ORIGINAL RESEARCH

Life Events as Environmental States and Genetic Traitsand the Role of Personality: A Longitudinal Twin Study

Christian Kandler • Wiebke Bleidorn •

Rainer Riemann • Alois Angleitner •

Frank M. Spinath

Received: 21 March 2011 / Accepted: 23 July 2011 / Published online: 7 August 2011

� Springer Science+Business Media, LLC 2011

Abstract The occurrence of many life events is not

entirely random but genetically influenced. The current

study examined the sources underlying the stability or

recurrence of life events and the developmental interplay

between personality traits and life events. In a longitudinal

study of 338 adult twin pairs we estimated (1) the genetic

and environmental sources of continuity in aggregates of

life events, (2) the sources through which personality

influences the experience of life events, and (3) the sources

through which life events influence personality. Unlike

personality which showed both genetic and environmental

influences on substantial continuity over time, stability of

life events was moderate and mainly influenced by genetic

factors. Significant associations between personality and

life events were specific to certain personality traits and

qualitative aspects of life events (controllable positive,

controllable negative, and less controllable negative), pri-

marily directional from personality to life events, and

basically genetically mediated. Controlled for these genetic

associations, we also found some small and basically

environmentally mediated effects of life events on per-

sonality traits. The results support the concept of genotype–

environment correlation as a propulsive mechanism of

development.

Keywords Twin study � Life events � Personality traits �Continuity � Genotype–environment correlation

Introduction

The generic term life event denotes major individual

experiences (e.g., accident, death of a close person, per-

sonally relevant significant success), meaningful changes

(e.g., relocation, birth of own child, lay-off by employer),

and normative transitions in life (e.g., starting a family,

retirement). While common sense suggests that life events

reflect external influences on an individual, behavioral

genetic research has shown heritability for most measures

of life events suggesting that individual differences in the

experience of life events can at least partly be explained by

genetic differences (e.g., Kendler et al. 1993; Plomin et al.

1990). In search of mediating individual characteristics,

multivariate behavior genetic studies have found that this

genetic variance may be accounted for by genetic variance

in personality measures (e.g., Billig et al. 1996; Saudino

et al. 1997). Also rather unexpected, phenotypic longitu-

dinal studies have shown a substantial continuity of indi-

vidual differences in the frequency of experiencing certain

life events and, again, that personality traits seem to play a

role in the probability of the experience and the recurrence

of certain events (e.g., Andrews 1981, Billings and Moos

1982; Headey and Wearing 1989). Thanks to these studies,

we know that the occurrence of life events is not entirely

random. Though taking the same line, these developmental

and biometric approaches have rather traveled separate

paths resulting in a lack of genetically informative longi-

tudinal studies.

The current study is the first longitudinal twin study

aimed to examine the genetic and environmental sources of

Edited by Deborah Finkel.

C. Kandler (&) � W. Bleidorn � R. Riemann � A. Angleitner

Department of Psychology, Bielefeld University, Universitatsstr.

25, 33615 Bielefeld, Germany

e-mail: christian.kandler@uni-bielefeld.de

F. M. Spinath

Department of Psychology, Saarland University, Saarbrucken,

Germany

123

Behav Genet (2012) 42:57–72

DOI 10.1007/s10519-011-9491-0

(1) the significant continuity of individual differences in

the frequency of certain life events and (2) the interplay

between life events and personality traits over a period of

10 years. Showing that the continuity in life events and the

links between personality and life events are a function of

genetic differences would account for the well-established

finding that similar events tend to reoccur in individuals’

lives. Certainly of greater interest for individual develop-

ment, the partly evoked effects of life events back on

personality would suggest genotype–environment correla-

tions (Plomin et al. 1977): Genotypic differences in per-

sonality affect differences in life events which, in turn,

affect differences in phenotypic personality. The design of

the present study allowed us to address the aforementioned

issues by testing multivariate longitudinal twin models of

life events and comparing unidirectional and bidirectional

models of genetic and environmental pathways between

personality and life events across time. In this way, the

present study extends the knowledge about the genetic and

environmental sources of variance in life events and the

links between life events and personality to a develop-

mental behavioral genetic perspective.

Sources of variance in life events

As the incidence of one specific event may be very small,

researchers have investigated different (primarily qualita-

tive) aspects of events by classifying these aspects in

broader categories. The initial biometric study (Plomin

et al. 1990) collected self-reports on several life events

from identical and fraternal twins reared together and apart

during the last half of the life span. They reported that 40%

of the variance in the frequency of life events was genet-

ically influenced. The remaining variance was explained by

environmental influences not shared by twins. More spe-

cifically, they demonstrated larger genetic influence on

controllable life events (43%) compared to less controlla-

ble life events (18%).

Kendler et al. (1993) investigated self-reports on life

events during the first half of adulthood in a large sample of

twins reared together. In the total number of life events,

26% of the variance was attributable to genetic factors and

18% resulted from environmental factors shared by twins.

The largest amount of variance was due to nonshared

environmental influences (56%). A further study on inter-

view-based data affirmed these results and showed that

there are no significant differences between men and

women (Bolinskey et al. 2004).

Billig et al. (1996) examined interview-based reports on

life events in an adolescent twin sample and categorized

family events, nonfamily events independent of respon-

dent’s behavior, and nonfamily events dependent on

behavior. They found genetic effects (49%) only on

dependent events. A recent twin and adoption study con-

firmed the results from Billig et al.(Bemmels et al. 2008).

The findings of genetic influences on life events are not

limited to the total number or clusters. Heritability was also

estimated for more specific categories, such as marriage,

divorce, illnesses, accidents, and annoyance at work (e.g.,

Hershberger et al. 1994; Johnson et al. 2004; Middeldorp

et al. 2005).

In view of these findings it remains to explain how genes

may affect individual differences in experiencing life

events. The genetic effects have either been interpreted as a

genetically influenced perception of environments and the

genetic control of the exposure to certain events (Kendler

2001). From this perspective, researchers have looked for

individual traits that are qualified to mediate these genetic

effects on events. The most promising candidates are per-

sonality traits (Plomin and Bergeman 1991).

Links between life events and personality

Personality is strongly genetically influenced (Kandler

et al. 2010b) and genetic factors contribute primarily to

continuity in personality traits (Bleidorn et al. 2009;

Blonigen et al. 2008; Johnson et al. 2005; Viken et al.

1994). Personality traits may not only affect how individ-

uals interpret and perceive their environments but also

influence which experience individuals do. Behavioral

genetic studies on correlations between personality and

several aggregates of life events have shown that these

links were primarily due to common genetic factors (Billig

et al. 1996; Saudino et al. 1997). In other words, genotypic

differences in personality may affect (1) how life events are

perceived or (2) the probability of (active and evocative)

exposure to certain events that in turn may affect pheno-

typic differences in personality.

