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The use of multiple thresholds and segregation analysis in analyzing the phenotypic heterogeneity of multifactorial
Abstract:
(1) Three models based on multifactorial inheritance are introduced to account for phenotypic heterogeneities. These models are used to determine whether subforms of a triat are: (a) different degrees of the same process, (b) non-familial environmental variants of the same process, and (c) independently transmitted processes. (2) The parameters of each model consist of two population prevalences and either one, two, or three correlation coefficients which reflect the three hypotheses given above. The models are formulated so that a likelihood ratio test may be performed to discriminate between them. (3) The following types of analyses are described: (a) analysis of prevalence data with separate population prevalence estimates, (b) analysis of prevalence data with the proband a parent with specified spouse, (c) analysis of prevalence data with the proband an offspring with specified parents, and (d) the full segregation distribution of families using Complex Segregation Analysis. (4) When compared with the Analysis of Prevalences, Complex Segregation Analysis has the following advantages: (a) the number of degrees of freedom for parameter estimates is greater and separate estimates of the population prevalences are not necessary, (b) standard errors of the parameters are smaller, and (c) the power to discriminate models is increased. (5) Phenotypic heterogeneities such as age of onset, severity, and sex effect can be more completely understood by the methods of analyses described above. The nosology of familial disorders can also be clarified, and environments relevant to the transmission of the trait can be detected. This approach is particularly suitable for the analysis for behavioural traits since it does not require the assumption that environmental effects common to relatives be ignored. (6) Finally, our experience indicates that incorporating both prevalence and pedigree data into a single analysis decreases the time required to perform the analysis.