Abstract
We describe a method for obtaining a linear discriminant function to identify monogenic segregation in multivariate pedigree data. It differs from Fisher's linear discriminant function in that it does not assume that the genotype of each individual in the pedigree already known. The method consists of finding that linear function of the variables that maximizes the likelihood of a set of pedigree data, under the hypothesis of single gene segregation, subject to the constraint that the total sample variance of the function remains constant. To simplify the computation the variables are first transformed to their standardized principal components. Reanalysis of a set of pedigree data suggests that age and powers of age should be considered as extra variables from which the principal components are obtained, and virtually all of the variance should be accounted for by the principal components used to obtain the discriminant function.
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