ازدواج و طلاق: دیدگاه ژنتیکی
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|37128||2010||6 صفحه PDF||سفارش دهید||محاسبه نشده|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Personality and Individual Differences, Volume 49, Issue 5, October 2010, Pages 473–478
Abstract Marriage is considered one of the most important sources of social support that an individual receives as an adult. Although hypotheses have been formulated as to why individuals may dissolve a marriage, the determinants of marital success or failure are still relatively unknown. Behavioral geneticists have found that both marriage and divorce are, in part, genetically influenced. The goal of this research was to determine the degree of shared genetic and environmental variance between the two marital statuses. Participants were 6225 twin pairs from the Vietnam Era Twin Registry. Data were obtained on marital history, and if the individual was no longer married, how the marriage ended. Univariate analyses were performed to determine the extent of genetic and environmental influences each of the marital statues (i.e., marriage and divorce), followed by a novel bivariate analysis to test the shared variance between marriage and divorce. Results from this analysis revealed that the two different marital statuses were influenced by entirely distinct genetic and environmental factors.
1. Introduction Marriage is considered one of the most important forms of social support for adults, and population-based studies have found that most adults will marry at some point in their lifetime (Bjorksten & Stewart, 1984). Despite the benefits of marriage, the divorce rate has been rising since the middle of the twentieth century (Coontz, 2007). One potential origin for this trend is the genetic influence on getting married and ultimately divorced. Johnson, McGue, Kreuger, and Bouchard (2004) found considerable genetic influences on the propensity to marry over the course of the lifespan. Longitudinally, the genetic influences on getting married have been found to increase at midlife and then decrease in older adulthood (Trumbetta, Markowitz, & Gottesman, 2007). Divorce, like marriage, has also been found to be highly heritable. McGue and Lykken (1992) found the proportion of genetic variance in the risk of getting a divorce was slightly greater than 50%. In addition, D’Onofrio et al. (2007) reported an increased risk of marital instability in offspring of divorced parents (i.e., intergenerational transmission). Jockin, McGue, and Lykken (1996) found that up to 40% of the variance in the heritability of divorce is from genetic factors that affect the personality of one spouse. Traditionalism and social potency were the most important correlates of divorce risk, as were high scores in both neuroticism and extraversion. Spotts et al. (2004) found that the way that spouses interact with one another stems from genetically influenced characteristics; however, they posit that the same influences do not always operate similarly in different social settings. In addition, various pathologies may have negative effects on marital quality or vice versa. For example, Dehle and Weiss (1998) found that low marital quality predicted an increase in depressed mood and at the same time initially higher scores of depression predicted greater decline in marital quality. Divorced individuals have also shown increases in various pathologies such as affective disorders, gambling and substance abuse (Jerskey et al., 2001) all of which have shown to have genetic influences (e.g., see Plomin, DeFries, McClearn, & Rutter, 1997). Trumbetta and Gottesman (2000) proposed two distinct ‘endophenotypes’ for marital status; one oriented toward pair-bonding and the other toward mate diversification. Pair-bonding would reflect a greater likelihood of maintaining a marriage over a lifespan, while mate diversification is associated with the greater likelihood of multiple marriages. They found significant genetic influences on both endophenotypes with unique environmental influences accounting for the remainder of the variance. However, it is unclear the extent to which both phenotypes might share genetic variance. While influences on marriage and divorce can be separately evaluated using traditional twin and non-twin approaches, the analysis of shared risk or protective factors across marriage and divorce poses a unique methodological dilemma, as divorce is contingent upon having ever married. Indeed, in studies of unrelated individuals, determining the influence of shared factors on marriage and divorce is an intractable problem. In contrast, twin studies have advantages over non-twin designs in that data on a co-twin’s marital status can also be used simultaneously with data on the twin’s own marital status. Nevertheless, standard multivariate twin models are not designed to assess covariation among two traits in which the status of one trait is conditional upon the other. A similar situation has been observed in substance use literature when modeling the relationship of substance initiation to various outcomes ( Heath and Martin, 1993, Koopmans et al., 1999 and Madden et al., 1999). In such cases, a specific type of bivariate analysis is needed to determine the degree of covariance between the two traits. This “stage” approach, which is captured by the Causal-Contingent-Common (CCC) pathway model, may be used to accurately describe the degree to which the variables are independent constructs from one another (Kendler et al., 1999). While these CCC models have been applied to prior studies of substance use (e.g., Maes et al., 2004), to our knowledge, this would be the first study to use the CCC approach to determine the extent of shared genetic and environmental influences on the social constructs of marriage and divorce. Thus, the purpose of the present study was to explore the genetic and environmental influences on marriage and divorce, with a particular emphasis on the nature of the relationship between the two constructs. We hypothesized the existence of genetic and environmental factors specific to both marital statuses as well as a significant phenotypic association
نتیجه گیری انگلیسی
