LRP4 has diverse roles in neuromuscular junctions and in disorders of the nervous system, including Alzheimer’s disease and amyotrophic lateral sclerosis 36 , STX4 is implicated in synaptic growth and plasticity 37 , and MMAB, which catalyses the final step in the conversion of cobalamin (vitamin B12) into adenosylcobalamin (biologically active coenzyme B12), all of which have broad implications for brain function, including those in relation to methylmalonic acidaemia 38 . Low levels of plasma vitamin B12 have been found to be associated with higher depression cases in multiple studies 39 .

We investigated the relationship of these personality traits with other psychiatric traits, cognitive functions and disorders in a broad phenome-wide scan of genetic correlations with 1,437 traits. A total of 325 traits showed significant genetic correlations with at least one of the five personality traits following multiple testing correction. Two important traits that had some of the strongest associations were MDD and anxiety. Whereas the association of neuroticism with depression and anxiety has been previously considered 4 , 13 , our analysis revealed that another personality trait, agreeableness, is also strongly associated with both anxiety and depression but in the opposite direction to neuroticism, showing a potential protective relationship. MR indicated a strong bidirectional causal relationship between neuroticism with anxiety and depression, while showing a bidirectional protective relationship for agreeableness for both traits. Variance explained for neuroticism was attenuated upon conditioning for MDD but remained significant, indicating some independent genetic component for neuroticism despite the strong overlap. Similar, but with a less strong effect, was seen of anxiety on neuroticism, which may be partly due to lower power of available anxiety summary statistics. Larger studies of anxiety disorders are needed to better understand this relationship. Conversely, when we conditioned on agreeableness, for MDD and anxiety we observed a nominal but non-significant change in SNP-based heritability. We conducted MR to further discern these patterns and it showed bidirectional causal effects with neuroticism, confirming a high degree of inter-relatedness between the traits. Given the high degree of genetic overlap between trait neuroticism and the expectation of personality trait expression preceding age of onset for MDD, a high trait neuroticism may be considered an early risk factor for anxiety, depressive and related psychopathology. Indeed, studies have shown persistent elevated neuroticism through adolescence is a risk factor for later susceptibility to anxiety and MDD diagnosis 40 .

Personality phenotyping in The MVP sample were done using self-report for the short BFI-10 inventory. As such, data are relatively sparse compared with more robust instruments and do not have more in-depth features such as facets found in the NEO inventory. The nature of large biobank studies such as the MVP comes with a crucial advantage in recruitment and sample size, but comes with the sacrifice of deep phenotyping. Future studies that compare findings from more deeply phenotyped samples to more sparse phenotyping used by the MVP would be valuable to address this limitation. Additionally, while we greatly expand on the amount of data available for agreeableness, conscientiousness, openness and extraversion, they still lag behind what has been accomplished for neuroticism. This means genetic instruments defined for the other four traits may lack the precision available for neuroticism. Larger samples still need to be collected to better understand these other traits.

Personality traits are known to have complex interactions with other human behaviours. In this work we have conducted comprehensive genomic studies of personality traits. We performed a GWAS in the MVP sample, the largest and most diverse biobank in the world, in both EUR and AFR to better understand genetic factors underlying personality traits. We combined this information with previously published results in a large meta-analysis, identifying novel genetic associations with five personality traits studied. We identified interactions in a phenome-wide genetic correlation analysis, finding novel relationships between complex traits. We used in silico analysis techniques to identify genetic overlap and causal relationships with depression and anxiety disorders. We also characterized underlying biology using predicted changes in gene and protein expression, biological pathway enrichment and drug perturbation analysis. These results substantially enhance our knowledge of the genetic basis of personality traits and their relationship to psychopathology.

Inclusion and ethics statement

This research was not restricted or prohibited in the setting of any of the included researchers. All studies were approved by local institutional research boards and ethics review committees. MVP was approved by the Veterans Affairs central institutional research board. We do not believe our results will result in stigmatization, incrimination, discrimination or personal risk to participants.

