Family Matters: Happiness in Nuclear Families and Twins

Transcription

1 DOI /s x ORIGINAL RESEARCH Family Matters: Happiness in Nuclear Families and Twins Ragnhild Bang Nes N. Czajkowski K. Tambs Received: 11 August 2009 / Accepted: 21 April 2010 Ó Springer Science+Business Media, LLC 2010 Abstract Biometric studies have shown that happiness is strongly affected by genes. The findings are mainly based on twin data, however, and the full validity of the results has been debated. To overcome some limitations in classical twin research, we examined aetiological sources of subjective well-being (SWB), using two independent population-based samples, one including nuclear families (N = 54,540) and one including twins (N = 6,620). Biometric modelling using R was conducted to test for a data structure implying either non-additive genetic effects or higher environmental co-twin correlation in MZ than DZ pairs (violation of the EEA). We also estimated non-random mating, cultural transmission and shared environments specific for regular siblings and twins. Two sets of nested models were fitted and compared. The best explanatory model shows that family matters for happiness predominantly due to quantitative sex-specific genetic effects, a moderate spousal correlation and a shared twin environment. Upper limits for broad-sense heritability were estimated to be 0.33 (females) and 0.36 (males). Our study constitutes the most elaborate biometric study of SWB to Edited by Chandra A. Reynolds. R. B. Nes (&) N. Czajkowski K. Tambs Division of Mental Health, The Norwegian Institute of Public Health, PB 4404, Nydalen, 0403 Oslo, Norway Ragnhild.bang.nes@fhi.no N. Czajkowski Nikolai.czajkowski@fhi.no K. Tambs Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, USA Kristian.tambs@fhi.no date and illustrates the utility of including responses from multiple types of relatives in quantitative genetic analyses. Keywords Subjective well-being Happiness Life satisfaction Behaviour genetics Twin research Introduction Subjective well-being (SWB) has recently reigned as the primary well-being construct in psychological research (Ryan and Deci 2001). The construct is grounded in firstperson experiences and commonly conceptualised as a multidimensional subjective appraisal reflecting the degree to which people think and feel that their life is going well. It is therefore often summarised as happiness. An abundance of studies has shown that SWB provides important information of experienced utility (Kahneman 1994) and reliably predicts multiple positive personal outcomes such as successes in marriage, friendship, health, and income (Lyubomirsky et al. 2005). Psychologists and economists alike have therefore recommended the implementation of broad scale population surveys to trace SWB levels over time so that structural characteristics for wellbeing can be identified and policy decisions optimized to increase happiness for all (Lucas and Diener 2008). Research indicates, however, that well-being levels may be fairly unresponsive to most changes in life circumstances (Lucas and Diener 2008), and that genetic influences predominantly explain individual variation in SWB (Lykken and Tellegen 1996). Few objective life circumstances are strongly associated with SWB, and demographic variables are consistently shown to account for no more than 10 15% of the variance in well-being scores (Andrews and Withey 1976; Argyle 1999). By contrast, a

2 number of biometric studies have reported genetic influences to account for as much as 40 50% of the time-specific variance in SWB in humans (Tellegen et al. 1988; Lykken and Tellegen 1996; Røysamb et al. 2002; Eid et al. 2003; Stubbe et al. 2005; Nes et al. 2006; Schnittker 2008; Bartels and Boomsma 2009) and in apes (Weiss et al. 2002). The genetic effects on SWB have been linked to personality (extraversion and neuroticism) through common genes (Weiss et al. 2008) possibly involved in susceptibility to experience positive and negative affect (Watson and Clark 1997). This may largely explain why SWB is relatively stable across both situations and the life span (e.g. Eid and Diener 2004; Diener and Lucas 1999). Two separate longitudinal twin studies of young adults estimated the stable variance in SWB to be 0.50 over 6 and 10 year intervals and indicate that as much as 80% of these stable levels are attributable to genes (Lykken and Tellegen 1996; Nes et al. 2006). The evidence is therefore consistent with a theory positing a general positivity factor, or readiness to perceive and interpret the world more or less positively, which is stable across time and strongly influenced by genes. Further support for a genetically based positive orientation (Caprara et al. 2009, p. 278), or covitality factor (Weiss et al. 2002, p. 1147), is given by the finding of a common higher-order factor for individual well-being indicators including life satisfaction, optimism and self-esteem, which is stable across time (Caprara and Steca 2005, 2006) and linked through common genes (Caprara et al. 2009). A partly genetically determined positivity factor is therefore likely to parallel a genetic comorbidity factor for neuroticism and internalizing disorders (Hettema et al. 2006) such as major depression (Fanous et al. 2002) and generalised anxiety disorder (Hettema et al. 2004) as well as for neuroticism and subthreshold symptoms of anxiety and depression (Jardine et al. 1984). The environmental influences on SWB have been shown to be largely or entirely non-shared (e.g. Lykken and Tellegen 1996; Røysamb et al. 2003; Stubbe et al. 2005; Nes et al. 2006), indicating that familial resemblance essentially is due to shared genes, and not to shared environments (i.e. environmental characteristics which tend to make family members similar). This fits well with biometric findings on related constructs (e.g. extraversion, neuroticism, liability to depression) which quite consistently indicate that being raised within the same household usually does not contribute to sibling similarity (e.g. McGue et al. 1993; Rettew et al. 2008; Keller et al. 2005; Rijsdijk et al. 2003). The biometric findings on happiness and well-being appear impressively robust, presenting a consistent story in which family matters for happiness predominantly due to genes. The results are based on thousands of twins from several different nations, and include twins reared together (e.g. Nes et al. 2006) as well as twins reared apart (Tellegen et al. 1988; Lykken and Tellegen 1996). However, despite consistency and power (sample sizes) important questions remain unresolved. The set of biometric studies is small, and a notable limitation concerns the findings being based almost entirely on twins. Classical twin studies offer a number of potential advantages and afford a range of analytical tools, but do not permit concurrent exploration of non-additive genetic effects (i.e. interaction between alleles) and different types of shared environments. Genetic non-additivity and shared environments may both influence variation in SWB, however. A number of twin studies