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Do Schools Moderate the Genetic Determinants of Smoking?

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Abstract

This paper uses data from the National Longitudinal Study of Adolescent Health to examine the extent to which school-level social and institutional factors moderate genetic tendencies to smoke cigarettes. Our analysis relies on a sub-sample of 1,198 sibling and twin pairs nested within 84 schools. We develop a multilevel modeling extension of regression-based quantitative genetic techniques to calculate school-specific heritability estimates. We show that smoking onset (h 2 = .51) and daily smoking (h 2 = .58) are both genetically influenced. Whereas the genetic influence on smoking onset is consistent across schools, we show that schools moderate the heritability of daily smoking. The heritability of daily smoking is the highest within schools in which the most popular students are also smokers and reduced within schools in which the majority of the students are non-Hispanic and white. These findings make important contributions to the literature on gene-environment interactions.

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Notes

  1. Our data consists of a subset of the original sibling and twin pair data (see Harris et al. 2006 for a detailed description of these data). Our analysis is limited to siblings and twins who attended the same school, those with information on smoking at wave II, sibling pairs whose ages are at most three years apart from one another, schools with network data on friend nominations, schools with at least five pairs of siblings, and schools that contained sampling weights. After these deletions our final sample consisted of 1,198 pairs nested within 84 schools.

  2. Because mechanisms of social control and social expression may be happening simultaneously, it is possible that there is no effect of social norms at or near the average of the distribution and it is only in the extreme ends of this distribution in which social forces manifest. This most closely resembles a cubic distribution with a positive linear, negative quadratic, and positive cubic term, but these models are difficult to estimate with the limited number of schools that we have in the data. Accordingly, we took the extreme schools to capture expression and control, respectively, in which their combined effects are interpreted in relation to the average normative environment.

  3. We use Mx, a structural equation modeling package that contains a number of standard procedures, to calculate quantitative genetic parameter estimates for genetic and environmental components of phenotypic variance. There are a number of Mx scripts developed by the GenomeUTwin group (Posthuma et al. 2003) that are freely available at: http://www.psy.vu.nl/mxbib/. We use the contingency table script ctVCut2c.mx to estimate the ACE parameters presented in Table 1.

  4. The GLLAMM procedure is comprised of a bundle of statistical procedures that are remarkably flexible and well-suited to the complex design of the Add Health study. As an example, all parameter estimates in this paper are calculated using the sampling weights for both individuals (GSWGT2) and schools (SCHWT1) provided by the Add Health study (Chantala and Tabor 2004). Because we estimate random effects at the school level, individual and school weights were calculated using a PWIGLS macro in STATA 9.0 that was designed for survey data obtained from more than one level (see Chantala et al. 2006 for a detailed discussion of this method).

  5. Jaffee and Price (2007) define gene-environment correlations as “genetic differences in exposure to a particular environment” (p. 2) and describe both causal and non-causal variants. We are primarily concerned with two forms of rGE; both of which are causal. The first is the passive form of rGE where children inherit both genes and the environment from their parents. Therefore, if a parent smokes because of genetic reasons, then these genes will be shared with their children but their children will also be raised in an environment in which cigarettes are easier to obtain and smoking behaviors are not negatively sanctioned. For example, Hill et al. (2005) show that, controlling for other important factors, the onset of smoking among adolescents is strongly shaped by both parental smoking and household norms about smoking but these environmental factors may have a genetic component that needs to be modeled explicitly. Active correlation is the second major form of rGE. This model describes a situation in which individuals with genetically oriented tendencies to smoke cigarettes will select in to social environments in which smoking is normative or rewarded. Some of the strongest evidence for active rGE related to adolescent smoking comes from a study by Cleveland et al. (2005) who use traditional behavioral genetic models to estimate the heritability of substance using or non-substance using friendship networks. That is, do genetics shape the composition of adolescents’ friends as related to smoking, drinking, and drug use? According to their estimates, nearly two-thirds of the variance (h 2 = .64) of the substance using behaviors of adolescents’ networks has a genetic component. This estimate is derived by comparing the pair-wise correlations across identical twin (r = .61), fraternal twin (r = .27), full-sibling (r = .28), and half-sibling (r = .18) pairs. As Jaffee and Price (2007) highlight, these two forms or rGE are particularly problematic for research on gene-environment interactions. According to these authors “rGE does not have to reach statistical significance to profoundly affect the interpretation of G x E estimates” (6). To deal with the possibility that our environmental measures may have a genetic component, we include statistical controls that reduce the likelihood of rGE confounding our gene-environment interaction results.

  6. Using data from Add Health, Slomkowski et al. (2005) show that the similarity of sibling pairs is strongly conditional upon the social connectedness of the pair. That is, pairs that report spending more time with one another, sharing similar friends, and having affection for one another are much more alike one another than those who do not report this same level of closeness. Importantly, in wave II of the study, identical twins with high levels social connectedness reported a sibling correlation for smoking of .83 compared to a value of .54 for DZ twins with the same level of connection. However, when these sibling correlations were compared for those who reported low social connection, the identical pair correlation was only .63 and the fraternal twin correlation remained roughly the same (r = .52). Although their analysis of the sibling pair data does not show systematic differences in the heritability estimate by social connection status, it is an important caveat to consider. Kendler and Gardner (1998) analyze three aspects of the EEA using retrospective data from an adult sample of twin pairs: a) childhood treatment (i.e., how similarly they two were treated by others), b) co-socialization (i.e., how much the twins tried to act like one another), and c) similitude (i.e., were they treated as individuals or were they always treated as a “pair”). These factors were then used to compare concordance rates for smoking across the groups. While treatment and similitude were not associated with smoking initiation, the sibling correlations were significantly moderated by their responses to the co-socialization measure. High co-socializing identical and fraternal twins had a tetrachoric correlation of .87 and .51, respectively, but their low-socializing counterparts had scores of .74 and .37. Again, these differences may have important implications for the heritability estimate.

  7. Because regression-based techniques are typically used in proband-based designs, the twins and siblings are double entered in the multivariate models to adjust for the lack of an a priori proband in this sample. Standard errors are adjusted by a constant factor (2.5) when calculating test statistics and corresponding statistical significance.

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Acknowledgements

This paper is part of a larger study funded by the National Institute of Child Health and Human Development (NIH/NICHD K01 HD 50336). Resources were also provided by the Population Center at the University of Colorado (NIH/NICHD R21 HD 51146). This research uses data from Add Health, a program project designed by J. Richard Udry, Peter S. Bearman, and Kathleen Mullan Harris, and funded by a grant P01-HD31921 from the National Institute of Child Health and Human Development, with cooperative funding from 17 other agencies. Special acknowledgment is due Ronald R. Rindfuss and Barbara Entwisle for assistance in the original design. Persons interested in obtaining data files from Add Health should contact Add Health, Carolina Population Center, 123 W. Franklin Street, Chapel Hill, NC 27516-2524.

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Boardman, J.D., Saint Onge, J.M., Haberstick, B.C. et al. Do Schools Moderate the Genetic Determinants of Smoking?. Behav Genet 38, 234–246 (2008). https://doi.org/10.1007/s10519-008-9197-0

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