Since active and evocative types of genotype–environ-

ment correlations play a major role in adulthood (Scarr and

McCartney 1983), it seems fair to assume that genetically

influenced personality traits affect how people experience

life events and which life events people experience (Billig

et al. 1996). The active type occurs when individuals seek

out environments (e.g., starting a training), create or

change situations (e.g., change of address) that are com-

patible with their genetically influenced individual char-

acteristics (e.g., high openness). The evocative type refers

to individuals’ experiences (e.g., break of friendship,

demotion in the work place) that arise as a consequence of

social interaction and reactions to their genetically influ-

enced individual characteristics (e.g., low agreeableness).

The concept of genotype–environment correlation is in

line with the developmental theory of Gottlieb (1991,

2003). He proposed development as a fully bidirectional

coactional system with four major levels moving from

58 Behav Genet (2012) 42:57–72

123

lower levels of genetic and neural activity (neurons, hor-

mones, and genes) to the higher levels of behavior as well

as external consequences in the environment (physical,

social, cultural) and back again (see left side of Fig. 1).

This model implicates that the impetus for the continuity or

recurrence of certain experiences such as certain life events

comes from the genotype (Scarr and McCartney 1983).

Personality traits as genetically influenced relatively stable

behavioral tendencies may mediate genetic effects on life

events and may thus contribute to stability of individual

differences in the frequency of certain life events that in

turn may have an effect on individuals’ development (right

side of Fig. 1).

A genetically informative longitudinal analysis of con-

tinuity in life events and reciprocal relations between

personality genotypes and the external environment (i.e.,

life events) allows for testing such a model. The afore-

mentioned behavioral genetic studies on the links between

personality and life events, however, were cross-sectional

and thus did not allow for an investigation of directional

and bidirectional effects over time.

Until now, only a handful of phenotypic longitudinal

studies have addressed the continuity of aggregates (or

recurrence) of life events and the developmental interplay

between personality and life events. These studies have

consistently revealed two important findings. First, the

number of experienced events showed moderate up to

substantial levels of continuity, which could be confirmed

for aggregates of negative and positive occurrences as well

as subjective and objective reports (e.g., Andrews 1981;

Billings and Moos 1982; Headey and Wearing 1989).

Second, the total number of life events and clusters in

respect of several experiential qualities (e.g., valence or

controllability) can be predicted by personality traits,

whereas the effects from life events on personality traits are

rather low. For example, Neuroticism predicts negative

events and Extraversion predicts positive events, whereas

Openness is a significant predictor of both (Headey and

Wearing 1989). Openness to experiences is a good expla-

nation for the interesting finding that people who report

more positive events also tend to report more negative

events. The pattern of links holds for subjective reports, for

reports on events that are sufficiently salient (Magnus et al.

1993) and objective measures corrected for reporting bias

(Kendler et al. 2003). Therefore, the interpretation of

reports on life events and correlations between life events

and personality as retrospective or perceptual bias can be

rejected in favor of a bidirectional model of genotype–

environment correlation.

The findings of most longitudinal studies, however,

rather support unidirectional causality of the relationship

between personality and situations. That is, individuals

seek out events or evoke reactions from the social envi-

ronments due to their individual characteristics, but events

in turn do not significantly affect personality (Magnus et al.

1993). As a consequence, genetic effects on and continuity

of individual differences in aggregate measures of life

events might be due to the stability of genetically influ-

enced personality traits, but not vice versa. These findings

do not support a theory of bidirectional person-environ-

ment relations (Gottlieb 2003).

Although measures of life events have not accounted for

a substantial proportion of variability in personality, some

studies have found small but indeed significant effects of

events on personality. For example, Costa et al. (2000)

reported significant effects of marital and job status change

on Neuroticism and Conscientiousness. Negative events

lead to an increase in Neuroticism (Lockenhoff et al. 2009;

Middeldorp et al. 2008), whereas positive events appear to

affect an increase in Extraversion (Vaidya et al. 2002).

Taken together, these findings are in line with bidirectional

causality between personality and life events and support

the theory of reciprocal influences (Gottlieb 2003).

Aims of the current study

Our study extends previous studies (1) investigating to

what extent the phenotypic continuity in defined categories

of life events (positive and negative, controllable and less

controllable) is attributable to genetic and environmental

factors and (2) testing whether the relationship between

personality and life events is unidirectional or bidirectional

genetically and (or) environmentally mediated. As we

captured personality by a questionnaire which mapped the

five-factor model (McCrae and John 1992; Costa and

McCrae 1992), we were able to extend previous behavioral

genetic studies not only with regard to the longitudinal

perspective but also to the broad personality traits Agree-

ableness and Conscientiousness.

On the basis of previous research and theories reviewed

above, we can articulate three main hypotheses. First,

continuity of individual differences in the frequency of

experiencing events should be primarily attributable to

Fig. 1 This figure illustrates a simplified scheme of the devel-

opmental systems view, showing a hierarchy of four mutually

interacting components in which there are ‘‘top–down’’ as well as

‘‘bottom–up’’ bidirectional influences (according to Gottlieb 1991,

p. 6). Genetic effects on life events may be mediated through

personality (solid path) and life events may in turn affect personality

(dashed path)

Behav Genet (2012) 42:57–72 59

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genetic differences (Scarr and McCartney 1983). Second,

genetic effects on personality should account for a signif-

icant amount of this genetic variance in certain aggregates

of life events (Billig et al. 1996; Saudino et al. 1997). More

specifically, we expected positive relations between Neu-

roticism and negative events, between Extraversion and

positive events, as well as between Openness and events

independent of valence and controllability (Headey and

Wearing 1989). Finally, in line with the developmental

system view, the relationship between personality and life

events should be reciprocal (Gottlieb 2003). To address the

latter hypothesis, we tested if there was an influence of life

events on personality domains when former links between

personality and life events were controlled, and estimated

genetic and environmental links to understand the sources

of these relationships.

Method

Participants and procedure

The sample was drawn from the Bielefeld Longitudinal

Study of Adult Twins (BiLSAT), a study of twins reared

together which has been described in detail previously

(Spinath et al. 2005). For the present analyses, we utilized

data from twin pairs collected at the second (time 1), third

(time 2), and forth (time 3) wave of BiLSAT collected

between 1994 and 2006. The measurements were taken

about 5 years apart. At all three points of time, participants

provided self-reports on their personality. At time 2 and

time 3, twins received a list of life events and were

instructed to report whether certain events occurred or not

within the preceding 5 years. The sample who participated

at time 1 and time 2 consisted of 224 monozygotic (MZ)

and 114 dizygotic (DZ) twin pairs (27 opposite-sex pairs).

Participants ranged in age from 22 to 74 years (M = 39.56,

SD = 13.46) at time 2, 105 of them were males. At the last

measurement occasion, at least one twin sibling of 230

pairs (68%) provided complete data and in 59% of twin

pairs (136 MZ and 62 DZ twin pairs) complete data were

available.

