Parametric estimation in a genetic mixture model with application to nuclear family data.

The apparent conflict between the biometrician and Mendelian genetics has been recently resolved by the introduction of a genetic mixed model to analyze continuous traits measured on human families and to elucidate the mechanism of underlying major genes. The mixed model formulated by Elston and Stewart (1971, Human Heredity 21, 523-542), extended by Morton and MacLean (1974, American Journal of Human Genetics 26, 489-503), and reviewed, with further extensions, by Boyle and Elston (1979, Biometrics 35, 55-68) has become an extremely useful tool of wide applicability in the field of genetic epidemiology. This model allows for segregation at a major locus, a polygenic effect, and a sibling environmental variation. The main concern of this paper is with estimating the model parameters by the method of maximum likelihood. The expectation-maximization (EM) algorithm is developed to derive the estimates iteratively. An approximation of the information matrix when using the EM algorithm is given. We illustrate the methodology by fitting the model to the arterial blood pressure data collected by Miall and Oldham (1955, Clinical Science 14, 459-487).