3. Results Prior to beginning our univariate and bivariate analyses, we ran fully saturated1 models which estimated thresholds for both marriage and divorce separately for twin 1 and twin 2, as well as for MZ and DZ twins. We then systematically tested whether the thresholds could be equated across each member of the pair and across zygosity. These threshold-equal models fit the data well, indicating that rates of marriage and divorce do not systematically differ across twins within a pair, or among MZ versus DZ twins. 3.1. Univariate analyses of ever-married The full univariate model reflected a substantial contribution of genes and unique environmental influences. Table 1 displays the parameters estimates for the univariate analyses and Table 2 displays the results for the biometrical model fitting. The best-fitting model for ever married was the AE model suggesting that twins’ resemblances to each other were due to genetic and non-shared environmental factors. Table 1. Parameter estimates for univariate analyses on ever-married. Full and nested models Heritability h2 (95% CI) Family environment c2 (95% CI) Non-family environnent e2 (95% CI) ACE .41 .15 .44 (.16–.62) (.00–.37) (.37–.51) AE .58 – .42 (.51–.64) (.36–.49) CE – .48 .52 (.42–.54) (.46–.58) E – – 1.00 – – (1.00–1.00) Note: Best-fitting model in bold. Table options Table 2. Univariate model fitting results for ever-married. Ever married Absolute model fit Relative model fit −2 LL df LRCa Δdfa p-Value a AIC LRCb LRC dfb p-Value b 0 Saturated model 6500.936 9872 n/a n/a n/a n/a – – – 1 Full model (ACE) 6503.377 9875 2.441 3 .486 −3.559 n/a n/a n/a 2 AE model 6505.180 9876 4.244 4 .374 −3.756 1.803 1 .179 3 CE model 6513.740 9876 12.804 4 .012 +4.804 10.363 1 .001 4 E model 6704.166 9877 203.230 5 <.001 +193.23 200.789 2 <.001 Note: LRC = likelihood ratio chi-square; df = degrees of freedom; AIC = Akaike’s Information Criterion. Best-fitting model in bold. a Significance of model is based on comparison with the saturated model. b LRC obtained from comparison with full model (Model 1). Table options 3.2. Univariate analyses for ever-divorced Similar to the univariate results for ever marrying, the full univariate model reflected a substantial contribution of genes and unique environmental influences with less common environmental influences. Again, the AE model fit the data the best once the common environmental influences were removed. Table 3 and Table 4 reflect the parameter estimates and modeling fitting for ever divorced. Table 3. Parameter estimates for univariate analyses on ever-divorced. Full and nested models Heritability h2 (95% CI) Family environment c2 (95% CI) Non-family environnent e2 (95% CI) ACE .32 .00 .68 (.13–.38) (.00–.16) (.62–.74) AE .32 – .68 (.26–.38) – (.62–.74) CE – .25 .75 – (.20–.30) (.70–.80) E – – 1.00 – – (1.00–1.00) Note: Best-fitting model in bold. Table options Table 4. Univariate model fitting results for ever-divorced. Ever divorced Absolute model fit Relative model fit −2 LL df LRCa Δdfa p-Value a AIC LRCb LRC dfb p-Value b 0 Saturated model 11249.455 8819 n/a n/a n/a n/a – – – 1 Full model (ACE) 11251.357 8822 1.902 3 .593 −4.098 n/a n/a n/a 2 AE model 11251.357 8823 1.902 4 .754 −6.098 0.000 1 1.00 3 CE model 11261.451 8823 11.996 4 .002 +3.996 10.094 1 .001 4 E model 11347.776 8824 98.321 5 <.001 +88.321 96.419 2 <.001 Note: LRC = likelihood ratio chi-square; df = degrees of freedom; AIC = Akaike’s Information Criterion. Best-fitting model in bold. a Significance of model is based on comparison with the saturated model. b LRC obtained from comparison with full model (Model 1). Table options However, since ever divorcing is dependent on getting married, the univariate model may be misleading given that there may be a strong shared genetic and environmental influence between getting married and subsequently getting divorced. Thus, the last analysis was the bivariate CCC model to determine the extent to which there are overlapping determinants. 3.3. Bivariate analysis of ever-married and ever-divorced We ran three alternative versions of the CCC model. The first model assumes that the determinants for divorce are solely related to the genetic and environmental influences on getting married (i.e., having ever married and ever divorced on the same continuum). In this model, a good fit to the data would suggest that marriage and divorce share a substantial proportion of the same genetic and environmental influences. However, this model was not a good fit to the data given the significant p-value and AIC (X2 = 107.72; p = 0.00; AIC = 97.77). The second model allows the b-pathway to be free to determine the extent of shared variance between ever married and ever divorced. Table 5 lists the model fitting results for bivariate analyses on ever-married and ever-divorced. Due to the underidentification of the model noted above, there is no saturated model comparison to the CCC model. Table 5. Model fitting results for bivariate analysis. EP −2 log-likelihood ΔX2 Δdf p-Value AIC 1. Full CCC model with a free b parameter 9 17754.214 1.150 2 0.563 −2.850 2. Full model withbparameter set to zero 8 17755.033 1.969 3 0.579 −4.031 Note: Best-fitting model in bold. EP = estimated parameters; df = degrees of freedom; AIC = Akaike’s Information Criterion; CCC = causal, contingent, common-pathway. Table options The results from the full CCC model suggest that almost all the variance in divorce is independent of that for ever getting married. This was reflected in the b-parameter estimate which was close to zero (b = 0.0854). In other words, 0.7% of the variance in ever getting divorced (0.0852 = 0.0072) is in common with ever marrying ( Fig. 2). Full CCC model. Note: With the exception of b, the values presented are the ... Fig. 2. Full CCC model. Note: With the exception of b, the values presented are the standardized variance components for each phenotype along with the 95% confidence intervals. Figure options The last model set the regression pathway to zero (i.e., b = 0), under the assumption that the genetic and environmental influences on marriage and divorce are completely independent. This model fit the data better than the previous two models ( Fig. 3, p = 0.579, AIC = −4.031). Since the variance attributed to the regression of divorce on marriage in the full CCC model was so small, the parameters estimates were almost identical for the full CCC model and the CCC model in which b was set to zero. Best-fitting CCC model minus the regression line. Fig. 3. Best-fitting CCC model minus the regression line.