Ken B. Hanscombe, Maciej Trzaskowski, Claire M. A. Haworth, Oliver S. P. Davis, Philip S. Dale, Robert Plomin. (original article)
[...] We found greater variance in intelligence in low-SES families, but minimal evidence of GxE interaction across the eight ages. A power calculation indicated that a sample size of about 5000 twin pairs is required to detect moderation of the genetic component of intelligence as small as 0.25, with about 80% power – a difference of 11% to 53% in heritability, in low- (-2 standard deviations, SD) and high-SES (+2 SD) families. With samples at each age of about this size, the present study found no moderation of the genetic effect on intelligence. However, we found the greater variance in low-SES families is due to moderation of the environmental effect – an environment-environment interaction.
Results
The means, standard deviations, and analysis of variance by sex and zygosity for IQ at every age are presented in Table 3. There was no indication of any differences by zygosity or sex. In general, we find no significant effect of sex for intelligence [38]. For all subsequent analyses, we considered the IQ scores for males and females together.
Because similarity due to age and sex can contribute to phenotypic similarity and inflate estimates of C, as is standard practice in twin analyses [51], all verbal and nonverbal scales were corrected for the effects of age and sex before conducting twin analyses. Correlations between IQ measured at each age are presented in Table 2.
Below, results are presented for continuous moderation analyses of IQ moderated by three indices of SES: SES index 1, Parental education and occupation acquired at first contact (age 18 months); SES index 2, Parental education and occupation at age 7; and SES index 3, Parental income at age 9. At the end of this section, we present results for a discontinuous analysis, i.e., IQ as a function of stratified SES.
SES index 1: Parental education and occupation at contact (age 18 months)
Phenotypic correlations between SES (a unit-weighted composite of parental education and occupation acquired at contact) and IQ are presented in Table 4. From infancy to adolescence we found an increasing correlation between SES and IQ, from .08 to .37, as expected from the literature. A graphical summary of the continuous moderation analyses is presented in Figure 2. This visual summary of the SES moderation of IQ across the eight ages suggests three conclusions. First, the total variation in IQ changed with SES level: at ages 2, 4, 9, and 10 we found greater variance in low-SES families; at ages 3, 7 and 12 only small differences; and at age 14, greater variance at both ends of the SES distribution than around the mean.
Second, except for a large drop in the A contribution with increasing SES at age 10, we found no substantial change in A across the eight ages: little or no change at ages 2, 3, 9, and 14, and small increases with increasing SES at ages 7 and 12. This suggests no consistent GxE interaction. Moreover, it should be noted that the only substantial GxE interaction at age 10 is in the opposite direction from that suggested in the literature: heritability is greater in low-SES families.
Third, differences in C were somewhat more consistent: at ages 2, 4, 7, 9, and 12, there was a drop in C with increasing SES. This suggests the presence of greater C in low-SES families.
Intra-class correlations (coefficients of twin similarity; [52]) are presented in Table 5. Doubling the differences between the MZ and DZ correlations provides a rough estimate of the heritability of IQ. These estimates show the expected pattern of increasing heritability with age, from 30% at age 2 to 46% at age 14. The extent to which MZ correlations are not explained by heritability provides an estimate of shared environment. These estimates show the expected pattern of decreasing shared environmental influence with age, from 61% at age 2 to 14% at age 14.
Table 6 shows the parameter estimates at each age derived from the full GxE interaction model with full information maximum likelihood estimation. Squaring the path estimate and dividing by the sum of the squared paths gives the standardized variance component: e.g., heritability or h² = (a+βAM)²/((a+βAM)²+(c+βCM)²+(e+βEM)²). (A formal test of the significance of each moderated term in the interaction model, at each age, is shown in Table S1.)