have indicated only additive genetic effects (Røysamb et al. 2002, 2003; Nes et al. 2006; Weiss et al. 2008; Schnittker 2008), but some studies have reported substantial non-additive genetic effects (Lykken and Tellegen 1996; Stubbe et al. 2005). Using a twin-sibling design (N = 5,024), Bartels and Boomsma (2009) recently reported contributions from both common additive and common non-additive genetic sources to four different well-being constructs (quality of life in general, quality of life at present, life satisfaction, subjective happiness). Conclusions about non-additive genetic effects are tentative, however, as the power to reliably separate additive and non-additive genetic effects is limited in studies using only twins and regular siblings. Conclusions regarding the shared environment should also be made with caution. Despite the majority of studies not indicating shared environmental effects, some studies show significant effects (e.g. Tellegen et al. 1988; Baker et al. 1992; Gatz et al. 1992; Weiss et al. 2002). Very large samples are also required for detection of moderate to small effects and the magnitude of the effects are likely to vary across development (Burt 2009). The validity of twin findings is also inextricably linked to significant methodological assumptions being met which are important to put to test such as the equal environment assumption (EEA) and random mating. To reliably address these substantial issues, extensions of the standard twin study to include informants from multiple familial relationships are clearly needed. Twin and family analyses Apart from two studies combining responses from twins and non-twin siblings (Stubbe et al. 2005; Bartels and Boomsma 2009), biometric research on happiness (human) is fully based on the classical twin design (CTD). The CTD compares the similarity in monozygotic (MZ) and dizygotic (DZ) co-twins to estimate parameters of additively (A) and non-additively (D) acting genes, and shared (C)

3 and nonshared (E) environments. These parameters are derived by specifying a mathematical model according to the differential degree to which pairs of MZ and DZ twins are correlated for genetic and environmental effects. Simultaneous estimation of all these four components is not feasible when data is available only from twins, however, as the CTD offers only three observations (the total phenotypic variance, and the covariance between MZ twins and between DZ co-twins). By using extended twin kinship designs that include data from twins and their relatives (e.g. parents, spouses, siblings), or biometric designs that combine data from separate twin and family samples, non-additive genetic and shared environmental effects can be estimated concurrently due to the increase in covariance observations. Such extensions also permit more reliable separation of additive and non-additive genetic variance which is of particularly interest for SWB research. While the CTD is restricted to specify one category of shared environmental effects (the one shared by co-twins), extensions of the CTD also enable discrimination between several different types such as environmental influences from parents to offspring through parental identification and learning (vertical cultural transmission) and specific sibling or twin environments. Sibling effects which are unrelated to the parental phenotype (e.g. SWB) may still be caused by parents who sometimes encourage behaviour that they do not exhibit themselves, but may also be independent of parental influences (e.g. common peers, mutual sibling influences). Co-twins may share even more of the environment than do non-twin siblings (e.g. common classmates, teachers, identical clothing) and when combining data from twins and other family members, specific MZ and DZ twin environments may be estimated in case they differ. Thus, combined family and twin samples permit direct assessment of the validity of a key assumption of the twin model, namely the Equal Environment Assumption (EEA) which assumes same shared environments for MZ and DZ twins. Another important assumption is that of random mating which, if not accounted for, will lower the heritability estimates and inflate the effects from the shared environment in the CTD. Research has shown pervasive evidence for positive correlations between spouses on most behavioural traits, including well-being judgements (Tambs and Moum 1992; Schimmack and Lucas 2007), but previous twin studies of SWB have not accounted for such effects. This paper aims to address a number of these concerns. By combining data from twins (N = 6,620) and nuclear families (N = 54,540) on SWB we can directly test if results obtained from twins can be extrapolated to those found in other, more common, relationships as well as explore additional aetiological influences than allowed for in standard twin studies. In the present analyses we test for a data structure implying either a violation of the critical methodological assumption of EEA or non-additive genetic effects not necessarily detectable in a twin sample alone. We also observe the degree of non-random mating, estimate vertical cultural transmission and shared environments specifically for regular siblings and twins. In addition, we explore possible sex differences in the magnitude of these effects as some studies have reported sexspecific influences from genes and environments on SWB (e.g. Røysamb et al. 2002, 2003; Nes et al. 2006), whereas other reports have not indicated sex-specific effects (Bartels and Boomsma 2009). Method Sample Data used in this study originate from two separate Norwegian studies; The Norwegian Institute of Public Health Twin Panel (NIPHTP) and The Nord-Trøndelag Health Study (HUNT). NIPHTP NIPHTP is an ongoing longitudinal study with a cohort sequential design. Twins were initially identified through information on plural births in the Medical Birth Registry of Norway (MBRN) which began in 1967 and requires mandatory notification of all pregnancies from 16 weeks gestation and registration of standardized information regarding all births in Norway. Questionnaire data were first collected in 1992 (Q 1 ) from twins born , and in 1998 (Q 2 ) from twins born Responses on Q 1 were obtained from 2,570 complete pairs and 724 single twin responders (N = 5,864, response rate 74%) and on Q 2 from 3,334 complete pairs and 1,377 single twin responders (N = 8,045, response rate 63%). The present analyses are based on Q 2 responses from complete pairs (3,310) only, aged (mean 25.59; SD 3.67) at the time of assessment. Zygosity assignment was initially based on discriminant function analyses using questionnaire items, shown to categorize correctly more than 97% (Magnus et al. 1983). Twenty-four micro-satellite markers were then genotyped on a sub-sample of 676 like-sexed pairs in the sample. Seventeen out of 676 pairs with DNA information were found to be misclassified by the questionnaire data and zygosity corrected. This corresponds to a misclassification ratio for the total sample of 1.38%, a rate unlikely to substantially bias the results.

