### Abstract

Original language | English |
---|---|

Article number | 837 |

Number of pages | 16 |

Journal | Frontiers in Genetics |

Volume | 10 |

DOIs | |

Publication status | Published - 2019 |

### Fingerprint

### Keywords

- CONFIRMATORY FACTOR-ANALYSES
- CULTURE
- DISTRIBUTIONS
- ENVIRONMENT
- GENOTYPE
- ITEM-RESPONSE THEORY
- MATRICES
- MEASUREMENT INVARIANCE
- POWER
- STABILITY
- genetic correlations
- item response theory
- longitudinal data
- measurement error
- phenotypic stability
- psychometrics
- sum-scores
- twin study

### Cite this

*Frontiers in Genetics*,

*10*, [837]. https://doi.org/10.3389/fgene.2019.00837

}

*Frontiers in Genetics*, vol. 10, 837. https://doi.org/10.3389/fgene.2019.00837

**Psychometric modelling of longitudinal genetically informative twin data.** / Schwabe, Inga; Gu, Zhengguo; Tijmstra, Jesper; Hatemi, Pete; Pohl, Steffi.

Research output: Contribution to journal › Article › Scientific › peer-review

TY - JOUR

T1 - Psychometric modelling of longitudinal genetically informative twin data

AU - Schwabe, Inga

AU - Gu, Zhengguo

AU - Tijmstra, Jesper

AU - Hatemi, Pete

AU - Pohl, Steffi

PY - 2019

Y1 - 2019

N2 - 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 analyzing 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.

AB - 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 analyzing 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.

KW - CONFIRMATORY FACTOR-ANALYSES

KW - CULTURE

KW - DISTRIBUTIONS

KW - ENVIRONMENT

KW - GENOTYPE

KW - ITEM-RESPONSE THEORY

KW - MATRICES

KW - MEASUREMENT INVARIANCE

KW - POWER

KW - STABILITY

KW - genetic correlations

KW - item response theory

KW - longitudinal data

KW - measurement error

KW - phenotypic stability

KW - psychometrics

KW - sum-scores

KW - twin study

U2 - 10.3389/fgene.2019.00837

DO - 10.3389/fgene.2019.00837

M3 - Article

C2 - 31681400

VL - 10

JO - Frontiers in Genetics

JF - Frontiers in Genetics

SN - 1664-8021

M1 - 837

ER -