At ages 3, 7, and 12 the best-fitting model, as indicated by AIC, was one with no moderation of either genetic or environmental components. At age 2, the best fitting model, as indicated by AIC, was one with no genetic moderation. The p-value showing model fit for individually dropped parameters suggests only moderation of the C term is significant (βC = -.04). At age 4, the best-fitting model was one with moderation of both A and C terms (βA = -.03, βC = -.03). At age 9, moderation of only the C term was significant (βC = -.06). Age 10 showed a significant decrease in A with increasing SES (βA = -.10). At age 14, the best fitting model, as indicated by AIC, suggested significant moderation of the C term (βC = -.19). All significant genetic and environmental moderation was in the direction of greater variance in IQ explained at lower levels of SES.
SES index 2: Parental education and occupation at age 7
Phenotypic correlations between SES (a unit-weighted composite of parental education and occupation assessed at age 7) and IQ show a similar pattern of increasing correlation with age, and are in the range 0.22–0.33 (Table 4). Intra-class correlations for twins with data on 7-year parental education and occupation are presented in Table 5. Rough estimates of variance components calculated by doubling the differences between the MZ and DZ correlations are similar to estimates for twins with SES index 1 data.
A graphical summary of the continuous moderation analyses is presented in Figure 3. Inspection of the visual summary of the interaction analyses reveals a consistent increase in the effect of the shared environment on IQ with decreasing SES, coupled with an increase in the variance in IQ in low-SES families – most notably at ages 9, 10 and 12.
Table 6 shows the parameter estimates at each age derived from the full GxE interaction model with full information maximum likelihood estimation. (A formal test of the significance of each moderated term in the interaction model, at each age, is shown in Table S2.)
At ages 7 and 14, the best fitting model as indicated by AIC was one with no moderation of genetic or environmental components of intelligence. At all other ages (9, 10, and 12) the best fitting model included only moderation of the C component (βC = -.06, βC = -.05, and βC = -.09 respectively).
SES index 3: Parental income at age 9
Phenotypic correlations between SES (family income at age 9) and IQ are presented in Table 4. As for SES indices 1 and 2, we find a pattern of increasing correlation between IQ and SES index 3 with age, with correlations in the range 0.17–0.26. Intra-class correlations by zygosity for twins with 9-year family income data are presented in Table 5. Again, rough estimates of variance components found by doubling the differences between the MZ and DZ correlations are similar to estimates for twins with SES index 1 and 2 data.
A graphical summary of the continuous moderation analyses at ages 9, 10, 12 and 14 is presented in Figure 4. As for the other indices of SES, the visual summary of the interaction analyses reveals an increase in the variance in IQ in low-SES families, an increase in the effect of the shared environment on IQ with decreasing SES, and inconsistent differences in genetic effect.
Table 6 shows the parameter estimates at each age derived from the full GxE interaction model with full information maximum likelihood estimation. (A formal test of the significance of each moderated term in the interaction model, at each age, is shown in Table S3.)
At all ages (9, 10, 12, and 14), the best fitting model as indicated by AIC includes (in addition to the main effect of SES) only moderation of the shared environmental component (βC = -.05, βC = -.04, βC = -.16, and βC = -.16 respectively).
What is the most parsimonious account of the moderating effect of SES?
Summarized in Table 7 are the best-fitting models at each age, for each of the three indices of SES. An asterisk indicates the best-fitting model (as indicated by AIC). It should be noted that at each age, in testing the significance of each parameter in the model, AIC suggests very little difference between each of the accounts of the data (see last column in Tables S1, S2, and S3). Accepting this small difference, three results are worth highlighting. First, the only significant GxE interaction with SES index 1 found for g at age 10 (higher heritability in low-SES families) disappears with the more proximal measures of SES at ages 7 and 9. Second, the best fitting model indicates no interaction of any kind at three ages for SES index 1 (ages 3, 7, and 12), and for two ages for SES index 2 (ages 7 and 14). Third, moderation of the shared environmental component of g is indicated at four of eight ages for SES index 1, three of five ages for SES index 2, and four of four ages for SES index 3. Thus, the most consistent result across ages and across the three indices of SES is moderation of the influence of shared environment on children’s intelligence – an environment-environment interaction.
