Update note: The article below has been updated slightly from its original form to emphasize that the level of comparison in the research study is the “school level.”
Although the topic is not politically correct, genes account for 50% or more of the variability in human intelligence, over large populations. The quest for specific “intelligence genes” has been a frustrating one for Robert Plomin and other researchers. Genetic influence on intelligence is mediated through dozens of genes — in a similar way as the genetic influence on height is mediated through scores of genes. But occasionally researchers will select specific genes in particular to study in relation to IQ, and sometimes they get lucky.
Meng Hu’s blog recently presented a study of human genes and IQ that used data from US schools with varying racial composition. The study looked at high and low-risk alleles of three specific dopamine genes — DRD4, DRD2/ANKK1, and DAT1 — and compared on the basis of race, verbal IQ, and genetic allele.
The analysis begins by first estimating the association between each of the dopaminergic polymorphisms and individual-level IQ scores. Table 1 presents the means, standard deviations, sample sizes, and correlations for each of the genotypes. The results indicate that DAT1 and DRD2 maintain statistically significant and negative associations with IQ scores, while the effect of DRD4 on IQ is non-significant. To further explore the association between dopaminergic polymorphisms and IQ, we employed the additive dopamine scale as a predictor of IQ scores. The results of this analysis indicated a statistically significant and negative association between IQ and dopamine scores, where higher scores on the dopamine index correspond to lower IQ scores (r=−.15, p<.05, two-tailed test).
We continue our analysis of the individual-level data by examining whether IQ scores and dopamine scores vary significantly across the 36 schools. Our aggregate-level analyses hinge on significant variation across schools in both IQ and dopamine scores, otherwise it would be akin to trying to explain a constant with a constant, a variable with a constant, or a constant with a variable. The results of the F-tests revealed that IQ scores varied significantly across schools (F=11.227, p<.05) as do dopamine scores (F=2.239, p<.05). Fig. 1 reveals additional support that IQ scores and dopamine scores vary significantly across schools. The distributions in this figure reveal the scores for IQ and dopamine, respectively, across schools and clearly indicate a significant amount of dispersion for both variables.
The next set of analyses examines the association between school-level dopamine scores and school-level IQ. Model 1 in Table 2 shows the results of the bivariate analyses revealing a strong and statistically significant negative association between dopamine scores and IQ scores (as measured with a standardized regression coefficient [i.e., Beta]). Given that allelic distributions for certain genes and IQ scores both vary across race/ethnicity, it is possible that the results would be rendered spurious by the confounding effects of race. As a result, in Model 2 we introduce the percentage of African American variable. As can be seen, even after including race in the analysis, the partial correlation between school-level dopamine scores and school-level IQ scores remained large and statistically significant.
Last, to examine convergence in the results generated at the individual level with those generated at the school level, we plotted predicted IQ scores across scores on the dopamine scale index. The dopamine scale indexes were z-transformed so that the individual-level analysis could be compared with the school-level analysis. Fig. 2 portrays these plots and shows a high degree of convergence in the slopes and by implication the predicted values, where IQ scores decrease as the total number of risk alleles increases.
Research has consistently revealed that IQ and other measures of cognitive abilities vary significantly across macro-level units of analysis, such as states, nations, and even schools. Although various explanations have been set forth to explain variation in IQ at the macro-level, the most controversial explanation is that genetic variation across macro-level units explains variation in IQ. To this point, however, empirical research had not directly examined this potential link. The current study partially addressed this gap in the literature by examining whether variation in IQ at the school level was associated with dopaminergic scores aggregated to the school-level. Analysis of data drawn from the Add Health revealed support in favor of this position, where schools that had higher dopamine scores were the same schools that had, on average, lower IQ scores.
Our results also examined the association between dopaminergic polymorphisms and IQ at the individual level. Consistent with prior research (e.g., Beaver, DeLisi et al., 2010; Berman & Noble, 1995), the associations between dopaminergic genes and individual-level IQ scores were either small and statistically significant or non-significant. Recall, however, that the association between school-level dopamine scores and school-level IQ scores was relatively large in magnitude, which necessarily begs the question of why the effects differed so markedly. While not exhaustive we offer two potential explanations. First, given the small sample size that was employed in the school-level analysis, our statistical power to detect small-to-moderate effect sizes was severely compromised and detecting large effect sizes could be due, in part, to methodological and statistical artifacts. We addressed this possibility by comparing the predicted values of IQ scores at the individual- and school-levels of analysis. The results of these models converged suggesting that the significant effects at the school-level are not solely due to a methodological or statistical artifact.
