Identifiability of segregation parameters using estimating equations

To eliminate the need for distributional assumptions and to reduce the computational burden associated with the method of maximum likelihood, several researchers have proposed using estimating equations techniques for segregation analysis. One concern with the application of this technique has been that the first and second order moments may not carry sufficient information for identifying all of the parameters in segregation models. It is shown that in addition to the marginal means and covariances from nuclear family data, up to the third order product moments need to be used in estimating equations for identifying all of the segregation parameters in a major gene model. A polygenic component and potentially a common family environment parameter can also be identified using up to the fourth order moments. Two weighting functions are developed to improve statistical efficiency.