Performance of the continuous moderator model with simulated data
In order to estimate power of the continuous model to detect genetic moderation under conditions of genetic moderation only, we set parameters as follows; a = c = e = 1; βC = βE = 0. We simulated a range of genetic moderation (βA) between 0.05 and 0.50. We generated 1000 replicates for a range of sample sizes, with equal numbers of MZ and DZ twin pairs. Figure 5 shows that a sample size of about 2500 pairs of MZ and DZ twins each is needed to detect an effect size (genetic moderation) of between 0.25 and 0.30 with 80% power. A genetic moderation of 0.25 translates to a difference in heritability of about 11% at -2SD of the moderator to about 53% at +2SD of the moderator (at the simulated parameter values).
Second, to explore how the model performed when moderation of all three terms is present, we simulated data with parameters set as follows: a = c = e = 1; and, βC = βE = βA = a range of values between 0.05 and 0.50. Again, we generated 1000 replicates for each sample and effect size, and estimated the model’s ability to detect the presence of the genetic moderation only, i.e. a 1df test. Figure 6 is more informative about model performance than power per se. With equal moderation of the genetic, shared, and nonshared environmental components, above a moderation of 0.30 (moderated coefficient 30% of the unmoderated coefficient, i.e., βA = 0.30*a) the model does not perform well when assessing the significance of just the genetic moderation (a 1df test). Purcell’s [18] simulations suggest that this would also be the case when testing only moderation of the shared environment.
The simulations summarized in Figures 3a and 3b perhaps illustrate the best and worst case scenario for the continuous moderator model. In the case of genetic moderation only, the model performs well, and increasing sample size increases power to detect genetic moderation. However, as noted by Purcell [18], the model does not do well at distinguishing between genetic and shared environmental moderation when both are present, and one proceeds by testing one term at a time.
Discontinuous analysis of low-SES versus high-SES groups
Because several studies explored GxE interaction by comparing ACE estimates, or twin correlations, in low-and high-SES groups (see Table 1), we compared results of our continuous moderator analysis with the results for a discontinuous analysis. We estimated variance components in low- and high-SES groups, and tested whether these could be equated – a heterogeneity analysis. Although these discontinuous analyses have usually ignored variance differences between groups by using twin correlations (which standardize variances between groups), heterogeneity analysis provided components of raw variance which we present along with the standardized estimates to highlight the difference between components of raw and standardized variance.
We present results for age 9 IQ, which showed the most consistent C interaction across the three SES indices. We split the sample into quartiles and compared the variance components derived for the top and bottom 25% of the SES distribution. In Figure 7, rows 1, 2, and 3 show age 9 IQ components as a function of SES indices 1, 2, and 3 respectively; in the left column are the components of raw variance, in the right hand column are the standardized estimates. The unstandardized estimates show greater total variance for the low-SES groups and this excess variance can be attributed to greater shared environment for the low-SES group. Shared environment is significantly greater in the low-SES group for SES indices 1 (low-SES C = .40 {95% confidence interval (CI) = .27–.53}; high-SES C = .19 {95% CI = .08–.31}) and 2 (low-SES C = .48 {.34–.62}; high-SES C = .25 {.13–.37}). Equating C in low- and high-SES groups significantly reduced model fit (SES index 1: Δ-2lnL = 5.45, Δdf = 1, ΔAIC = 3.45, p = .02; SES index 2: Δ-2lnL = 3.572, Δdf = 1, ΔAIC = 5.57, p = .02). In contrast, heritability estimates are identical for the low- and high-SES groups. The standardized estimates also show greater C in the low-SES group for SES indices 1 and 2; however, standardizing the variance components in the two groups artificially increases estimates of A in the high-SES group.
In summary, this discontinuous analysis of low-SES versus high-SES groups generally confirms the results of our continuous moderator analysis for the largest interaction effect, despite a great loss in power for the discontinuous analysis [18].
Discussion
We attempted to replicate the finding that parental SES moderates the heritability of children’s intelligence, with a greater genetic contribution to IQ in high-SES families compared to low-SES families. In a large UK-representative sample, we did not find evidence for the presence of such a gene-environment interaction across childhood and adolescence. At only one of the eight ages, age 10, did we find a significant moderation of the genetic contribution to IQ. However, the GxE interaction was in the opposite direction from that predicted by the environmental disadvantage hypothesis, and moreover, was not significant with a more proximal measure of parental education and occupation. Instead, using three different indices of SES, at eight ages from infancy through adolescence the emerging pattern appears to be one of environment-environment interaction rather than gene-environment interaction: shared experiences explain more of the variance in children’s performance on IQ tests in more disadvantaged backgrounds.
