Category: Behavior Genetics (Page 1 of 2)

The Classical Twin Design (CTD) has always been criticized for being oversimplistic, and consistently overestimating heritability estimates due to not accounting for GxE, GxG, rGE and equal environmental effects. It is almost never mentioned the bias is not systematic. The criticism largely exaggerates the flaws of the CTD, often misleadingly so, and apparently cherry-picking their evidence whenever large discrepancies in heritabilities are reported due to ignoring key assumptions inherent to the twin design. This article will show why the CTD and its extensions are robust methods, but with a strong focus on the Equal Environment Assumption (EEA).

The classical twin design withstood past criticisms, duo to employing a large variety of methods to test the key assumptions (Plomin & Bergeman, 1991; Andrew et al., 2001; Johnson et al., 2002; Christensen et al., 2006; Kendler & Prescott, 2006, ch. 6; Segal & Johnson, 2009; Plomin et al., 2013, ch. 6 & 12 & 17), and will likely withstand current ones (Tarnoki et al., 2022) in spite of some recent developments (e.g., Sunde et al., 2024) to improve the standard ACE models used to decompose genetic (h²) and shared and non-shared environmental (c² and e²) variances.
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Classical twin data comprise of phenotypic measurements on monozygotic (MZ) and dizygotic (DZ) twin pairs who were raised together. To derive estimates of behavioral genetic parameters (e.g., heritability) from such data, the ACDE model is most often used. In principle, the model provides estimates of the effects of additive genes (A), the shared environment (C), non-additive genes (D), and the unshared environment (E).

However, if only classical twin data are available, there is not enough information to estimate all four parameters, that is, the system of equations is underdetermined or underidentified. To enable parameters to be estimated, it is customary to fix either D or C to zero, leading to the ACE and ADE models which are identified. The problem with this approach is that if the influence of the omitted parameter is not actually zero, the estimates will be biased. Additional data on other types of family members, such as adoptees, would be needed for the full model but such data are usually not readily available.

Against this backdrop, Jöreskog (2021a) proposed that the full ACDE model can be estimated with only classical twin data. (A version of the ACDE model for categorical data was developed in Jöreskog [2021b], while Jöreskog [2021a] concerns only continuous data. I will discuss only the latter, but the same arguments apply to the categorical case.) This is a startling claim because the ACDE model has long been regarded as obviously impossible to estimate as there is simply not enough information in the twin variances and covariances for the full model (MZ and DZ variance-covariance matrices are sufficient statistics for the typical twin model, i.e., no other aspect of the sample data provides additional information on the parameter values). Nevertheless, Jöreskog claimed that it can be done, demonstrating it in several examples. Karl Jöreskog is not a behavioral geneticist but he is a highly influential statistician whose work on structural equation models has had a major influence on twin research. Therefore, even though his claims sounded implausible, they seemed worth investigating.

After studying Jöreskog’s model in detail I conclude that it does not deliver what it promises. It does generate a set of estimates for A, C, D, and E, but there is no reason to believe that they reflect the true population parameters. As nice as it would be to estimate the ACDE model with ordinary twin data, it just cannot be done.

This post has the following structure. I will start with a brief overview of twin models, describing some of the ways in which their parameters can be estimated. Then I will show how Jöreskog proposes to solve the ACDE identification problem, and where he goes wrong. I will end with a discussion of why I think twin models are useful despite their limitations, and why they have continuing relevance in the genomic era. The Appendix contains additional analyses related to the ACDE model.

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In the classic twin study design, identical (MZ) twin pairs are compared to fraternal (DZ) twin pairs so as to estimate the relative contributions of heredity and environment to individual differences. The classic twin design depends on the equal environments assumption (EEA) according to which the shared environment of MZ twins is not more similar than that of DZ twins.

The claim that the EEA is an unrealistic assumption which is routinely violated in reality is perhaps the most common criticism of the classic twin design. Violations of the EEA generally bias estimates of the effect of heredity upwards and those of the environment downwards. For this reason, there have been a number of studies where the assumption has been put to test with research questions such as:

• Are twin pairs who are misinformed about their actual zygosity as similar as pairs who know their real zygosity?
• Are twin pairs with objectively more similar environments more similar phenotypically?
• Are the results of twin studies consistent with the results of other kinds of behavioral genetic designs, such as adoption studies?

This research has indicated that the EEA is generally valid and that even when it’s violated, the effect on parameter estimates is small (Barnes et al., 2014; Felson, 2014).

I think sex differences offer an underappreciated way of further evaluating the EEA. Half of DZ pairs are same-sex (male-male or female-female) and half are opposite-sex (male-female), whereas MZ pairs are, of course, all same-sex. Differences in twin correlations across these sex categories are informative about the EEA because if the shared environment differs by zygosity, you would expect it to differ by sex, too. Continue reading

There’s a long-standing debate about if and how parental socioeconomic status moderates the heritability of IQ. Research has often but not always found that heritability is lower in low-SES families. See Turkheimer and Horn’s excellent review for details (although some of Turkheimer’s own research on this is less than convincing).

Robert Kirkpatrick and colleagues have conducted what may be the best study on the question so far. They use a big Minnesota sample, comprising about about 2500 pairs of adolescent twins, non-twin biological siblings, and adoptive siblings, and investigate if SES moderates either genetic or environmental determinants of IQ. Continue reading