The aim of the present study was to investigate individual differences in intelligence in adults while taking non-random mating of spouses into account. To this end, two different assortment models were fitted to the data, a social homogamy (SH) model and a phenotypic assortment (PA) model. For general intelligence, as well as for verbal and performance intelligence, the SH model fitted the data comparatively worse than the PA model. The most parsimonious model under SH was a model including genetic dominance, CT, and non-shared environmental factors. The effect of additive genetic factors was estimated close to zero. Under PA, we ended up with two alternative models as the estimates of genetic dominance and negative CT were confounded: (i) a model including negative CT but not genetic dominance, or (ii) a model including genetic dominance but not negative CT. Both PA models also included additive genetic variance, variance explained by assortative mating, and non-shared environmental variance. Similar results were obtained for verbal intelligence and performance intelligence subscales (see online supplement for details).
The overall misfit observed for the SH model is likely to be due to the high estimate of genetic dominance, which is, in the light of practically absent additive genetic effects, biologically unlikely (Falconer and Mackay 1989
). We assume that the estimate of genetic dominance is increased in order to accommodate the observed decrease in spousal correlations with increasing genetic distance, a pattern of correlations that is not expected under an SH model (see Fig. ).
With respect to the PA models, the present study lacked the information to disentangle effects of genetic dominance and negative CT. Based on test statistics, the PA model including genetic dominance (but not CT) fitted the data relatively better than the PA model including negative CT (but not genetic dominance). Moreover, significant negative CT seems somewhat unlikely in the context of general intelligence, as it would, for example, imply that smarter parents suppress their children’s cognitive abilities. An alternative explanation of negative CT, however, is possible incomplete genetic isomorphism across adult ages with e.g., increased genetic contribution in young to middle adulthood and decreased genetic contribution at later ages (Pedersen et al. 1992
; Brandt et al. 1993
; Finkel et al. 1998
) or different (sets of) genes that might be related to general intelligence in different stages in life (e.g., Deary et al. 2002
). Further analyses are required to disentangle different explanations for negative CT in the context of general intelligence.
In the present design, negative CT and genetic dominance are largely confounded, consequently genetic dominance could only be detected when effects of CT were eliminated from the model. Results for general intelligence showed that estimates of genetic dominance increased from 0 to 27% if negative CT was eliminated from the model. Based on test statistics and interpretation of the parameter estimates, we suggest that the PA model including additive genetic factors, genetic dominance deviation, positive assortative mating, and non-shared environmental factors (Model PA-3 in Table ) provides the most plausible description of the observed data. Such a model would support the hypothesis that in adults, genetic dominance might go undetected due to the presence of assortative mating when assortment is not adequately modeled. Note that negative CT, if present, could mask the presence of genetic dominance as well.
Although, the design applied in the present study allows one to model the effects of assortative mating on the estimates of the variance components of intelligence, some limitations should be noted. First, mating behavior was assumed to be due to pure SH or pure PA, in which SH was defined as purely environmental similarities and PA as assortment purely based on phenotypic similarities. It is however not unlikely that mating behavior is influenced by both processes, i.e., mixed assortment. Moreover, social stratification may itself be driven by genetic stratification between populations such that assortment due to SH may in fact have a genetic background. Similarly, PA may be purely environmental in the case that the trait under study is not influenced by genetic factors.
Second, it is possible that cohort differences in assortment exist, i.e., that the process underlying assortative mating differs for different birth cohorts. For example, mating in the first half of the 20th century may generally have been based on similarity in social milieus for spouses, while urbanization and increasing equality of educational opportunities between men and women may have increased the influence of PA in latter generations. Studies including large generational cohorts are required to model both processes simultaneously, or to model assortment changes over time.
Third, satisfying the distinction between negative CT and genetic dominance was difficult as these two effects were largely confounded in the present study design. Different relatives, such as half-sibs, adoptees, or twins that have grown up in separate households, would need to be included to disentangle those two processes.