Measures

Life events

At time 2 and time 3, participants were asked about the

occurrence of 31 life events (LE, Table 1). The list of life

events was similar to that used by Plomin et al. (1990) and

Saudino et al. (1997). Response categories were (1) never,

(2) one-time, and (3) repeatedly experienced within the last

5 years. In the current study the categories were reduced to

never (0) and at least one-time (1) experienced. The list

basically includes factual externally verifiable events

reducing the probability of distortions due to retrospection

or other judgment biases. In addition, twins rated the

valence of the experiences on a scale ranging from very

negative (-3), negative (-2), rather negative (-1), nor

(0), rather positive, (?1), positive (?2) to very positive

(?3). Since twins were not asked about the controllability

of events in BiLSAT, 30 independent raters were recruited

who assessed to what extent the occurrence of events was

under a person’s control: non-controllable (0), less con-

trollable (1), rather controllable (2), and controllable (3).

The inter-rater reliability was ICC = .97. Control was

significantly correlated with valence (r = .64) indicating

that more controllable life events were perceived as more

positive or more positive life events were more desirable.

Based on the standardized mean values (between 0 and

1) of control and valence within the 31 LE-items (see

Table 1) and across items (M = .53 for controllability and

M = .50 for valence), the 31 life events can be classified

into three clusters (cutoff = .50): controllable positive life

events (LECP: items 1, 2, 6, 9, 12, 15, 18, 19, 20, 21, 22, 27,

29), controllable negative life events (LECN: items 3, 5, 7,

8, 16, 24, 26, 28), and less controllable negative life events

(LELN: items 4, 10, 11, 13, 14, 17, 23, 25, 30, 31). The set

of life events did not include less controllable positive life

events. This might be due to the rareness of such events

(e.g., lottery win) or to the fact that positive events are

rather attributed to be controllable (Weiner 1986). The

averaged frequency of occurrence of LECP, LECN,

and LELN were M = 4.45 (SD = 2.40), M = 1.79

(SD = 1.46), and M = 2.35 (SD = 1.66) at second mea-

surement occasion (i.e., the retrospective number of LE-

occurrence between time 1 and 2), and were M = 3.21

(SD = 2.18), M = 1.37 (SD = 1.27), and M = 2.05

(SD = 1.53) at the third measurement occasion (i.e. the

retrospective number of LE-occurrences within the second

interval of 5 years, time 2–3).

Personality

Personality was measured using the German version of the

NEO-Personality Inventory-Revised (Costa and McCrae

1992; Ostendorf and Angleitner 2004) for all measurement

occasions. This measure is a 240-item inventory to assess

the five broad personality dimensions of the FFM:

Neuroticism (N), Extraversion (E), Openness (O), Agree-

ableness (A), and Conscientiousness (C). Detailed charac-

teristics of the scales, evidence on the reliability and

validity, as well as the genetic and environmental structure

are presented in the manuals and previous studies (e.g.,

Bleidorn et al. 2009).

60 Behav Genet (2012) 42:57–72

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Pre-analyses

Missing value analyses

We wanted to analyze all available data and thus tested

whether the missing values were completely at random

using T- and MCAR-tests (Little 1988). Missing values in

the data set were not completely at random (v2 = 39.07,

d.f. = 19, p \ .01). For each personality variable, zygos-

ity, sex, and LECN, T-tests showed no significant differ-

ences between the group with complete data and the group

with missing values. Significant but small differences were

detected for age (d = .29). That is, dropout was larger for

younger people. Effects for LECP (d = -.14) and LELN

(d = -.15) could be evaluated as weak (Cohen 1988).

Thus, the following analysis could be assumed to be

unbiased with regard to missing values.

Age and sex correction

Age and sex differences had to be taken into account. Age

differences regarding the LE-scores have been well estab-

lished (e.g., Headey and Wearing 1989): younger people

experience more life events and transitions. Sex differences

have generally been found to be small or non-significant

depending on the events in the investigation. In our study,

controllable positive life events showed significant age and

slight sex differences (male and younger people experi-

enced more events) for time 2 (bAGE = -.41, bSEX =

-.10, R2 = .19) while only the age differences remained

Table 1 Percentage of

individuals for whom an event

occurred, item values of

perceived control on occurrence

and valence of life events

Note. Perceived control and

self-rated valence were adapted

to take values between 0 and 1.

Higher values indicate more

controllable and more positive

events. Values above .50 are

shown in boldface

Life events Frequency in % Perceived

control

Positive

valence

Time 1 Time 2

1. Change of address 62 42 .83 .92

2. Major improvement in financial status 57 41 .59 .99

3. Separation from spouse for an extended period 18 14 .58 .42

4. Unfulfilled wish to have children 18 9 .23 .24

5. Excessive consumption of drugs or alcohol 23 15 .83 .30

6. Promotion in the work place 48 38 .57 .98

7. Unintentional pregnancy 9 4 .60 .48

8. Major conflict with close relatives 30 19 .55 .19

9. Start or finish of professional training 48 23 .82 .97

10. Serious illness or injury (self) 32 30 .09 .23

11. More than three month of unemployment 11 10 .48 .36

12. Entering into a serious new romantic relationship 43 23 .61 .95

13. Death of a close person 53 46 .02 .14

14. Lay-off by employer 15 6 .31 .18

15. Personally relevant significant success 62 54 .73 .98

16. Child in trouble 21 22 .55 .12

17. Demotion in the work place 10 10 .45 .10

18. Drastic changes in social activities 49 40 .79 .69

19. A further adult person added to the household 7 6 .82 .63

20. Changing to a new work place 34 25 .76 .92

21. Birth of own child 18 20 .68 1.00

22. A spiritual and religious crisis 11 6 .51 .63

23. Major deterioration in financial status 23 22 .45 .19

24. Anger or serious dispute with others 57 49 .56 .07

25. Miscarriage or stillborn birth (in family) 6 4 .04 .12

26. Withdrawal of the drivers license 2 1 .87 .32

27. Military or civil service 7 1 .72 .91

28. Job related separation from partner for a while 20 13 .53 .36

29. Retirement 7 10 .68 .91

30. Traffic or job related accident 27 19 .18 .20

31. Serious illness or injury of a close person 53 49 .02 .05

Behav Genet (2012) 42:57–72 61

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significant for time 3 (bAGE = -.51, R2 = .25). Negative

events (LECN and LELN) did not show significant gender or

age effects. As the existence of age and sex effects can bias

the estimates of twin similarity, personality self-reports and

LE-scores were corrected for significant age and gender

effects within each measurement occasion using the

regression procedure. This correction did not affect the age

differences across measurement occasions but adjusted for

age effects at a given point in time. Residual scores were

used in the subsequent analyses.

