Risch N
Department of Epidemiology and Public Health, Yale University School of Medicine, New Haven, CT 06510.
Am J Hum Genet. 1990 Feb;46(2):222-8.
In order to investigate linkage detection strategies for genetically complex traits, multilocus models of inheritance need to be specified. Here, two types of multilocus model are described: (1) a multiplicative model, representing epistasis (interaction) among loci, and (2) an additive model, which is shown to closely approximate genetic heterogeneity, which is characterized by no interlocus interaction. A ratio lambda R of risk for type R relatives that is compared with population prevalence is defined. For a single-locus model, lambda R - 1 decreases by a factor of two with each degree of relationship. The same holds true for an additive multilocus model. For a multiplicative (epistasis) model, lambda R - 1 decreases more rapidly than by a factor of two with degree of relationship. Examination of lambda R values for various classes of relatives can potentially suggest the presence of multiple loci and epistasis. For example, data for schizophrenia suggest multiple loci in interaction. It is shown in the second paper of this series that lambda R is the critical parameter in determining power to detect linkage by using affected relative pairs.
为了研究遗传复杂性状的连锁检测策略，需要明确多基因座遗传模型。本文描述了两种类型的多基因座模型：（1）乘法模型，代表基因座间的上位性（相互作用）；（2）加法模型，该模型被证明能紧密近似遗传异质性，其特征是不存在基因座间相互作用。定义了与群体患病率相比较的R型亲属风险比λR。对于单基因座模型，λR - 1随着亲缘关系程度每增加一级就降低一半。加法多基因座模型也是如此。对于乘法（上位性）模型，λR - 1随着亲缘关系程度降低的速度比减半更快。检查各类亲属的λR值可能会提示多基因座和上位性的存在。例如，精神分裂症的数据表明存在相互作用的多个基因座。本系列第二篇论文表明，λR是通过使用患病亲属对来确定连锁检测效能的关键参数。
