Fitting Problems: Evaluating Model Fit in Behavior Genetic Models

Garrison, S. Mason; Rodgers, Joseph Lee

doi:10.31234/osf.io/qys83

Cited by 3 publications

(2 citation statements)

References 56 publications

(84 reference statements)

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“…Third, although AIC has been widely used in behavior genetics to determine the 'best model' (Sullivan & Eaves, 2002), its accuracy as a selection approach remains under-examined. A lengthy appendix in Garrison and Rodgers (2021) hints at potential issues with AIC as a selection criterion, and the worst-fitting 'gorge' seen across all AIC result matrices further point to potential shortcomings of this approach. Therefore, more comprehensive research should be done to investigate this model selection approach.…”

Section: Discussionmentioning

confidence: 99%

Effects of Genetic Relatedness of Kin Pairs on Univariate ACE Model Performance

Lyu,

Garrison

2023

Twin Res Hum Genet

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The current study explored the impact of genetic relatedness differences (ΔH) and sample size on the performance of nonclassical ACE models, with a focus on same-sex and opposite-sex twin groups. The ACE model is a statistical model that posits that additive genetic factors (A), common environmental factors (C), and specific (or nonshared) environmental factors plus measurement error (E) account for individual differences in a phenotype. By extending Visscher’s (2004) least squares paradigm and conducting simulations, we illustrated how genetic relatedness of same-sex twins (HSS) influences the statistical power of additive genetic estimates (A), AIC-based model performance, and the frequency of negative estimates. We found that larger HSS and increased sample sizes were positively associated with increased power to detect additive genetic components and improved model performance, and reduction of negative estimates. We also found that the common solution of fixing the common environment correlation for sex-limited effects to .95 caused slightly worse model performance under most circumstances. Further, negative estimates were shown to be possible and were not always indicative of a failed model, but rather, they sometimes pointed to low power or model misspecification. Researchers using kin pairs with ΔH less than .5 should carefully consider performance implications and conduct comprehensive power analyses. Our findings provide valuable insights and practical guidelines for those working with nontwin kin pairs or situations where zygosity is unavailable, as well as areas for future research.

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Section: Discussionmentioning

confidence: 99%

Effects of Genetic Relatedness of Kin Pairs on Univariate ACE Model Performance

Lyu,

Garrison

2023

Twin Res Hum Genet

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“…al. 2008; Heene et al 2011; Montoya & Edwards 2021), including within behavior genetics(Garrison and Rodgers 2021). Instead, if the researcher emphasizes investigating the heritability of traits like height or BMI, using available parameters to calculate the power of the heritability estimation before tting the ACE model will be a preferable approach.…”

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confidence: 99%

Effects of Genetic Relatedness of Kin Pairs on Univariate ACE Model Performance

Lyu

Garrison

2022

Preprint

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Statistical power is important for genetically informed research, especially when using publicly available datasets. Such datasets can make research conclusions more generalizable, but accurate records of zygosity are not always obtainable. Some researchers tend to fit models with other kin pairs rather than MZ and DZ twins, who have a less than .5 genetic relatedness difference (ΔR). However, no research has systematically investigated the impact of using such two groups of kin pairs on ACE model performance. In our study, we did mathematical derivations and simulations to illustrate how genetic relatedness of same-sex twins (RSS) and sample sizes influence ACE model performance. Specifically, we analyzed those factors’ impact on statistical power of heritability (h2) estimation, the overall power, and the frequency of negative estimates based on univariate ACE models. Our algebraic and simulation results suggest that heritability power, overall power, and reduction of negative estimates are positively associated with larger RSS and larger sample sizes. We also found addressing sex limitations would cause slightly worse model performance under most circumstances. Simulation results were discussed from both statistical and empirical perspectives, and suggestions are proposed for studies using kin pairs with ΔR < .5.

show abstract

Effects of Genetic Relatedness of Kin Pairs on Univariate ACE Model Performance

Lyu,

Garrison

2023

Preprint

Self Cite

View full text Add to dashboard Cite

The current study explored the impact of genetic relatedness differences (ΔH) and sample size on the performance of non-classical ACE models, with a focus on same-sex and opposite-sex twin groups. By extending Visscher (2004)’s least squares paradigm and conducting simulations, we illustrated how genetic relatedness of same-sex twins (HSS) influences the statistical power of additive genetic estimates (A), AIC-based model performance and the frequency of negative estimates. We found that larger HSS and increased sample sizes were positively associated with increased power to detect additive genetic components and improved model performance, and reduction of negative estimates. We also found that the common solution of fixing the common environment correlation for sex-limited effects to .95 caused slightly worse model performance under most circumstances. Further, negative estimates were shown to be possible and were not always indicative of a failed model, but rather, they sometimes pointed to low power or model misspecification. Researchers using kin pairs with ΔH less than .5 should carefully consider performance implications and conduct comprehensive power analyses. Our findings provide valuable insights and practical guidelines for those working with non-twin kin pairs or situations where zygosity is unavailable, as well as areas for future research.

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Fitting Problems: Evaluating Model Fit in Behavior Genetic Models

Abstract: Final Paper Version of my Dissertation

Cited by 3 publications

References 56 publications

Effects of Genetic Relatedness of Kin Pairs on Univariate ACE Model Performance

Effects of Genetic Relatedness of Kin Pairs on Univariate ACE Model Performance

Effects of Genetic Relatedness of Kin Pairs on Univariate ACE Model Performance

Effects of Genetic Relatedness of Kin Pairs on Univariate ACE Model Performance

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