4 HUNT In , all persons aged 20 years and older and living in the Norwegian county of Nord-Trøndelag were invited to participate in a comprehensive health survey (HUNT-I). Altogether 74,599 (88.1%) attended. The data collection was repeated in (HUNT-II). A total of 92,936 persons were found eligible for participation in the HUNT-II. The invitation was sent by mail attached to a three-page questionnaire (HUNT-II 1 ) which was to be completed prior to a physical health screening and to be returned at attendance to the screening site. A second questionnaire (HUNT-II 2 ) was handed out at the screening site and should be completed and returned by prepaid mail. A total of 66,140 persons (71.2% of the persons found eligible) attended the physical examinations and returned the HUNT-II 1 questionnaire. Out of these respondents, 57,316 (87%) also returned the HUNT-II 2 questionnaire. The current study only uses data from HUNT-II 2 responders as some SWB variables are from the HUNT-II 2 questionnaire. First-degree relationships were obtained from the MBRN, identifying mother-offspring pairs with absolute certainty but with a slight chance that the father registered at birth is not the biological father. In addition to first degree relatives, data linking spouses was supplied. Further details on the HUNT study can be found in Holmen et al. (2003). Measures SWB is commonly operationalised as consisting of three related components (life satisfaction, positive affect, negative affect), which are separable both theoretically and empirically, but assumed to reflect one single underlying dimension, or higher-order construct (Lucas et al. 1996). In the present study, SWB was measured by a set of items originally suggested by Moum et al. (1990) comprising a cognitive aspect (life satisfaction), positive affect (happy, strong) and negative affect (worn out, dejected), thereby conforming to the generally accepted operationalisation of SWB. The index was constructed using a mean score of four items: (1) When you think about your life at present, would you say that you are mostly satisfied with your life, or mostly dissatisfied? (response categories ranging from extremely satisfied to very dissatisfied. (2) Are you usually happy or dejected? (response categories ranging from dejected to happy ). (3) Do you mostly feel strong and fit, or tired and worn out? (response categories ranging from very strong and fit to tired and worn out ). (4) Over the last month, have you suffered from nervousness (felt irritable, anxious, tense, or restless)? (response categories ranging from almost all the time to never ) (Details on the index are found in Røysamb et al. 2002). Cronbach s a for the index was estimated to be 0.70 and 0.79 for the NIPHTP and HUNT-II sample, respectively. In the HUNT data, items 1 3 had seven response categories whereas item 4 had four response categories. In the NIPHTP Q 2 questionnaire, item 1 had six response categories, item 2 had five response categories, and items 3 and 4 had four response categories. As the number of response categories was different, ranging from 4 to 6 (NIPHTP) and from 4 to 7 (HUNT-II), all items were standardised (z-scored) before being included into a summative index. To test whether the SWB scores were comparable across samples, the HUNT-II items were recoded into the same categories as used in the NIPHTP. Correlations between the original score and the recoded score for items 1 3 (which had different number of response categories) ranged from 0.97 to 0.99, indicating that transformations have not affected the results. The Expectation Maximization (EM) imputation option in SPSS was used to impute missing values for each SWB item. In cases with two or fewer missing values, the remaining SWB items and 6 additional items with highly related content were used to predict values substituting missing values. Data from respondents with more than 2 values missing on the 4 SWB items were excluded from the analyses. Imputation of missing values increased the total effective sample size from 52,557 (no missing items) to 54,540 subjects in HUNT-II and from 6,522 to 6,620 in NIPHTP. Statistical analyses Prior to computing the correlations, age effects on means and variances were removed by transforming SWB scores to z-scores in age groups with 5 year intervals because the effect of age was complex and partly non-linear. These transformations were conducted separately for males and females. Correlations between the various kinds of relatives were then computed and subsequently fitted to a structural equation model by weighted least squares estimation (WLS) using the statistical open source software package R (R Development Core Team 2005). The WLS method gives estimates that are close to those from maximum likelihood in kinship studies, is far less computationally demanding, and the risk of attaching undue precision to individual parameter and to falsely reject a true model, has been shown to be small (Eaves et al. 1989; McGue et al. 1984). As there were no solid grounds for a priori choosing a specific model, we fitted two sets of models expressing alternative hypotheses about the aetiology of SWB. These two model sets were used as starting points for the comparison of different nested models in which one or more parameters were dropped or equated. The full models in terms of path analytic notation are shown in Figs. 1 and 2.

