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ACE model
Statistical model used to separate phenotypic variance into three components

The ACE model is a statistical model commonly used to analyze the results of twin and adoption studies. This classic behaviour genetic model aims to partition the phenotypic variance into three categories: additive genetic variance (A), common (or shared) environmental factors (C), and specific (or nonshared) environmental factors plus measurement error (E). It is widely used in genetic epidemiology and behavioural genetics. The basic ACE model relies on several assumptions, including the absence of assortative mating, that there is no genetic dominance or epistasis, that all genetic effects are additive, and the absence of gene-environment interactions. In order to address these limitations, several variants of the ACE model have been developed, including an ACE-β model, which emphasizes the identification of causal effects, and the ACDE model, which accounts for the effects of genetic dominance.

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See also

Further reading

  • Maes, Hermine H. (2014-09-29). "ACE Model". Wiley Stats Ref: Statistics Reference Online. Wiley StatsRef: Statistics Reference Online. Chichester, UK: John Wiley & Sons, Ltd. doi:10.1002/9781118445112.stat06752. ISBN 9781118445112.

References

  1. Germine, Laura; Russell, Richard; Bronstad, P. Matthew; Blokland, Gabriëlla A.M.; Smoller, Jordan W.; Kwok, Holum; Anthony, Samuel E.; Nakayama, Ken; Rhodes, Gillian (October 2015). "Individual Aesthetic Preferences for Faces Are Shaped Mostly by Environments, Not Genes". Current Biology. 25 (20): 2684–2689. Bibcode:2015CBio...25.2684G. doi:10.1016/j.cub.2015.08.048. ISSN 0960-9822. PMC 4629915. PMID 26441352. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4629915 ↩

  2. Maes, Hermine H. (2005-10-15). ACE Model. Chichester, UK: John Wiley & Sons, Ltd. doi:10.1002/0470013192.bsa002. ISBN 978-0470860809. {{cite encyclopedia}}: |journal= ignored (help) 978-0470860809 ↩

  3. Kohler, Hans-Peter; Behrman, Jere R.; Schnittker, Jason (2011). "Social science methods for twins data: integrating causality, endowments, and heritability". Biodemography and Social Biology. 57 (1): 88–141. doi:10.1080/19485565.2011.580619. ISSN 1948-5565. PMC 3158495. PMID 21845929. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3158495 ↩

  4. Beauchamp, Jonathan P.; Cesarini, David; Johannesson, Magnus; Lindqvist, Erik; Apicella, Coren (2010-07-06). "On the sources of the height–intelligence correlation: New insights from a bivariate ACE model with assortative mating". Behavior Genetics. 41 (2): 242–252. doi:10.1007/s10519-010-9376-7. ISSN 0001-8244. PMC 3044837. PMID 20603722. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3044837 ↩

  5. Lawlor, Debbie A.; Lawlor, Deborah A.; Mishra, Gita D. (2009-04-02). Family Matters: Designing, Analysing and Understanding Family Based Studies in Life Course Epidemiology. OUP Oxford. pp. 252–3. ISBN 9780199231034. 9780199231034 ↩

  6. Kohler, Hans-Peter; Behrman, Jere R.; Schnittker, Jason (2011). "Social science methods for twins data: integrating causality, endowments, and heritability". Biodemography and Social Biology. 57 (1): 88–141. doi:10.1080/19485565.2011.580619. ISSN 1948-5565. PMC 3158495. PMID 21845929. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3158495 ↩

  7. Kohler, Hans-Peter; Behrman, Jere R.; Schnittker, Jason (2011). "Social science methods for twins data: integrating causality, endowments, and heritability". Biodemography and Social Biology. 57 (1): 88–141. doi:10.1080/19485565.2011.580619. ISSN 1948-5565. PMC 3158495. PMID 21845929. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3158495 ↩

  8. Wang, Xueqin; Guo, Xiaobo; He, Mingguang; Zhang, Heping (2011-02-09). "Statistical Inference in Mixed Models and Analysis of Twin and Family Data". Biometrics. 67 (3): 987–995. doi:10.1111/j.1541-0420.2010.01548.x. ISSN 0006-341X. PMC 3129472. PMID 21306354. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3129472 ↩