Cohort and phenotype

We used data release version 4 of the MVP 41 . The BFI-10 was included as part of a self-report Lifestyle survey provided to MVP participants, with two items for each of the personality traits (Supplementary Fig. 3 ). For the MVP EUR participants, the mean age was ~65.5 years for each of the five traits and 8% of the sample was female. For MVP AFR, the mean age was ~60.6 years for each trait while 14.0% of the sample was female.

Genotyping and imputation

Genotyping and imputation of MVP subjects has been described previously 41 , 42 . A customized Affymetrix Axiom Array was used for genotyping. MVP genotype data for biallelic SNPs were imputed using Minimac4 43 and a reference panel from the African Genome Resources panel by the Sanger Institute. Indels and complex variants were imputed independently using the 1000 Genomes phase 3 panel 44 and merged in an approach similar to that employed by the UKB. Ancestry group assignment within the MVP has been previously described 45 . Briefly, designation of broad ancestries was based on genetic assignment with comparison to 1000 Genomes reference panels 44 . Principal components to be used as covariates were generated within each assigned broad ancestral group.

GWAS and meta-analysis

We performed individual GWAS for each of the five personality traits in the MVP cohort 41 . The personality information along with genotype data were available for a total of 270,000 individuals with 240,000 EUR and 30,000 AFR. The GWAS was performed separately for each of the traits in the EUR and AFR datasets and the effect values were computed using linear regression.

MVP GWAS was conducted using linear regression in PLINK 2.0 using the first ten principal components, sex and age as covariates 46 . Variants were excluded if call missingness in the best-guess genotype exceeded 20%. Alleles with minor allele frequency (MAF) <0.1% were excluded. Additionally, only variants with an imputation accuracy of ≥0.6 were retained. After applying all filters, genotype data from 233,204, 235,742, 235,374, 234,880 and 220,015 participants were included for neuroticism, extraversion, agreeableness, conscientiousness and openness, respectively.

For meta-analysis, summary statistics generated in this study (referred to as MVP study) were combined using METAL 17 with that from Nagel et al. and GPC phase I and II studies (Fig. 1a ) based on the availability of data for respective traits. The z -scores of variants provided in the summary statistics were converted into β scores 47 . The inverse variance weighing scheme of METAL was applied to weight the effect sizes of SNPs from the different source studies. For neuroticism, summary statistics from MVP and Nagel et al. studies 13 (excluding 23 and Me) were combined, increasing the total sample size to 682,688. For extraversion, summary statistics from MVP and GPC phase II study 10 were combined, while summary statistics from MVP and GPC phase I study 8 were combined for the respective meta-analysis of agreeableness, openness and conscientiousness. GPC data were already included in the neuroticism meta-analysis of Nagel et al.

The independent GWS loci for each of the personality traits were identified by clumping all SNPs using PLINK v1.9 software 48 . P value cut-off of 5 × 10 −8 , MAF >0.0001, distance cut-off of 1 MB and r 2  < 0.1 were used to define the lead SNPs using the 1000 Genomes phase 3 European reference panel 44 . The genes are mapped for the identified lead SNPs using biomaRt package in R 49 . The same parameters were used to define novel independent loci for comparison from the Nagel et al. 13 and Becker et al. 14 summary statistics (excluding 23 and Me).

Trans-ancestry analysis for each of the five personality phenotypes was performed by combining their respective summary statistics from AFR and EUR analyses using METAL 17 . As with the EUR-only meta-analysis, the inverse variance weighing scheme of METAL was applied to weight the effect sizes of SNPs from the two ancestries. We identified independent SNPs in the same manner as described above for the ancestry-stratified GWAS.

LDSC and SNP heritability

LDSC was performed based on the linkage disequilibrium reference from the 1000 Genomes data for all EUR cohorts and SNP heritability for each of the five personality traits was calculated 50 . To investigate the relation among the different personality traits, the LDSC-based correlation was also calculated between each pair of traits 51 . LDSC was also used to calculate genetic correlation of the personality traits with multiple other phenotypes (1,437 traits) with the Complex Traits Genetics Virtual Lab webtool 22 . A P value cut-off of 6.9 × 10 −6 (0.05/(1437 × 5)) was applied to filter the significant correlating pair of traits after multiple test correction.