Fourth, within the present study we did not model the correlation between genetic and non-shared environmental factors as this correlation is not identified as long as no specific non-shared environmental factors are measured and included in the model. In the context of general intelligence, correlation between genes and non-shared environmental factors such as e.g., education and profession, has, however, been suggested by Haworth et al. (2010
). Ignoring the correlation between genes and non-shared environmental factors may lead to an overestimation of the genetic effects (Purcell 2002
Thus far, only a few studies have suggested the presence of genetic dominance for general intelligence in adults (Chipuer et al. 1990
; Fulker and Eysenck 1979
). The results of these studies were, however, based on combined samples (i.e., different samples from different studies were combined within one study) with different measures of intelligence. A disadvantage of such a combined design is that general intelligence is assessed using different intelligence tests at different points in time, which may affect estimates of the correlation between relatives. Correlations between individuals measured with different tests and/or in different points in time may be relatively decreased compared to correlations between relatives assessed with the same test and/or at a similar moment in time. This, in turn, may lead to biased estimates of the variance components. The advantage of the present study is that a single intelligence test was used for all participants. The present study is also unique in its design as it includes adult MZ and DZ twins, their non-twin siblings and the parents, spouses and adult offspring of the twins and non-twin siblings.
Reynolds et al. (2000
) emphasized the importance of considering assortative mating in a twin-family study on educational attainment and fluid ability in adults. In that study, effects of SH and PA were modeled simultaneously (i.e., mixed assortment) in a sample of 116 twin-spouse sets; effects of CT and genetic dominance were however not considered in this study. Both SH and PA contributed to the spousal similarities for educational attainment and fluid ability in a multivariate design. Considering both SH and positive PA in the context of general intelligence requires larger sample sizes than we had currently available (Heath and Eaves 1985
). A mixed model might however nicely fit to the pattern of phenotypic correlations between relatives that we observe in the present study, i.e., a decrease in correlations with increasing genetic distance (attributable to PA) and generally high correlations between relatives with no genetic relationship (attributable to SH).
Results from the present study have several implications. First, our results suggest that the well recognized high influence of additive genetic factors on individual differences in intelligence in adults may partly reflect more complex processes such as genetic dominance and positive assortative mating. The extended twin-family design evidently allows the disentanglement of various sources of individual differences in intelligence, and this design could also prove important in the context of a wide variety of other traits for which assortative mating has been reported, such as human height, body mass index, smoking behavior, personality traits, and psychiatric disorders (Silventoinen et al. 2003
; Agrawal et al. 2006
; Glicksohn and Golan 1999
; Maes et al. 1998
). Heritability estimates for these traits are generally based on twin correlations, while effects of assortative mating are not considered. Consequently, current knowledge about causes of individual differences in numerous traits may need to be reconsidered with effects of assortative mating are taken into account.
Second, our finding that genetic dominance explains part of the variance in adult intelligence is interesting in the context of the well-known increase in heritability of intelligence over age. It is generally recognized that shared environmental influences disappear after adolescence as children leave their parental home. An alternative is that dominance variance is present in children as well, but goes undetected due to larger shared environmental variance or effects of CT in childhood. In addition, the reported effects of shared environmental variance in childhood may be overestimated due to assortative mating that is not accounted for. To test this hypothesis, the CTD should be extended with parents of young twins.
Third, the conclusion that in adulthood, the genetic variation of general intelligence may not be merely additives in nature may be important in the context of gene finding studies for general intelligence. Genome wide association (GWA) studies generally test for main effects of alleles, and do not consider interaction (Plomin et al. 2001b
; Seshadri et al. 2007
; Butcher et al. 2008
), this has two implications. The extent of ‘missing heritability’ is lower, since it is only the unexplained part of the additive variance that is missing, not the non-additive genetic variance. Moreover, considering non-additive genetic effects within GWA studies for general intelligence might enhance their gene finding success. A major problem in this context, however, is that even larger sample sizes are required to detect non-additive alleles in GWA studies. Other approaches, such as the candidate gene approach, or functional pathway analyses might prove more suited to better our understanding of the contribution of the additive genetic factors and genetic dominance deviation as these studies do not suffer from power problems such as GWA studies.
To conclude, we demonstrated that the high heritability of intelligence is not only due to additive genetic factors but also to non-additive genetic factors or to negative CT, and the consequences of assortment. Analyses of verbal intelligence and performance intelligence support these results. Future studies of intelligence need to accommodate both assortment and non-additive genetic influences. Such studies could for example use genomic marker data to distinguish underlying mechanisms of spouse correlations (e.g., assortative mating due to PA would show increased genetic relatedness between spouses relative to random individuals of a population, whereas assortative mating due to SH would not). Moreover, gene finding studies may benefit from genetic resemblance between spouses since genetic variants that are shared between spouses more often than expected by chance, are possibly the same variants that account for part of the variance in general intelligence.