Background/Aims Structural Equation Modeling (SEM) is an analysis approach that accounts for both the causal relationships between variables and the errors associated with the measurement of these variables. computationally feasible, and has good statistical properties. Conclusion Our framework may be used to build and compare causal models using family data without any genetic marker data. It also allows for a nearly endless array of genetic association and/or linkage assessments. A preliminary Matlab program is usually available, and we are currently implementing a more complete and user-friendly R package. observed characteristics in pedigrees. Suppose also that there are individuals in each pedigree. Let be a 1 vector of observed 252017-04-2 characteristics for the = [be a 1 vector of observed covariates, including a 1 in the first position to account for the mean. Let be a matrix of coefficients relating to = [ 1 vector of unobserved or latent factors by the factor loading matrix . Note that latent growth curve models may be implemented by specifying certain forms for 252017-04-2 . Let = [Trandom effects Table 1 Some model notation ?T? and related to each VC. We then have the measurement model: Table 2 Some possible variance components be an matrix relating to itself and be an matrix of coefficients relating to and are selected to have a specific structure. Many of the entries in these matrices will be forced to 0 while others will be free parameters that need to be estimated. It is often simpler to represent the model graphically. Bollen [3] gives a good introduction to how the matrix and graphical representation of SEM are related. The basic intuition behind (2) is usually that each variable in is usually influenced by other variables in characteristics of 252017-04-2 are exogenous. Then the bottom rows Rabbit polyclonal to CD14 of will be zero, and the lower right entries in will be free parameters for all those 1 . It is also important to note that when we perform SEM we usually believe that most of 252017-04-2 the covariance between variables can be explained by the causal associations between those variables. That is, the random genetic and environmental factors are generally assumed to only affect the entries of one at a time. Thus we pressure many of the off-diagonal entries in to zero because they are not correlated. When we specify a SEM, we expect that certain conditional independencies will apply, both within the pedigree and on the population level. That is, we often expect to be able to sample unrelated individuals from the population and apply the same SEM with no additional VCs. Thus we would argue that often the same constraints should be placed on for all those 1 and that, similarly, the same constraints should be placed on for all those 1 . Otherwise, counterintuitive populace level results may be encountered. Of course, it may at times be affordable to break this theory. For example, we may wish to have the major gene VC contribute directly to only one trait and not to others. Similar arguments have appeared in the multilevel modeling literature, as may be seen on p. 582 of Longford and Muthn [24] and on p. 219 of McDonald and Goldstein [25]. By using the Kronecker notation it becomes fairly simple to separate out the development of the SEM part of the model from the modeling of the familial correlations. A statistician may therefore put together a model by mixing and matching various features for both the SEM and common VC models of familial correlation. Identifiability and the Implied Mean and Covariance A SEM is usually globally identifiable if all of its parameters can be decided uniquely from its associated mean and covariance structure. It is shown in Appendix A that there is a particularly simple form for the mean and covariance structure of the model discussed. Let =.