TY - JOUR
T1 - Using non-normal SEM to resolve the ACDE model in the classical twin design
AU - Ozaki, Koken
AU - Toyoda, Hideki
AU - Iwama, Norikazu
AU - Kubo, Saori
AU - Ando, Juko
PY - 2011/3
Y1 - 2011/3
N2 - One of the biggest problems in classical twin studies is that it cannot estimate additive genetic (A), non-additive genetic (D), shared environmental (C), and non-shared environmental (E) effects, simultaneously, because the model, referred to as the ACDE model, has negative degrees of freedom when using Structural Equation Modeling (SEM). Therefore, instead of the ACDE model, the ACE model or the ADE model is actually used. However, using the ACE or ADE models almost always leads to biased estimates. In the present paper, the univariate ACDE model is developed using non-normal Structural Equation Modeling (nnSEM). In SEM, (1st- and) 2nd-order moments, namely, (means and) covariances are used as information. However, nnSEM uses higher-order moments as well as (1st- and) 2nd-order moments. nnSEM has a number of advantages over SEM. One of which is that nnSEM can specify models that cannot be specified using SEM because of the negative degrees of freedom. Simulation studies have shown that the proposed method can decrease the biases. There are other factors that have possible effects on phenotypes, such as higher-order epistasis. Since the proposed method cannot estimate these effects, further research on developing a more exhaustive model is needed.
AB - One of the biggest problems in classical twin studies is that it cannot estimate additive genetic (A), non-additive genetic (D), shared environmental (C), and non-shared environmental (E) effects, simultaneously, because the model, referred to as the ACDE model, has negative degrees of freedom when using Structural Equation Modeling (SEM). Therefore, instead of the ACDE model, the ACE model or the ADE model is actually used. However, using the ACE or ADE models almost always leads to biased estimates. In the present paper, the univariate ACDE model is developed using non-normal Structural Equation Modeling (nnSEM). In SEM, (1st- and) 2nd-order moments, namely, (means and) covariances are used as information. However, nnSEM uses higher-order moments as well as (1st- and) 2nd-order moments. nnSEM has a number of advantages over SEM. One of which is that nnSEM can specify models that cannot be specified using SEM because of the negative degrees of freedom. Simulation studies have shown that the proposed method can decrease the biases. There are other factors that have possible effects on phenotypes, such as higher-order epistasis. Since the proposed method cannot estimate these effects, further research on developing a more exhaustive model is needed.
KW - Biases in estimators
KW - Higher-order moments
KW - Model identification
KW - Non-normality
KW - Univariate ACDE model
KW - nnSEM
UR - http://www.scopus.com/inward/record.url?scp=79952447755&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=79952447755&partnerID=8YFLogxK
U2 - 10.1007/s10519-010-9386-5
DO - 10.1007/s10519-010-9386-5
M3 - Article
C2 - 20703791
AN - SCOPUS:79952447755
SN - 0001-8244
VL - 41
SP - 329
EP - 339
JO - Behavior Genetics
JF - Behavior Genetics
IS - 2
ER -