For MVP AFR, linkage disequilibrium scores were computed from the approximately 123,000 AFR individuals’ genotype data in the MVP cohort using covariant LDSC software 21 . This linkage disequilibrium reference panel was then utilized to calculate SNP heritability in the MVP AFR cohort using LDSC.

We used LAVA 23 to calculate local heritability for the five personality traits and local genetic correlations for each pair. The genome was divided into 2,495 genomic chunks/loci to attain minimum linkage disequilibrium between them and maintain an approximate equal size of around 1 MB. The local heritability of each of the five personality traits was calculated for each of the 2,495 loci. For a given personality trait pair, local genetic correlations were calculated only for pairs that had significant local heritability (Bonferroni-corrected P value at 5% false discovery rate (FDR)) for both traits of the pair. Bonferroni multiple testing correction was also applied to genetic correlated P value to consider significant correlated pairs.

FUSION software 18 was used to perform TWAS. FUSION first estimates the SNP heritability of steady-state gene and uses the nominally significant ( P  < 0.05) genes for training the predictive models. The predictive model with significant out-of-sample R 2 (>0.01) and nominal P  < 0.05 in the five-fold cross-validation was then used for the predictions in the GWAS data. The process is performed for all five personality EUR GWAS data with 10,386 unique genes spanning over the 13 selected tissues. The expression weight panels for 13 a priori selected tissues were taken from GTEx v8 19 . We selected the different available brain tissues and whole blood as the tissues of interest, where Bonferroni corrections at FDR <0.05 were applied with the 10,386 genes test for the 13 tissues to find the genes with significant hits (P < 3.703 × 10 −7 ).

We performed PWAS to test the association between genetically regulated protein expression and different personality traits individually using FUSION software 18 . The weights for genetic effect on protein expression for the PWAS were from the Wingo et al. study 52 . In the PWAS, we integrated the protein weights with the summary statistics from the GWAS of each of the personality traits, respectively. Next, to decrease the probability of linkage contributing to the significant association in the PWAS, we performed colocalization analysis using COLOC 53 . In COLOC, we determined if the genetic variants that regulate protein expression colocalize with the GWAS variants for the personality trait. Significant proteins in the PWAS that also have COLOC posterior probability of hypothesis 4 (PP4) >50% have a higher probability of being consistent with a causal role in the personality trait of interest.

Fine-mapping

To identify likely causal variants, we performed variant fine-mapping using Polyfun software 24 . Since the fine-mapping was performed on the same EUR data, SNP-specific prior causal probabilities were taken directly from the pre-computed causal probabilities of 19 million imputed UKB SNPs with MAF >0.01 based on 15 UKB traits analysis. The fine-mapping was performed on the GWAS sumstats for each of the five personality traits. SuSiE 54 was used to map the posterior causal probabilities of the SNPs. The SNPs with posterior inclusion probability (PIP) value >0.95 were considered as significant for neuroticism, while a more relaxed cut-off of PIP >0.80 was used for other four personality traits to avoid loss of causal variant information due to the relatively less power in their respective datasets.

Conditional analysis was performed to investigate the possible mediating effects between depression or anxiety and neuroticism or agreeableness. Neuroticism meta-data GWAS summary statistics were used and conditioned on MDD and anxiety in individual runs. The MDD summary statistics were from Levey et al. study 55 and include a meta-analysis from the MVP, UKB, PGC and FinnGen. The anxiety summary statistics were taken from Levey et al. study 42 . With depression/anxiety studies as covariate traits, the conditional analysis of neuroticism (target trait) was carried out using multi-trait-based conditional and joint analysis utility of genome-wide complex trait analysis 56 . Similarly, the same method was used to perform conditional analysis of agreeableness on MDD and anxiety.