Analyses of life events

Before running longitudinal biometric analyses, we com-

puted phenotypic correlations via Pearson’s product-

moment correlation within and across measurement occa-

sions using a pairwise deletion procedure for handling

missing values. As individuals who report more events of

one kind (e.g., positive, negative, controllable, and

uncontrollable) also tend to report more events of another

kind (Magnus et al. 1993; Plomin et al. 1990), we expected

positive correlations between all LE-scores reflecting

common occurrence of different life events. In line with

previous research, cross-time correlations within same life

event clusters should be larger than between different life

event clusters (Headey and Wearing 1989). That is, sta-

bility of individual differences should rather be due to

qualitative aspects of life events than due to the frequency

of common occurrence within a given interval of time.

Investigating the sources of continuity patterns in life

events, we extended phenotypic latent-state-trait models

(Steyer et al. 1999) to genetically informative models. We

used this biometric latent-state-trait model to disentangle

genetic and environmental stable and instable components

as well as common and specific LE-score components of

different qualitative aspects (i.e., controllable vs. less

controllable and positive vs. negative).

As a baseline, we began with a biometric model

allowing for a common factor within but not across mea-

surement occasions (LEFAC) reflecting the frequency of

common occurrence of different life events within a ret-

rospective 5-year interval (see Fig. 2, model variant in bold

type). The model also provides estimates of specific genetic

and environmental LE-score residuals, reflecting frequency

of occurrences in dependence of the life events’ control-

lability and valence.

The baseline model was compared to (1) a model that

allowed for a common ‘‘trait’’ factor (T-LEFAC; see Fig. 2,

model variant in bold and solid type) and (2) a model that

allowed for LE-score-specific ‘‘trait’’ factors (T-LECP,

T-LECN, and T-LELN; see Fig. 2, model variant in bold and

dashed type). T-LEFAC reflects continuity of common

occurrence of different life events across measurement

occasions. The LE-score-specific trait factors reflect con-

tinuity of occurrence in dependence of qualitative aspects

(controllability and valence).

Subsequently, these models were compared to a full

model (entire model in Fig. 2). Based on the best fitting

Fig. 2 This figure illustrates a

latent state-trait model of life

events (LE) that is genetically

informative. For simplicity, the

model is shown only for one

twin and allows for additive

genetic factors (A) and

nonshared environmental

factors (E) only; indices 1 and2: measurement occasions;

indices CP, CN, LN, and FAC:

controllable positive,

controllable negative, less

controllable negative, and

common factors reflecting

co-occurrence; T: ‘‘trait’’ factor

in sense of a stable factor.

Further description is presented

in the text

62 Behav Genet (2012) 42:57–72

123

phenotypic model, we finally tested biometric parameters

(e.g., A and E in Fig. 2). The model assumes environ-

mental effects equal for MZ and DZ twins, the absence of

assortative mating of twins’ parents, and no correlation and

interaction between genetic and environmental factors, but

can be extended to nonadditive genetic factors (d2) or

environmental effects shared by twins (c2). Both effects

contribute to twins’ resemblance (COVMZ = a2 ? d2 and

COVDZ = � a2 ? � d2, or COVMZ = a2 ? c2 and COV-

DZ = � a2 ? c2), but in a model of twins reared together

these effects cannot be detected in the presence of each

other. When both d2 and c2 are effective, a2 would be

overestimated. Studies including twins reared together and

reared apart have not found significant contributions of c2

but d2 (e.g., Plomin et al. 1990; Saudino et al. 1997). The

relative small sample size of our study reduced the power

to detect d2 in the presence of a2. However, a2 derived from

twins reared together is a quite good estimation of broad-

band heritability (a2 ? d2; Visscher et al. 2008). This

argues (1) against a violation of the equal environment

assumption and (2) for a2 as broad heritability estimation.

Nested model comparisons allowed us to examine, first,

whether the stability of individual differences in the fre-

quency of LE-occurrence depended on specific qualitative

aspects of experiences, and second, whether stability was

genetically or environmentally mediated. It is important to

note that within-time effects do not exactly reflect occa-

sional specificity in the sense of a latent-state-trait model

(Steyer et al. 1999), because LE-scores include retrospec-

tive reports on life events which occurred over an anterior

period of 5 years.

The models were fitted to the raw data via full infor-

mation maximum likelihood for handling missing values

using the statistical software package AMOS 18.0

(Arbuckle 2009). Different nested models were compared

using the v2-difference test and the comparative fit index

(CFI; Bentler 1990). A higher CFI indicates a better fit

(values range from 0 to 1). In addition, we evaluated the

significance of model parameters using the critical ratio

(C.R. = path coefficient/standard error).

Analyses of the links between life events

and personality

To investigate the reciprocal relations between personality

and life events, we analyzed biometric models with reci-

procal effects across time. Figure 3 represents the full

model (reflecting Gottlieb’s 1991, system view; see Fig. 1).

It is an extension of a genetic simplex model (allowing for

additive genetic, A, and nonshared environmental effects,

E; Boomsma and Molenaar 1987) to a model with bidi-

rectional relations between personality (P) and LE-scores

(LE). This model can be extended to nonadditive genetic

factors (d2) or environmental effects shared by twins (c2,

for facility of inspection not shown in Fig. 3). Please note

that there is a strict chronological order of variables, that is

personality at three measurement occasions (PTime1, PTime2,

and PTime3) and aggregates of life events for the two ret-

rospective intervals of time between the three measurement

occasions (LETime1-2, and LETime2-3).

The model allows for estimates of independent genetic

and environmental stability in personality (a31 9 a53 and

e31 9 e53) and in LE-scores (a42 and e42). Since three

personality measurements are available, measurement error

can be taken into account (e). Previous studies have shown

that continuity in personality is due to both genetic and

environmental components (Johnson et al. 2005; Kandler

et al. 2010a). Thus, we did not test the relevant paths (a31,

a53, e31, and e53) which were freely estimated. Regression

coefficients between personality and LE-scores (a21, a32,

a43, a54, e21, e32, e43, and e54) reflect reciprocal effects

attributable to genetic and environmental factors. All

parameters with index ‘‘R’’ represent measurement-occa-

sional specific components.

For model identification, variances of latent variables

were fixed to one and paths were estimated (see Neale and

Maes 2004). We assumed that measurement errors (e) for

personality measures were equal across measurement

occasions. In addition, we fixed a2, a3, a4 and a5 as well as

e2, e3, e4 and e5 to one in order to estimate higher-order

paths that represent specific components of these fixed

parameters. The full model was fitted to the data via full

information maximum likelihood and was compared to

more restrictive (nested) models in which several recipro-

cal paths were fixed. That is, we tested for (1) unidirec-

tional models allowing for directional effects from

personality to LE-scores or (2) vice versa versus (3) bidi-

rectional models allowing for reciprocal relations. In

addition, we tested for (a) genetic effects, (b) environmen-

tal effects, or (c) both between personality and LE-scores

(e.g., genetic paths from personality to LEs and environ-

mental paths from LEs to personality, see the associated

section of the Results for more details). The v2-difference

test was used for nested model comparisons and the CFI for

descriptive comparisons of non-nested models.

