In the Swedish data, we found a significant correlation between educational attainment and intelligence (r
= .56, p
< .001), In the MTFS data, we also found a significant correlation (r
= .32, p
< .001). includes raw twin correlations for the study variables. Genetic influences were indicated by greater MZ correlations than DZ correlations, shared environmental influences by DZ correlations that were greater than half the MZ correlations, and nonshared environmental influences by MZ correlations less than 1.00. We found that all correlations were greater in the Swedish data than in the Minnesotan data. Overall, shared environmental influences were more important in Sweden than in Minnesota. This difference was much more marked for educational attainment than for general intelligence. shows the model fit statistics we used to determine whether all moderating terms estimated were needed to describe the Swedish data parsimoniously. Using standard information-theoretic fit statistics (Akaike’s information criterion—Akaike, 1983
; Bayesian information criterion Raftery, 1995
), we determined that the best model for our study was one that allowed moderation of genetic and nonshared environmental influences unique to educational attainment, with small moderating effects of genetic and shared environmental influences common to intelligence and educational attainment.
Twin Correlations of Study Variables in the Swedish and Minnesotan Populations
Fit Statistics From the Models of Variance Components Allowing for Gene-Environment Interaction and Correlation
The parameter estimates from this model indicated that in Sweden, genetic variance in educational attainment increased with greater intelligence (, ). The variance components were raw across the intelligence range, so that at any given level of intelligence variance components did not necessarily add up to 1. Across the intelligence range, from 2 standard deviations below the mean to 2 standard deviations above the mean, shared environmental variance increased sharply from .13 to .42 across the 4-standard-deviation range of intelligence. Genetically influenced variance more than doubled, from .31 to .73. Nonshared environmental variance increased as well, but much more modestly, from .21 to .29. Shared environmental influences were completely correlated: All shared environmental influences on intelligence also influenced educational attainment. The moderate-to-large genetic correlation between intelligence and educational attainment varied little across the range (.53–.56)3
, indicating the presence of substantial genetic influence common to the two traits. Nonshared environmental influences (which included measurement error) were significantly but not substantially correlated (.16–.14; see ). By definition, the components of variance in intelligence did not vary. Genetic variance was .56, shared environmental variance was .25, and nonshared environmental variance was .16.
Estimates of Genetic and Environmental Variance Components in General Intelligence and Educational Attainment at Three Levels of General Intelligence in Sweden and Minnesota
Fig. 2 Variance in educational attainment in Sweden by 2004, as a function of IQ (in standard deviation units) measured at 18 years of age. Results are shown for three sources of variance: genetic (A), shared environmental (C), and nonshared environmental ( (more ...)
Correlations Between Influences on Intelligence and Education at Three Levels of General Intelligence in Sweden and Minnesota
, the right-hand columns of , and show analogous results for MTFS. Both similarities and differences were apparent. Some of the similarities were superficial. Within each region, we standardized the intelligence and educational-attainment variables. The variables for the two samples were thus placed on the same relative scale, and it is impossible to determine whether there was more absolute variability in one sample or the other. It would have been interesting to be able to do this, but because the scales on which the variables were measured differed, there was no good way to accomplish such a comparison. Within the standardization, however, it was possible to compare relative levels of environmental and genetic variance components in the two samples.
Fig. 3 Variance in educational attainment in Minnesota by 24 years of age as a function of IQ (in standard deviation units) measured at 17 years of age. Results are shown for three sources of variance: genetic (A), shared environmental (C), and nonshared environmental (more ...)
Overall, nonshared environmental variance in educational attainment was very similar in the two samples. There was a small increase in nonshared environmental variance across the range of intelligence in the Swedish group and not in the MTFS group, although this probably reflected the greater statistical power in our analysis of the Swedish data. The biggest difference between the two sets of results was in the shared environmental variance in educational attainment. In Sweden, the shared environmental variance was roughly half the genetic variance across the intelligence range, increasing across that range in a manner very similar to the increase in genetic variance. In sharp contrast, in the MTFS group, shared environmental variance was very large (.70; almost 6 times the genetic variance) when the intelligence level was low, and decreased dramatically to .07 (about 10% of genetic variance) when the intelligence level was high.
There were also similarities and differences in the environmental and genetic correlations between intelligence and educational attainment in the two samples. In both, the shared environmental correlation was 1.00 across the range of intelligence. In other words, all sources of shared environmental variance in intelligence also contributed to variance in educational attainment. This indicated a direct contribution from shared environmental influences on intelligence to educational attainment. In both cases, nonshared environmental variance was almost completely unique to educational attainment. Of course, nonshared environmental variance included measurement error, which is by definition unsystematic and therefore unlikely to be correlated with any other factors.
The patterns of genetic correlations in the two samples differed. In Sweden, genetic correlation was steadily in excess of .50 across the range of intelligence, indicating a genetically influenced direct effect of intelligence on educational attainment that was weaker than the shared environmental effect on educational attainment. In the MTFS population, however, genetic correlation was in excess of .50 when level of intelligence was low, but was halved at higher levels of intelligence. This indicated that genetic influences on intelligence tended to limit educational attainment when the level of intelligence was low, but not when the level of intelligence was average or high.