Second, it is well known that findings detected at one level of analysis cannot be extrapolated to other levels of aggregation (Piantadosi, Byar, & Green, 1988; Samuelson, 1955). This phenomenon is particularly salient in the social sciences where research often spans multiple units of analysis, but the effects can differ considerably among units of analysis (Kramer, 1983). Criminological research, for instance, consistently reveals a strong and robust association between poverty and crime rates among macrosocial units (e.g., states or neighborhoods), while the association between poverty and criminal involvement at the individual-level is weak and oftentimes non-significant. It is quite possible that this pattern also applies to genetic research, where the usual small effects of single genes detected at the individual level become much larger at higher levels of aggregation. Future research will need to explore this possibility in much greater detail.
To our knowledge, this is the first study to aggregate DNA markers to a unit of analysis higher than the individual. Moreover, this is the first study to our knowledge that has revealed that variation in aggregate IQ scores is associated with variation in aggregate DNA markers. These results are in line with Lynn and Vanhanen’s (2002, 2006) (see also Hart, 2007; Rushton, 1997) thesis that the average IQ of nations is the result of genetic differences across those nations. Of course, the current study used schools, not nations, as the unit of analysis, meaning that the results reported here may not generalize to other levels of aggregation, including the nation level. There is good reason to believe, however, that the association between DNA and IQ would be even stronger at the nation level in comparison with the school level. There is much more variation in both genetic markers and IQ scores cross-nationally than there is across schools. Schools in the current study were all drawn from the same country (i.e., the United States) creating more genetic homogeneity among schools than there is among nations. Given that nations can vary quite drastically in terms of the allelic distributions of certain genes (Cavalli-Sforza, Menozzi, & Piazza, 1994), it stands to reason that this increased genetic variation would be able to explain more of the variance in IQ scores. Future research is needed to address this issue more fully and examine whether the link between DNA markers and IQ scores would be detected at other levels of aggregation.
The results of the current study provide some of the first evidence indicating that IQ scores across macro-level units are the result of genetic factors. As with all research, though, the current study is host to at least three limitations that need to be rectified in follow-up studies. First, only three dopaminergic genes were used to create the dopamine scale. Although the dopaminergic systemhas previously been linked to IQ (Beaver, DeLisi et al., 2010; Berman & Noble, 1995; Previc, 1999), future research would benefit by examining a broader range of genes from the dopaminergic system and other systems of genes that may be linked to IQ. Second, the data that were available only allowed for IQ and DNA to be aggregated to the school level. It would be interesting to examine what types of associations are visible at other levels of aggregation, including the neighborhood level, the state level, and the nation level. Third, the measure of IQ was based on scores garnered from the PVT, a test designed to assess verbal skills. Whether the results would be observed using different measures of IQ is an empirical question awaiting future research. Until these limitations are addressed, the results of the current study should be interpreted with caution. If future researchers are able to replicate these findings, then the results would begin to provide additional support that cross-national inequalities may be produced, in part, by genetic variation.
Updated Note: This study uses “school-level” data in its statistical comparisons. I was impressed by the novelty of that approach when first reading the Meng Hu blog entry a few days ago, but failed to emphasize that aspect of the study above in the first published draft.
This type of study amounts to “nibbling around the edges,” and represents the use of particular genes as “biomarkers” of cognition rather than as “intelligence genes.” It is, however, an interesting approach.
Yes, the research is subject to criticism as the authors admit. But as more genetic data becomes available in parallel with cognitive testing data for the same subjects (or other levels of analysis), many of the weaknesses in the study can be overcome. Eventually, we will be able to compare genetic data, conventional psychometric data, neural processing speeds, and functional neuroimaging data in large numbers of subjects (or schools, regions, nations, etc.).
The study of the neuro-processing apparatus (receptors, transporters, etc.) for neurotransmitters such as dopamine is a logical starting point for focused genetic/aptitude studies such as the one above.
We have barely begun to look at the relationship between genes, brain anatomy and function, aptitudes, and behaviours.
Not every scientist wants these issues to be studied rigorously, and it can be difficult to get funding for studies that have the potential to provide scientific data contradicting official politically correct dogma.