Results

Analyses of life events

LE-score-correlations within and across measurement

occasions, that is correlations of LE aggregates within and

across intervals of 5 years, are presented in Table 2.

Within measurement occasions (i.e., within intervals of

time), we found moderate correlations among LE-scores

Behav Genet (2012) 42:57–72 63

123

(between r = .26 and r = .43) indicating that individuals

for whom more controllable positive life events occurred

more often also experienced less controllable and negative

life events. Across measurement occasions (i.e., across

intervals of time), correlations were higher within than

among the LE-scores indicating moderate to substantial

stability within qualitative aspects of life events (r = .32

up to r = .50) but low to moderate stability for common

occurrence (r = .08 up to r = .27) indicating that conti-

nuity in aggregates of life events is to some extent common

but also attributable to the specific qualitative aspects of

experiences.1 The pattern of correlations within and across

time was in line with previous research (e.g., Headey and

Wearing 1989).

We investigated the sources of continuity in LE-scores

by comparing different modifications of genetically infor-

mative latent-state-trait models (Fig. 2).2 There was no

Fig. 3 This figure illustrates a

bidirectional genetic simplex

model of the relationship

between personality variables

(P) and LE-scores (LE). For

simplicity, the model is shown

only for one twin: A and a:

latent additive genetic factor

and path; E and e: latent

nonshared environmental factor

and path; e: measurement error

of personality self-reports; Time1, 2, and 3: measurement

occasions 5 years apart; Time1-2 and Time 2-3: first and

second interval of 5 years;

Index R: residuals; further

descriptions in the text

1 We ran additional multivariate regression analyses with all three

life event aggregates at the first measurement occasion as predictors

for each score at the second measurement occasion. We found that

only the continuity within qualitative aspects were significant when

controlling for prior links between qualitative aspects of life events

(b = .39 for positive events; b = .48 for controllable negative events;

b = .27 for less controllable events). There was one exception:

Controllable negative events at the first measurement occasion

Footnote 1 continued

additionally accounted for a significant amount of variance in less

controllable life events at the second measurement occasion

(b = .17).2 Prior to longitudinal analyses, we estimated within-time MZ and

DZ twin correlations. For positive life events, MZ twin correlations

were .24 at time 2 and .31 at time 3, whereas DZ correlations were .09

and .19, respectively; for controllable negative life events, MZ and

DZ twin correlations were .34 and .12 at time 2 and .35 and .12 at

time 3; and for less controllable negative life events, we found MZ

and DZ twin correlations of .31 and .15 at time 2 and .42 and .30 at

time 3. For all scores and both measurement occasions, an univariate

twin model allowing for additive genetic (a2) and nonshared

environmental effects (e2) provided the best model fit compared to

more complex models additionally allowing for shared environmental

effects or nonadditive genetic effects and compared to more

64 Behav Genet (2012) 42:57–72

123

improvement of fit when we additionally allowed for

shared environmental or genetic dominance effects. We

began with the baseline model (allowing for a common

factor of LE-scores at each measurement occasion) and

compared it with more complex models. Parameter esti-

mates of models are presented in Table 3. A common trait

factor model (Dv2(2) = 91.76; p = .00; CFI = .876) as

well as a model allowing for specific trait factors on each

LE-score (Dv2(6) = 110.84; p = .00; CFI = .898) led to

significant improvements of fit against a baseline model.

The full model (Dv2(8) = 168.40; p = .00; CFI = .979)

provided improvements in fit against all other models. That

is, continuity of individual differences in the frequency of

experiencing life events was accounted for by a common

source (T-LEFAC) as well as by factors specific for different

aspects of life events (T-LECP, T-LECN, and T-LELN).

Subsequently, we systematically dropped all insignifi-

cant and small biometric model parameters (A and E, see

Fig. 2) based on critical ratio to obtain a more parsimoni-

ous model. This modification procedure was terminated

when the model fit significantly worsened against the full

model. The best fitting model (last column of Table 3)

fitted the data not significantly worse compared to the full

model (Dv2(7) = 3.86; p = .80; CFI = .984).

From this longitudinal model (Fig. 4) we could infer: (1)

stable variance in the common occurrence of several life

events (15–29%) was exclusively influenced by genetic

factors; (2) specific stability in controllable positive life

events (16%) was also exclusively genetically affected; (3)

specific stable variance in controllable negative life events

(29%) was genetically and environmentally mediated; (4)

specific continuity in less controllable life events (7%) was

exclusively environmentally influenced; and (5) the largest

component of each LE-score (52–70%) was instable and

almost exclusively environmentally affected. In line with

previous research (Kendler and Baker 2007), about one-

third of the variance in life events was due to genetic

effects and two-thirds were due to nonshared environ-

mental effects at both time intervals. The clear pattern of

genetic and environmental sources of stability and insta-

bility in life events helps to explain the phenotypic conti-

nuity of life events, but also established a genetic pattern

that needs to be explained. Personality traits can poten-

tially help to explain these patterns.

Analyses of the links between life events

and personality

Correlations between personality traits and LE-scores are

given in Table 4. P?LE links reflect correlations between

personality domains at a given point in time and retro-

spective LE-scores at a proximate measurement occasion.

LE?P links describe correlations between personality

domains and retrospective LE-scores measured at the same

point in time. In line with previous research, correlations

between personality and LE variables were rather small (all

correlations fell short of r = .30; see Table 4). However,

there was a distinct pattern of significant correlations: if

P?LE correlations were positive, the LE?P correlations

were also positive and the same was true for negative

correlations. It should be noted that the LE?P correlations

were not corrected for the former P?LE links of variables.

Testing unidirectional versus reciprocal relations and

genetic as well as environmental mediations, we ran

biometric model fitting analyses for all cases of rela-

tionships since genetic and environmental sources might

have opposite effects, which would have led to pheno-

typic zero correlations. First, we compared the full model

(model 1; Fig. 3) with four restricted bidirectional mod-

els: (a) a model allowing for genetic links only (model 2a:

dropping e21, e32, e43, and e54), (b) a model allowing for

environmental links only (model 2b: dropping a21, a32,

a43, and a54), (c) a model allowing for genetic effects

through personality on life events and environmental

effects from life events to personality (model 2c: dropping

e21, a32, e43, and a54), and (d) a model allowing for

genetic effects through life events on personality and

environmental effects from personality to life events

(model 2d: dropping a21, e32, a43, and e54). In addition,

we compared the full model with two unidirectional

models (e) allowing for only unidirectional effects from

personality to LE-scores (model 2e: dropping a32, a54, e32,

e54) and (f) vice versa (model 2f: dropping a21, a43, e21,

Table 2 LE-score correlations within and across measurement

occasions

LE-score Time 2 Time 3

LECP LECN LELN LECP LECN

Time 2 LECN .31*

LELN .39* .43*

LECP .39* .12* .14*

Time 3 LECN .17* .50* .23* .26*

LELN .08 .27* .32* .29* .42*

Note. LECP controllable positive events, LECN controllable negative

events, LELN less controllable negative events, correlations were

estimated for both twin and co-twin subsamples, Fisher’s z trans-

formed, averaged, and transformed back; stability of LE-scores is

shown in boldface

* Correlations are significant on level p \ .05

Footnote 2 continued

restrictive models only allowing for nonshared environmental effects.

