Guo S W
Institute of Human Genetics, University of Minnesota, Minneapolis, MN 55454-1015, USA.
Am J Hum Genet. 1998 Jul;63(1):252-8. doi: 10.1086/301928.
One widely used measure of familial aggregation is the sibling recurrence-risk ratio, which is defined as the ratio of risk of disease manifestation, given that one's sibling is affected, as compared with the disease prevalence in the general population. Known as lambdaS, it has been used extensively in the mapping of complex diseases. In this paper, I show that, for a fictitious disease that is strictly nongenetic and nonenvironmental, lambdaS can be dramatically inflated because of misunderstanding of the original definition of lambdaS, ascertainment bias, and overreporting. Therefore, for a disease of entirely environmental origin, the lambdaS inflation due to ascertainment bias and/or overreporting is expected to be more prominent if the risk factor also is familially aggregated. This suggests that, like segregation analysis, the estimation of lambdaS also is prone to ascertainment bias and should be performed with great care. This is particularly important if one uses lambdaS for exclusion mapping, for discrimination between different genetic models, and for association studies, since these practices hinge tightly on an accurate estimation of lambdaS.
一种广泛使用的家族聚集性测量方法是同胞复发风险率，它被定义为在其同胞患病的情况下疾病表现风险与一般人群中疾病患病率的比值。它被称为λS，已在复杂疾病的定位中广泛应用。在本文中，我表明，对于一种严格非遗传且非环境的虚构疾病，由于对λS原始定义的误解、确诊偏倚和报告过多，λS可能会被大幅夸大。因此，对于完全由环境引起的疾病，如果风险因素也是家族聚集性的，那么由于确诊偏倚和/或报告过多导致的λS膨胀预计会更加显著。这表明，与分离分析一样，λS的估计也容易出现确诊偏倚，应该非常谨慎地进行。如果有人将λS用于排除定位、区分不同遗传模型以及进行关联研究，这一点尤其重要，因为这些做法紧密依赖于对λS的准确估计。
