Twin studies with unmet assumptions are biased towards genetic heritability

SummaryFor a century [1,2], studies of monozygotic and dizygotic twins have yielded estimates of trait heritability. The clever logic behind them is that while both types of twins share environments, their genetic overlap is different. Hence, larger trait correlations between monozygotic compared to dizygotic twins indicate heritability (nature), whereas similar correlations indicate shared environmental influences (nurture), and low correlations indicate shaping through non-shared environments (external influences and measurement error). While many have written on the assumptions that both types of twins share equal environments [3–5], and that parental genetics and environment are independent [6,7]; fewer have put their data where their mouth is. Here, the impacts of unmet assumptions were investigated using a generative mixture model of twin phenotypes. The results indicated that violations of the equal environments assumption yielded large overestimations of heritability and underestimations of shared environmental influences. On the other hand, when parental genetics shaped twins’ shared environments, only minor non-linear biases against heritability emerged. Finally, realistic levels of measurement error uniformly depressed estimates for genetic and shared environmental factors. In sum, twin studies are particularly susceptible to overestimation of genetic and non-shared environmental influences. This bias could explain why some traits, such as attitudes towards property taxes [8], show suspiciously high heritability without a biologically plausible mechanism. Particularly in the context of traits with convincing mechanisms of cultural transmission [9–11] and complex gene-environment interactions [6], researchers should not allow biases in twin studies to overestimate heritability.

Musings on Visscher et al. (2006)

The classical twin design relies on a number of strong number of assumptions in order to yield unbiased estimates of heritability. This includes the equal environments assumption — that monozygotic and dizygotic twins experience similar degrees of environmental similarity — an assumption that is likely to be violated in practice for many traits of interest. An alternative method of estimating heritability that does not suffer from many of these limitations is to model trait similarity between sibling pairs as a function of their empirical genome-wide identity by descent sharing, estimated from genetic markers. In this review, I recount the story behind Nick Martin’s and my development of this method, our first attempts at applying it in a human population and more recent studies using the original and related methods to estimate trait heritability.

Sociopolitical Attitudes Through the Lens of Behavioral Genetics: Contributions from Dr Nicholas Martin

Professor Nicholas (Nick) Martin spearheaded initial investigations into the genetic basis of political attitudes and behaviors, demonstrating that behaviors that are perceived as socially constructed could have a biological basis. As he showed, the typical mode of inheritance for political attitudes consists of approximately equal proportions of variance from additive genetic, shared environmental and unique environmental sources. This differs from other psychological variables, such as personality traits, which tend to be characterized by genetic and unique environmental sources of variation. By treating political attitudes as a model phenotype, Nick Martin was able to leverage the unique pattern of observed intergenerational transmission for political attitudes to reexamine the quintessential assumptions of the classical twin model. Specifically, by creatively leveraging the nuances of the genetic architecture of political attitudes, he was able to demonstrate the robustness of the equal environments assumption and suggest corrections to account for assortative mating. These advances have had a substantial impact on both the fields of political science, as well as behavioral and quantitative genetics.