5 ρ ρ A E E A A D E E D A a f e f e m a m a f d f e f e m d m a m μ 0.5 Mother Father Mother μ Father 0.5 v f v f v m v m A E E A A 0.5 D E 1.00/0.25 E 0.5 D A a f /a m e f /e m t m MZ m t e f /e m a f /a m Child 1 Child 2 a f /a m d f /d m Child 1 e f /e m t T σ T t e f /e m Child 2 d f /d m a f /a m Fig. 1 Path diagram of the first reference model. Capital letters A, D, and E in circles denote the latent variables for additive genetic, nonadditive genetic and environmental effects, whereas T denotes residual environmental similarity in twins. Parameters (small letters): a additive genetic effect, d non-additive genetic effect, e environmental effect, v vertical cultural transmission, t environmental effect shared by twins, q gene environment correlation resulting from correlation between parental genotypes and phenotypes, l assortative mating. The r is estimated in unlike-sex pairs, but fixed to 1 in samesex pairs s f /s m S T tz σ T S s f /s m Fig. 2 Path diagram of the second reference model. Capital letters A and E in circles denote the latent variables for additive genetic and environmental effects. S denotes residual environmental sibling similarity, T residual environmental similarity in twins, and MZ residual environmental similarity in MZ twins. Parameters (small letters): a additive genetic effect, e environmental effect, s environmental effects shared by siblings (sibling effect), t environmental effect shared by twins, m environmental effect shared by MZ twins, r correlation between male and female sibling/twin effect, l assortative mating. The r is estimated in unlike-sex pairs, but fixed to 1 in samesex pairs In line with conventions, the observed phenotype (SWB) in each relative is drawn as rectangles whereas the latent variables are drawn as circles. The (additive) genetic effects, a, are drawn as paths from the paternal and maternal genotypes to the genotypes of their children. Cultural transmission, v, is specified from the parental phenotypes to the environments of the offspring. The parameters of these paths were allowed to differ across males and females to allow for sex-specific contributions from genes and environments to SWB (i.e. scalar sex limitation). The first model set (Fig. 1) specified both additive and non-additive genetic effects along with vertical cultural transmission and one horizontal environmental component the special twin environment (T). The non-additive genetic effect (Fig. 1) was specified despite the fact that the co-twin correlations alone did not clearly indicate such effects (the ratio of MZ to DZ correlations was less than 2.0). The MZ correlations did, however, exceed twice the size of the sibling and DZ correlations combined and the parent-offspring correlations. In addition, effects from genetic non-additivity and shared environments may disguise each other when both are present giving a correlational pattern as indicated here. The ongoing debate raised by inconsistent findings regarding the relative role of additive and non-additive genetic effects in previously published reports, further added to the relevance of testing for non-additive effects here. The second model set (Fig. 2) omitted genetic nonadditivity and vertical transmission, but specified three horizontal environmental components. These components were all specified to impact through the general latent environmental variable (E) and include: (i) sibling similarity (s) from latent factor S, unrelated to the parents SWB/parental environment (e.g. common peers, sibling cooperation and competition), (ii) twin similarity (t) from latent factor T (i.e. sibling similarity not accounted for by S such as common experiences of twins due to their paired presence, shared classmates), regardless of zygosity, and (iii) MZ twin similarity (m) not accounted for by either S or T (identical clothing, more similar habits, self-images, and parental treatment). The two twin environments (T, MZ) thus account for resemblance in co-twins over and above

6 the sibling effect. A significant t effect indicates that cotwins share environmental influences of relevance to SWB to a greater extent than do non-twin siblings. A significant m effect indicates a possible violation of the EEA. The correlation between latent S and T factors, r, was constrained to be fully shared by like-sexed siblings and to vary freely in opposite-sex pairs (i.e. it reflects the correlations between male and female sibling environments). A phenotypic correlation between spouses will, depending on the magnitude and cause, give rise to different expectations of sibling and parent-offspring similarity. A significant spouse correlation may for example primarily be due to phenotypic assortment, where spousal similarity is assumed to be partly due to spouses selecting each other on the basis of the relevant trait, or to social homogamy, where spousal similarity results from phenotypic similarity within social groups. These two forms of non-random mating are not mutually exclusive, but cannot both be included in the same model. In line with conventions, we specified phenotypic assortment, represented by a co-path (l) (Cloninger 1980). The expected correlations between relatives for the first model and for the simplified version of the second are shown in Table 1. The models are highly similar to standard models described in detail elsewhere (Truett et al. 1994). In nested submodels, parameters were deleted or equated across sex. Finally all models were compared using a chi-square test. Difference in model fits between reduced sub-models can be interpreted as approximately chi-square distributed, with degrees of freedom equal to the numbers of parameters dropped. Certain assumptions are violated for this to be exactly true in our data set since a given respondent can be included in multiple family relationships. This will result in the chi-square test being slightly more conservative (i.e. having lower p-values), but is not expected to bias parameter estimates (McGue et al. 1984). Choice between alternative models is a decision process subject to error, but generally the best compromise between parsimony and fit is assumed to indicate the best fit to the data. We used the Akaike Information Criterion (AIC) which provides a summary index of both parsimony and fit (v 2-2df) (Akaike 1987). Confidence intervals (CI) for model parameters were estimated through bootstrap sampling, and the models were refitted to 30,000 samples drawn with replacement. Results Correlation analyses Table 2 shows the SWB correlations for the different types of relatives with 95% confidence intervals (CIs). As evident by their CIs, all nuclear family correlations are significant. The two MZ correlations are high compared to those for other pairs of relatives. The large difference (ratio exceeding 2) between the MZ correlations