Improving accuracy and precision of heritability estimation in twin studies through hierarchical modeling: reassessing the measurement error assumption
Introduction: The conventional approach to estimating heritability in twin studies implicitly assumes either the absence of measurement error or that any measurement error is incorporated into the nonshared environment component. However, this assumption can be problematic when it does not hold or w...
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Frontiers Media S.A.
2025-04-01
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| Series: | Frontiers in Genetics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fgene.2025.1522729/full |
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| author | Gang Chen Dustin Moraczewski Paul A. Taylor |
| author_facet | Gang Chen Dustin Moraczewski Paul A. Taylor |
| author_sort | Gang Chen |
| collection | DOAJ |
| description | Introduction: The conventional approach to estimating heritability in twin studies implicitly assumes either the absence of measurement error or that any measurement error is incorporated into the nonshared environment component. However, this assumption can be problematic when it does not hold or when measurement error cannot be reasonably classified as part of the nonshared environment.Methods: In this study, we demonstrate the need for improvement in the conventional structural equation modeling (SEM) used for estimating 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 are typically based on the assumption of no measurement errors.Results: We argue that measurement errors in the form of intra-individual variability are an intrinsic limitation of finite sampling and should not be considered as part of the nonshared environment. Previous studies have shown that the intra-individual variability of psychometric effects is significantly larger than the inter-individual counterpart. Here, to demonstrate the appropriateness and advantages of our hierarchical linear modeling approach in heritability estimation, 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: i) The conventional SEM may underestimate heritability. ii) A hierarchical model provides a more accurate assessment of heritability. iii) Large samples, exceeding 100 observations or thousands of twins, may be necessary to reduce imprecision.Discussion: Our study highlights the impact of measurement error on heritability estimation and introduces a hierarchical model as a more accurate alternative. These findings have significant implications for understanding individual differences and improving the design and analysis of twin studies. |
| format | Article |
| id | doaj-art-9da56bb81c4e4d1dab88d084ac2dfb88 |
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| issn | 1664-8021 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Genetics |
| spelling | doaj-art-9da56bb81c4e4d1dab88d084ac2dfb882025-08-20T01:55:37ZengFrontiers Media S.A.Frontiers in Genetics1664-80212025-04-011610.3389/fgene.2025.15227291522729Improving accuracy and precision of heritability estimation in twin studies through hierarchical modeling: reassessing the measurement error assumptionGang Chen0Dustin Moraczewski1Paul A. Taylor2Scientific and Statistical Computing Core, National Institute of Mental Health, Bethesda, MD, United StatesData Science and Sharing Team, National Institute of Mental Health, Bethesda, MD, United StatesScientific and Statistical Computing Core, National Institute of Mental Health, Bethesda, MD, United StatesIntroduction: The conventional approach to estimating heritability in twin studies implicitly assumes either the absence of measurement error or that any measurement error is incorporated into the nonshared environment component. However, this assumption can be problematic when it does not hold or when measurement error cannot be reasonably classified as part of the nonshared environment.Methods: In this study, we demonstrate the need for improvement in the conventional structural equation modeling (SEM) used for estimating 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 are typically based on the assumption of no measurement errors.Results: We argue that measurement errors in the form of intra-individual variability are an intrinsic limitation of finite sampling and should not be considered as part of the nonshared environment. Previous studies have shown that the intra-individual variability of psychometric effects is significantly larger than the inter-individual counterpart. Here, to demonstrate the appropriateness and advantages of our hierarchical linear modeling approach in heritability estimation, 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: i) The conventional SEM may underestimate heritability. ii) A hierarchical model provides a more accurate assessment of heritability. iii) Large samples, exceeding 100 observations or thousands of twins, may be necessary to reduce imprecision.Discussion: Our study highlights the impact of measurement error on heritability estimation and introduces a hierarchical model as a more accurate alternative. These findings have significant implications for understanding individual differences and improving the design and analysis of twin studies.https://www.frontiersin.org/articles/10.3389/fgene.2025.1522729/fullheritabilitytwin studiesACE modelFalconer’s methodintra-individual variabilityhierarchical modeling |
| spellingShingle | Gang Chen Dustin Moraczewski Paul A. Taylor Improving accuracy and precision of heritability estimation in twin studies through hierarchical modeling: reassessing the measurement error assumption Frontiers in Genetics heritability twin studies ACE model Falconer’s method intra-individual variability hierarchical modeling |
| title | Improving accuracy and precision of heritability estimation in twin studies through hierarchical modeling: reassessing the measurement error assumption |
| title_full | Improving accuracy and precision of heritability estimation in twin studies through hierarchical modeling: reassessing the measurement error assumption |
| title_fullStr | Improving accuracy and precision of heritability estimation in twin studies through hierarchical modeling: reassessing the measurement error assumption |
| title_full_unstemmed | Improving accuracy and precision of heritability estimation in twin studies through hierarchical modeling: reassessing the measurement error assumption |
| title_short | Improving accuracy and precision of heritability estimation in twin studies through hierarchical modeling: reassessing the measurement error assumption |
| title_sort | improving accuracy and precision of heritability estimation in twin studies through hierarchical modeling reassessing the measurement error assumption |
| topic | heritability twin studies ACE model Falconer’s method intra-individual variability hierarchical modeling |
| url | https://www.frontiersin.org/articles/10.3389/fgene.2025.1522729/full |
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