Abel L, Golmard J L, Mallet A
INSERM Unité 194, Service de Biostatistiques et d'Informatique Médicale, Hôpital Pitié-Salpétrière, Paris, France.
Am J Hum Genet. 1993 Oct;53(4):894-907.
Regressive logistic models specify the probability distribution of familial binary traits by conditioning each individual's phenotype on those of preceding relatives; therefore, the expression of the joint probability of the familial data necessitates ordering the observations. In the present paper, we propose an autologistic model of this familial dependence structure, which does not require specification of a particular ordering of the phenotypic observations. Genetic effects are introduced into the model in order to perform segregation analysis that is aimed at detecting the role of a major locus in the expression of familial phenotypes. In this model, the conditional probabilities have a logistic form, and large patterns of dependence between relatives can be considered with a simple interpretation of the parameters measuring the relationship between two phenotypes. The model is compared with the regressive logistic approach in terms of odds ratios and by using a simulation study.
回归逻辑模型通过将每个个体的表型以前面亲属的表型为条件来指定家族二元性状的概率分布；因此，家族数据联合概率的表达式需要对观测值进行排序。在本文中，我们提出了一种关于这种家族依赖结构的自逻辑模型，该模型不需要指定表型观测值的特定排序。为了进行旨在检测主基因座在家族表型表达中作用的分离分析，将遗传效应引入模型。在该模型中，条件概率具有逻辑形式，并且通过对测量两个表型之间关系的参数进行简单解释，可以考虑亲属之间的大的依赖模式。通过优势比并使用模拟研究将该模型与回归逻辑方法进行比较。