and the correlations between the remaining siblings (including DZ twins), points either to non-additive genetic effects or a specific MZ environment, whilst the spousal correlation (r = 0.26) shows a positive assortment for SWB. DZ correlations and, to a lesser extent, sibling correlations, are suggestive of sex-specific effects, whereas the parent-offspring correlations are not. This pattern indicates environmental effects of factors shared by like-sexed, but not fully shared by unlike-sexed siblings and twins. Age effects Age effects on means and variances were removed prior to the model fitting analyses. However, since the co-twins have the same age and other relatives in nuclear families vary widely in age, possible effects of age differences were explored. Age-specific genetic and time-specific environmental effects may dilute some of the observed resemblance between family members distant in age. If ignored, such age-specific effects may lower the parent-offspring correlations considerably, the sibling correlations moderately, and leave the twin correlations unchanged. We therefore tested for (i) possible differences in sibling correlations within age groups (20 31, 32 44, 45 57, and years), and (ii) differences in sibling correlations with increasing age differences between siblings. No significant differences were found, indicating that age differences do not substantially affect the results and that fitting models without parameters for age effects are justified. Model fitting The model-fitting results are shown in Table 3. Model set 1 The reference model in this model set (Fig. 1) allowed for sex-specific differences (quantitative differences) in both additive and non-additive genetic variance and sex-specific cultural transmission. The reference model fitted well (v 2 = 5.56, df = 4, p = 0.240). However, the cultural transmission effects were estimated to be 0.01 and 0.00 in males and females, respectively. Consequently, equating the cultural transmission effects across sex (model 2), or dropping them altogether (model 3) did not worsen the fit (v 2 = 6.03, df = 5, p = vs. v 2 = 6.16, df = 6, p = 0.406). Models dropping the additive genetic effect (model 4 compared with model 3, Dv 2 = , Ddf = 1,

7 Table 1 Expected correlations in terms of path coefficients Model set Relationship Gender specific 1 Mother son 0:5 a m ða f þ e f q þ lða m þ q e m ÞÞþe m ðv f þ lv m Þ Mother daughter 0:5 a f ða f þ e f q þ lða m þ q e m ÞÞþe f ðv f þ lv m Þ Father son 0:5 a m ða m þ e m q þ lða f þ q e f ÞÞþe m ðv m þ lv f Þ Father daughter 0:5 a f ða m þ e m q þ lða f þ q e f ÞÞþe f ðv m þ lv f Þ Brothers 0:5 a 2 m a þ 0:25 d2 m þ e2 m e þ 2e ma m q Sisters OS-siblings Male DZ twins Female DZ twins OS DZ twins Male MZ twins Female MZ twins 0:5 a 2 f a þ 0:25 d2 f þ e2 f e þ 2e fa f q 0:5 a m a f a þ 0:25 d m d f þ e m e f e þ ðe m a f þ e f a m Þq 0:5 a 2 m a þ 0:25 d2 m þ e2 m e þ 2e ma m q þ e 2 m t2 0:5 a 2 f a þ 0:25 d2 f þ e2 f e þ 2e fa f q þ e 2 f t2 0:5 a m a f a þ 0:25 d m d f þ e m e f e þ ðe m a f þ e f a m Þq þ e f e m t 2 r a 2 m þ d2 m þ e2 m e þ 2e ma m q a 2 f þ d2 f þ e2 f e þ 2e fa f q Spouses l 2 Mother son 0:5 a m a f þ a 2 m l Mother daughter 0:5 a 2 f þ a2 f l Father son 0:5 a 2 m þ a fa m l Father daughter 0:5 a m a f þ a 2 f l Brothers 0:5 a 2 m þ a3 m a fl þ s 2 m e 2 m Sisters 0:5 a 2 f þ a3 f a ml þ s 2 f e 2 f OS-siblings 0:5 a m a f þ a 2 m a2 f l þ sm s f e m e f r DZ males 0:5 a 2 m þ a3 m a fl þ s 2 m e 2 m þ t2 e 2 m DZ females 0:5 a 2 f þ a3 f a ml þ s 2 f e 2 f þ t2 e 2 f DZU 0:5 a m a f þ a 2 m a2 f l þ sm s f e m e f r þ t 2 e m e f r tz MZ males a 2 m þ e2 m s2 m þ t2 þ m 2 MZ females a 2 f þ e2 f s 2 f þ t2 þ m 2 Spouses l Path coefficients: a additive genetic, d non-additive genetic, e total environment effect, v cultural transmission, s sibling environment, l assortative mating, r correlation between sibling or twin environments in opposite sexed pairs (fixed to 1 in like-sexed pairs). Subscripts m and f refers to male and female, respectively q ¼ 0:5 ðða f ðv f þ lv m Þþqe f ðv f þ lv m ÞÞþða m ðv m þ lv f Þþqe m ðv m þ lv f ÞÞÞ q ¼ ða f v f þ a m v m þ a f lv m þ a m lv f Þ= ð1 ðe f v f þ e m v m þ e f lv m þ e m lv f ÞÞ a ¼ ð1 þ ða mþ q e m Þða fþ q e f ÞlÞ e ¼ v 2 m þ v2 f þ 2v mlv f p \ 0.001) or the non-additive genetic effects (model 5, Dv 2 = 17.26, Ddf = 1, p \ 0.001) were clearly rejected. Neither the additive genetic effects (model 6 compared with model 3, Dv 2 = 17.26, Ddf = 1, p \ 0.001) nor the non-additive genetic effects (model 7, Dv 2 = 4.49, Ddf = 1, p = 0.034) could be equated across sex without significant deterioration in fit, indicating that both additive and non-additive genetic effects on SWB vary across males and females. The best-fitting model under this model set was therefore model 3, indicating that (i) there is no cultural transmission, (ii) the magnitude of the genetic effects on SWB differ in males and females, (iii) these genetic effects are partly additive and partly non-additive, and that (iv) twins share more of the trait relevant environment than do regular siblings (t effect). The a and d effects were estimated to be 0.41 ( ) and 0.44 ( ) in males and to be 0.52 ( ) and 0.26 ( ) in females, respectively. The confidence intervals for the additive genetic influences for males and females clearly indicated sex differences in the magnitude of additive genetic effects. The a and d effects accounted for 17 and 19% of the variance in males, and 27 and 7% of the respective estimates in females. Broad sense heritability (including both additive and non-additive genetic effects) for SWB was estimated to be 0.36 ( ) and 0.33 ( ) in males and females,

8 Table 2 Correlations for SWB between members of nuclear families Family relation Correlation (95% confidence interval) Pairs Mean age (standard deviation) Data set Older relative Younger relative MZ male 0.45 ( ) (3.60) NIPHTP MZ female 0.40 ( ) (3.72) NIPHTP DZ male 0.21 ( ) (3.65) NIPHTP DZ female 0.25 ( ) (3.72) NIPHTP DZ unlike-sex 0.13 ( ) (3.73) NIPHTP Mother son a 0.13 ( ) 7, (11.13) (9.48) HUNT Mother daughter a 0.15 ( ) 8, (11.00) (9.05) HUNT Father son a 0.11 ( ) 5, (10.56) (8.76) HUNT Father daughter a 0.14 ( ) 6, (10.44) (8.36) HUNT Sisters a 0.20 ( ) 3, (10.40) HUNT Brothers a 0.14 ( ) 3, (10.83) HUNT Unlike-sex siblings a 0.14 ( ) 5, (10.54) HUNT Spouses 0.26 ( ) 13, (13.53) (13.15) HUNT The correlations are corrected for effects of age on means and variances NIPHTP The Norwegian Institute of Public Health Twin Panel, HUNT The Nord-Trøndelag Health Study, MZ monozygotic, DZ dizygotic a Each person can be included in more than one family relation. Spouses are in the order of male first estimating the confidence intervals by bootstrapping. The effect of the twin environment, 0.35 ( ), was clearly significant accounting for 