Reich T, Rice J, Cloninger C R, Wette R, James J
Ann Hum Genet. 1979 Jan;42(3):371-90. doi: 10.1111/j.1469-1809.1979.tb00670.x.
(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.
（1）引入了基于多因素遗传的三种模型来解释表型异质性。这些模型用于确定性状的子形式是否为：（a）同一过程的不同程度，（b）同一过程的非家族性环境变异，以及（c）独立传递的过程。（2）每个模型的参数由两个群体患病率以及一个、两个或三个相关系数组成，这些系数反映了上述三个假设。对模型进行了公式化处理，以便可以进行似然比检验来区分它们。（3）描述了以下几种分析类型：（a）使用单独的群体患病率估计值对患病率数据进行分析，（b）以先证者为有特定配偶的父母对患病率数据进行分析，（c）以先证者为有特定父母的后代对患病率数据进行分析，以及（d）使用复杂分离分析对家庭的完整分离分布进行分析。（4）与患病率分析相比，复杂分离分析具有以下优点：（a）参数估计的自由度更大，无需单独估计群体患病率，（b）参数的标准误差更小，以及（c）区分模型的能力增强。（5）通过上述分析方法可以更全面地理解诸如发病年龄、严重程度和性别效应等表型异质性。家族性疾病的疾病分类也可以得到澄清，并且可以检测到与性状传递相关的环境。这种方法特别适合于行为性状的分析，因为它不需要假设亲属共有的环境效应可以忽略不计。（6）最后，我们的经验表明，将患病率和系谱数据纳入单一分析可以减少进行分析所需的时间。
