Impact Factor 3.517 | CiteScore 3.60
More on impact ›

Methods ARTICLE Provisionally accepted The full-text will be published soon. Notify me

Front. Genet. | doi: 10.3389/fgene.2019.00837

Psychometric Modelling of Longitudinal Genetically-Informative Twin Data

 Inga Schwabe1*, Zhengguo Gu1, Jesper Tijmstra1,  Pete Hatemi2 and  Steffi Pohl3
  • 1Tilburg School of Social and Behavioral Sciences, Tilburg University, Netherlands
  • 2Pennsylvania State University, United States
  • 3Freie Universität Berlin, Germany

The often-used A(C)E model that decomposes phenotypic variance into parts due to additive genetic and environmental influences can be extended to a longitudinal model when the trait has been assessed at multiple occasions. This enables inference about the nature (e.g., genetic or environmental) of the covariance among the different measurement points. In the case that the measurement of the phenotype relies on self-report data (e.g., questionnaire data), often, aggregated scores (e.g., sum-scores) are used as a proxy for the phenotype. However, earlier research based on the univariate ACE model that concerns a single measurement occasion has shown that this can lead to an underestimation of heritability and that instead, one should prefer to model the raw item data by integrating an explicit measurement model into the analysis. This has, however, not been translated to the more complex longitudinal case. In this paper, we first present a latent state twin A(C)E model that combines the genetic twin model with an item response theory (IRT) model as well as its specification in a Bayesian framework. Two simulation studies were conducted to investigate 1) how large the bias is when sum-scores are used in the longitudinal A(C)E model and 2) if using the latent twin model can overcome the potential bias. Results of the first simulation study (e.g., AE model) demonstrated that using a sum score approach leads to underestimated heritability estimates and biased covariance estimates. Surprisingly, the IRT approach also lead to bias, but to a much lesser degree. The amount of bias increased in the second simulation study (e.g., ACE model) under both frameworks, with the IRT approach still being the less biased approach. Since the bias was less severe under the IRT approach than under the sum-score approach and due to other advantages of latent variable modelling, we still advise researcher to adopt the IRT approach. We further illustrate differences between the traditional sum-score approach and the latent state twin A(C)E model by analysing data of a two-wave twin study, consisting of the answers of 8,016 twins on a scale developed to measure social attitudes related to conservatism.

Keywords: Psychometrics, heritability, IRT, Phenotypic stability, measurement error, Sum-scores, genetic correlation, longitudinal data, twin study

Received: 16 Oct 2018; Accepted: 13 Aug 2019.

Copyright: © 2019 Schwabe, Gu, Tijmstra, Hatemi and Pohl. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Dr. Inga Schwabe, Tilburg School of Social and Behavioral Sciences, Tilburg University, Tilburg, 5037 AB Tilburg, Netherlands,