12.3% of the environmental variance, or 7.8 and 8.1% of the total variance in males and females, respectively. Model set 2 This model set (Fig. 2) omitted cultural transmission and genetic non-additivity, but included three horizontal environmental components (S, T, MZ). Both sex-specific heritability (quantitative differences) and sex-specific sibling effects (quantitative and qualitative differences) were specified. To test the importance of genetic influences on SWB, all genetic effects were eliminated in a nested submodel (model 11). This model was firmly rejected by the v 2 -test (Dv 2 = , Ddf = 2, p = 0.00), clearly attesting the importance of genetic influences on SWB. Next we tested whether the magnitude of the genetic effects (model 12), or the magnitude of the sibling effects (model 13), could be equated across sexes without deteriorating fit. The magnitude of the shared sibling effects could be equated across sex (Dv 2 = 0.01, Ddf = 1, p = 0.920), but not the genetic effects (Dv 2 = 4.40, Ddf = 1, p = 0.036). Further constraints (comparing with model 13), fixing the correlation between male and female sibling environment (r) at one (model 14, Dv 2 = 19.25, Ddf = 1, p \ 0.001), or dropping the special MZ environment (model 17, Dv 2 = 5.47, Ddf = 1, p = 0.019) gave significantly worse fit. Dropping the twin environment resulted in a near-significant loss of fit (model 16, Dv 2 = 3.78, Ddf = 1, p = 0.052). The bestfitting model nested under model 10 in terms of AIC value was therefore model 13, which specified sex-specific heritability and all three sibling-specific environments (S, T, MZ). This suggests that (i) the magnitude of the genetic influences on SWB differ in males and females, (ii) sibling effects on SWB may be important, (iii) twins may share more of the environment than do non-twin siblings (t effect), and (iv) the EEA may to some extent be violated (significant environmental effect shared only by MZ cotwins). Heritability of SWB was estimated to be 0.25 ( ) in females and to be 0.18 ( ) in males and thus substantially lower than previously reported estimates for SWB. The S, T, and MZ environments accounted for 6.2% ( ), 8.4% ( ) and 11.2% ( ) of the environmental variance, respectively. The correlation between male and female sibling and twin environments (r) was estimated to be 0.16, suggesting that environmental factors shared by siblings is at least partly sex-specific (qualitative sex differences). Model set comparison In spite of the large sample, the best fitting models under the two models sets (model 4 and model 9) yield highly similar fit (v 6 2 = 6.53, AIC =-5.84 vs. v 6 2 = 6.16, AIC =-5.47). The fit measures alone therefore leave us unable to choose a best fitting model to our data.

9 Table 3 Results from model fitting Model v 2 Df p AIC a m a f d m d f e m e f s m s f v m v f r t m l v m = v f v = v = 0, a = v = 0, d = v = 0, a m = a f v = 0, d m = d f v = 0, r = v = 0, t = 0, r = a = a m = a f S m =S f r = Sm = Sf, m = S m = S f,t= S m = S f,t= 0, m = Note: The best-fitting models are shown in bold. The magnitude of the parameters must be squared to equal the proportion of variance accounted for by the latent factors. Path coefficients: a additive genetic, d non-additive genetic, e total environment effect, s sibling environment, v vertical cultural transmission, r correlation between environmental effects for siblings or twins in opposite sexed pairs, t twin environment, m MZ twin environment, l assortative mating. Subscripts m and f refers to male and female, respectively

10 Discussion This study aimed to further resolve the genetic and environmental influences on variation in SWB by examining the magnitude and types of genetic influences involved, by estimating vertical and horizontal environmental effects and sex specific effects, and by testing the validity of important assumptions (EEA, random mating) underpinning classical twin studies. The results clearly show that family matters for happiness in large part due to genes, and that the magnitude of these genetic effects differs in males and females (quantitative differences). Cultural transmission appears to be negligible for SWB, but twins clearly share more of the trait relevant environment than do regular siblings, indicating that some types of shared environments are of importance for variation in SWB. The fit measures alone leave as unable to decide with certainty between the two best-fitting models which provide different explanations for what makes MZ twins different - one specifying non-additive genetic effects (model 3) and one specifying the alternative explanation of environmental effects shared only by MZ co-twins (model 13). Evidence for genetic non-additivity In spite of the fit measures being virtually identical, the two best-fitting models (models 3 and 13) are probably not equally realistic. Several extended twin-family studies provide robust evidence for non-additive genetic effects on neuroticism and extraversion (e.g. Eaves et al. 1998; Rettew et al. 2008; Keller et al. 2005). These traits are shown to be strongly and genotypically related to SWB (Weiss et al. 2008). Figueredo and Rushton (2009) have also recently reported on shared non-additive genetic variance for covitality and a general factor of personality. Concordance with these replicable findings of non-additive genetic variance for closely related phenotypes, particularly those based on extended kinship data, favours model 3 as the best-fitting model. Model 3 also yields the lowest AIC values and gives parameter estimates for broad-sense heritability that are consistent with previously published estimates for SWB and SWB related constructs (e.g. neuroticism and extraversion, satisfaction with life) which usually range between 0.35 and 0.50 (Eaves et al. 1998; Keller et al. 2005; Stubbe et al. 2005; Bartels and Boomsma 2009). These broad-sense estimates are generally stable despite considerable fluctuations in the additive and non-additive genetic components (Eaves 1972). Evidence from adoption studies have also showed MZ twins reared apart to be as similar in happiness and well-being as MZ twins reared together (Tellegen et al. 1988; Lykken and Tellegen 1996) further supporting model 3 as the best-fitting model. The alternative explanation of a special MZ environment has not previously been explored for SWB. This is probably due to a combination of a scarcity of data from additional types of relatives, lack of statistical power, and perhaps due to the numerous studies supporting the validity of the EEA (Kendler and Eaves 2005). Nevertheless, Tambs et al. (1995) showed greater social closeness between MZ than DZ co-twins to be significantly although moderately related to co-twin resemblance for anxiety and depression scores. Likewise, social interaction in MZ co-twins has been found