Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí sinh học quốc tế đề tài: Inferring causal phenotype networks using structural equation models | Rosa et al. Genetics Selection Evolution 2011 43 6 http content 43 1 6 GSE Ge n et i cs Selection Evolution REVIEW Open Access Inferring causal phenotype networks using structural equation models 4 15 Guilherme JM Rosa Bruno D Valente Gustavo de los Campos Xiao-Lin Wu Daniel Gianola Martinho A Silva3 Abstract Phenotypic traits may exert causal effects between them. For example on the one hand high yield in dairy cows may increase the liability to certain diseases and on the other hand the incidence of a disease may affect yield negatively. Likewise the transcriptome may be a function of the reproductive status in mammals and the latter may depend on other physiological variables. Knowledge of phenotype networks describing such interrelationships can be used to predict the behavior of complex systems . biological pathways underlying complex traits such as diseases growth and reproduction. Structural Equation Models SEM can be used to study recursive and simultaneous relationships among phenotypes in multivariate systems such as genetical genomics system biology and multiple trait models in quantitative genetics. Hence SEM can produce an interpretation of relationships among traits which differs from that obtained with traditional multiple trait models in which all relationships are represented by symmetric linear associations among random variables such as covariances and correlations. In this review we discuss the application of SEM and related techniques for the study of multiple phenotypes. Two basic scenarios are considered one pertaining to genetical genomics studies in which QTL or molecular marker information is used to facilitate causal inference and another related to quantitative genetic analysis in livestock in which only phenotypic and pedigree information is available. Advantages and limitations of SEM compared to traditional approaches commonly used for the analysis of multiple traits as well as some indication of .