Confidence intervals indicated no significant differences in the

amount of genetic variance among LE-scores and across time.

Re-running the analyses without opposite-sex DZ pairs led to almost

identical results but slightly reduced statistical power.

Behav Genet (2012) 42:57–72 65

123

e43). Subsequently, we tested more restricted models

allowing for only unidirectional genetic versus environ-

mental effects from personality to LE-scores (model 3a:

dropping a32, a54, e21, e32, e43, and e54; model 3b:

dropping e32, e54, a21, a32, a43, and a54) and from

LE-scores to personality (model 3c: dropping a21, a43, e21,

e32, e43, and e54; model 3d: dropping e21, e43, a21, a32,

a43, and a54). Finally, all models were compared to an

Table 3 Genetically informative latent-state-trait models: explained variance components

Baseline model Common trait factor Specific trait factors Full model Best fitting model

Explained phenotypic components

LEFAC1 ? LECP1 .29* .26* .30* .29* .29*

LEFAC1 ? LECN1 .36* .50* .27* .36* .37*

LEFAC1 ? LELN1 .53* .43* .49* .53* .53*

LEFAC2 ? LECP2 .27* .24* .27* .27* .26*

LEFAC2 ? LECN2 .34* .47* .24* .34* .34*

LEFAC2 ? LELN2 .51* .40* .46* .51* .50*

T-LEFAC ? LEFAC1 – .68* – .54* .50*

T-LEFAC ? LEFAC2 – .76* – .59* .57*

T-LECP ? LECP1 – – .14* .14* .16*

T-LECP ? LECP2 – – .15* .14* .16*

T-LECN? LECN1 – – .35* .28* .28*

T-LECN? LECN2 – – .36* .29* .29*

T-LELN ? LELN1 – – .15* .06* .07*

T-LELN ? LELN2 – – .16* .06* .07*

Explained genetic (A) and environmental components (E)

A ? LECP1 .20* .21* .06* .06* –

A ? LECN1 .10* .02 .00 .00 –

A ? LELN1 .08* .12* .06* .09* .09*

A ? LECP2 .21* .21* .06* .06* –

A ? LECN2 .11* .02 .00 .00 –

A ? LELN2 .09* .13* .07* .09* .09*

E ? LECP1 .51* .54* .50* .51* .55*

E ? LECN1 .53* .48* .39* .36* .36*

E ? LELN1 .38* .45* .30* .32* .31*

E ? LECP2 .52* .55* .52* .52* .57*

E ? LECN2 .55* .51* .40* .37* .37*

E ? LELN2 .40* .47* .32* .34* .34*

A ? LEFAC1 .43* .00 .33* .00 –

A ? LEFAC2 .59* .00 .49* .08 –

E ? LEFAC1 .57* .32* .67* .46* .50*

E ? LEFAC2 .41* .24* .51* .33* .43*

A ? T-LEFAC – .79* – .86* 1.00*

E ? T-LEFAC – .21* – .14* –

A ? T-LECP – – 1.00* 1.00* 1.00*

A ? T-LECN – – .48* .37* .37*

A ? T-LELN – – .55* .00 –

E ? T-LECP – – .00 .00 –

E ? T-LECN – – .52* .63* .63*

E ? T-LELN – – .45* 1.00* 1.00*

Note. The full maximum likelihood procedure was used fitting the raw data to the model and parsimonious modifications presented in Fig. 2.

Components were computed from squared standardized path coefficients

* Variance components were significant on p B .05 (C.R. [ 1.96)

66 Behav Genet (2012) 42:57–72

123

Fig. 4 This figure illustrates the

best fitting model variant (last

column of Table 3) of the full

model shown in Fig. 2. For

simplicity, the model is shown

only for one twin: A: latent

additive genetic factor and path;

E: latent nonshared

environmental factor and path;

indices 1 and 2: measurement

occasions; indices CP, CN, LN,and FAC: controllable positive,

controllable negative, less

controllable negative, and

common factor reflecting

co-occurrence; T: ‘‘trait’’ factor

in sense of a stable factor.

Model parameters reflect the

standardized path coefficients.

Dashed model elements were

not significant and fixed to zero.

Further description is presented

in the text

Table 4 Reciprocal genetic simplex models: best fitting models of the relationship between personality and LE-scores

Variables Phenotypic correlations Best fitting model Model fit Model descriptions

Personality Life events P?LE LE?Pa Dv2 (df) Dp

N LECP -.06/.01 -.05/.02 4 7.78 (8) .46 No significant relation

LECN .09*/.13* .11*/.15* 3a 6.27 (6) .18 Genetic P?LE

LELN -.00/.05 .05/.09 3d 5.06 (6) .54 Environmental LE?P

E LECP .14*/.15* .13*/.14* 3a 1.73 (6) .94 Genetic P?LE

LECN .07/.05 .08/.05 4 12.28 (8) .14 No significant relation

LELN .06/.07 .06/.04 4 4.68 (8) .79 No significant relation

O LECP .11*/.28* .10*/.25* 3a 6.75 (6) .35 Genetic P?LE

LECN .06/.08 .10*/.09 2c 3.15 (4) .53 P?LE?P reciprocal effects

LELN .10*/.10* .11*/.10* 3a 4.16 (6) .66 Genetic P?LE

A LECP -.02/-.04 -.06/-.06 4 8.59 (8) .38 No significant relation

LECN -.12*/-.20* -.11*/-.20* 2c 2.60 (4) .63 P?LE?P reciprocal effects

LELN -.08/-.13* -.08/-.12* 3a 3.14 (6) .79 Genetic P?LE

C LECP .07/.06 .06/-.03 4 6.91 (8) .55 No significant relation

LECN -.03/-.05 -.06/-.04 4 10.79 (8) .21 No significant relation

LELN .02/-.02 -.02/-.09 4 9.19 (8) .33 No significant relation

Note. N Neuroticism, E Extraversion, O Openness, A Agreeableness, C Conscientiousness, LECP controllable positive events, LECN controllable

negative events, LELN less controllable negative events, P?LE and LE?P: correlations between personality and life events, Dv2: v2 difference

to the full model, phenotypic correlations were estimated for the first (prior slash) and the second interval of time (onto slash); the full maximum

likelihood procedure was used fitting the raw data to the model presented in Fig. 3 and parsimonious modifications

* Significant on p \ .05a Not corrected for former correlations between personality and life events

Behav Genet (2012) 42:57–72 67

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independent model (model 4) fixing all paths between

personality and LE-scores to zero.

