How Close to the Mark Might Published Heritability Estimates Be?
Author:
Maraun Michael,Heene Moritz,Sckopke Philipp
Abstract
The behavioural scientist who requires an estimate of narrow heritability, h2, will conduct a twin study, and input the resulting estimated covariance matrices into a particular mode of estimation, the latter derived under supposition of the standard biometric model (SBM). It is known that the standard biometric model can be expected to misrepresent the phenotypic (genetic) architecture of human traits. The impact of this misrepresentation on the accuracy of h2 estimation is unknown. We aimed to shed some light on this general issue, by undertaking three simulation studies. In each, we investigated the parameter recovery performance of five modes- Falconer’s coefficient and the SEM models, ACDE, ADE, ACE, and AE- when they encountered a constructed, non-SBM, architecture, under a particular informational input. In study 1, the architecture was single-locus with dominance effects and genetic-environment covariance, and the input was a set of population covariance matrices yielded under the four twin designs, monozygotic-reared together, monozygotic-reared apart, dizygotic-reared together, and dizygotic-reared apart; in study 2, the architecture was identical to that of study 1, but the informational input was monozygotic-reared together and dizygotic-reared together; and in study 3, the architecture was multi-locus with dominance effects, genetic-environment covariance, and epistatic interactions. The informational input was the same as in study 1. The results suggest that conclusions regarding the coverage of h2 must be drawn conditional on a) the general class of generating architecture in play; b) specifics of the architecture’s parametric instantiations; c) the informational input into a mode of estimation; and d) the particular mode of estimationemployed. The results showed that the more complicated the generating architecture, the poorer a mode’s h2 recovery performance. Random forest analyses furthermore revealed that, depending on the genetic architecture, h2, the dominance and locus additive parameter, and proportions of alleles were involved in complex interaction effects impacting on h2 parameter recovery performance of a mode of estimation. Data and materials: https://osf.io/aq9sx/
Publisher
Linnaeus University
Reference68 articles.
1. Afifi, T. O., Asmundson, G. J., Taylor, S., & Jang, K. L. (2010). The role of genes and environment on trauma exposure and posttraumatic stress disorder symptoms: A review of twin studies. Clinical Psychology Review, 30(1), 101–112. 2. Benyamin, B., Pourcain, B., Davis, O. S., Davies, G., Hansell, N. K., Brion, M.-J., Kirkpatrick, R. M., Cents, R. A., Frani ́c, S., & Miller, M. B. (2014). Childhood intelligence is heritable, highly polygenic and associated with FNBP1L. Molecular Psychiatry, 19(2), 253–258. 3. Bischl, B., Lang, M., Kotthoff, L., Schiffner, J., Richter, J., Jones, Z., Casalicchio, G., Gallo, M., Bossek, J., Studerus, E., Judt, L., Kuehn, T., Kerschke, P., Fendt, F., Probst, P., Sun, X., Thomas, J., Vieira, B., Beggel, L., . . . Coors, S. (2017). Mlr: Machine Learning in R. Retrieved May 15, 2017, from https://cran.r-project.org/web/packages/mlr/index.html 4. Bowles, S., Gintis, H., et al. (2001). The inheritance of economic status: Education, class and genetics [Publisher: Oxford University Press New York]. International encyclopedia of the social and behavioral sciences: Genetics, behavior and society, 6, 4132–141. 5. Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324
|
|