FUMA was used to carry out the MAGMA-based gene-association tests to find significantly associated genes for a trait from its GWAS summary data 15 . Drugs were searched for both neuroticism and MDD individually using their respective significantly associated genes derived from neuroticism meta-analysis summary statistics and MDD GWAS summary statistics from the Levey et al. summary statistics. To predict drug candidates for a given trait, significant genes associated with neuroticism/depression were given as input to gene2drug R-package 25 . Pre-computed Pathway Expression Profiles of the Connectivity Map data were taken from Drug Set Enrichment Analysis (DSEA) website. For each query gene, a maximum of five predicted drugs were predicted. Further, the drugs showing an E s core >0.5 and a P value less than 1 × 10 −6 were considered significant. The process was repeated for MDD.

MR was performed to study the causal relationship between four pairs of traits: neuroticism and MDD, neuroticism and anxiety, agreeableness and MDD, and agreeableness and anxiety. These traits had the highest genetic correlation. The summary statistics described previously for conditional analysis for all four traits were used for carrying out MR analysis as well. TwoSample MR package was used to perform the MR 57 . For each pair of traits, the TwoSample MR was run twice to see the effect of exposure of each of the two traits on the outcome of the other. After harmonizing the exposure and outcome instruments sets, clumping of SNPs (distance of 500 kb, r 2  = 0.05) was performed before conducting the MR analysis. Because some of our samples included in the analysis of personality overlap with our outcomes and exposures of interest, and a TwoSample MR is not robust to sample overlap, we also performed a sensitivity analysis for each trait pair using the MRlap package 26 . MRlap is specifically designed to account for many assumptions of MR, including sample overlap. It first calculates observed MR-based effect values and then a corrected effect value by using the genetic covariance calculated by LDSC.

The Yale–Penn cohort includes participants recruited from sites in the eastern United States 58 . A total of 11,705 participants completed the 240-item revised NEO PI-R, which assesses the domains of the five-factor model of personality: neuroticism, extraversion, openness to experience, agreeableness and conscientiousness 59 . Each domain has six facets. For example, the facets of neuroticism are anxiety, angry hostility, depression, self-consciousness, impulsiveness and vulnerability. Each item is rated on a five-point scale. Of the Yale–Penn participants with a NEO score, 4,582 were assigned to the broadly defined EUR group using the same methods as in the MVP sample and were unrelated. We used PRS-CS, Python software that uses Bayesian regression and continuous shrinkage priors, to calculate posterior effect sizes per SNP 60 . The 1000 Genomes linkage disequilibrium reference panel was used. The training datasets were summary statistics from the EUR meta-analysis for each of the five personality factors. The target dataset was a PLINK-formatted binary file set containing genotype information from the Yale–Penn participants 48 . Once score per SNP was generated by PRS-CS and PLINK was used to generate a score for each individual by summing SNP effect 48 . The lm (linear model) function in R was used to regress NEO PI-R scores on PRS, using age, sex and the first ten within-ancestry principal components as covariates 61 .

Ethics oversight

Research involving MVP in general is approved by the Veterans Affairs Central institutional research board; the current project was also approved by institutional research boards in West Haven, CT.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

All MVP summary statistics are made available through dbGAP request at https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs001672.v11.p1 . Meta-analysis summary statistics are available through the Levey lab website at https://medicine.yale.edu/lab/leveylab/data/ . Meta-analysis data will also be made available via the Complex Trait Genetics Virtual Lab at https://vl.genoma.io/ .

Code availability

No custom code was developed for analyses in this manuscript. All code used is cited and described in the methods. Software versions are accessible via PLINK v1.9 at https://www.cog-genomics.org/plink/1.9/ , PLINK v2.0 at https://www.cog-genomics.org/plink/2.0/ and Polyfun: version 1.0.0 SuSiE package version: 0.11.92.

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Acknowledgements

This research is based on data from the Million Veteran Program, Office of Research and Development, Veterans Health Administration, and was supported by award no. 5IK2BX005058. This publication does not represent the views of the Department of Veteran Affairs or the United States Government. A.W. was supported by a BLRD CDT award from the US Department of Veterans Affairs no. 1IK4BX005219 and grant I01 BX005686. A.W. and T.W. were supported by R01 grant no. AG072120. J.G. was supported by US Department of Veterans Affairs grant 5I01CX001849-04 and NIH grants R01DA037974 and R01DA058862. H.K. was supported by US Department of Veterans Affairs grant I01 BX004820 and the VISN4 Mental Illness Research, Education and Clinical Center of the Crescenz VAMC. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. Detailed MVP Core team acknowledgements are included in the supplement.

