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 techniq ues for segregation analysis. One concern with the application of this technique has been that the first and second order moments may not ca rry sufficient information for identifying all of the parameters in se gregation models. It is shown that in addition to the marginal means a nd covariances from nuclear family data, up to the third order product moments need to be used in estimating equations for identifying all o f the segregation parameters in a major gene model. A polygenic compon ent and potentially a common family environment parameter can also be identified using up to the fourth order moments. Two weighting functio ns are developed to improve statistical efficiency.