The best fitting models are shown in Table 4. Balancing

fidelity and parsimony, we chose the models which did not

fit the data significantly worse than more complex (nested)

models or the full model and also showed the highest CFI.

In addition, further reduction of parameters should result in

significantly worse fit. Model comparisons with the full

model (i.e., the v2-difference tests) are shown for each

relationship in Table 4.

In all cases of significant correlations between person-

ality and life events, the models 2c, 3a, or 3d fitted the data

best. That is, the effects from personality on life events

were genetically mediated whereas the effects from LE-

scores on personality were environmentally mediated.

Based on model fitting results, we estimated averaged

genetic and environmental links derived from the respec-

tive best fitting models (pictured in Fig. 5). The overall

genetic links were calculated as [a21 ? (a31 9 a21 9 a42

? a43)]/2 and the environmental links as [e32 ? (e42 9

e32 9 e53 ? e54)]/2. In the following, we describe the

results with respect to each specific personality domain. In

line with previous research (e.g., Kandler et al. 2009),

about half of the variance in self-reports on personality was

attributable to genetic effects.

For Neuroticism, genetic factors (a2) accounted for

51%, whereas the other half was due to nonshared envi-

ronmental effects (e2 = 32%) and measurement error

(e2 = 17%). The 10-year genetic and environmental con-

tinuities were rG = .92 and rE = .85 (rE was corrected for

e2). Neuroticism was positively related to negative events,

especially to controllable negative life events. This relation

was genetically mediated and unidirectional. Neuroticism

explained about 2% of the variance in controllable negative

LE (about 4% of the genetic variance). Nearly all shared

genetic variance was stable through genetic continuity in

controllable negative events. The direction of the effects

was inverted for the relationship between Neuroticism and

less controllable negative life events. That is, these life

events led to an increase in Neuroticism (accounting for

about 1% of the environmental variance in Neuroticism).

Extraversion (a2 = 54%, e2 = 31%, e2 = 15%, rG =

.95, rE = .74) was positively related to controllable posi-

tive life events with only one significant direction that was

genetically mediated indicating that individuals high on

Extraversion experienced more controllable positive

events. Genetic influences on Extraversion accounted for

about 2% of the variance in such events (about 12% of the

genetic variance, primarily stable through the genetic

continuity of such events).

Openness (a2 = 55%, e2 = 32%, e2 = 13%, rG = .89,

rE = .60) was positively related to all LE-scores (but in

particular to positive events). These relationships were

primarily genetically mediated and unidirectional from

Openness to LE-scores. This indicates that individuals who

are genetically predisposed to be more open tend to

experience a greater number of life events, especially

controllable positive events. Genetic factors on Openness

accounted for about 4% of the variance in controllable

positive events (17% of the genetic variance). Openness

also accounted for variance in controllable negative (about

1%; 2% of the genetic variance) and less controllable

negative events (about 2%; 4% of the genetic variance). In

addition, there was an effect from controllable negative

events on Openness that was environmentally mediated

(accounting for about 1% of the environmental variance in

the personality trait).

Agreeableness (a2 = 48%, e2 = 34%, e2 = 18%,

rG = .88, rE = .76) was negatively related to negative life

events through the genetic pathway from this personality

dimension to controllable (explained about 2% of variance,

6% of the genetic variance) and less controllable events

(accounting for about 1% of variance, 3% of the genetic

variance). For the controllable undesirable events, how-

ever, there was also an environmental pathway back to

Agreeableness (explained about 1% of the environmental

variance). The relationships indicate that genetically based

Agreeableness may prevent negative experiences and, in

(a)

(b)

(c)

Fig. 5 Genetic and environmental unidirectional and reciprocal

effects between personality and life events are illustrated. Genetically

mediated effects are shown in solid paths and environmentally

mediated effects are presented in dashed paths. Path coefficients

reflect latent genetic and environmental correlations

68 Behav Genet (2012) 42:57–72

123

turn, negative experiences tend to make people less

agreeable. Finally, Conscientiousness (a2 = 53%, e2 =

34%, e2 = 13%, rG = .92, rE = .62) was not related to the

life events assessed in our study.

Discussion

We studied the sources of continuity in experiencing life

events and the directionality and sources of the relation-

ships between personality traits and life events. Our results

support the following conclusions. First, the continuity of

aggregates (or recurrence) of negative and positive events

can be attributed to genetic factors, whereas the larger

proportion of variance is not stable and influenced by

environmental effects. There is even stable common vari-

ance in different clusters of life events that is exclusively

genetically influenced. Second, prospective associations

from personality traits to life events are mediated by

genetic factors and genetic effects on life events can partly

be explained by genetic effects on personality. Third,

controlling for genetic factors on and between personality

traits and life events, there are very small but significant

effects from negative life events on Neuroticism, Open-

ness, and Agreeableness accounting for a small proportion

of environmental variance in these personality traits. We

will discuss these conclusions and their implications in

turn.

Life events as genetic traits and environmental states

The expected positive correlations of LE clusters across

two measurement occasions indicate that certain classes of

events tend to be repeated in individuals’ lives (Headey and

Wearing 1989). Our results indicate that this continuity is

primarily a function of genetic effects supporting the the-

ory that occurrences and reoccurrences of certain experi-

ences are primarily directed by genotypes (Scarr and

McCartney 1983). Moreover, positive cross-cluster cross-

time correlations are exclusively due to a common genetic

factor which explains that some individuals tend to expe-

rience more events than other people independent of the

controllability and valence of events. These genotypic

differences might affect that some people are more pro-

active, are more open to different experiences, or have

more social contacts than other people, which in turn

increase the frequency of different life events and

experiences.

With respect to controllability and valence, genetic

factors play the major role in the continuity or repetition

specific for controllable and positive life events. This

directly supports the concept of the genetic control of the

exposure to certain events which are primarily positive and

desirable (Kendler 2001; Plomin et al. 1990). In contrast,

continuity specific for negative life events is primarily

environmentally mediated. This might reflect stability of

the economic and social environment. For example,

unemployment, lay-off, and demotion in the work place

may more often reoccur in a high unemployment area.

Consumption of drugs, children in trouble, and major

conflicts may more often reoccur in a high crime area

(Headey and Wearing 1989).

The major proportion of individual differences in the

occurrence of life events is instable and environmentally

mediated. A significant amount of these environmental

factors seems to be common for different aspects of life

events but not shared by twins. A common environmental

factor may reflect rater biases. However, as already men-

tioned, to the degree to what events are rather factual and

externally verifiable this explanation is implausible (e.g.,

Kendler et al. 2003; Magnus et al. 1993). It is rather

plausible that events ‘‘flow from each other’’ (Headey and

Wearing 1989, p. 738). That is, events occur and reoccur

because of the occurrence of other events within and across

aspects of life events, such as valence and control. For

example, unemployment (less controllable negative event)

may increase the probability of financial problems (less

controllable negative event). Promotion in the work place

(positive event) may lead to job related separation from

partner (negative event). Beyond genetic factors and

common occurrences of life events, at least one-third of

variance in life events is aspect-specific and temporary.

