Liang K Y, Beaty T H
Department of Biostatistics, Johns Hopkins University, Baltimore 21205.
Genet Epidemiol. 1991;8(6):361-70. doi: 10.1002/gepi.1370080602.
Detection of familial aggregation of a disease is important for studying possible genetic and environmental factors contributing to disease etiology. Accurate quantification of familial aggregation can provide guidance for subsequent, more sophisticated genetic studies. This article presents a statistical model and method for detecting both inter- and intra-class aggregation of a binary trait with family data. The method used here is based on the logistic regression model which incorporates effects of individual covariates while measuring familial aggregation of risk as the odds ratios among classes of relatives. An estimation equation approach is presented where the joint distribution of binary traits among family members need not be fully specified. Data from a genetic epidemiologic study on liver cancer in Shanghai are analyzed for illustration, and reveal strong aggregation of risk even after adjusting for covariates. Effects of non-random sampling and ascertainment bias are also discussed.
疾病家族聚集性的检测对于研究导致疾病病因的可能遗传和环境因素非常重要。准确量化家族聚集性可为后续更复杂的基因研究提供指导。本文提出了一种统计模型和方法,用于利用家庭数据检测二元性状的类间和类内聚集性。这里使用的方法基于逻辑回归模型,该模型在测量风险的家族聚集性作为亲属类别间的比值比时纳入了个体协变量的影响。提出了一种估计方程方法,其中无需完全指定家庭成员间二元性状的联合分布。为作说明,分析了来自上海一项肝癌遗传流行病学研究的数据,结果显示即使在调整协变量后风险仍有很强的聚集性。还讨论了非随机抽样和确定偏倚的影响。