Environmental moderation of shared experiences
How can the present finding of SES moderation of the shared environmental effect on IQ, be reconciled to the reports of SES moderation of the genetic component of IQ? An increase in the contribution of C in lower-SES families would seem to require a reduction in the relative contribution of A because environmental and genetic variance components are complementary, and explain 100% of the variance. However, this is only the case for standardized components that are forced to sum to 100% regardless of total variance differences. Our most consistent finding is that total IQ variance is greater in lower-SES families, which must be caused by greater A, C, or E components of variance in lower-SES families. Although the power demands are daunting to disentangle A and C sources of this increased variance in lower-SES families, data from our large sample suggests that the source is C rather than A. The genetic effect does not differ for low- and high-SES groups using unstandardized estimates (A, C, and E) that take into account the greater total variance in the low-SES group, but the relative contribution of genes – heritability or h² = A/(A+C+E)) – is lower in low-SES families because the shared environmental effect increases.
Children from low-SES families face many physical and psychosocial environmental handicaps for their cognitive development [53]. For example, low-SES children are read to less, have fewer books, less access to computers, and tend to watch more television. Parents tend to be less responsive to children in low-SES families, participate less in their children’s school activities, and are more authoritarian. Children from more disadvantaged backgrounds tend to experience more instability, come from noisier, more crowded homes, and live in disadvantaged neighbourhoods with poorer facilities and inferior schools (for a recent review of the correlates of low-SES see [53]). To the extent that children growing up together experience these environments similarly, their cumulative effects are captured by the C component in a twin model; experiences such as these seem likely to contribute to the observed greater variation in the cognitive ability performance of children from low-SES families.
Sampling, age differences, and power to detect C
[...] We believe that sample age is a particularly important factor in the inconsistent findings. Because heritability increases and shared environmental influence decreases from childhood to adulthood [21,22], developmental differences in moderation could be expected. Two of the four studies in Table 1 that do not find greater heritability of IQ in higher SES are in older samples, ranging in age from 16 to 49 years [15,16]. The third non-replication was based on a small sample and unreliable estimates [14]. The last of the four non-replications involved an earlier analysis in the TEDS sample. This earlier analysis found no significant moderation of the heritability of age 4 IQ by SES, but did find moderation of the genetic effect by family chaos and parent-child communication [17]. Using the continuous moderator model, the present study suggests that SES does in fact moderate the relative contributions of A and C to variance in age 4 IQ – we suggest this is driven by a moderation of C.
Detecting modest shared environmental effects in the presence of larger genetic and nonshared environmental effects requires large twin samples [58]. This difficulty is compounded by the fact that the shared environmental contribution to general cognitive ability diminishes with age. We suggest moderation of the shared environmental effect on IQ could go undetected in smaller samples and that it could be misinterpreted as genetic moderation given the low power of the continuous moderator model to distinguish between moderation of the genetic and shared environmental variance components. Even with a relatively large sample, as in the present study, comparing the fit of nested models yields little difference in their ability to explain the data, as indicated by the small AIC differences at every age and for every SES index (Tables S1, S2, and S3).
Conclusion
[...] Although the genetic influence on IQ is the same in lower-SES families, shared environmental influence appears to be greater in lower-SES families, suggesting that family-based environmental interventions might be more effective in these families. However, two further aspects of the results temper the policy implications of this finding. First, shared environmental influence is found in both lower- and higher-SES families and the difference in shared environmental influence between them is modest. Second, shared environmental influences on IQ decline from childhood to adulthood so that these influences might not have an impact in the long run.
J’ai du mal à me figurer la signification générale des résultats de ces études (même les conclusions ne me parlent pas vraiment ). Pour une large public non spécialise mais curieux, une petite aide pédagogique sous forme par exemple de conclusions personnelles de l’auteur du blog serait bienvenue.
