Demenais F M
Division of Biostatistics and Epidemiology, Howard University Cancer Center, Washington, DC 20060.
Am J Hum Genet. 1991 Oct;49(4):773-85.
Statistical models have been developed to delineate the major-gene and non-major-gene factors accounting for the familial aggregation of complex diseases. The mixed model assumes an underlying liability to the disease, to which a major gene, a multifactorial component, and random environment contribute independently. Affection is defined by a threshold on the liability scale. The regressive logistic models assume that the logarithm of the odds of being affected is a linear function of major genotype, phenotypes of antecedents and other covariates. An equivalence between these two approaches cannot be derived analytically. I propose a formulation of the regressive logistic models on the supposition of an underlying liability model of disease. Relatives are assumed to have correlated liabilities to the disease; affected persons have liabilities exceeding an estimable threshold. Under the assumption that the correlation structure of the relatives' liabilities follows a regressive model, the regression coefficients on antecedents are expressed in terms of the relevant familial correlations. A parsimonious parameterization is a consequence of the assumed liability model, and a one-to-one correspondence with the parameters of the mixed model can be established. The logits, derived under the class A regressive model and under the class D regressive model, can be extended to include a large variety of patterns of family dependence, as well as gene-environment interactions.
已经开发出统计模型来描绘导致复杂疾病家族聚集的主基因和非主基因因素。混合模型假定存在疾病的潜在易感性，主基因、多因素成分和随机环境对其有独立贡献。患病状态由易感性量表上的一个阈值定义。回归逻辑模型假定患病几率的对数是主基因型、前驱症状的表型及其他协变量的线性函数。这两种方法之间的等价关系无法通过解析推导得出。我基于疾病的潜在易感性模型的假设提出了回归逻辑模型的一种表述。假定亲属之间存在疾病的相关易感性；患病个体的易感性超过一个可估计的阈值。在亲属易感性的相关结构遵循回归模型的假设下，前驱症状的回归系数可以根据相关的家族相关性来表示。简约参数化是假定的易感性模型的一个结果，并且可以与混合模型的参数建立一一对应关系。在A类回归模型和D类回归模型下推导得到的对数几率，可以扩展到包括多种家族依赖性模式以及基因 - 环境相互作用。