to be related to twin resemblance for neuroticism and extraversion (Rose et al. 1988). Although these reports could not resolve the direction of causation whether more similar co-twins get closer, or more close co-twins get more similar, further analyses restricted only to MZ co-twins, and for whom personality differences necessarily are due to non-genetic influences, support the direction of causation from social closeness to personality resemblance (Rose and Kaprio 1988). Most known environmental factors fail to make a discernible difference in co-twin personality resemblance, however (Rose et al. 1988). Considering the evidence, particularly the consistency and replicability of non-additive genetic effects in extended twin-family studies of personality-related phenotypes along with the vast research literature documenting surprisingly few violations of the EEA, a special MZ environment is perhaps less likely to explain our data than genetic non-additivity. Data including multiple sibling groups of varying genetic relatedness, and specifically half-siblings, could provide a practical and feasible method for strengthening the conclusion and provide a more final answer to the questions about the mode of genetic inheritance for SWB. Evidence for shared environments A remarkable finding to emerge from biometric research in adult populations is the absence of shared environmental influences on most phenotypes relevant to well-being despite social relationships being fundamental to mental health (e.g. Myers 2004) and serving as important sources of SWB (Reis and Gable 2003). Contrary to most previous reports, our findings challenge this widespread assumption, clearly indicating that environmental influences of importance for SWB systematically differ between twins and regular siblings due to some aspect of the twin environment. This important finding is consistent with results reported in a previous study showing mean differences between regular siblings and twins on Social Closeness in three separate samples (Johnson et al. 2002). This subscale contributes to the Positive Emotionality factor (extraversion) of the Multidimensional Personality Questionnaire (MPQ). Another study, also using the MPQ and

11 including both twins reared together and twins reared apart (Tellegen et al. 1988), reported substantial contributions from the shared environment on Social Closeness (c 2 = 0.19), Well-Being (c 2 = 0.13) and Positive Emotionality (c 2 = 0.22). These specific measures are characterized by communal and inter-communicative aspects, perhaps making them particularly responsive to and reflective of the surrounding social climate. Siblings close in age are likely to be play-mates and to develop a relationship characterised by reciprocity, cooperation and trust (Aisworth 1991). Mutual understanding, social and emotional closeness are therefore likely to be particularly strong among co-twins who have the same age, and often share classmates and peers, as well as experiences due to their paired presence in other social groupings. Shared environments are therefore likely to be particularly influential in younger age groups, when siblings are still living together and share more of the environment. This is in line with recent research indicating small to moderate influences from shared environments to SWB-related traits (such as anxiety and depressive symptoms) during childhood and adolescence (Burt 2009). Most shared environmental influences appear to cease once the co-twins leave their shared family home and become more independent, however. Although our sample does not include children, we use data from twins and regular siblings from the ages of 18 and 20, respectively. Some of these pairs are probably living together. Testing of differences in sibling correlations (i) within different age groups and (ii) with increasing age differences between siblings, did not indicate that sibling resemblance vary with age differences in our sample, however. Evidence for non-random mating Consistent with the majority of studies on mating for personality-related characteristics, our findings show that mating is hardly entirely random for SWB. The spousal correlation of 0.26 exceeds all remaining correlations in the family sample (HUNT-II), and although moderate, this correlation would have deflated the heritability estimate and inflated the effect of the shared environment by a few percent in the CTD, if ignored. Along with most biometric studies, our analytical model assumed that spousal resemblance was due to phenotypic assortment. The mechanisms and strategies involved in generating spousal similarity for personality-related constructs are probably complex, however and spousal similarity could also reflect social homogamy, or convergence over time. These possible causes of similarity between spouses are not mutually exclusive. Schimmack and Lucas (2007) have for example reported both dyadic trait similarity and dyadic similarity in new state variance for global and domain satisfaction, suggesting that spousal similarity in well-being reflects active selection as well as mutual social influences over time. The majority of research, however, indicates that spousal resemblance more typically results from selection effects (Kendler and Eaves 2005) and a former study using the HUNT-I data and a life satisfaction measure similar to the one used here, reported spousal resemblance to be unrelated to marital duration (Tambs and Moum 1992), indicating that although the explanation of convergence over time is intuitively appealing, the available evidence does not support it. In twin studies the heritability estimate is essentially based on the difference in MZ-DZ correlations. Ignoring increased genetic correlation in DZ co-twins due to assortative mating would deflate the observed difference, and accordingly inflate effects of common environment and deflate genetic effects. In model set 2, the twin data are not genetically informative. This is due to the MZ correlation being partly explained by an environmental effect, m, shared only by MZ co-twins. In addition, high DZ twin and sibling correlations compared to parent-offspring correlations can be explained by environmental effects shared only by twins, t, and by twins and siblings, s. Ignoring or misspecifying assortative mating as social assortment in this model would result in inflated heritability. Conversely, if we have misspecified social assortment as assortative mating, our heritability estimate from model 2 is somewhat deflated. In model 1, misspecifying social assortment as assortative mating would give too high expected genetic correlations in all types of relationship but MZ twins. This would probably produce deflated additive genetic effects. As the expected difference between MZ and DZ co-twins in genetic similarity would be artificially low (\0.5), another consequence would probably be slightly inflated non-additive genetic effects. Even if some of the observed spouse correlation should be due to social assortment rather than assortative mating, however, the bias associated with the moderate spouse correlation would be modest. Limitations and implications Our combined twin and family sample, comprising more than 60,000 adult individuals from multiple biological and social relationships, constitutes the largest and most elaborate study of genetic and environmental influences on SWB to date. However, some limitations should be recognised. The use of WLS rather than strict maximum likelihood (ML) estimation saves computer time and allows for estimation of CIs on the parameter estimates, but violates the assumptions of (i) independent observations of pairs of relatives (the same persons may be included in more than one pair), and (ii) independent correlations (the same person is usually included in more than one type of