Cordiales salutations.
J’avais prévu d’en parler dans un ultime article (en cours) qui résume tous les arguments pour et contre sur l’hérédité du QI, les différences raciales et les possibles moyens de l’augmenter. Globalement, je trouve les arguments “anti-racialistes” très faibles.
Grosso modo, les auteurs de la présente étude tentent de répliquer l’étude de Turkheimer d’après laquelle la contribution génétique au QI est faible chez les pauvres, forte chez les riches. (Notez que dans sa conclusion, Turkheimer lui-même nie en bloc que l’environnement peut être séparé de la variable génétique puisque c’est surtout l’intelligence qui déterminera si vous êtes riche ou pauvre)
http://analyseeconomique.files.wordpress.com/2012/09/turkheimer-2003-socioeconomic-status-modifies-heritability-of-iq-in-young-children.pdf
C’est la théorie que l’on nomme communément GxE interaction (gene-environment). Les gènes influencent l’environnement et vice versa. C’est aussi comme cela que Flynn et Dickens ont tenté de réconcilier l’effet Flynn avec la forte héritabilité du QI. Savoir si l’effet Flynn est un pur artéfact ou non est une autre question.
Hanscombe, Plomin, et autres ont donc trouvé un résultat contraire, dans un échantillon plus grand. La part génétique (A) n’est pas modifiée en fonction du statut social, par contre la part environnementale non partagé (E) et partagée (C), oui. La part de C est plus grande, ce qui fait que E est plus faible, chez les familles pauvres. Cela signifie que l’impact de l’éducation parentale est plus grande chez les pauvres, sauf qu’en raison du fait que la part génétique augmente avec l’âge (voir le paragraphe juste sous la Table 5), les effets de l’éducation parentale sont susceptibles de s’estomper, au moins en partie. En outre, il y a peu de différence entre la contribution de C chez les familles pauvres et chez les familles riches.
Merci de votre réponse,
qui éclaire les conclusions des auteurs et m’a permis de les relire plus correctement.
A bientôt pour de prochains articles, j’attendrai avec impatience celui que vous annoncez.
Le débat sur ces sujets très “sensibles” n’est pas clos et reste au centre de vives controverses (et de pressions diverses). Il est difficile de tirer une synthèse de tous les travaux menés essentiellement dans les pays anglo-saxons (ça semble être le désert chez nous, et bien pire que celà, en fait un trou noir qui aspire tous les contenus).
Je parle des travaux suffisamemt récents pour prendre en compte l’ensemble des dernis acquis dans les domaines connexes : de la neurologie (et l’exploration fonctionnelle) , l’anthropologie anatomique, la biologie, la génétique …
Peut-être se dégage t’il finalement un consensus réellement scientifique (à la “Watson” ?).
Cordiales salutations renouvelées.
A bientôt aussi éventuellement sur d’autres blogs. …
Une synthèse, à ma connaissance, seuls Rushton et Jensen l’ont fait, du moins, parmi les chercheurs en sciences sociales.
http://psychology.uwo.ca/faculty/rushtonpdfs/2010%20Review%20of%20Nisbett.pdf
Mais ce n’est pas tout à fait complet. Il manque une critique de Fryer, de Duckworth, de Jaeggi et de Eppig par exemple. Chuck a rédigé une énorme synthèse. Il s’est également attaqué à Fryer, Turkheimer, Flynn et Dickens, à la théorie du stereotype threat. Il a couvert encore bien d’autres sujets. Son blog est d’ailleurs bien meilleur que le mien, et de tous les autres que j’ai connu.
http://occidentalascent.wordpress.com/2012/06/10/the-facts-that-need-to-be-explained/
Quant à moi, je n’ai pas prévu d’écrire quelque chose sur Fryer, mais j’ai des mots à dire sur les 3 autres. J’enverrai un commentaire chez l’Ubiquiste quand j’aurai publié tout ça (ce sera en français, mais ce sera très long, un peu comme le post de Chuck, que je recommande si cela vous intéresse).