Demenais F M, Laing A E, Bonney G E
Division of Biostatistics, Howard University Cancer Center, Washington, D.C.
Genet Epidemiol. 1992;9(6):419-35. doi: 10.1002/gepi.1370090605.
Segregation analysis of discrete traits can be conducted by the classical mixed model and the recently introduced regressive models. The mixed model assumes an underlying liability to the disease, to which a major gene, a multifactorial component, and random environment contribute independently. Affected persons have a liability exceeding a threshold. The regressive logistic models assume that the logarithm of the odds of being affected is a linear function of major genotype effects, the phenotypes of older relatives, and other covariates. A formulation of the regressive models, based on an underlying liability model, has been recently proposed. The regression coefficients on antecedents are expressed in terms of the relevant familial correlations and a one-to-one correspondence with the parameters of the mixed model can thus be established. Computer simulations are conducted to evaluate the fit of the two formulations of the regressive models to the mixed model on nuclear families. The two forms of the class D regressive model provide a good fit to a generated mixed model, in terms of both hypothesis testing and parameter estimation. The simpler class A regressive model, which assumes that the outcomes of children depend solely on the outcomes of parents, is not robust against a sib-sib correlation exceeding that specified by the model, emphasizing testing class A against class D. The studies reported here show that if the true state of nature is that described by the mixed model, then a regressive model will do just as well. Moreover, the regressive models, allowing for more patterns of family dependence, provide a flexible framework to understand gene-environment interactions in complex diseases.
离散性状的分离分析可通过经典混合模型和最近引入的回归模型来进行。混合模型假定存在一种潜在的疾病易感性，主要基因、多因素成分和随机环境各自独立地对其产生影响。受影响个体的易感性超过某个阈值。回归逻辑模型假定受影响几率的对数是主要基因型效应、年长亲属的表型以及其他协变量的线性函数。最近有人基于潜在易感性模型提出了回归模型的一种形式。根据相关的家族相关性来表示前因的回归系数，从而能够建立与混合模型参数的一一对应关系。进行计算机模拟以评估回归模型的两种形式与核心家庭中的混合模型的拟合度。就假设检验和参数估计而言，D类回归模型的两种形式对生成的混合模型都有很好的拟合度。更简单的A类回归模型假定子女的结果仅取决于父母的结果，对于超过模型指定值的同胞-同胞相关性缺乏稳健性，这突出了对A类模型与D类模型进行检验的重要性。此处报告的研究表明，如果自然的真实状态如混合模型所描述，那么回归模型也能同样适用。此外，回归模型考虑了更多的家庭依赖性模式，为理解复杂疾病中的基因-环境相互作用提供了一个灵活的框架。