12 relationship). Siblings in families with many children are thus unproportionally highly weighted in the correlation coefficients. This approach has been shown to result in nearly unbiased parameter estimates, but may lead to errors in confidence intervals and in goodness-of-fit, thus introducing a small risk of attaching undue precision to the individual parameters and to reject true models (McGue et al. 1984; Eaves et al. 1989). Unreliability or measurement error is a substantial problem in most studies of psychological traits. Measurement error is completely confounded with the non-shared environmental influences in the CTD and with the total environmental variance in family studies. Alpha reliabilities between 0.70 and 0.80 indicate that from to of the general effect of the environment, which account for 64 82% of the total variance in the two best-fitting models, reflects measurement error. Accordingly, non-shared environmental effects may be somewhat inflated on the expense of the other estimates (Riemann et al. 1997). Another limitation concerns our study not accounting for effects of gene environment interaction. In addition, the results were obtained from two Norwegian samples and may not extrapolate to other ethnic groups. Notwithstanding limitations, our results add strong support to the idea that family resemblance for happiness is in large part due to genes. Genes seem to operate both additively and non-additively and are thus only partly passed from parents to offspring. The genetic influences also appear to act differently in males and females. Judging from the models specifying genetic non-additivity, broadsense heritability for SWB is fairly similar across the sexes, whereas narrow-sense (additive) estimates clearly differ 2 2 (a male = 0.17, a female = 0.27). Failure to model for sexspecific architecture in some studies of SWB may therefore have hampered our understanding of its genetic and environmental aetiology. Weiss et al. (2002) have previously suggested that happiness may be a sexually selected fitness indicator, a cue that females can use in choosing males who will contribute high fitness genes to their offspring (p. 1147). There is considerable evidence that sexually selected traits show higher levels of additive genetic variance compared to other fitness related traits and that the additive genetic variance often is higher in the same, nonsexually selected trait in females (Pomiankowski and Møller 1995). The validity of the sexual selection hypothesis for SWB remains to be further explored. We note, however, that the additive genetic variance in this study is significantly higher in females than in males. The ways in which genes affect SWB are as yet unknown. Genetic influences may have a direct effect on SWB through emotional intensity or reactivity, but may also operate in causal paths far away from the DNA. Various indirect pathways through individual exposure or choice of environments, or through physiological and biochemical processes, may therefore provide important links between happiness and genes. Neurobiological research have for example shown that resilience and wellbeing are associated with high levels of left prefrontal activation, effective modulation of activation in the amygdala and fast recovery in response to negative life events (See Davidson 2004 for a review). These cortical areas are also associated with sociability in toddlers and have been shown to be involved in dopamine transmission, which appears to be important for both positive affect and extraversion (Depue 1995; Eid et al. 2003). The genetic effects may therefore represent activity in complex behavioural, biochemical, and physiological systems, which partly affect well-being indirectly. Although our study is not able to permit exact resolution of the entire set of genetic and environmental influences responsible for variation in SWB, it illustrates the higher sensitivity afforded by inclusion of multiple social and biological relationships. Our study suggest that studies which lack either the size or the structure to simultaneously analyse sex differences, genetic non-additivity and shared environments for variation in SWB, may be biased. To better capture the dynamic interplay between genes and environments for happiness, further advanced studies, using additional types of relatives, are needed. Acknowledgments Nord-Trøndelag Health Study (The HUNT Study) is a collaboration between HUNT Research Centre (Faculty of Medicine, Norwegian University of Science and Technology NTNU), Nord-Trøndelag County Council and The Norwegian Institute of Public Health. The twin program of research at the Norwegian Institute of Public Health is supported by grants from The Norwegian Research Council, The Norwegian Foundation for Health and Rehabilitation, and by the European Commission under the programme Quality of Life and Management of the Living Resources of 5th Framework Programme (no. QLG2-CT ). We are very thankful to the twins for their participation. References Aisworth MDS (1991) Attachments and other affectional bonds across the life cycle. In: Parkes CM, Stevenson-Hinde J, Marris P (eds) Attachment across the lifecycle. Routledge, New York, pp Akaike H (1987) Factor analysis and AIC. Psychometrika 52: Andrews FM, Withey SB (1976) Social indicators of well-being. Plenum, New York Argyle M (1999) Causes and correlates of happiness. In: Kahneman D, Diener E, Schwarz N (eds) Well-being: the foundations of hedonic psychology. Russell Sage Foundation, New York Baker LA, Cesa IL, Gatz M, Mellins C (1992) Genetic and environmental influences on positive and negative affect: support for a two-factor theory. Psychol Aging 7: Bartels M, Boomsma D (2009) Born to be happy? The etiology of subjective well-being. Behav Genet. Published online 03 September 2009