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Division of Human Genetics, Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA

Priya Gupta, Marco Galimberti, Sarah Beck, Keyrun Adhikari, Joel Gelernter & Daniel F. Levey

Department of Psychiatry, Veterans Affairs Connecticut Healthcare Center, West Haven, CT, USA

Department of Neurology and Human Genetics, Emory University School of Medicine, Atlanta, GA, USA

Yue Liu & Thomas Wingo

Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, USA

Aliza Wingo

Atlanta Veterans Affairs Medical Center, Atlanta, GA, USA

Crescenz Veterans Affairs Medical Center, Philadelphia, PA, USA

Henry R. Kranzler

Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA

Psychiatry Service, VA San Diego Healthcare System, San Diego, CA, USA

Murray B. Stein

Departments of Psychiatry, School of Medicine, and Herbert Wertheim School of Public Health, University of California San Diego, La Jolla, CA, USA

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VA Million Veteran Program

• Priya Gupta
• , Marco Galimberti
• , Sarah Beck
• , Henry R. Kranzler
• , Murray B. Stein
• , Joel Gelernter
•  & Daniel F. Levey

Contributions

D.F.L. and P.G. designed the study. P.G. and D.F.L. drafted the manuscript. J.G. and M.B.S. provided ongoing feedback and refinement of the analytical plan, as well as early feedback on the drafted manuscript. D.F.L. and P.G. conducted GWAS on included cohorts. D.F.L. and P.G. discussed, created and refined the phenotype in the MVP. P.G. and M.G. discussed and refined MVP analytic plans. P.G. and Y.L. conducted TWAS and PWAS analysis with guidance from A.W., T.W. and D.F.L. S.B conducted out-sample PRS into the Yale–Penn cohorts with guidance from J.G. and H.R.K. P.G. D.F.L., M.G., S.B., Y.L., A.W., T.W. and K.A. conducted original analyses. D.F.L., T.W. and A.W. supervised original analyses. All authors critically evaluated and revised the manuscript.

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Correspondence to Daniel F. Levey .

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Competing interests.

H.R.K. is a member of advisory boards for Dicerna Pharmaceuticals, Sophrosyne Pharmaceuticals, Clearmind Medicine and Enthion Pharmaceuticals; a consultant to Sobrera Pharmaceuticals; the recipient of research funding and medication supplies for an investigator-initiated study from Alkermes; and a member of the American Society of Clinical Psychopharmacology’s Alcohol Clinical Trials Initiative, which was supported in the past 3 years by Alkermes, Dicerna, Ethypharm, Lundbeck, Mitsubishi and Otsuka. J.G. and H.R.K. are holders of US patent 10,900,082 titled: ‘Genotype-guided dosing of opioid agonists’, issued 26 January 2021. J.G. is paid for editorial work on the journal Complex Psychiatry. The remaining authors declare no competing interests. J.G. is named as an inventor on PCT patent application no. 15/878,640 entitled ‘Genotype-guided dosing of opioid agonists’, filed 24 January 2018, and issued on 26 January 2021, as US patent no. 10900082. M.B.S. has stock options in Oxeia Biopharmaceuticals and EpiVario. He has been paid for his editorial work on Depression and Anxiety (Editor-in-Chief), Biological Psychiatry (Deputy Editor) and UpToDate (Co-Editor-in-Chief for Psychiatry). No other authors report competing interests.

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Supplementary information

Supplementary information.

Supplementary Figs. 1–3, Tables 1–4 and VA Million Veteran Program core acknowledgement.

Reporting Summary

Supplementary tables 1–31.

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Gupta, P., Galimberti, M., Liu, Y. et al. A genome-wide investigation into the underlying genetic architecture of personality traits and overlap with psychopathology. Nat Hum Behav (2024). https://doi.org/10.1038/s41562-024-01951-3

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Received : 16 January 2024

Accepted : 09 July 2024

Published : 12 August 2024

DOI : https://doi.org/10.1038/s41562-024-01951-3

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