Using the Flanker Task to Examine Genetic and Environmental Contributions in Inhibitory Control Across the Preschool Period

Hung, I-Tzu; Ganiban, Jody M.; Saudino, Kimberly J.

doi:10.1007/s10519-022-10129-4

Cited by 1 publication

(1 citation statement)

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“…Data aggregation is a common practice in heritability estimation. Examples can be found in the fields of psychometrics (e.g., Rea-Sandin et al, 2023; ; Hung et al, 2023; Gustavson et al, 2023; Vellani et al, 2022; Viktorsson et al, 2022; Yeom et al, 2022; Wang et al, 2020; Routledge et al, 2018; Stins et al, 2005; Schachar et al, 2010; Fan et al, 2001) and neuroimaging (Kastrati et al, 2022; van Drunen et al, 2021; Chen et al, 2019; Harper et al, 2019; van der Meulen et al, 2018; Anokhin et al, 2017; Blokland et al, 2011; Matthews et al, 2007; Polk et al, 2007). This practice is also popular in test-retest reliability estimation in psychometrics (e.g., Hedge et al, 2018) and neuroimaging (e.g., Elliott et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Improving accuracy and precision of heritability estimation in twin studies: Reassessing the measurement error assumption

Chen

Moraczewski

Taylor

2023

Preprint

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In this study, we demonstrate the need for improvement in the conventional ACE model used for esti- mating heritability when applied to trait data with measurement errors. The critical issue revolves around an assumption concerning measurement errors in twin studies. In cases where traits are measured using samples, data is aggregated during preprocessing, with only a centrality measure (e.g., mean) being used for modeling. Additionally, measurement errors resulting from sampling are assumed to be part of the nonshared environment and are thus overlooked in heritability estimation. Consequently, the presence of intra-individual variability remains concealed. Moreover, recommended sample sizes (e.g., 600 twin pairs) are typically based on the assumption of no measurement errors. We argue that measurement errors are an inherent limitation of finite sampling and should not be regarded as part of the nonshared environment. More importantly, recent studies have provided converging evidence indicating that the intra-individual variability of psychome- tric effects is much larger than the inter-individual counterpart. To accurately capture the underlying data generating process, we propose a hierarchical model that explicitly incorporates measurement errors. To demonstrate the appropriateness and advantages of our modeling approach, we utilize simulations as well as a real dataset from the ABCD (Adolescent Brain Cognitive Development) study. Moreover, we showcase the following analytical insights for data containing non-negligible measurement errors: 1) The conventional ACE model may underestimate heritability. 2) A hierarchical model provides a more accurate assessment of heritability. 3) Large samples, exceeding 100 observations or thousands of twins, may be necessary to reduce ambiguity. In summary, our study sheds light on the impact of measurement errors on heritability estimation and pro- poses a hierarchical model as a more accurate alternative. These findings have important implications for understanding individual differences and for the design and analysis of twin studies.

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