That is, a large proportion of environmental differences of

individuals reflect the random nature of (qualitative aspects

of) life events as short-time situational effects.

In sum, the tendency to have more major individual

experiences and the continuity of aggregates of certain

experiences, in particular positive experiences, is affected

by the genetic makeup. Whether the underlying process is

an active one—like seeking out positive environments or

transforming situations in a positive direction—or a reac-

tive—like evoking sympathy, attraction, or support from

others—is an important question for future research. Con-

tinued negative experiences on the other hand might be

explained by the stability of the social and economic

environments the people live in. The short-time common

occurrence of positive and negative or controllable and less

controllable events may reflect the dynamics of the envi-

ronment like one (bad or good) thing leads to another.

Genetic links between personality and life events

We hypothesized that genetic differences in personality

may account for a significant amount of genetic variance in

future life events. In line with previous research, Neuroti-

cism showed a moderate positive relation to negative

Behav Genet (2012) 42:57–72 69

123

events, Extraversion to positive events, and Openness to

both (Headey and Wearing 1989; Saudino et al. 1997). In

addition, our study indicates negative associations between

Agreeableness and negative events. The impetus of these

associations was due to genetic factors on personality.

Thus, genetic influences on life events may partly arise as

genetic control of exposure to certain events via personality

(Kendler 2001), or in other words as genotype–environ-

ment correlation (Plomin et al. 1977).

However, there was a large residual proportion of

genetic variance in LE-scores not accounted for by genetic

factors on personality traits considered in our study.

Potentially, other genetically influenced individual char-

acteristics related or not related to personality may play an

additional role. Cognitive abilities, for example, are asso-

ciated with job success (e.g., Schmidt and Hunter 1998)

and may systematically influence the occurrence of specific

economic events. Physical attractiveness and self-esteem

may facilitate benefits in social interaction (e.g., starting a

firm relationship with a new partner, social support). Also,

an increase in psychiatric symptoms can affect an increase

in negative life events (e.g., Billings and Moos 1982).

Other dispositional characteristics may mediate the genetic

control of exposure to certain events. Motivational vari-

ables, such as major life goals (Bleidorn et al. 2010) and

individual interests (Kandler et al. in press) appear to have

unique genetic basis that is independent from the genetic

factors influencing personality traits. Life goals and inter-

ests are very plausible candidates affecting individuals’

exposure to certain events.

Environmental links between life events

and personality

Controlling for effects of personality on life events, we

found significant influences from negative life events on

Neuroticism, Openness, and Agreeableness. This finding

partly supports the idea of reciprocal influences between

personality and life events (Gottlieb 2003). However, it

should be noted that the revealed effects were rather small

and there were no significant effects from positive events

on personality traits. This might be attributable to the

desired nature of positive events that needs no effort for

individual adaptation.

The influences from negative life events are environ-

mentally mediated accounting for a small proportion of

environmental effects on Neuroticism, Openness, and

Agreeableness not shared by twins. That is, the influence

from life events on personality is independent of twins’

shared genetic makeup. On the one hand, personality

genotypes affect the probability of exposure to certain

environments which are correlated within twin pairs as a

function of their genetic relatedness. On the other hand,

these environments appear to allocate experiences which

affect phenotypic differences within twin pairs. Such a

mechanism provides a good explanation for the increase of

continuity in personality and the decrease of heritability of

personality across the life span (Kandler et al. 2010a).

The low and environmentally mediated effects of life

events on personality supports the idea that the causal

impact of any single event may be very small, unsystem-

atic, and (or) nonlinear (Plomin and Daniels 1987; Turk-

heimer and Waldron 2000). The cumulative effect of

multiple events may cause detectable personality changes

(Dunn and Plomin 1990; Plomin et al. 2001). In addition,

the impact of one event (e.g., personally relevant signifi-

cant experience of success) may buffer another one (e.g.,

degrading in work) and only very extreme experiences

might have a large direct influence (Ge et al. 2009;

Lockenhoff et al. 2009).

Nonshared environmental influences can also reflect

statistical genotype–environment interaction if this is not

included in the model (Purcell 2002). That is, the indi-

vidual adaptation given a life event depends on the indi-

vidual context. Consequently, the consideration of an

overall picture of the interplay between personality and

environments needs more complex interactionist perspec-

tives (South and Krueger 2008) and research strategies that

requires large sample sizes such as modeling genotype–

environment correlation in the presence of genotype–

environment interaction (Purcell 2002).

Limitations

The present study extends previous research by using a

multivariate biometric design to disentangle continuity in

occurrences of life events and links between personality

and life events into genetic and environmental sources.

However, several limitations have to be discussed that

could be overcome by future research.

First, the sample is only modest in size. Thus, the sta-

tistical power of our analyses was relatively low to disen-

tangle small associations between personality and life

events in genetic and environmental effects. In fact, our

model tests provide support for only one source of effects

respectively: Genetic effects through personality on life

events and environmental effects from life events on per-

sonality. Thus, there may be additional mediating effects

not detectable in our study.

Second, as in most voluntary twin studies, there is an

overrepresentation of women relative to men and MZ rel-

ative to DZ twins. However, the revealed pattern of phe-

notypic correlations among life events across time as well

as between personality and life events generally replicates

previous studies with larger and more representative sam-

ples (e.g., Headey and Wearing 1989).

70 Behav Genet (2012) 42:57–72

123

A further limitation regarding our sample concerns the

rather wide range of age. Although we corrected for age

effects, different age groups might differentially evaluate

one and the same event (e.g., retirement, death of a close

person). Also, stability of life events (and the correspond-

ing role of heritable traits) might be higher or lower at

different times in adulthood. Future genetically informative

studies might focus on potential age differences (e.g.,

before and after leaving the childhood home or before and

after retirement). Finally, the system used to classify life

events may obscure important patterns that involve specific

events or other possible distinctions, such as normative and

non-normative events.

Summary

The entire picture of our results supports the concept of

genotype–environment correlation (Scarr and McCartney

1983) and provides partial support for a bidirectional per-

son-environment model as a crucial element of human

development (Gottlieb 1991, 2003). Genotypic differences

significantly impact individual differences in the exposure

to certain events (Kendler 2001). In particular, genetic

factors influence the stability or the recurrence of positive

events. These environments allocate experiences which, in

turn, may retroact on the individual. Since genetic bases for

personality explain some of the heritability of later life

events, personality indeed mediates genetic influences on

life events. Moreover, that life events explain some of the

nonshared environment in future personality is a demon-

stration that life events reflect some of the nonshared

environmental influences on personality. Thus, it appears

that ‘‘nature proposes’’ and ‘‘environment disposes’’ the

human development (Scarr 1993, p. 1333).

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