Zlotnik L H, Elston R C, Namboodiri K K
Am J Med Genet. 1983 Jun;15(2):307-13. doi: 10.1002/ajmg.1320150214.
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.
我们描述了一种用于获得线性判别函数以识别多变量家系数据中单基因分离的方法。它与费希尔线性判别函数的不同之处在于，它不假定家系中每个个体的基因型已知。该方法包括在单基因分离的假设下，找到变量的线性函数，该函数在函数的总样本方差保持恒定的约束下，使一组家系数据的似然性最大化。为了简化计算，首先将变量转换为其标准化主成分。对一组家系数据的重新分析表明，年龄和年龄的幂应被视为从中获取主成分的额外变量，并且几乎所有的方差都应由用于获得判别函数的主成分来解释。
