Multivariate modelling of endophenotypes associated with the metabolic syndrome in Chinese twins.
OBJECTIVE
The common genetic and environmental effects on endophenotypes related to the metabolic syndrome have been investigated using bivariate and multivariate twin models. This paper extends the pairwise analysis approach by introducing independent and common pathway models to Chinese twin data. The aim was to explore the common genetic architecture in the development of these phenotypes in the Chinese population.


METHODS
Three multivariate models including the full saturated Cholesky decomposition model, the common factor independent pathway model and the common factor common pathway model were fitted to 695 pairs of Chinese twins representing six phenotypes including BMI, total cholesterol, total triacylglycerol, fasting glucose, HDL and LDL. Performances of the nested models were compared with that of the full Cholesky model.


RESULTS
Cross-phenotype correlation coefficients gave clear indication of common genetic or environmental backgrounds in the phenotypes. Decomposition of phenotypic correlation by the Cholesky model revealed that the observed phenotypic correlation among lipid phenotypes had genetic and unique environmental backgrounds. Both pathway models suggest a common genetic architecture for lipid phenotypes, which is distinct from that of the non-lipid phenotypes. The declining performance with model restriction indicates biological heterogeneity in development among some of these phenotypes.


CONCLUSIONS
Our multivariate analyses revealed common genetic and environmental backgrounds for the studied lipid phenotypes in Chinese twins. Model performance showed that physiologically distinct endophenotypes may follow different genetic regulations.
Pang
Z
Z
Qingdao Center for Disease Control and Prevention, No 175, Shandong Road, Sifang District, 266033 Qingdao, China. cdcpang@126.com
Zhang
D
D
Li
S
S
Duan
H
H
Hjelmborg
J
J
Kruse
T A
TA
Kyvik
K O
KO
Christensen
K
K
Tan
Q
Q
D000328:Adult; D044466:Asian People / Q000235:genetics / Q000706:statistics & numerical data; D058068:Endophenotypes / Q000032:analysis*; D004777:Environment; D005260:Female; D006801:Humans; D008297:Male; D024821:Metabolic Syndrome / Q000453:epidemiology / Q000209:etiology* / Q000235:genetics; D008962:Models, Theoretical; D015999:Multivariate Analysis; D010641:Phenotype; D014427:Twins